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Exploring Neurocognitive Processes That Underlie Reading Exploring Neurocognitive Processes That Underlie Reading

Performance in Children: A Foundational Study Performance in Children: A Foundational Study

Ayan Mitra

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EXPLORING NEUROCOGNITIVE PROCESSES THAT UNDERLIE READING

PERFORMANCE IN CHILDREN: A FOUNDATIONAL STUDY

by

Ayan Mitra

Bachelor of Arts (Honors)

The University of Calcutta, 2010

Master of Arts

Presidency University, 2012

Master of Philosophy

Jadavpur University, 2016

Submitted in Partial Fulfillment of the Requirements

For the Degree of Doctor of Philosophy in

Teaching and Learning

College of Education

University of South Carolina

2021

Accepted by:

Lucy K. Spence, Major Professor

Scott L. Decker, Committee Member

Angela C. Baum, Committee Member

Michele Myers, Committee Member

Tracey L. Weldon, Interim Vice Provost and Dean of the Graduate School

ii

© Copyright by Ayan Mitra, 2021

All Rights Reserved.

iii

DEDICATION

I dedicate this dissertation to my father Dr. Abhijit Mitra who was a

neuropsychiatrist. He was devoted to his patients and always set a very high example

of what service to others meant. Even when he was undergoing chemotherapy for

lung cancer he would go out in the community and see his patients in between his

weekly treatment sessions. We frequently discussed research on brain, neuroscience,

and early childhood education, and life in general. Those experiences motivated me

to better understand how children come to read, write, and speak. What does it mean

for a child to learn something? During that time, I asked my dad why he thought

nursery rhymes stay with us forever. “How do we remember it from our childhood

and what happens in the brain when such verses are introduced to us?” My dad

smiled and looked at me, and said, “I guess that's a real question and you have to

find the answer for yourself.” When my father passed, I set off to the United States

to find the answers to my questions. The reason for taking up this challenge was to

work for the community of school children who faced reading difficulties. That is

why I decided to bridge reading education and neuroimaging.

iv

ACKNOWLEDGEMENTS

A page of acknowledgement is not enough to express the gratitude that I feel

towards people who supported me through this dissertation. First and foremost, I would

like to acknowledge my advisers, Dr. Lucy K. Spence and Dr. Scott L. Decker, and my

dissertation committee members, Dr. Michele Myers and Dr. Angela Baum, for their

unwavering belief, conviction, and support in my project. Among my past mentors, I

would like to thank Dr. Anuradha Mukherjee, Dr. Arpita Chattaraj Mukhopadhay, Aloka

Reba Sarkar, and Dr. Rimi B Chatterjee, whose training during the formative part of my

research career in India helped me understand the rigor associated with research and

pursue a doctoral degree. Among my family and loved ones, I would like to thank my

friend Kyryl Kozenko for his love and support during the most crucial last two years of

my dissertation research. My late Mother Ira was, is, and always will be my muse for

inspiration for any challenge and for teaching me how to dream. My sister Bidisha Mitra,

has been one of the strongest support systems for me in the last five years of this

dissertation journey, beginning from making my life as comfortable as possible in the

United States, while dealing with all the issues back in India, after Mom and Dad passed

away. I would also like to thank my best friend Shreerupa Banerjee, my lab mates

Michael and Chris, my buddies Harveen, Swanandi, Xiao, Stevie, and Kelvin and my

friend, philosopher, and guide John Spence for making me smile throughout this journey.

Finally, I would like to thank my schoolteachers Koely Ghosh and Joyeeta Mukherjee for

inspiring hope in an otherwise ordinary school kid. I appreciate all of you.

v

ABSTRACT

With advancement in brain research, neuroscience researchers have collectively

informed our understanding of reading-related processes. Despite an extensive body of

literature, many educators are not aware of specific neuroimaging findings related to

phonological processing and word reading. Therefore, the study builds on this body of

research by exploring the connection between the brain and reading scores. Quantitative

EEG and standardized academic achievement analyses were performed on 60 school-

aged children. Intrahemispheric coherence analysis at rest were conducted across the

sample of participants and several coherence networks were extracted and compared to

standardized reading achievement scores. Specifically, networks that included Brodmann

area 44 and 45 (Brocas Area-associated with reading) whose coherence values were

significantly correlated with standardized reading scores were examined. Results indicate

total of five coherence networks across the two brain hemispheres, that are correlated

with reading achievement scores in children. In addition to Brodmann area 44 and 45,

these coherence networks include BAs in the left frontotemporal lobe, right

occipitotemporal lobe, left temporoparietal lobe, and the right occipital lobe. This

dissertation seeks to disseminate this information to an audience of educators. Findings

discussed in this dissertation include the QEEG coherence patterns specifically associated

with letter word identification, reading fluency, passage comprehension, and broad

reading scores measured by the Woodcock Johnson III Test of Achievement contributing

to educators’ understanding of brain connectivity in relation to reading performance.

vi

PREFACE

As someone who was always fascinated by how children learn to read —

especially in India — with activities like nursery rhymes, I wanted to explore the

neuroscientific basis of literacy. My passion for early childhood education and the brain,

and my vision of having a greater impact in informing classroom practices, led me down

this path in education. Not being a schoolteacher, I nevertheless gravitated toward the

classroom environment. I wanted to approach reading from the ground level and align it

with the best practices in neuroscience.

My research attempts to bridge the gap between education and neuroscience. I

want to understand how connectivity across different regions of the brain (coherence) is

predictive of reading measures in widely used cognitive reading assessments. With

rapidly evolving neuroimaging techniques providing better spatial and temporal

resolution to brain imaging, it is increasingly important for literacy scholars to theorize

the neural basis of reading.

My research will help teachers and reading specialists understand the brain in

relation to literacy. This can help them implement appropriate strategies in the classroom.

Understanding how the brain processes written and spoken language might help them

devise new curricula. My engagement with this field has already forged connections

between education and psychology leading to research, presentations, and the U of SC

Literacy Lab.

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TABLE OF CONTENTS

Dedication .......................................................................................................................... iii

Acknowledgements ............................................................................................................ iv

Abstract ................................................................................................................................v

Preface................................................................................................................................ vi

List of Tables ................................................................................................................... viii

List of Figures ......................................................................................................................x

List of Symbols .................................................................................................................. xi

List of Abbreviations ........................................................................................................ xii

Chapter 1: Introduction ........................................................................................................1

Chapter 2: Methodology ....................................................................................................22

Chapter 3: Results ..............................................................................................................33

Chapter 4: Discussion ........................................................................................................53

References ..........................................................................................................................72

Appendix A: Adapted Prisma Flow Diagram ....................................................................83

Appendix B: Principal Component Analysis Loading Weights ........................................84

viii

LIST OF TABLES

Table 1.1 Associated Regions of Interest to Reading and Their Functions .........................7

Table 2.1 Demographic Characteristics .............................................................................22

Table 2.2 Sample Descriptive Statistics for WJ-III Ach

Reading Standard Scores .......................................................................................22

Table 3.1 Extracted components and Eigenvalue variance

explained for Brodmann Area 44 ..........................................................................41

Table 3.2 Extracted components and Eigenvalue variance

explained for Brodmann Area 45 ...........................................................................43

Table 3.3 Significant Pearson’s Correlations between PCA

components reading tests in BA 44 .......................................................................45

Table 3.4 Significant Pearson’s Correlations between PCA

components reading tests in BA 45 .......................................................................46

Table 3.5 Multiple linear regression model using coherence components

to see their association with Broad Reading composite score ...............................49

Table 3.6 Multiple linear regression models using coherence components

to see their association with reading subtests ........................................................50

Table 3.7 Significant Pearson’s Correlations between reading coherence

components and Broad Math scores ......................................................................52

Table B.1 Rotated Delta component #2 matrix in the

left hemisphere (BA 44).........................................................................................84

Table B.2 Rotated Theta component #2 matrix in the

right hemisphere (BA 44) ......................................................................................85

Table B.3 Rotated Beta 2 component #3 matrix in the

right hemisphere (BA 44) ......................................................................................85

ix

Table B.4 Rotated Alpha 1 component # 3 matrix in the

left hemisphere (BA 45).........................................................................................86

Table B.5 Rotated Beta 2 component # 3 matrix in the

left hemisphere (BA 45).........................................................................................86

x

LIST OF FIGURES

Figure 1.1 The Reading Brain ..............................................................................................6

Figure 2.1 10/20 Electrode Placement ...............................................................................32

Figure 3.1 Associated coherence components for BA 44 ..................................................47

Figure 3.2 Associated coherence components for BA 45 ..................................................48

Figure A.1 Adapted Prisma Flow Diagram .......................................................................83

xi

LIST OF SYMBOLS

β Standardized Coefficient

p Significance Value

F F Statistics

xii

LIST OF ABBREVIATIONS

ADHD .................................................................. Attention Deficit Hyperactivity Disorder

BA 44 ...................................................................................................... Brodmann Area 44

BA 45 ...................................................................................................... Brodmann Area 45

BOLD ........................................................................ Blood-Oxygenation Level Dependent

EEG ................................................................................................. Electroencephalography

fMRI ..................................................................... Functional Magnetic Resonance Imaging

KMO ..................................................................................................... Kaiser-Meyer-Olkin

LDT ....................................................................................................Lexical Decision Task

LH ...............................................................................................................Left Hemisphere

LORETA ..................................................... Low Resolution Electromagnetic Tomography

MRI ........................................................................................ Magnetic Resonance Imaging

PCA ..................................................................................... Principal Components Analysis

PDT .......................................................................................... Phonological Decision Task

PET ..................................................................................... Positron Emission Tomography

QEEG ......................................................................... Quantitative Electroencephalography

RH ............................................................................................................ Right Hemisphere

TBI ................................................................................................... Traumatic Brain Injury

VWFA .............................................................................................Visual Word Form Area

WJ III Ach..................................................... Woodcock Johnson III- Test of Achievement

WJ III Cog............................................ Woodcock Johnson III- Test of Cognitive Abilities

1

CHAPTER 1

INTRODUCTION

With current neuroimaging technology, such as Electroencephalography (EEG),

Functional Magnetic Resonance Imaging (fMRI), Positron Emission Tomography (PET),

along with other methods that provide improved spatial and temporal resolution to brain

imaging, it is possible for educational neuroscientists and literacy scholars to explore the

neural basis of reading. This allows us to explore the structures of the brain. EEG gauges

electrical activity, fMRI measures the BOLD (Blood-oxygenation level dependent)

signal, and PET measures glucose utilization. These methods provide us with information

about the neuronal activity associated with specific reading processes and their location.

The images of the brain provide increased understanding of the reading pathways and the

development of white matter tracts across the brain when children engage with reading

(Saygin et al., 2013). There have been several attempts to integrate neuroscience and

reading education (e.g., Berninger & Richards 2002; Hruby & Goswami, 2019), to

explore connectivity across regions of the brain, and neural networks. This research

allows educators to better understand the ways in which children learn to read. This

growing body of literature on brain and reading-related processes, provides insights into

neuroscience and reading education (e.g., Martin et al., 2015)

Brain regions associated with phonological analysis and recoding have been the

most substantial area of research within this field (Schlaggar & McCandliss, 2007).

2

Research has also shown the importance of word reading skills and subsequently the

relationship between phonological processing and word reading skills (McCandliss et al.,

2003). Beyond phonological processing and word reading, studies of connectivity across

areas of the brain are beginning to provide a more nuanced understanding of the reading

brain.

Reading is a complex and multidimensional process that engages cognitive,

social-cultural, and the aesthetic processes (Alexander & Jetton, 2000). Human feelings,

connections, and experiences are involved in reading texts (Alexander & Jetton, 2000).

For example, basic aspects of human cognition such as processes of expectation,

anticipation, and prediction are engaged when reading a suspenseful narrative (Lehne et

al., 2015). Interactions among these helps to develop the reader’s knowledge, strategic

processing, and motivation (Alexander & Fox, 2019). Although, complex and

interconnected processes are involved in reading, neuroimaging studies concerning

reading focus on discreet functions and connectivity across the brain. New research

findings cumulatively add to a growing knowledge of the reading brain.

Reading is one of the most vital cognitive skills that an individual can learn

during early childhood. Subsequently, the level of reading is associated with

socioeconomic factors and is a predictor of academic success or failure in the future

(Allington, 2001). Given the increased likelihood of comorbid deficits for those with

reading difficulties, it is crucial for educators and school psychologists to adequately and

efficiently identify children who may need early reading interventions. Despite recent

improvements to the procedures that have been put in place by school systems

3

surrounding the identification of students with specific learning disabilities (SLDs), there

is still much room for growth and improvement.

It is estimated that 5-15 % of students in the United States suffer from a form of

reading disability. The identification of learning disabilities (LDs) in children, guided by

U.S. federal and state legislation, is fraught with problems (Decker et al., 2012; Fletcher

et al., 2007). One of the methods of identification, the IQ-Discrepancy Model, also

referred to as the “wait-to-fail” approach is often deployed when children have already

fallen behind in a school subject area and must catch up to peers through years of

intervention. Response to Intervention (RTI) approach, on the contrary, was designed to

address this problem and improve early identification and obtain better results (Balu et

al., 2015). Additionally, despite the early intervention of the RTI method, the failure to

respond to an intervention does not provide sufficient diagnostic information in

identifying a disability. As such, the potential for supplementary and/or more reliable

methods to identify children with LDs within the school systems, including reading LDs,

exists and should therefore be pursued.

Improper diagnosis and intervention for children with specific learning disabilities

explains the relatively large gap in the neurocognitive literature around these topics.

Despite advancements in the use of neurological markers to identify those with and

without learning disabilities (Alahmadi, 2015), there has been no research investigating

the patterns of brain connectivity to empirically predict reading ability in children. The

current study aims to explore children’s basal neuro-electrophysiological activity to

determine if brain coherence at rest is associated with general reading skills. This

information will help to shed light on the underlying neurocognitive mechanisms

4

fundamental to reading ability and thus, guide future research on performance-based

reading assessment batteries as well as reading interventions for struggling children

Neuroimaging research has revealed the operation of multiple processes across

areas of the brain. Admittedly, neuroimaging research focuses on the study of brain

response times when exposed to stimuli and connectivity across specific areas of the

brain. As a result, neuroimaging investigates discreet functions within the process of

reading and does not provide an overview of the entire reading process. Nevertheless,

even with this limited focus, neuroimaging has revealed distributed and multiple

overlapping processes across multiple brain regions.

Educators across the world are increasingly becoming interested in findings from

neuroscience and how an enhanced understanding of connectivity across regions of the

brain can inform educational practices. This dissertation is an attempt to add to this

project by exploring how coherence across brain regions at rest might be associated with

standardized scores of letter-word identification, reading fluency, passage comprehension

and overall broad reading performance. This research adds to what researchers are

discovering regarding the reading network of the child and its subsequent development as

they learn to read. A review of literature on neuroscience and reading will provide a

detailed background for the dissertation study that follows.

LITERATURE REVIEW

The following section is a systematic review of the extant literature on the

neuroimaging studies of the reading process with focus on three main areas of

knowledge: (1) phonological and visual word form processing, (2) models of the reading

process, and (3) reading development.

5

1.1 ELIGIBILTY CRITERIA

For this dissertation, articles were selected with the help of keywords: reading,

neuroimaging, phonological, literacy, brain, EEG (electroencephalography), fMRI

(functional magnetic resonance imaging), phonemic perception, orthography, word

reading, and reading development in different combinations in subject, title, and keyword

fields. Studies that investigated phonological processing, word reading and their

intersection with neuroimaging were selected for the synthesis.

1.2 LITERATURE SEARCH

The process of identifying studies and literature for this dissertation was designed

to be inclusive of neuroscience, phonological processing and word reading. Using the

PRISMA flow guidelines (see Appendix A) set by Moher et al. (2009), a systematic

literature review was conducted across databases including ERIC, Ed Source,

PsychINFO, and PscyhArticles for years 1980 through 2020. These databases were used

because they specialize in education, psychology, and their intersection with

neuroscience research. Articles were selected from 1980 through 2020 because

educational neuroscience has evolved over these past four decades. Keywords in different

combinations in subject, title, and keyword fields included: reading, neuroimaging,

phonological, literacy, brain, EEG (electroencephalography), fMRI (functional magnetic

resonance imaging), phonemic perception, orthography, word reading, and reading

development. Each abstract was screened to align with the purpose of this dissertation.

Pertinent articles were read, and additional articles were located through bibliographic

branching. This led to inclusion of other research articles in the review to provide a more

6

complete picture of reading and neuroimaging. The final review yielded 52 appropriate

articles.

1.3 ANATOMY OF THE READING BRAIN

The literature on neuroimaging of reading has informed us about the different

brain regions associated with reading and how connectivity develops as children learn to

read. It has also informed us about the underlying sub-processes of reading. These

findings have led to models of the reading brain. Each of these areas are discussed in this

dissertation, with reference to figure 1.1, which illustrates pertinent anatomical regions in

the brain. Below the illustration, functions associated with these brain regions are also

listed (see table 1.1).

Figure 1.1 The Reading Brain

7

Table 1.1 Associated Regions of Interest to Reading and their Functions

Region of the Brain Function

Inferior Prefrontal Cortex Sentence Comprehension

Sensorimotor Regions Semantic Representations

Temporoparietal Cortex Phonological Analysis & Recoding

Occipitotemporal Region Visual Word Form Area

Visual Cortex Recognizing Print

Superior Temporal Gyrus Spoken Word Recognition

1.4 PHONOLOGICAL PROCESSING AND VISUAL WORD FORM PROCESSING

While researchers have identified regions of the brain involved during reading,

connectivity across the regions continues to be explored. One of the things that

researchers largely agree upon is that the temporoparietal cortex is an important region

for phonological processing and recoding (e.g., Fiez & Petersen, 1998; Pugh et al., 1996,

1997; Schlaggar & McCandliss, 2007; Turkeltaub et al., 2002). This process enables the

phonetic decoding of unknown words (Pugh et al., 1996, 1997; Schlaggar & McCandliss,

2007). Furthermore, a phonological network develops during early reading acquisition

and includes phonological, visual, oral, lexical, and semantic pathways (Yu, et al., 2018).

Readers draw upon their visual experiences to represent orthographic units with

the help of the left ventral occipitotemporal cortex, theorized as the “visual word form

area” or VWFA (Dehaene & Cohen, 2011; McCandliss et al., 2003). Through the

retrieval of orthographic whole-word representations (Ludersdorfer et al., 2015),

frequently encountered words are processed without the need for phonological processing

(Glezer et al., 2016). Functional specialization within this area emerges with early

reading acquisition (Pleisch et al., 2019). However, it is not determined if the VWFA is a

8

modality specific area or if there are overlapping areas that are recruited for reading

words (Price et al., 2003).

Understanding rapid and fluent reading is an important goal for reading educators

and neuroimaging researchers. Looking at processing speed, Klein et al. (2015) found a

fast-naming response to target words when the reader is first shown a pseudoword with

the same initial phoneme. This study suggests that in order to process the target word,

orthographic analysis was not necessary. Studies such as this have shown that words can

be rapidly recognized by readers without decoding and neuroscientists are beginning to

understand where and how the brain engages in this rapid processing. Balthasar et al.

(2011) indicated there might be an underlying route of access to word form knowledge.

Neuroanatomical models describe cortical networks for reading that facilitate

orthographic to phonological mapping and elicit word meaning from semantic memory

(McNorgan et al., 2015, Oberhuber et al., 2013). Other studies have reported data that is

best interpreted in terms of interactions between language processing and visual word

form processing (Twomey et al., 2011). This means that the visual word form area and

phonological processing are interconnected during reading. Other areas of the brain also

interact to provide meaning and experience to phonemic and visual subprocesses.

Phonological processing and visual word form processing are part of a reading

network. The reading network refers to linked areas in the brain that are responsive to

semantic, phonological, grapheme, and morpheme structures that map to language.

Becoming a skilled reader requires the gradual building of integrated neuronal pathways

of printed language processing which are in alignment with spoken language processing.

9

A study done by Preston et al. (2015) examined the print-speech coactivation implicated

in the reading network. The results indicated a left hemisphere reading network in

emergent readers that was predictive of later reading development. This indicates that as

children learn to read, they build connections across regions associated with visual and

phonemic processing. Learning to read builds connectivity across brain regions.

The meaning of words and prior experiences are used to recognize words in print.

Reading words and naming pictures involves the association of a visual stimuli with

phonological and semantic knowledge (Buchel et al., 1998). This involves the left interior

prefrontal cortex (Poldrack et al., 1999; Sandak et al., 2004) associated with accessing

meaning. Word processing has been found to be strongly associated with activations in

regions associated with semantic processing. (McNorgan et al., 2015). These studies

describe how the semantic aspect of reading is associated with other areas of the brain

described above and in Figure 1.1 To summarize, semantic processing is important for

readers to be able to comprehend the meaning of the text. Indicating that semantic

comprehension processes are required along with phonological and word decoding skills

in order to fully comprehend a text. This aligns with the connectionist model of reading

development where phonological and semantic processes interact with each other.

1.5 MODELS OF READING

Neuroscientists have proposed a dual-stream model of reading (Pugh et al., 1996;

Sandak et al., 2004). One is a dorsal stream that is critical for extracting relations between

orthography, phonological form, morphological and lexical-semantic dimensions of print.

Another is a ventral stream, a memory-based word form area supporting fluent word

identification (Pugh et al., 2001; Glezer et al., 2016). The dorsal route is involved in word

10

decoding (Fiez & Petersen, 1998; Jobard et al., 2003) and access to phonemes (Wu et al.,

2012), phonological decision-making (Rumsey et al., 1997) and phonological output

(Taylor et al., 2012). The ventral route is engaged with orthographic representations, how

words are represented by letter strings, and is finely tuned to read whole words (Glezer et

al., 2016). Further delineating this area, the insular cortex, an area with connectivity

across cortical and subcortical brain regions, is implicated for both lexical and sub-lexical

reading processes (Borowski, et al., 2006) further implicating the coordination of both

routes during reading. The activation in these areas, specifically phonological and lexical,

confirms the importance of the language network for spelling thus reinforcing the dual

route model of spelling (Norton et al., 2007). Hence the dorsal route with its focus on

phonological processing and word decoding and the ventral route with its focus on

orthographic processing and visual word-form recognition in reading whole words

functions in a coordinated manner in the development of the reading network.

Neuroscientists have studied areas that overlap in activation. Borowski, et al.

(2007) found the perceptual component of reading was unique with very little shared

processing, whereas the analytical component was unique but also involved shared

processing. Perceptual component of reading involves visuospatial information

processing, whereas the analytical component underlines comprehension and semantic

processing. The development of the reading network is shared by the Brocas area and

attention-related brain regions, a word-reading related left temporal lobe participating in

semantic processes, and a pseudoword related basal occipitotemporal region. These

subnetworks propose a functional model of reading where orthographic, phonological,

and semantic processes are recruited to access the most efficient form of reading.

11

In reading words and pseudowords, the processes and areas involved during silent

reading and reading aloud were found to overlap to a large extent (Hagoort et al., 1999).

In reading familiar words, Jobard, et al. (2011) found an orthographic-to-semantic

pathway for skilled readers, while lower skilled readers had greater involvement of

phonological regions. This means that skilled readers connect the spelling pattern in a

word with meaning whereas lower skilled readers rely on letter-sound correspondence.

This indicates that during frequent word reading some of these pathways may function in

an exclusive fashion depending on the proficiency of handling the written material.

However, some studies also show that the dual route model encompassing the dorsal

phonological and ventral orthographic does not hold for pre-readers. (Vanderauwera et

al., 2018). Indicating that the letter-sound correspondence is not yet developed in pre-

readers. These studies suggest there is inter-individual variability in how reading is

processed that may depend on the type of written material and the development of

reading skill.

1.6 READING DEVELOPMENT

Learning to read changes how the brain functions as the reading network builds.

Strong connections across brain regions develop that integrate spoken language with

written language. However, there are individual differences in brain integration among

readers such as the integration of sublexical phonological processing during visual word

recognition (Twomey et al., 2015). This means that there may be differences in readers

using parts of words to recognize words. Fischer-Baum et al. (2017) established

individual differences in word reading, with some participants drawing more on

phonology and some drawing more on semantics. For example, Simos et al. (2005) found

12

that kindergarten students with higher scores on a primary reading inventory evidenced

brain activation similar to older skilled readers who use more semantics or meaning-

based processes for the purpose of reading. Thus, integration of brain regions associated

with phonological, visual, and semantic processing is indicative of greater reading

proficiency.

Children begin to develop a reading network when they are exposed to print and

begin to read (Vanderauwera et al., 2018; Simos et al., 2005). Alcauter et al. (2017) Vas

six. However age-related differences in activation have been found. Young readers use

the left temporo-parietal circuit involved with phonology-based reading, whereas adults

use more whole-word recognition processes (Martin et al., 2015; Sela, et al., 2012;

Shaywitz, et al. 2007). As children develop as readers, they rely less on phonology and

begin to rely more on whole-word recognition in their meta-analysis of reading and

neuroimaging, Martin et al. (2105) show how, with advanced literacy in adults, there is

greater activation in the occipitotemporal cortex when compared to children, who show

significantly more activation in the left superior temporal gyrus, suggesting that for

emergent readers, grapheme-to-phoneme mapping is fundamental (Glezer et al., 2016).

Longitudinal studies contribute to our understanding of neural network development over

time (Horowitz-Kraus et al., 2015; Marosi et al., 1997) These studies show perceptible

increase in coherence across time periods, indicating a strong connection between

coherence in the brain and reading development.

Looking at the neuroimaging studies cited in this literature synthesis, a significant

amount of work has been done concerning phonological processing and word reading,

modelling reading, and reading development. First, readers activate multiple regions of

13

the brain to access the most efficient function for reading a particular text. When

phonological processing is required, such as when encountering an unknown word in an

unfamiliar context, readers activate phonological processing. When the word is familiar,

the visual word form area is activated. To summarize, phonological processing and visual

word form processing are both integral to the reading network which entails areas of

brain responsive to semantic, phonological, grapheme and morpheme structures. A

skilled reader gradually builds this neuronal network in order to integrate print-speech

processing. This has led to neuroimaging studies revealing the interconnections among

brain areas. Educators concerned with the process of learning to read can be informed by

this growing understanding of functional connectivity across different regions of the

brain.

Second, within the field of neuroscience, models have been developed,

questioned, revised, and expanded. Researchers continue to build on the dual-stream

model of reading: a dorsal stream that is critical for extracting relations between

orthography, phonological form, morphological, and lexical-semantic dimensions of

print, and a ventral stream of a memory-based word-form area supporting fluent word

identification (Pugh et al., 2001). This model has been expanded through various studies

to understand the sub-processes at work during reading. Additionally, interactive theories

suggest that transformations occur across areas of the brain and that experience with

language changes the connections between visual, phonological, and semantic

information, with transformations occurring across areas of the brain (Fisher-Baum, et

al., 2017). With increased studies, more nuanced models of the reading brain are

emerging.

14

Third, it has been determined that the reading network develops as children learn

to read. For emergent readers, phoneme-grapheme mapping is fundamental, whereas with

increasing experience with print, later readers use more whole word recognition. With

reading experience, multiple regions of the brain are accessed, depending on the task.

When phonological processing is needed, such as when encountering an unknown word

in an unfamiliar context, the reader will activate phonological processing. When the word

is familiar, the visual word form area is activated. These two processes are activated as

needed during the reading task. If the reader has greater connectivity, both processes are

utilized, and greater reading proficiency is observed. Therefore, educators are necessarily

concerned with providing educational experiences that build connections across regions

of the brain.

Neuroscience researchers have been able to collectively inform our understanding

of reading-related processes. Thus, we take up Hruby’s trifecta challenge (2012) that an

educational neuroscience requires: thorough attention to intellectual coherence; matching

expertise in both neuroscience and educational, research, theory, and practice; and

attention to “ethical issues, concerns, and obligations” (p. 3) related to the general public

and their children. The neuroscientific literature continues to advance our understanding

of reading processes in the brain; however, this literature must be viewed as on-going and

partial as we seek to understand the complex interactions that take place during reading.

Although much has been learned about phonological processing and word reading, it is

clear that these processes are interconnected with other processes that involve memory,

semantic processing, and experiences. Other processes such as sensory motor processing

and emotion have been recently studied but were beyond the focus of this dissertation.

15

These areas of study are at the forefront of neuroscience research in reading. Going

forward, it will be necessary to provide an account of the neuroscience research on

semantic processing, sensory motor processing and emotion for the benefit of reading

educators.

The audience for such research includes educators, neuropsychologists, school

psychologists, and clinical psychologists who deal with reading-related issues. Since

teachers are given the responsibility of educating young minds, it is imperative that they

too, have some working knowledge or understanding about how the brain functions as

they plan instructional interventions for reading (Berninger & Richards, 2002). Teachers’

access to brain research and their experiential knowledge could inform a better

understanding of the process of reading development and how the brain, in turn, is

changed by the capacity to read. Such collaboration would enable reading researchers to

participate in ongoing discussions regarding the implications of teaching reading based

on current neuro-scientific knowledge. As such, this synthesis and future literature on

neuroscience research on reading will lay a foundation to facilitate a dialogue among

reading theory, policy, classroom instruction, and brain research.

1.7 QEEG

Quantitative electroencephalography (qEEG) has an extensive history of being

used to assess underlying brain functions for various neuropsychological disorders. For

example, results from several studies have demonstrated that qEEG measures can

accurately discriminate between individuals who have experienced a Traumatic Brain

Injury (TBI) and those who have not. Thatcher et al. (1989) report that qEEG was able to

differentiate TBI patients from non-TBI patients with 90%-95% accuracy. Further studies

16

utilizing qEEG provide evidence to support its utility for classifying TBI severity with

96% accuracy (Thatcher et al., 2001a). Numerous studies have also utilized qEEG

measures to examine its relationship with measures of intelligence, several of them

reporting significant relationships between coherence and standardized intelligence

measures (Thatcher et al., 2005). Additional studies have further supported utilizing

qEEG for studying neuropsychological differences in individuals with Attention Deficit

Hyperactivity Disorder (ADHD), demonstrating its utility in this domain with several

decades’ worth of literature (Barry et al., 2009; Clarke et al., 1998, 2001; Janzen et al.,

1995; Satterfield et al., 1972).

Despite an extensive history of utilizing qEEG for studying various

neurocognitive phenomena, a relatively small base of literature exists outlining its utility

for examining children’s academic skills and abilities. While recent EEG studies have

begun to focus on children’s reading abilities and disorders (e.g., dyslexia) (Arns et al.,

2007; Lehongre et al., 2013; Rippon & Brunswick, 2000), very few studies examining the

cognitive mechanisms involved in specific reading skills exist.

While reading abilities have been studied for many years within the frame of an

educational context, neurocognitive research on reading abilities is a relatively recent

field of study (Ardila et al., 2016). Recent research literature exploring the utility of

qEEG as it relates to reading skill and ability is scarce, though mounting. Specifically,

this study aimed to evaluate the coherence levels in children with differing reading skill

scores while they performed a reading related task.

Coherence is a type of qEEG analysis often used when examining EEG data (John

et al., 1988; Thatcher et al., 2005a; Thatcher et al., 2001b, 1989). It provides a measure of

17

the phase angle consistency between two brain regions in a set of continuous EEG data

(Thatcher et al., 2005b). Essentially, coherence is a quantitative value representing a

denotation of which regions of the brain are oscillating at the same frequency

simultaneous to one another. Thus, examining the coherence among brain regions can

provide valuable information with regards to functional brain connectivity and cognitive

functioning (González-Garrido et al., 2018). In relation to this it might be important for

us to review the domain-specific model of language to further unpack the reading brain.

1.8 DOMAIN SPECIFIC MODELS

In order to understand functional connectivity in the brain in relation to reading, it

is important to understand the domain-specific model of language. Within the language

system of the brain, there are regions that are involved in reading sequences of words and

non-words that can be pronounced. Despite the fact that reading as a whole may summon

networks other than those associated with spoken language, there is enough evidence to

suggest that there are additional regions which may be activated during reading. Hence

along with the left fronto-temporal- parietal network engaged in reading and language

systems, there are also associated activations in the right hemisphere (Fedorenko, 2014).

This right hemisphere activation in the fronto-temporal region may also account for a

domain-specific language network to include broader language functions like lexicality,

syntax, semantics, and pragmatics despite semantics and pragmatics being classically

associated with cognitive functions (domain-general) that do not involve language

(Campbell & Tyler, 2018). In relation to this, the Brocas area located in the frontal lobe

of the left hemisphere is important for speech production, sensorimotor learning, and

language comprehension. It is made up of two areas: pars opercularis (Brodmann area 44)

18

and pars triangularis (Brodmann area 45). The anterior portion of the Brocas area is

associated with semantics or meaning of words and the posterior region is related to

phonology or the sounding of words. Hence Brocas area is critical to reading. Looking at

the structural architecture of the Brocas area, Fedorenko et al. (2012) found that it

contains both domain-specific (language) and domain-general subregions. This explains

the complex manifestations of the “Brocas aphasia” where several domain general

functions are compromised along with linguistic functions. This necessitates a deeper

dive into the functional organization of the Brocas area (Brodmann area 44 and 45) to

explore functional profiles like reading and other academic skills.

1.9 BRODMANN AREA 44 AND AREA 45 (BA 44 AND BA 45)

Utilizing fMRI, activation in the Broca’s area (BA 44 and BA 45) was observed

during the processing of visually presented words and pseudowords while performing a

phonological decision task (PDT) and a lexical decision task (LDT). These tasks tested

the use of grapheme to phoneme conversion and word recognition. Contrary to the PDT,

the LDT had longer reaction times to pseudowords over words. The researchers

demonstrated that the left BA 44 and BA 45 had a stronger activation during

pseudowords as opposed to words. A separate analysis of the PDT and LDT revealed that

BA 44 was activated during both the tasks, whereas BA 45 was activated only during

LDT. The results support the dual route model of reading with the left BA 44 being

implicated in the grapheme to phoneme conversion and the left BA 45 being explicitly

involved in lexical search. (Heim et al., 2005). Therefore, children with greater

connectivity circuits near BA 44 and BA 45, likely fair better in reading performance

than those that have less. Additionally, studies investigating the neural activations

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underlying verbal fluency (Amunts et al., 2004), semantic processing (Bookheimer,

2002), lexicality: spelling to sound consistency (Fiez et al., 1999), auditory sentence

processing (Friederici, 2002), pseudoword reading (Herbster et al., 1997), and phonology

and orthography in word reading (Rumsey et al., 1997) implicate the Brodmann area 44

and 45 very heavily. The BA 45 is implicated in semantic aspects of language processing

whereas BA 44 is associated with speech production.

CURRENT STUDY

Children with greater connectivity circuits near BA 44 and BA 45, likely fair

better in reading performance than those that have less. The current study aims to utilize

qEEG to explore children’s reading performance by analyzing default brain activity. The

goal is to determine if the existence of brain networks, and the strength of coherence

within them, is associated with general and specific reading skills as measured through

subtests like letter-word identification, passage comprehension, and reading fluency.

Recent translational research has begun investigating the utility of neurophysiological

resting-state paradigms (Takamura & Hanakawa, 2017). The current study utilizes a

qEEG resting-state paradigm to explore children’s basal electrophysiological brain

activity to determine if coherence values among brain regions at rest are associated with

reading scores. Although task-based EEG and fMRI research on reading assessments

elucidates key brain areas actively involved in completing specific reading tasks, there

are several advantages to resting-state paradigms. For example, to uncover associations

between brain networks and reading performance. This gives us more “stable” data since

the epochs are longer; the resting-state research is more indicative of developmental

differences and how every-day/general brain activity predicts response to

20

stimuli/performance demands as opposed to ERP data. By gathering continuous sets of

EEG data from participants and extracting coherence values, the current study looks

specifically at BA 44 and BA 45 and its intrahemispheric connectivity with the rest of the

brain. Determining brain regions that are significantly coherent with BA 44 and BA 45

allows us to identify specific brain networks that can be likely linked to specific reading

(Ardila et al., 2016). We can then assign variables to each of these networks and

determine if the levels of coherence (connectivity) among them can be utilized to explore

associations with reading skills as measured by standardized reading composite scores of

letter-word identification, passage comprehension and reading fluency from the

Woodcock Johnson-III Test of Achievement (WJ III- Test of Ach).

By obtaining bio-signatures of specific sets of reading skills, such as the ones

outlined above, educational researchers can be better equipped to understand how

children learn to read. Identification of these additional factors implicated in reading

skills may enable a more comprehensive, integrated depiction of an individual student’s

learning trajectory leading to better reading instruction.

To summarize, neuroscience research has described specific brain areas, the

dorsal pathway, and the reading network that are involved in letter and word processing.

It has also highlighted the importance of the visual word form area and the role of reading

development on rapid word retrieval. Finally, it has shown that reading comprehension is

distributed across various brain regions through white matter connections. To understand

how these bio-signatures can help educators with a wider understanding of reading

research in relation to reading scores of children on standardized achievement tests like

Woodcock Johnson III-, I center the following questions:

21

RESEARCH QUESTIONS

1. How is resting-state EEG coherence across regions of the brain associated

with standardized letter-word identification scores from WJ-III, Test of Ach? This

information will provide educators with brain research related to letter-word reading

outcomes.

2. How is resting-state EEG coherence across regions of the brain associated

with standardized reading fluency scores from WJ-III, Test of Ach? This information

will provide educators with brain research related to fluency outcomes.

3. How is resting-state EEG coherence across regions of the brain associated

with standardized passage comprehension scores from WJ-III, Test of Ach? This

information will provide educators with brain research related to reading comprehension.

4. How is resting-state EEG coherence across regions of the brain associated

with standardized broad reading composite scores from WJ-III, Test of Ach? This

information will provide educators with brain research related broad reading

performance.

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CHAPTER 2

METHODOLOGY

This study is based on archived data from a previous research study that

investigated cognitive profiles in children with math and learning disabilities. In

replicating previous research examining QEEG and academic performance, the current

study followed the analytical methods outlined by Anzalone et al. (2020). Reading and

Math subtests of the Woodcock Johnson III Test of Achievement and resting state qEEG

coherence data were collected. The participant selection criteria were based in part on the

Woodcock Johnson III Math achievement scores. (1) 30 children with math learning

difficulties (2) 30 typically developing children, (3) appropriate age (7-12 years), and (4)

score below the 25th percentile on the WJ-III Ach Math Calculation test and/or Math

Fluency test. Children were excluded from the study if they were deemed to have an

intellectual disability, as determined by their Broad Cognitive Ability score from the WJ-

III Cog falling below the score of 70. Descriptive statistics for the overall sample,

collapsed across both groups are include in Table 2.1.

Table 2.1 Demographic Characteristics

Participants

N 60

Gender (%)

Male 53.3

Female 46.7

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Age (years)

Mean 9.58

SD 1.38

Ethnicity (%)

Caucasian – Non-hispanic 83.3

African American – Non- hispanic 8.3

Latino, Hispanic, Spanish Origin 1.7

Asian, South-East Asian 6.7

The children were also tested on composite reading measures including letter

word identification, passage comprehension, and reading fluency, from the WJ-III Ach

which did not have any separate inclusion or exclusion criteria.

Hence, data used in my study came from these 60 school- children with the goal

of looking at the full range of standardized reading scores. The recruitment of these

children was done through local advertisements and agencies in the Southeastern part of

United States. Specifically, a school for learning disabilities and tutoring program were

recruited as the primary data collection site. Inclusion criteria: all reading scores and

brain imaging data from the math study, there were no exclusions based on reading

scores.

Table 2.2. Sample Descriptive Statistics for WJ-III Ach Reading Standard Scores

Subtest M SD Min/Max

Letter-word Identification 100.54 16.95 61-135

Reading Fluency 101.54 21.18 44-148

Passage Comprehension 97.61 16.39 55-133

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2.1 MEASURES

The current study used the Woodcock-Johnson Tests of Achievement (WJ-III

Ach) in order to determine reading scores. This battery is designed to measure an

individual’s academic skills who are aged two to 90 or more years, and it has been

validated for its reliability and consistency in research studies. Its core subtests have

median reliability coefficients of r11 = .81. The WJ III- Test of Achievement is designed

to measure several areas of achievement, which in turn gives us an idea about the

composite measures in the specified areas. The raw scores of the participants can be

converted into standardized scores with the help of either grade or age norms. The grade

norms were constructed with a group of adults enrolled in a school or a university and the

total achievement score was found to have an internal consistency of .98. The three sub

scores that embody the clusters of achievement are Broad Reading, Broad Math, and

Broad Written Language scores which have an internal consistency of .94. .95, and .94,

respectively. In relation to other major standardized measures of achievement their

correlation coefficients have been reported to be between .65 and .79 (McGrew &

Woodcock, 2001, as cited by Cressman & Liljequist, 2012). The WJ-III Ach has been

previously used in a study to understand the effects of repeated reading methods in

relation to reading fluency and passage comprehension for children who are slow

learners. The study investigated the importance of reading repetition for children who

faced reading difficulties measured with the help of the reading fluency and passage

comprehension subtests of the WJ-III Ach. Results indicated that the experimental group

that received the English program intervention showed significant improvement in

reading fluency and passage comprehension scores as opposed to the control group, that

25

did not receive the intervention. This validates the use of WJ-III Ach reading battery in

evaluation of reading outcomes for children. (Bendak, 2018).

Assessment Battery. The following assessments were administered as a part of the

assessment battery in order to get the composite scores on reading.

Letter Word Identification: This oral test assesses reading skills by the subjects

reading a list of words from an increasingly difficult vocabulary list and evaluating their

pronunciation (Woodcock et al., 2001b)

Passage Comprehension: This test focuses on the child’s understanding the

meaning of the text where a child reads a sentence silently and from the context decides

to fill in the blank spaces with specific words that complete the sentence. There is a

gradual increase in the level of the vocabulary, as the child progresses through the

sections. (Woodcock et al., 2001b)

Reading Fluency: This three-minute subtest allows the child to read simple

sentences and circle “Y” or “N” in the answer sheet in order to accurately respond to as

many items as possible within the allotted time. (Woodcock et al., 2001b)

After administering the battery, the participants were taken to the Applied

Cognitive Neuropsychology Lab for collecting EEG data.

EEG data was collected to examine whether specific frequency bands (i.e., delta,

theta, alpha 1, alpha 2, beta 1, beta 2, beta 3, and high beta) and the coherence patterns

among them were able to predict the degree of a child’s letter word identification,

passage comprehension, and reading fluency. The frequency bands are individually

associated with important factors like delta is related to sleep and drowsiness, theta is

related to shift in attention; emotional stress, alpha connotes shift in focus and attention;

26

beta is related to problem solving and memory, and gamma indicates learning, cognition

and processing. EEG data was recorded from 19 channel electrodes distributed across the

scalp using the 10/20 standard placement methods, via electro-caps by Electro-cap

International (See Figure 2.1). The standard placement of each of the 19 electrodes are as

follows: FP1 and FP2 are electrodes placed over the prefrontal cortex, while F3, F4, F7,

and F8 are electrodes placed over the frontal lobe. Electrodes T3, T4, T5, and T6 are

placed over the temporal lobe, while the parietal lobe has electrodes P3 and P4. O1 and

O2 are placed over the occipital lobe. FZ, CZ, and PZ measure midline brain activity,

while C3 and C4 are placed between the temporal lobe to measure centro-temporal brain

activity. Finally, A1 and A2 within Figure 2.1 represent ground leads (i.e., ear clips)

Data were sampled at 1026 Hz using a BrainMaster Discovery 24E amplifier. This device

is used due to its FDA approval classification as well as its compatibility with the

Neuroguide program 6.6.4 (Thatcher, 2011). A 60Hz notch filter was used to remove

electrical interference/signal caused by electronics from the surrounding environment and

the bandwidth range was set to record frequencies between 1.0 and 30 Hz. The frequency

bands used in this study are defined as follows: delta (1.0 - 4.0 Hz), theta (4.0 - 8.0 Hz),

alpha 1 (8.0 -10.0 Hz), alpha 2 (10.0 - 12.0 Hz), beta 1 (12.0 - 15.0 Hz), beta 2 (15.0 -

18.0 Hz), beta 3 (18.0 - 25.0 Hz), and high-beta (25.0 - 30.0 Hz). Impedance values ear

reference electrodes were kept below 5KΩ, and all other electrode impedance values

were kept below 10KΩ for all subjects. The quality of connection of the electrodes to the

scalp was measured by the amount of electrical impedance found. Additionally, ear

references can be broadly used for recording the electrical activity at non-brain sites close

to the brain so that we can determine a general “baseline” of electrical activity in the area

27

that is not attributed to brain activity. Neuroguide 6.6.4 (Thatcher, 2011) was used for

removing EEG artifact in the data and to obtain normative values of qEEG spectral

coherence. MATLAB 2018a (MATLAB, 2018) was used for data transformation and

organization.

2.2 PROCEDURES

Data used in the current study was derived from a prior research study aimed at

examining the relationships between brain function, math performance and anxiety. Prior

to conducting the study, approval to perform the research procedures was granted from

the University of South Carolina’s institutional review board. Participants were provided

child assent and parental consent forms and signatures were obtained. Preliminary

measures of reading skills, mathematical skills and cognitive abilities were obtained from

participants who agreed to partake in the study. Specifically, the WJ-III Ach and WJ-III

Cog measures were administered. Data from participants who met the study eligibility

criteria were retained and EEG data were recorded.

EEG recordings were obtained by fitting the participants with their appropriately

sized Electro-Cap and ground leads. The recordings were collected over three-minute

intervals while the participants were awake, at rest, with their eyes closed. All data used

for the current study was collected over the course of one to two study sessions. This

method of collecting resting-state data was used due to the advantages it offers for young

participants, specifically, three minutes is a relatively short amount of time for a child to

remain still and calm, but still allows for the adequate collection of qEEG data for

coherence analyses. Furthermore, having participants close their eyes does not

significantly impact brain activity in brain regions unrelated to visual processing, and

28

having eyes closed during an EEG recording provides a method to reduce common EEG

artifact associated with eye muscle movements (Barry et al., 2007). Following data

collection procedures, participant data was de-identified (i.e., participants’ names were

replaced with study ID numbers) to protect their confidentiality.

2.3 DATA ANALYSIS

Several procedures were required to allow for qEEG analyses to be performed.

Prior to conducting analyses, the first minute of each participant’s qEEG data was

manually inspected to identify a minimum of ten seconds of artifact-free data. The visual

inspection process involves a review of the raw EEG data, where the reviewer aims to

identify points in time with abnormal spikes in amplitude across multiple channels at the

same time. Artifacts of this type are typically related to scalp and/or facial muscle

movement and are therefore removed from the dataset individually for each participant.

Following the visual inspection, the Neuroguide software options were used to

automatically identify and reject EEG patterns consistent with artifacts relating to

drowsiness (e.g., slow rolling eye movements, specific changes in alpha rhythm, specific

changes in theta and beta ratios, etc.) and eye muscle movements. These flagged patterns

were subsequently reviewed for accuracy and manually removed by the reviewer if

necessary. By following this procedure, the Neuroguide software used the artifact-free

data from the manually identified ten-second sample as a reference. With this artifact-free

reference in place, the automated software program identified and selected artifact-free

data from the whole three-minute data file and discarded all portions of the data with

artifacts; thus, yielding artifact free samples for each participant. This procedure was

repeated for each participant. By using the automated artifact flagging and rejections

29

programs housed within the software several unique advantages are offered compared to

other methods. These methods are reliant upon objective quantitative values within each

participant’s dataset, as such, this more objective method of data cleaning removes the

potential for confounding inconsistencies when rejecting EEG artifact for each

participant.

Coherence measures between electrodes were obtained through qEEG

Neuroguide automated processes. The Neuroguide software contains a database with

information from 625 individuals, covering the age range two months to 82.6 years

(Johnstone & Gunkelman, 2003), pp. 42-43). By sourcing this database, Neuroguide

yields reports, which provide coherence values in raw Z-score units. Utilizing

standardized coherence values, discrepancies in coherence due to age-

related/developmental differences can be minimized. A subsequent automated procedure

utilizing Low Resolution Electromagnetic Tomography (LORETA) was performed in

Neuroguide to convert the data into a format that will produce standardized coherence

values between each of the 52 Brodamnn areas (BA) in either hemisphere.

LORETA is one of the most extensively used algorithms for localizing the source

of EEG signal detected on the scalp (Grech et al., 2008). By running the LORETA

program on the EEG dataset from this study, 3-dimensional statistical maps were

generated to model the distribution of brain coherence values. LORETA attributes

electrode activity to specific BAs by plotting the points on a standardized MRI atlas, it

has demonstrated its ability to provide accurate estimations of activity in subcortical

structures with better temporal resolution than can be provided by PET or fMRI (Pascual-

Marqui et al., 1994). This study utilized the LORETA program to convert the obtained

30

values of coherence between scalp electrodes into coherence values between each of the

52 BAs in each hemisphere. Ardila et al. (2016) proposed a neurological model based on

fMRI findings that suggests that BA 44 and BA 45 are heavily implicated in reading

ability. By obtaining models of EEG activity based on a Magnetic Resonance Imaging

(MRI) atlas, the current study used the findings by Ardila et al. (2016) to provide a

framework from which the subsequent analyses were predicated.

Following Ardila et al. (2016) report that BA 44 and BA 45 are crucial for reading

ability, MATLAB 2018a (Mathworks, Inc., 2018) was utilized to extract the coherence

data between BA 44, BA 45, and all other BAs from the full dataset. This data was then

exported to Microsoft Excel and IBM SPSS (version 24; IBM SPSS Statistics for

Windows, 2017) for final analyses.

Using IBM SPSS (IBM SPSS Statistics for Windows, 2017), coherence values

were collapsed across all participants for each BA. Principle component analyses (PCA)

with varimax rotation was applied individually to coherence values across each frequency

band of interest (delta, theta, alpha 1, alpha 2, beta 1, beta 2, beta 3, and high beta) for

BA 44, and BA 45 in the left hemisphere then separately for each frequency for BA 44,

and BA 45 in the right hemisphere to the rest of their respective hemispheres. PCA was

applied in order to reduce the number of EEG coherence variables and efficiently extract

the essential features from it, thus facilitating a more accurate interpretation of the

coherence properties between brain regions in either hemisphere. PCA is a traditional

method that has been used in EEG analysis due to the high number of variables it

produces and has been used previously to achieve similar analytic goals (Vigário et al.,

2000). Only components whose Kaiser-Meyer-Olkin (KMO) measure of sampling

31

adequacy was above the recommended value (KMO = .60) ((Dziuban & Shirkey, 1974),

and had eigenvalues greater than 1.0 (Gorsuch, 1983; Stevens, 1996), and passed the

scree test (Bro & Smilde, 2014) were considered for ensuing analyses.

Once the PCA results were obtained for BA 44 and BA 45, across various

frequencies in both hemispheres, bivariate Pearson’s correlation coefficients were

computed to quantify the correlation between these EEG coherence parameters and the

standardized letter-word identification, reading fluency, passage comprehension and the

broad reading composite scores. In a regression analysis we predict scores on one

variable from the scores on a second variable. The variable we are predicting is called a

criterion variable and, in this case, it is the individual scores on the tests. The variable

upon which we base our prediction is called a predictor variable and, in this case, it is the

QEEG coherence value of the individual participants. When there is only one predictor

variable the prediction method is called a simple linear regression and when there is more

than one predictor variable it is called a multiple linear regression. This study uses both

the methods to predict models for the three reading subtest scores and broad reading

performance scores. After the construction of the multiple regression model there are 3

key descriptive statistics that we would consider: β is the standardized coefficient, p is the

significance value, and F is the F statistics. F is a statistical value that sees if the factors I

am considering in the model make the model a good fit or not to explain the

phenomenon. The QEEG coherence components that were significantly correlated with

the three subtests (p <.05) were considered in subsequent regression analyses to assess

their association with the reading subtest score separately. Lastly, each of the significant

coherence components were used in a multiple regression model with broad reading and

32

broad math composite scores to determine if observed coherence components were

specifically associated with reading, or academic ability more generally (i.e., discriminant

validity).

Figure 2.1 10/20 Electrode Placement

33

CHAPTER 3

RESULTS

The present study sought to explore qEEG connectivity across regions of the brain

in school-aged children in relation to their reading performance on the WJ III- Ach, a

norm-referenced achievement test. The major reason for undertaking this study was to

investigate whether the coherence patterns at rest in the brain are associated with

children’s reading scores. QEEG measures of coherence can provide us with bio-

signatures that might be exclusively associated with a child’s reading scores.

The findings can be summarized using the following major pointers. First, there

were activations in frontotemporal lobe, occipital lobe, temporo-parietal lobe, and

occipitotemporal lobe, and these activations were significantly correlated with each other

(see figure 1.1). This was an expected outcome since prior neuroimaging research on

activations in these areas have been associated with reading. Second, the derived qEEG

coherence regions across five coherence components were associated with letter-word

identification, reading fluency, and passage comprehension subtests of the WJ-III- Ach

(see table 3.6). Third, the derived qEEG coherence regions across the five components

were associated with broad reading composite scores (see table 3.5. Finally, the derived

coherence components of interest to reading are not associated with broad math

composite scores, hence indicate domain specific activation (see Table 3.7). This ruled

out the possibility of shared activation for regions of the brain associated with Reading

and Math. Indicating that the derived components associated with reading hypothetically,

34

show a linear relationship with the reading scores and satisfy a broad Reading score

regression model but not the broad Math score model.

This chapter presents the principal components analysis, then the descriptive

statistics from the Woodcock-Johnson Reading Achievement Test. This is followed by

the bivariate Pearson's correlation analysis, the linear multiple regression analysis used in

understanding whether the five derived coherence components are associated with

individual reading subtest scores and broad reading composite scores. Finally,

discriminate validity related to Woodcock-Johnson Math Achievement results will be

presented.

In order to analyze the dataset, first I reduced the entire dataset from thousands of

variables to 128 variables without compromising on the information contained in the

original dataset. For doing this I conducted 32 Principal Component Analyses (PCA)

with the entire dataset. 16 of those PCA's were for Brodmann area 44 with eight in each

hemisphere representing the eight frequency bands of interest within that region. The

other 16 PCA’s were conducted on the Brodmann area 45 with eight in each hemisphere

representing the eight frequency bands of interest within that region. Once these 32

PCA's were done, each of them yielded 4 coherence components which could be of

interest to reading primarily based on the fact that they were extracted from Brodmann

area 44 and 45 which, from research, we are aware, is implicated in the reading process.

These four components from each of the PCA models explained more than 60% of the

variance in the data indicating that 60% of the attributes of the variables could be

correlated to reading. These components with their percentage of variance are an

intermediate product to reduce the data and arrive at the final coherence components that

35

could be of importance to reading. However, since the neuroimaging data was not

collected during the reading task, this is primarily an assumption of the model that they

are related to reading. Having said that, we do know that this process reduces the

dimensionality of the dataset without intrinsically changing the nature and the efficacy of

the data. Hence, it still becomes a true representation of the original dataset and avoids

loss of information as much as possible. To summarize, the principal component analysis

is another representation of the original dataset- a dimension-reduced version. Since I am

reducing the dimension of the dataset, I am bound to lose some information. The loss of

information can be quantified by the explanation of the variance in my dataset. The

original dataset explains 100% of the variance but since I reduced the dimension of the

dataset, it explains less than 100% of the variance, but still enough to consistently reflect

the true data.

Once I had a more manageable dataset, I could individually put these 128

coherence components in a bivariate Pearson’s correlation model with the three

individual reading subtest scores and see which of these components are correlated to the

reading subtest scores. The results of the Pearson’s correlation revealed five different

coherence components that were correlated to at least two of the three reading subtest

scores. Now, these components could be attributed to being reading components since

they satisfy the Pearson’s correlation model due to their association with reading scores.

These five components are then put together in a linear multiple regression model to see

if they are associated with the individual subtests and the broad reading composite scores.

Through this regression analysis we can infer if there is enough variance in these models

to be associated with broad reading performance in children. These components are then

36

used again to see if they are associated with broad math performance scores from the

same dataset, to confirm the idea of discriminant validity. This information helps us in

understanding if the five extracted components from the whole dataset are specifically

associated with reading or are associated with other general academic skills like math

performance.

Descriptive statistics for all participants’ scores on the WJ-III Ach reading

subtests including means, standard deviations, minimum scores, and maximum scores

have already been reported in Table 2.2. Examination of the descriptive statistics

indicates that the current study included a sample of participants whose reading

achievement scores are generally representative of the range of scores observed in the

sample. In case of letter word identification, the range of scores varied from a minimum

of 61 to a maximum of 135; the reading fluency scores varied from a minimum of 44 to a

maximum of 148; and the passage comprehension scores varied from a minimum of 55 to

a maximum of 133 (see table 2.2).

3.1 PRINCIPAL COMPONENTS ANALYSIS

A Principal Components Analysis (PCA) was conducted to reduce the number of

items into components that represent the data. Measures of sampling adequacy revealed

no issues with the factorization of the correlation matrix. Components with eigenvalues

greater than 1.0, that passed the scree test, and had a Kaiser-Meyer-Olkin measure

(KMO) value greater that .60 were extracted separately for each frequency bands in both

brain hemispheres. The scree plot identified a 4-8 component solution for the PCA’s. On

further examination, these three parameters were tested, but I selected a four-component

solution, based on interpretability and reliability (see table 3.1 and 3.2). Varimax rotation

37

was chosen because it aids interpretation when the components are to be used as

dependent variables (Tabachnick & Fidell, 2001). Complex loading items (i.e., those that

loaded on more than one component) and items that did not load >.40 on any of the

components were deleted. The remaining items again underwent PCA, and items with

low or complex loadings were deleted. Table 3.1 and 3.2 lists all components that were

extracted form Brodmann Area 44 and 45 meeting these criteria for each wavelength by

hemisphere with the percent of variance explained by the Eigenvalue of that component.

3.2.1 ASSOCIATIONS WITH READING PERFORMANCE

Brodmann area 44 and 45 anatomically constitute the Broca’s area. A

neuromorphometric analysis of the Broca’s area suggests that there is a clear anatomical

separation between BA 44 and BA 45 (Amunts et al., 1999; Brodmann, 1909). Years of

FMRI research has indicated that the two areas are indeed functionally separable.

Brodmann area 44 has been usually found to be activated during syntactic tasks (e.g.,

Friederici et al., 2000a, Friederici et al., 2000b) whereas Brodmann area 45 has been

found to be involved in semantic processing (Friederici et al., 2000b). The reasons for

extracting components from this region entail a direct correlation to reading skills.

However, since this EEG data were collected during rest and not while performing a

reading task, we cannot essentially attribute all the correlated components to reading.

Having said that, using the method of statistical analysis, we can create a model, whereby

we can explore, if the first four extracted components, from this region can generally

explain more than 60% of the variance in the data set. Implying, that these four variables

(principal components of the PCA’s) possibly constitute 60% of the reading-related

functions.

38

Functional Magnetic Resonance Imaging (fMRI) research on Broca’s area has

already shown that left Brodmann area 44 and 45 have strong activations during

pseudoword reading as opposed to words and BA 44 is activated during phonological and

lexical tasks whereas BA 45 is only activated during lexical tasks. Subsequently, this

finding supports the dual route model theory of reading with the left BA 44 being

involved in the grapheme to phoneme manipulation whereas the left BA 45 being

exclusively associated with lexicality (Heim et al., 2005). Overall, this supports the idea

that children with greater connectivity circuits across BA 44 and 45 would perform better

in reading than those who have lesser connectivity.

3.2.2 BRODMANN AREA 44 EXTRACTED COMPONENTS

In the right hemisphere, several components that included BA 44 were identified

in each frequency band. Within the alpha 1 band, the first four components were

extracted based on the criteria above. Initial Eigenvalues for these four components

indicated that they explained 65%, 13%, 5%, and 4% of the variance, respectively.

Within the alpha 2 band, the first four components were extracted. Initial Eigenvalues for

these four components indicated that they explained 50%, 21%, 9%, and 5% of the

variance, respectively. The beta 1 band also had four components extracted. The

eigenvalues for these components signified that they explained 22%, 20%, 12%, and 10%

of the variance respectively. The beta 2 band had an additional four components extracted

based on the criteria outlined above. These components’ initial Eigenvalues explained

29%, 20%, 12%, and 9% of the variance, respectively. Within beta 3 band, four

components were extracted; their initial Eigenvalues explained 36%, 24%, 8%, and 7%

of the variance respectively. In the high-beta frequency band, four components were

39

extracted. The initial Eigenvalues for these four components explained 37%, 23%, 6%,

and 5% of the variance, respectively. Within the Theta band four components were

extracted. Their initial Eigenvalues explained 24%, 22%, 13%, and 10% of the variance

respectively. Delta was the final frequency band examined in the right hemisphere; four

components were extracted. They explained 39%, 16%, 9%, and 6% of the variance,

respectively.

The left hemisphere analyses also identified several components that included BA

44 in each frequency band. Within the alpha 1 band, the first four components were

extracted based on the criteria outlined above. Eigenvalues for these four components

indicated that they explained 55%, 17%, 9%, and 5% of the variance, respectively.

Within the alpha 2 band, the first four components were extracted. Eigenvalues for these

four components indicated that they explained 51%, 21%, 9%, and 6% of the variance,

respectively. The beta 1 band had four components extracted. The eigenvalues for these

components signified that they explained 31%, 23%, 15%, and 12% of the variance,

respectively. The beta 2 band had a total of four components extracted. These

components explained 28%, 25% 11%, and 10% of the variance, respectively. Within the

beta 3 band, four components were extracted; they explained 38%, 21%, 9%, and 6% of

the variance, respectively. In the high-beta frequency band, four components were

extracted. These four components explained 32%, 24%, 9%, and 8% of the variance,

respectively. The theta frequency band yielded an additional four components. These

components explained 31%, 22%, 14%, and 9% of the variance, respectively. Lastly

within the delta band, four components were also extracted. They explained 51%, 11%,

7%, and 7% of the variance.

40

3.2.3 BRODMANN AREA 45 EXTRACTED COMPONENTS

In the right hemisphere, several components that included BA 45 were identified

in each frequency band. Within the alpha 1 band, the first four components were

extracted based on the criteria above. Initial Eigenvalues for these four components

indicated that they explained 62%, 15%, 6%, and 4% of the variance, respectively.

Within the alpha 2 band, the first four components were extracted. Initial Eigenvalues for

these four components indicated that they explained 49%, 21%, 9%, and 5% of the

variance, respectively. The beta 1 band also had four components extracted. The

eigenvalues for these components signified that they explained 32%, 18%, 11%, and 8%

of the variance respectively. The beta 2 band had an additional four components extracted

based on the criteria outlined above. These components’ initial Eigenvalues explained

36%, 20%, 11%, and 7% of the variance, respectively. Within beta 3 band, four

components were extracted; their initial Eigenvalues explained 33%, 26%, 12%, and 7%

of the variance respectively. In the high-beta frequency band, four components were

extracted. The initial Eigenvalues for these four components explained 36%, 24%, 7%,

and 6% of the variance, respectively. Within the Theta band four components were

extracted. Their initial Eigenvalues explained 29%, 22%, 14%, and 7% of the variance

respectively. Delta was the final frequency band examined in the right hemisphere; four

components were extracted. They explained 41%, 15%, 9%, and 6% of the variance,

respectively.

The left hemisphere analyses also identified several components that included BA

45 in each frequency band. Within the alpha 1 band, the first four components were

extracted based on the criteria outlined above. Eigenvalues for these four components

41

indicated that they explained 52%, 19%, 8%, and 5% of the variance, respectively.

Within the alpha 2 band, the first four components were extracted. Eigenvalues for these

four components indicated that they explained 51%, 21%, 8%, and 6% of the variance,

respectively. The beta 1 band had four components extracted. The eigenvalues for these

components signified that they explained 28%, 22%, 12%, and 10% of the variance,

respectively. The beta 2 band had a total of four components extracted. These

components explained 30%, 25% 13%, and 7% of the variance, respectively. Within the

beta 3 band, four components were extracted; they explained 42%, 21%, 11%, and 6% of

the variance, respectively. In the high-beta frequency band, four components were

extracted. These four components explained 37%, 17%, 10%, and 5% of the variance,

respectively. The theta frequency band yielded an additional four components. These

components explained 29%, 20%, 12%, and 10% of the variance, respectively. Lastly

within the delta band, four components were also extracted. They explained 59%, 8%,

7%, and 5% of the variance.

Table 3.1 Extracted components and Eigenvalue variance explained for Brodmann Area

44

Left Right

Wavelength

Variance

Explained Wavelength

Variance

Explained

Alpha 1 Alpha 1

Component 1 55% Component 1 65%

Component 2 17% Component 2 13%

Component 3

Component 4

9%

5%

Component 3

Component 4

5%

4%

Alpha 2 Alpha 2

Component 1 51% Component 1 50%

42

Component 2

Component 3

Component 4

21%

9%

6%

Component 2

Component 3

Component 4

21%

9%

5%

Beta 1 Beta 1

Component 1 31% Component 1 22%

Component 2 23% Component 2 20%

Component 3

Component 4

15%

12%

Component 3

Component 4

12%

10%

Beta 2 Beta 2

Component 1 28% Component 1 29%

Component 2 25% Component 2 20%

Component 3

Component 4

11%

10%

Component 3

Component 4

12%

9%

Beta 3 Beta 3

Component 1 38% Component 1 36%

Component 2 21% Component 2 24%

Component 3

Component 4

9%

6%

Component 3

Component 4

8%

7%

High-Beta High-Beta

Component 1 32% Component 1 37%

Component 2

Component 3

Component 4

24%

9%

8%

Component 2

Component 3

Component 4

23%

6%

5%

Theta Theta

Component 1 31% Component 1 24%

Component 2 22% Component 2 22%

Component 3

Component 4

14%

9%

Component 3

Component 4

13%

10%

Delta Delta

Component 1 51% Component 1 39%

43

Component 2

Component 3

Component 4

11%

7%

7%

Component 2

Component 3

Component 4

16%

9%

6%

Table 3.2 Extracted components and Eigenvalue variance explained for Brodmann Area

45

Left Right

Wavelength

Variance

Explained Wavelength

Variance

Explained

Alpha 1 Alpha 1

Component 1 52% Component 1 62%

Component 2 19% Component 2 15%

Component 3

Component 4

8%

5%

Component 3

Component 4

6%

4%

Alpha 2 Alpha 2

Component 1 51% Component 1 49%

Component 2

Component 3

Component 4

21%

8%

6%

Component 2

Component 3

Component 4

21%

9%

5%

Beta 1 Beta 1

Component 1 28% Component 1 32%

Component 2 22% Component 2 18%

Component 3

Component 4

12%

10%

Component 3

Component 4

11%

8%

Beta 2 Beta 2

Component 1 30% Component 1 36%

Component 2 25% Component 2 20%

Component 3

Component 4

13%

7%

Component 3

Component 4

11%

7%

Beta 3 Beta 3

44

Component 1 42% Component 1 33%

Component 2 21% Component 2 26%

Component 3

Component 4

11%

6%

Component 3

Component 4

12%

7%

High-Beta High-Beta

Component 1 37% Component 1 36%

Component 2

Component 3

Component 4

17%

10%

5%

Component 2

Component 3

Component 4

24%

7%

6%

Theta Theta

Component 1 29% Component 1 29%

Component 2 20% Component 2 22%

Component 3

Component 4

12%

10%

Component 3

Component 4

14%

7%

Delta Delta

Component 1 59% Component 1 41%

Component 2

Component 3

Component 4

8%

7%

5%

Component 2

Component 3

Component 4

15%

9%

6%

3.3 PEARSONS CORRELATION

Pearson’s correlations were used to examine the linear relationships between the

extracted EEG components (Table 3.1 and 3.2) and their correlations with WJ-III Ach

reading scores. The results of the analyses and indicates that there was a total of five

reading coherence components that were significantly correlated with at least two of the

three reading achievement standard scores; there were a total of 12 positive and

significant correlations between the five extracted components and the three WJ-III

reading scores (see table 3.3 and 3.4). The aim for this analysis was to identify significant

45

correlations for subsequent regression analyses. Table 3.3 and 3.4 depict correlations

between the identified coherence components and each of the WJ III-Ach reading

subtests in BA 44 and BA 45, respectively.

For BA 44, in the right hemisphere (RH) there were significant positive

correlations (p<.05) between two components (one component in theta and one

component in beta 2) and all three WJ-III reading achievement standard scores.

Component number two in the theta band had significant correlations with two reading

subtests (Letter-word Identification r (54) =0.340, p < .05, Reading Fluency r (58)

=0.305, p < .05) and was not correlated to Passage Comprehension. Component number

three in the beta 2 band had significant correlations with all the three reading subtests

(Letter-word Identification r (54) = -0.273, p < .05 and Reading Fluency r (54) = -0.325,

p < .05, and Passage Comprehension r (54) = -0.325, p < .05)

In the left hemisphere (LH), there were significant positive correlations (p<.05

and p<.01) among one additional component (in the delta band) and at least two of the

three WJ-III reading achievement standard scores. Component number two in the delta

band had significant correlations with the Reading Fluency and Passage Comprehension

subtests, but not Letter-word Identification (Reading Fluency r (54) = 0.271, p < .05 and

Passage Comprehension r (58) =0.356, p < .01).

Table 3.3 Significant Pearson’s Correlations between PCA components reading tests in

BA 44.

Letter-word

Identification

Reading

Fluency

Passage

Comprehension

BA 44, Delta, LH, Component

2

.258 (.060) .271 (.047) * .356 (.008) **

46

BA 44, Theta, RH, Component

2

.304 (.025) * .305 (.011) * .212 (.124)

BA 44, Beta 2, RH, Component

3

.273 (.046) * .325 (.016) * .325 (.016) *

* p < .05. ** p < .01. *** p < .001.

For BA 45, in the right hemisphere (RH) there were significant positive

correlations (p<.05) between one component in the beta 2 band and all the three WJ-III

reading achievement standard scores. Component number three in the beta 2 band had

significant correlations with all three reading subtests (Letter-word Identification r (54) =

-0.289, p < .05 and Reading Fluency r (54)= -0.276, p < .05, and Passage Comprehension

r(54)= -0.296, p < .05).

In the left hemisphere (LH), there were significant positive correlations (p<.05)

among one additional component (in the alpha 1 band) and at least two of the three WJ-

III reading achievement standard scores. Component number three in the alpha 1 band

had significant correlations with Reading Fluency and Passage Comprehension subtests,

but not Letter-word Identification (Reading Fluency r (54) = 0.300, p < .05 and Passage

Comprehension r (58) =0.276, p < .01).

Table 3.4 Significant Pearson’s Correlations between PCA components reading tests in

BA 45.

* p < .05. ** p < .01. *** p < .001.

Coherence Component

Letter-word

Identification

Reading

Fluency

Passage

Comprehension

BA 45, Alpha 1, LH, Component

3

.240 (.080) .300 (.028) * .276 (.043) *

BA 45, Beta 2, RH, Component

3

.289 (.034) * .276 (.043) * .296 (.030) *

47

Appendix B provides a summary of the post- PCA, rotated component loading

weights of each variable in these components that are significantly correlated to reading

standard scores. From the tables of the principal components loading weights and

coherence circuits associated with BA 44 and 45 shown in Appendix B, it can be inferred

that the reading components in the brain are distributed across both the right and the left

hemispheres in different lobes, as shown in the figure 3.1 and 3.2 below. Mapping the

derived components and clustering them in the form of Brodmann areas, one can clearly

see that these clusters are associated with different aspects of reading as informed by the

extant literature in this field. (see figure 1.1, chapter 1)

A. Frontotemporal Lobe B Occipitotemporal Lobe

C. Occipitotemporal Lobe

Figure 3.1 Associated coherence components for BA 44 A: Delta component 2, left

hemisphere; B: Theta component 2, right hemisphere; C: Beta 2 component 3, right

hemisphere

48

A. Temporoparietal Lobe B. Occipital Lobe

Figure 3.2 Associated coherence components for BA 45 A: Alpha 1 component 3, left

hemisphere; B: Beta 2 component 3, right hemisphere.

3.4 MULTIPLE REGRESSION

A multiple regression model was constructed with the help of the five identified

reading components to ascertain if they were associated with broad reading composite

scores. The model explains 26% of the variance and is significantly associated with the

Broad Reading WJ-ACH score, which is a composite of all the three reading subtests, F

(5, 54) = 3.306, p = .01, R2 = 0.26 (see Table 3.5). The Variance inflation factors (VIFs)

for three variables are less than 2 and for the other two variables they are equal to 5. This

suggests that three of the five variables are not collinear and the other two are moderately

collinear suggesting overall low multicollinearity within the model (Berninger &

Richards Knock & Lyn, 2012). Low multicollinearity indicates that the five independent

variables (components extracted) used in the regression model are not correlated to one

another and are fairly independent (VIF<2). This assumes that the model is a good fit and

interpretation of the results is accurate. If the independent variables are correlated with

one another, it can cause problems with the model fit and lead to erroneous interpretation

of results.

49

In order to understand the association of these five broad reading components

with individual reading skills, three other multiple regression models were derived (see

Table 3.6). In case of the letter-word identification subtest a model including all five

components significantly associated with Letter-word Identification standardized scores.

The model explains 23% of the variance, F (5, 54) = 2.781, p < .05, R2 = 0.23. For the

Reading Fluency subtest, a multiple linear regression model including the five

components significantly associated with the Reading Fluency standardized scores and

explains 27% of the variance, F (5, 54) =3.525, p < .01, R2 = 0.27. Lastly, for the Passage

Comprehension subtest, a multiple linear regression model including the five components

significantly associated with Passage Comprehension standardized scores explaining 24%

of the variance, F (5, 54) =3.084, p < .05, R2 = 0.24. The Variance inflation factors (VIFs)

for three variables are less than 2 and for the other two variables they are equal to 5. This

suggests that three of the five variables are not collinear and the other two are moderately

collinear suggesting overall low multicollinearity within the model (Knock & Lyn, 2012).

Table 3.5 Multiple linear regression model using coherence components to see their

association with Broad Reading composite score

.

β F t p

Broad Reading Model ** 3.306 =.01

Delta, LH, Component 2 .245 1.647 .106

Theta, RH, Component 2 .223 1.403 .167

Beta 2, RH, Component 3

Alpha 1, LH, Component 3

-.115

.246

-0.388

1.862

.700

.049*

Beta 2, RH, Component 3 .266 0.932 .356

* p < .05. ** p < .01

50

In the table (3.5) above β is the standardized coefficient, p is the significance

value, and F is the F statistics. From the table its apparent that an increased (positive)

delta (LH), theta (RH), alpha 1(LH), beta 2(RH) and a reduced (negative) beta 2 (RH)

coherence has a direct association with the broad reading composite scores of the

children. Specifically, a higher alpha 1 component in the Left hemisphere is implicated in

the broad reading composite scores of children.

Table 3.6 Multiple linear regression models using coherence components to see their

association with reading subtests.

β F t p

Letter-word Identification Model * 2.781 < .05

Delta, LH, Component 2 .256 1.690 .097

Theta, RH, Component 2 .283 1.745 .087

Beta 2, RH, Component 3 -.245 -0.805 .425

Alpha 1, LH, Component 3

Beta 2, RH, Component 3

.187

.326

1.382

1.116

.173

.270

Reading Fluency Model ** 3.525 < .01

Delta, LH, Component 2 .191 1.294 .202

Theta, RH, Component 2* .327 2.077 .043*

Beta 2, RH, Component 3 .079 0.266 .791

Alpha 1, LH, Component 3

Beta 2, RH, Component 3

.254

.009

1.927

0.031

.060

.975

Passage Comprehension Model * 3.084 < .05

Delta, LH, Component 2 .293 1.956 .056

Theta, RH, Component 2 .130 0.809 .422

Beta 2, RH, Component 3 -.039 -0.131 .897

Alpha 1, LH, Component 3

Beta 2, RH, Component 3

.193

.239

1.442

0.829

.156

.411

* p < .05. ** p < .01.

51

In the table (3.6) above β is the standardized coefficient, p is the significance

value, and F is the F statistics. For the Letter-word identification subtest, it can be

observed that an increased (positive) delta (LH), theta (RH), alpha 1(LH), beta 2(RH) and

a reduced (negative) beta 2 (RH) coherence has a direct association with the letter -word

identification scores of the children. For the Reading Fluency subtest, it can be observed

that an increased (positive) delta (LH), theta (RH), alpha 1(LH), beta 2(RH) and a

reduced (negative) beta 2 (RH) coherence has a direct association with the reading

fluency scores of the children. Specifically, a higher theta component in the Right

hemisphere is implicated in the reading fluency scores of children. For the Passage

Comprehension subtest, it can be observed that an increased (positive) delta (LH), theta

(RH), alpha 1(LH), beta 2(RH) and a reduced (negative) beta 2 (RH) coherence has a

direct association with the passage comprehension scores of the children.

3.5 DISCRIMINANT VALIDITY

Thatcher et al., (2005) indicate in their research that coherence is directly related

to IQ and general abilities hence it is incumbent upon us to validate the association with

the five resulting components for broad math composite scores as well. This would

clarify if these identified coherence circuits were specific to reading performance or

general academic performance (including math). All the five derived components were

examined to determine their correlations with the broad math performance using cluster

scores provided by the WJ-III Ach.

Results indicate that none of the five components are associated with the broad

math composite scores. None of the components are correlated to the broad math

performance (see Table 3.7). This suggests that the brain components including

52

Brodmann Area 44 and 45 are specific to reading and not broad math performance or

general academic performance. This indicates the presence of domain specific

components and regions in the brain exclusively related to reading performance.

In order to further solidify a region specific (BA 44 and BA 45) hypothesis for the

reading components a multiple regression model was examined. The resulting model

barely explains 11% of the variance and is not associated with broad math composite

scores, F (5, 54) =1.193, p >.05, R2 = 0.11, indicating domain specificity (for just

reading).

This also further reinforces the claim, that BA 44 and 45, which is generally

known as the Broca’s area in the brain is primarily associated with language production

and comprehension which are essential for reading. The connections of Broca’s area and

the lobes across the brain also indicate that there are several other regions of the brain

that might be implicated in the reading process. These regions include the frontotemporal

lobe, occipital lobe, temporo-parietal lobe, and the occipitotemporal lobe.

Table 3.7 Significant Pearson’s Correlations between reading coherence components and

Broad Math scores

Coherence Component Broad Math

Delta, LH, Component 2 .170 (.219)

Theta, RH, Component 2 .239 (.082)

Beta 2, RH, Component 3 .235 (.087)

Alpha 1, LH, Component 3

Beta 2, RH, Component 3

.157 (.257)

.207 (.132)

* p < .05. ** p < .01.

53

CHAPTER 4

DISCUSSION

Currently research examining neurocognitive underpinnings of brain functions

that are implicated in reading performance in children is an emerging area. This is

important because educators are increasingly using neuroscientific findings to understand

the distributed nature of the reading process. Findings from the dissertation indicate that

the Brocas area is in sync with the frontotemporal lobe, occipitotemporal lobe,

temporoparietal lobe and the occipital lobe. This means that the Brodmann areas 44 and

45 in the Brocas region and the derived Brodmann areas (highlighted in figure 3.1 and

3.2) in the four lobes are oscillating with the same frequency underlining functioning

connectivity among these regions of the brain. Looking at the structure and organization

of this connectivity, it is evident that it is spread out across all the four lobes of the brain

as suggested in the results section. All these different regions of the brain have different

functions, like orthography, phonology, semantic, and embodied and reading is an

orchestration of all those processes. Hence, we can say that the development of the

reading network is not built through reliance on one process but involves multiple

processes across brain regions adding to educators understanding the reading brain. To

develop this argument, I will first describe how coherence measures can be used to

determine functional connectivity in the brain. Next, I will explore how these measures

relate to reading scores. Finally, I will describe how the relationship of coherence and

54

reading scores supports a domain specific model of reading. This discussion will be

followed by limitations and implications of the study.

QEEG measures of coherence can provide us bio-signatures associated with areas

of the brain that contribute to the reading process and by extension reflect in children’s

reading scores. Hence, educators can use this information to understand that reading is a

multi-dimensional process with several regions of the brain in coherence with each other.

That is, there is functional connectivity across areas within the brain. Functional

connectivity basically connotes the cross-temporal correlation of activity measured in

different parts of the brain (Honey et al., 2009). Coherence is one among several other

metrics used for deriving functional brain connectivity in EEG (Decker et al., 2017).

Coherence provides information about the functional connectivity between the structures

lying beneath the electrode pairs. These electrodes are flat metal physical sensors placed

on the scalp, used to measure electrical activity resulting from the interactions between

neurons. Coherence compares the similarity in the power spectra and is usually

calculated after transforming from the time domain to the frequency domain with the

assumption that brain regions which show greater similarity in activation are functionally

connected (Bowyer, 2016). The current study theoretically includes the derived

coherence regions that would be of interest to reading.

From this theoretical perspective the information about the regions of the brain

involved in the reading process and children’s reading scores from standardized

assessments could also be used to understand reading difficulty in children. Armed with

this foundational knowledge of reading-related qEEG coherence, both educators and

school psychologists could provide better instructional interventions and assessments

55

taking into account how the brain processes reading. Adding on to the theoretical

understanding of how a child’s brain performs reading tasks, examination of neuro-

electrophysiological activity correlated with reading performance could help educators

understand that reading is a dynamic process, distributed across several regions of the

brain and not to be seen in isolation (Compton-Lilly et al., 2020). Understanding the

underlying neural processes is a critical step towards addressing the need for improved

reading assessment and intervention practices. The current study also helps to address

this need by providing the foundations for basic research in the future in relation to the

neurocognitive processes that are implicated in children’s broad reading performance

(Wu et al., 2008).

Research has already indicated that that BA 44 and 45, which constitutes the

Broca’s region are heavily implicated in phonological and semantic processing

(Friederici et al., 2000a; Friederici et al., 2000b). The current study adds to this

knowledge by exploring associations between children’s reading scores and

intrahemispheric brain connectivity levels with BA 44 and 45. Results of the study

clearly show that there are a total of five significant coherence networks (3 in the right

hemisphere and 2 in the left hemisphere) in the brain that might be associated with

children’s broad reading performance as determined by the standardized WJ-III Ach

reading test scores (see figure 3.1 and 3.2). Findings from this dissertation highlight that

the occipitotemporal and occipital lobe in the right hemisphere of the brain are in

coherence with the Brocas area (Brodmann area 44 and 45) (see figure 3.1 and 3.2).

Despite, classically language network being left dominant, activations found in the right

56

hemisphere debunk an exclusive left-lateralized reading network. This signals the

distributed nature of the reading process across both hemispheres.

This necessitates looking at the Woodcock Johnson III- Test of Achievement

reading battery in order to understand how the aforementioned coherence measures relate

to the scores of the children on the three subtests included in this study.

The three core reading subtests included in the current study were: Letter-word

identification, reading fluency, and passage comprehension. Letter-word identification

assesses reading skills by the subjects reading a list of words from an increasingly

difficult vocabulary list and evaluating their pronunciation. Passage comprehension

focuses on the child’s understanding the meaning of the text where a child reads a

sentence silently and from the context decides to fill in the blank spaces with specific

words that complete the sentence. There is a gradual increase in the level of the

vocabulary, as the child progresses through the sections. Reading fluency subtest allows

the child to read simple sentences and circle “Y” or “N” in the answer sheet in order to

accurately respond to as many items as possible within the allotted time.

There are three coherence networks in the BA 44 that are significantly associated

with at least two out of three reading subtest scores. The coherence network in the theta

frequency band (4-8 Hz) in the right hemisphere significantly associated with the

standardized scores for letter-word identification and reading fluency and is functionally

connected with occipitotemporal lobe.

This validates the idea that the occipitotemporal lobe, also known as the “visual

word form area” is implicated in rapid word-form processing without the need for

decoding (Glezer et al., 2016; Klein et al., 2015; Ludersdorfer et al., 2015) (see figure

57

3.1). Additionally, the coherence network in the beta 2 frequency band in the right

hemisphere significantly associated with standardized scores across all the three reading

subtests including letter-word identification, reading fluency, and passage comprehension

and also has functional associations with the occipitotemporal lobe. Within the left

hemisphere, the delta frequency band (1-4 Hz) significantly associated with the

standardized scores for reading fluency and passage comprehension, with a stronger

correlation with the passage comprehension reading scores and functional connectivity

across the frontotemporal lobe.

The frontotemporal region is associated with sentence comprehension and spoken

word recognition. In a study done by Sood and Sereno (2016) assessing the overlapping

regions in the brain for activation during reading comprehension and visual, auditory, and

somatosensory brain mapping, we observe that the frontal cortex and the superior

temporal cortex are heavily implicated. Recent research has also indicated that these

classical language areas (frontotemporal region and parietal cortex), that were erstwhile

considered to be beyond the scope of the occipital lobe (boundaries of vision), are

involved in reading as proven through retinotopic mapping (Hagler & Sereno, 2006;

Hagler et al., 2007; Kastner et al., 2007; Huk et al., 2002; Schluppeck et al., 2005; Sereno

& Huang, 2006; Huang and Sereno, 2013;). Results from this study indicate that there

was activation in specific frontal regions like the left inferior frontal gyrus and

dorsolateral prefrontal cortex. These regions are classically associated with attention,

which is required for reading processes and comprehension, indicating that attention is a

cortex-wide dynamic activity and is not dissociable from sustained reading practices

(Çukur et al., 2013, Peelen & Kastner, 2014).

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Hence my study adds on to the existing literature and makes a stronger case for

reading being distributed across the four lobes of the brain and the frontotemporal region

being implicated in reading comprehension on account of attentional processing (see

figure 3.1 and 3.2).

There are two coherence networks in BA 45 that are significantly associated with

at least two out of three reading subtest scores. The coherence network in the beta 2

frequency band in the right hemisphere is significantly associated with the standardized

scores across all three reading subtests including letter-word identification, reading

fluency, and passage comprehension and is functionally connected with occipital lobe.

Subsequently, the coherence network in the alpha 1 frequency band (8-10 Hz) is

significantly associated with standardized scores for reading fluency and passage

comprehension with functionally connectivity across the temporoparietal lobe.

To summarize, these results indicate that BA 44 and 45 are functionally

associated with frontotemporal, occipitotemporal, temporoparietal, and occipital lobe

which are spread across the entire brain. A region-specific functional association cannot

explain the associated connectivity across the whole brain. Hence, in the right hemisphere

an increased theta and beta 2 coherence between BA 44 and the occipitotemporal lobe

and an increased beta 2 coherence between BA 45 and the occipital lobe are associated

with better broad reading performance scores in children. Broad reading composite scores

is the total score across all the three reading subtests and was measured by adding up the

individual subtest scores and averaging the sum. Similarly, in the left hemisphere an

increased delta coherence between BA 44 and the frontotemporal lobe and an increased

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alpha 1 coherence between BA 45 and temporoparietal lobe are associated with better

broad reading performance scores in children.

To further the investigation of reading scores and associated brain activity,

models including these broad reading coherence networks and standardized reading

subtest scores were examined. Results reveal three statistically significant regression

models, one for each of the reading subtests. Letter-word identification, reading fluency,

and passage comprehension scores are significantly associated with a brain coherence

model composed of the following component variables: 1) BA 44 in the right hemisphere

having an increased theta and beta 2 coherence with the right occipitotemporal lobe; 2)

BA 44 in the left hemisphere having an increased delta coherence with the left

frontotemporal lobe; 3) BA 45 in the right hemisphere having an increased beta 2

coherence with the occipital lobe; 4) BA 45 in the left hemisphere having an increased

alpha 1 coherence with the temporoparietal lobe. A similar study on memory

improvement and QEEG changes in relation to a reading task with EEG biofeedback

therapy revealed the importance of having increased alpha, beta 1, and beta 2 coherence.

The experimental group performed above the normative reference for reading and

auditory memory tasks and showed significantly higher levels of alpha, beta 1, and beta 2

coherence with EEG biofeedback therapy (Thornton & Carmody, 2013).

The study shows that resting-state QEEG coherence in the theta, delta, beta 2,

and alpha 1 frequency bands for BA 44 and 45, across both hemispheres, has a direct

association with children’s reading performance on the WJ-III Test of Ach. reading tests.

These results suggest that there is a linear relationship between the QEEG coherence

parameters in the specified frequency bands and children’s reading scores. Indicating that

60

the higher the levels of connectivity across the brain (coherence), specifically in the theta,

delta, beta2, and alpha 1 band, the higher the scores on reading subtests. The field of

reading has been testing children with these subtests for a long time, hence it is

imperative that we understand the correlation between these tests and actual brain

connectivity. This not only validates the distributed reading network model but also

validates that educators have been focusing on the valid reading assessments in order to

measure reading performance in children. This finding is in line with the National

Reading Panel that centered five important aspects to reading: phonics, phonemic

awareness, vocabulary, reading comprehension, and fluency. As educational research on

reading keeps growing, this QEEG coherence study shows us that there is a strong

positive correlation between types of reading components that educators rely on during

assessment and what is actually happening in the brain. Some of the reading assessments

that are not represented in my study, should also be explored in future studies to get a

complete picture of the reading process. For instance, in addition to phonology-based

reading and reading comprehension, embodied processes are also crucial to reading. This

involves multimodal integration of linguistic and nonlinguistic knowledge, specifically

during meaning construction (Compton-Lilly et al., 2020). Research has shown that

reading natural sentences elicits brain activity within a neural network that is similar to

activation due to experiential and nonlinguistic knowledge (Anderson et al., 2019) as

opposed to reading pseudowords (Desai et al., 2016). Drawing from this literature it is

evident that embodied processes are an emerging area within the field of neuroimaging

studies of reading which further reinforces the idea of the distributed nature of the

reading process in the brain. Subsequently, exploring issues of executive functions like

61

cognitive control, working memory, and inhibition in relation to the reading process will

be imperative (Catwright, 2015).

Notably, the reading components that were discovered in the study were not

associated with math achievement scores from the same dataset. These findings provide a

rationale for the domain specific and domain general hypothesis that surrounds the

neuroscientific literature on learning domains and disabilities. From the research on

learning disabilities and deficits (stroke and lesion studies), we know that components

related to deficits can be classified as domain-general, indicating that individuals with

any type of learning disability can have underactive network connectivity in specified

areas. However, some networks may represent specific deficits only present in

individuals with a specific type of disability (i.e., math, reading, writing) (see Lutz Jäncke

et al., 2019).

This study is indicative of a domain specific model of brain activity underlying

reading performance exclusively indicating that areas of the brain including Brodmann

Area 44 and 45 are specific to reading and not broad math performance or general

academic performance. This indicates the presence of domain specific components and

regions in the brain exclusively related to reading performance. Consequently, the claim

that the Brocas area (BA 44 and BA 45) are majorly associated with language

comprehension and production which are necessary for reading is reinforced. Further, the

associated areas of the brain that are oscillating with the same frequency, indicating

functional connectivity across these regions (the frontotemporal lobe, occipital lobe,

temporo-parietal lobe, and the occipitotemporal lobe) might also be implicated in the

reading process.

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Future studies should compare identified connectivity networks for both reading

and mathematics performance to determine whether these networks are representative of

specific or general academic abilities. This is because, the data from the study looked at

both math and reading subtests and my research specifically looked at reading. This could

also provide more information on the domain-specific or general hypothesis in relation to

math and reading to clarify the regions of the brain that are exclusively associated with

these learning domains or share co-activation across these domains.

4.1 LIMITATIONS

One of the major limitations of the present study can be explained by the

exploratory nature of the analysis and the use of statistical modeling. On account of a

relatively small sample size of participants, several measures were undertaken to reduce

the number of variables in the dataset. As a result of this data-reduction procedure, two

major consequences arise. First, performing Principal Components Analysis (PCA) to

combine variables with redundancy complicates the results. By combining multiple

coherence variables into one variable, the spatial resolution of the anatomical structures

of the brain involved in the coherence networks were sacrificed. Also, the interpretability

of the brain regions represented by the coherence variables were compromised and

limited only to the general lobes of the brain and not distinct brain structures or the

Brodmann Areas underlying those structures. Second, the consequence of reducing the

total number of variables for examining their correlation to specific reading skillsets can

neither be explained or validated with the five coherence components that were extracted

for the analysis. This is because, only those coherence variables (reading components)

63

were considered in the PCA and the multiple linear regression, that had significant

correlations with at least two of the three reading subtests.

The subtests were used to create a statistical model; the data at rest cannot give us

a complete idea of the specific skill sets like letter word identification, reading fluency,

and passage comprehension since I selected reading components primarily based on

correlations with at least two out of three subtests. I left out the correlations that were just

significant with one test since that could be just a matter of chance. Unless I did multiple

comparison correction (which was not possible due to the low sample size), I cannot use

all the correlations. The large number of correlation tests run, and the relatively small

sample size introduced the issue of random false positive results due to the high

likelihood family-wise type 1 errors. To minimize the chances of Type I error across such

a large number of tests, we required a coherence component to correlate with at least two

out of the three reading subtests to meet criteria for inclusion in the subsequent regression

analyses. These criteria assumed it is unlikely that a component would correlate with two

(or all three) reading subtests by chance and enabled us to focus on brain activity related

to broad, rather than specific, reading skills. Thus, despite not correcting for multiple

comparisons in a traditional fashion (e.g., Bonferroni correction), this strategy does

account for and effectively reduce the occurrence of random false positive results.

Consequently, I ended up using only those correlations that were significant with

at least two out of the three subtests. And hence it can be safely assumed that the

interpretations of the results are limited to the neurocognitive underpinnings of broad

reading performance only and not specific reading skillsets. With a larger sample size,

PCA may not be necessary since the large sample would not necessitate the reduction of

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variables, thus allowing researchers to examine coherence variables individually rather

than in clusters of components. Though beyond the scope of the current study,

eliminating PCA from the methods used would allow for the examination of variables

that are related to specific reading skills rather than broad reading performance.

Furthermore, a large sample would allow additional analyses to examine differences

between high achieving and low achieving reading students.

Another significant limitation of the study entails that the coherence difference

across the participants were not evaluated. Examining differences among high and low

reading performance could yield valuable results as to the desired levels of brain activity

and coherence networks associated with reading processes. However, this was beyond the

scope of the present study due to the presence of a relatively large number of coherence

(EEG) variables obtained from a rather small sample size of participants.

Moreover, due to the inclusion of a large number of variables involved in the

PCA, the current study only examined intrahemispheric coherence and did not take into

consideration interhemispheric coherence. This was done in order to reduce the total

number of variables to be considered after the PCA’s. This might be seen as a major

limitation of the study since interhemispheric alpha coherence has been found to be

higher even for children with dyslexia in relation to their reading performance (Ben-

Soussan et al., 2014). Consequently, there are likely additional components that could

increase our current understanding of the electro-neurophysiological activity associated

with reading performance in children that need to be taken into consideration while

exploring reading.

65

Finally, the interpretation of the result is dependent upon the assumption that a

qEEG coherence network at rest correlating with a reading score is indicative of a

network that is directly involved in performance of that reading task. In reality, the five

different coherence networks identified in the current study represent the baseline

electrophysiological activity at rest. Resting-state brain activity does not imply that they

become active during the performance of the reading tasks. Despite this limitation, these

networks are associated with reading scores and broad reading performance and lend

credence to the distributed nature of the reading process. Hence, they have some utility

for understanding broad reading performance, although, the interpretations warrant

caution until further research can validate these findings.

4.2 IMPLICATIONS

Resting-state EEG implementation and analysis can provide us with a deeper

understanding of how brain development is associated with reading. With a nuanced

evaluation of the degree of brain synchronicity between specific regions of the brain at

rest, we can explore how these measures relate to broad reading performance. My study

adds to the foundational knowledge and research methodology upon which, future studies

examining the neurocognitive processes involved in general academic performance can

build.

There is a dearth of research combining functional neurocognitive data and

academic achievement scores in children. Consequently, gaining a firm understanding of

reading performance and processes at the biological level is paramount. Additionally,

there is a huge drive towards exploring the neurocognitive underpinnings of educational

assessments among educators and school psychologists. Rigorous research and awareness

66

are needed to better understand reading assessment, identification, and intervention

processes. Subsequently, educators also need to understand the distributed nature of the

reading process and how several areas of the brain are functionally connected. The

individual reading subtests only provide us with a partial view of the reading process and

focusing solely on the results from a subtest could often lead to a very reductive

assessment of all the possibilities that a child brings to a text during reading. As the child

navigates this complex process of reading his/her brain gradually builds a reading

network. The development of this reading network is not linear indicating that we cannot

focus on the results of subtests from either phonics or semantics and design curriculum.

This is because phonological processing is a lower order reading skill, while reading

comprehension is a higher order reading skill, and the brain adapts to them, depending

upon the requirement for a given task (Patael et al., 2018). Admittedly phonological

processing plays a vital role in the early years of reading development, but gradually with

the development of the reading network semantic processes also become active

(Compton-Lilly et al., 2020). Hence, there needs to be a more inclusive approach to

assessing children’s reading performance by considering both these factors along with

other precipitating factors like prior experiences, sensory motor processing, and social

interaction, and embodiment. Thus, there is a need to develop instructional interventions

and assessments keeping in mind this complexity of the reading process.

The current study looked at 60 school aged children 7-12 years of age. The linear

relationship found between QEEG coherence (baseline brain connectivity) and the

reading scores of children indicates the gradual development of a reading network across

this age group of children. The development of reading network is also associated with

67

the idea of some children using phonology-based decoding while others use

comprehension-based reading. Prior research indicates that the reading network can be

synchronized in a child as early as six years (Alcauter et al., 2017), and additionally, age-

related differences can also account for activation in different regions of the brain (Sela et

al., 2012). Consequently, it might be easier to understand why younger children might

use the temporoparietal connectivity found in the study while navigating phonology-

based reading, while the older readers might use the occipitotemporal connectivity found

in the study in order to perform whole-word recognition and semantic analysis. This is in

accordance with the findings from research which show a potential correlation between

increase in brain coherence and reading development across time periods (Horowitz-

Kraus et al., 2015).

The future of reading research and neuroscience is fraught with complexities that

need to be unpacked. The association between education and neuroscience could lead the

way for better instruction and interventions and simultaneously translate into better

teaching and learning methodologies in school settings.

4.3 FUTURE DIRECTIONS

Linking neuroscience with literacy instruction can help reading theorists and

educators draw connections between neuroimaging research and the theories and

practices used in education settings. Neuroscientists have described a language network,

made up of different regions located in separate parts of the brain, with information

transferred between them (Friederici, & Gierhan, 2013). Consequently, as the reader

encounters letter patterns and words, semantic activation occurs along with phonemic

processing and word recognition. (Sandak et al., 2004). Additionally, Yu et al. (2018)

68

identified an anatomical overlap between semantic and phonological subnetworks within

the language network. Research also states that at the word, sentence, or discourse levels,

semantic processes involve integrated brain areas that are differentially activated

depending upon the difficulty and abstractness of a text (Binder, Desai et al., 2009).

Finally, neuroscience reading research additionally explores embodied and social factors

in reading (Desai, Binder, Conant, & Seidenberg, 2010; Noble et al., 2006).

From this, it is exceedingly clear that educational neuroscience needs education

scholars to facilitate a dialogue among reading theory, policy, and brain research. The

most sophisticated and impressive attempts to bridge neuroscience and education have

been in relation to reading processes. However, there is a need to incorporate the voice of

reading researchers engaged with the instructional aspect of this interdisciplinary

confluence. The scholars and educators with an interest in teaching reading would benefit

from a thoughtful and thorough understandings of not only these various processes but

also knowledge about how these processes work together.

Neuroscientists and educators, caution against simplistic and reductive

extrapolations of correlations from brain-based imaging studies of reading, since many of

the results are not replicated, and the images of the brain (which are mostly an averaged

difference between the comparison condition and the target condition, hence subject to

variation) do not tell us whether the differences are neurological/genetic/environmental or

instructional (Hruby & Goswami, 2019). Hence, despite the promise of biomarkers in

understanding instructional difficulties and future development with the help of early

interventions and behavioral assessments (Beddington et al., 2008), neuroscientists warn

against false positives in clinical assessments, premature tracking, and the subsequent

69

selection of subprocesses for intensive remediation of struggling readers (Hruby and

Goswami, 2019).

Reading neuroscience has reached a point where it can provide valuable

information for educators. Researchers have been able to collectively inform our

understanding of reading-related processes. The neuroscientific literature continues to

advance our understanding of reading processes in the brain; however, this literature must

be viewed as on-going and partial as research continues to understand the complex

interactions that take place during reading. The neuroscientific models of reading are still

being developed and seem to be focusing on the interaction among areas of the brain with

individual differences in activation depending upon the reader, the task, and the text. It

would be important to keep abreast of the development of these newer models of reading.

The purpose of this dissertation was to take up Hruby’s trifecta challenge (2012)

that an educational neuroscience requires (1) thorough attention to intellectual coherence,

(2) matching expertise in both neuroscience and educational, research, theory, and

practice, and (3) attention to “ethical issues, concerns, and obligations” (p. 3) related to

the general public and their children. The audience for such research includes educators,

neuropsychologists, school psychologists, and clinical psychologists who deal with

reading-related issues. Since teachers are given the responsibility of educating young

minds, it is imperative that they have a working knowledge or understanding about how

the brain functions as they plan reading instruction. Teachers’ access to brain research

and their experiential knowledge could inform a better understanding of how the brain

influences the process of reading development and how the brain, in turn, is changed by

the capacity to read. Such collaborations could enable reading researchers to participate

70

in ongoing discussions regarding the implications of teaching reading based on current

neuroscientific knowledge.

Educators and neuroscientists are both invested in a better future for children.

With integrated approaches, sharing knowledge, and a more open dialogue, I believe both

can come to a consensus about what is best for the child. For the future I sincerely hope

there are collaborations and grants for studies that will drive advancement in education

and neuroscience in order to create a “thinktank” of faculty members for addressing

interdisciplinary research across existing departments. Continuing this line of research

and instead of focusing just on literacy, expanding it to create possibilities of newer

research tracks for addressing pressing issues in science education, math education,

social, cognitive, and affective skills.

Additionally, if we want to know, what parts of our brain (neural networks)

develop/ are activated as we become better at science/reading/math/history/social and

cognitive skills? We could follow a group of children longitudinally who received an

intervention as opposed to a group of peers who did not, and then compare and see which

one of the groups had a better conceptual understanding of the subject areas or concepts;

we will be able to substantiate our claims about which mode of instruction is better for

children with the neuroimaging data. This would encourage collaborations across a

diverse community of research groups, schools, and children and strengthen

interdisciplinary research for the department of learning sciences and assessment

academic group.

Increasingly as educators and neuroscientists start engaging with each other

educational neuroscience lab focused on the aforementioned areas and neuroimaging

71

could be established. Establishing labs in conjunction with the educational outcomes,

learning sciences, and assessment groups will help develop research-in-practice programs

where teaching and learning in education and neuroscience research could complement

each other leading to dynamic and applied work in the field. While work at the

intersection of neuroscience and education continues to develop, what exists cannot be

easily translated into practice. Therefore, I hope to inspire educators and neuroscientists

to work collaboratively mapping new veins of research that prioritize children’s needs

and translate basic scientific research into better educational outcomes for children.

72

REFERENCES

Alahmadi, N. (2015). Classifying children with learning disabilities on the basis of

resting state EEG measures using a linear discriminant analysis. Zeitschrift Für

Neuropsychologie.

Alcauter, S., Garcia-Mondragon, L., Garcia-Tabuenca, Z., Moreno, Martha.B., Oritz, J.

J., & Barrios, F. A. (2017). Resting state functional connectivity of the anterior

striatum and prefrontal cortex predicts reading performance in school-age

children. Brain and Language, 174, 94-102.

Alexander, P. A., & Fox, E. (2019). Reading research and practice over the decades: A

historical analysis. In D.E. Alvermann, N.J. Unrau, M. Sailors, & R.B. Ruddell

(Eds.) Theoretical Models and Processes of Literacy. New York: Routledge.

Alexander, P. A., & Jetton, T. L. (2000). Learning from text: A multidimensional and

developmental perspective. In M. L. Kamil, P. B. Mosenthal, P. D. Pearson, & R.

Barr (Eds.), Handbook of reading research: Vol. III (pp. 285-310). Mahwah, NJ:

Erlbaum.

Allington, R. L. (2001). What really matters for struggling readers: Designing research-

based interventions. New York: Longmans.

Amunts, K., Weiss, P. H., Mohlberg, H., Pieperhoff, P., Eickhoff, S., Gurd, J. M.,

Marshall, J. C., Shah, N. J., Fink, G. R., & Zilles, K. (2004). Analysis of neural

mechanisms underlying verbal fluency in cytoarchitectonically defined

stereotaxic space—The roles of Brodmann areas 44 and 45. NeuroImage, 22(1),

42.

Anderson, A. J., Binder, J. R., Fernandino, L., Humphries, C. J., Conant, L. L., Raizada,

R. D. S., Feng Lin, & Lalor, E. C. (2019). An Integrated Neural Decoder of

Linguistic and Experiential Meaning. Journal of Neuroscience, 39(45), 8969–

8987.

Anzalone, C., Luedke, J. C., Green, J. J., & Decker, S. L. (2020). QEEG coherence

patterns related to mathematics ability in children. Applied neuropsychology.

Child, 1–11. Advance online publication.

Ardila, A., Bernal, B., & Rosselli, M. (2016). Why Broca's Area Damage Does Not

Result in Classical Broca's Aphasia. Frontiers in human neuroscience, 10, 249.

73

Arns, M., Peters, S., Breteler, R., & Verhoeven, L. (2007). Different brain activation

patterns in dyslexic children: Evidence from EEG power and coherence patterns

for the double-deficit theory of dyslexia. Journal of Integrative Neuroscience,

6(01), 175–190.

Balthasar, A. J. R., Huber, W., & Weis, S. (2011). A supramodal brain substrate of word

form processing — An fMRI study on homonym finding with auditory and visual

input. Brain Research, 1410, 48–63.

Balu, R., Zhu, P., Doolittle, F., Schiller, E., Jenkins, J., & Gersten, R. (2015). Evaluation

of Response to Intervention Practices for Elementary School Reading. NCEE

2016-4000. National Center for Education Evaluation and Regional Assistance.

Barry, R. J., Clarke, A. R., Johnstone, S. J., McCarthy, R., & Selikowitz, M. (2009).

Electroencephalogram θ/β Ratio and Arousal in Attention-Deficit/Hyperactivity

Disorder: Evidence of Independent Processes. Biological Psychiatry, 66(4), 398–

401.

Beddington, J., Cooper, C. L., Field, J., Goswami, U., Huppert, F. A., Jenkins, R., Jones,

H. S., Kirkwood, T. B., Sahakian, B. J., & Thomas, S. M. (2008). The mental

wealth of nations. Nature, 455(7216), 1057–1060.

Bendak, L. (2018). Effects of Applying Repeated Readings Method on Reading Fluency

and Passage Comprehension of Slow Learners. Middle East Journal of Family

Medicine, 16(1), 232–237

Ben-Soussan, T. D., Avirame, K., Glicksohn, J., Goldstein, A., Harpaz, Y., & Ben-

Shachar, M. (2014). Changes in cerebellar activity and inter-hemispheric

coherence accompany improved reading performance following Quadrato Motor

Training. Frontiers in Systems Neuroscience, 8, 81.

Berninger, V. W., & Richards, T. L. (2002). Brain literacy for educators and

psychologists. San Diego, CA, US: Academic Press.

Binder, J. R., Desai, R. H., Graves, W. W., & Conant, L. L. (2009). Where is the

semantic system? A critical review and meta-analysis of 120 functional

neuroimaging studies. Cerebral Cortex, 19(12), 2767-2796.

Bookheimer, S. (2002). FUNCTIONAL MRI OF LANGUAGE: New Approaches to

Understanding the Cortical Organization of Semantic Processing. Annual Review

of Neuroscience, 25(1), 151-188.

Borowski, R., Cummine, J., Owen, W. J., Friesen, C. K., Shih, F., & Sarty, G. E. (2006).

FMRI of ventral and dorsal processing streams in basic reading processes: Insular

sensitivity to phonology. Brain Topography, 18(4), 233–239.

74

Borowski, R., Esopenko, C., Cummine, J., & Sarty, G. E. (2007). Neural representations

of visual words and objects: A functional MRI study on the modularity of reading

and object processing. Brain Topography, 20(2), 89–96.

Bowyer, S. M. (2016). Coherence a measure of the brain networks: Past and present.

Neuropsychiatric Electrophysiology, 2(1), 1.

Bro, R., & Smilde, A. K. (2014). Principal component analysis. Analytical Methods, 6(9),

2812–2831

Büchel, C., Price, C., & Friston, K. (1998). A multimodal language region in the ventral

visual pathway. Nature, 394(6690), 274-277.

Campbell, K. L., & Tyler, L. K. (2018). Language-related domain-specific and domain-

general systems in the human brain. Current Opinion in Behavioral Sciences, 21,

132–137.

Cartwright, K. B. (2015). “Executive function and reading comprehension: The critical

role of cognitive flexibility”. In S. R. Parris & K. Headley (Eds.), Comprehension

instruction: Research-based best practices (3rd ed., pp. 56–71). New York:

Guilford

Clarke, A. R., Barry, R. J., McCarthy, R., & Selikowitz, M. (1998). EEG analysis in

attention-deficit/hyperactivity disorder: A comparative study of two subtypes.

Psychiatry Research, 81(1), 19–29.

Clarke, A. R., Barry, R. J., McCarthy, R., & Selikowitz, M. (2001). EEG-defined

subtypes of children with attention-deficit/hyperactivity disorder. Clinical

Neurophysiology, 112(11), 2098–2105.

Compton‐Lilly, C.F., Mitra, A., Guay, M., & Spence, L.K. (2020). A Confluence of

Complexity: Intersections Among Reading Theory, Neuroscience, and

Observations of Young Readers. Reading Research Quarterly, 55(S1), S185–

S195.

Cressman, M. N., & Liljequist, L. (2014). The effect of grade norms in college students:

Using the woodcock–johnson III tests of achievement. Journal of Learning

Disabilities, 47(3), 271–278.

Çukur, T., Nishimoto, S., Huth, A. G., & Gallant, J. L. (2013). Attention during natural

vision warps semantic representation across the human brain. Nature

Neuroscience 16, 763– 770.

Decker, S. L., Englund, J., & Albritton, K. (2012). Integrating multi‐tiered measurement

outcomes for special education eligibility with sequential decision‐making

methodology. Psychology in the Schools, 49 (4), 368–384.

Decker, S. L., Fillmore, P., Roberts, A.M. (2017). Coherence: The measurement and

application of brain connectivity. NeuroRegulation, 4(1), 3-13.

75

Dehaene, S., & Cohen, L. (2011). The unique role of the visual word form area in

reading. Trends in Cognitive Sciences, 15, 254– 262.

Desai, R. H., Binder, J. R., Conant, L. L., & Seidenberg, M. S. (2010). Activation of

sensory–motor areas in sentence comprehension. Cerebral Cortex, 20(2), 468–

478.

Desai, R. H., Choi, W., Lai, V. T., & Henderson, J. M. (2016). Toward semantics in the

wild: Activation to manipulable nouns in naturalistic reading. The Journal of

Neuroscience, 36(14), 4050–4055.

Dziuban, C. D., & Shirkey, E. C. (1974). When is a correlation matrix appropriate for

factor analysis? Some decision rules. Psychological Bulletin, 81(6), 358

Fedorenko, E. (2014). The role of domain-general cognitive control in language

comprehension. Frontiers in Psychology, 5.

Fedorenko, E., Duncan, J., & Kanwisher, N. (2012). Language-Selective and Domain-

General Regions Lie Side by Side within Broca’s Area. Current Biology, 22(21),

2059–2062.

Fiez, J. A., & Petersen, S. E. (1998). Neuroimaging studies of word reading. Proceedings

of the National Academy of Sciences, 95, 914– 921.

Fiez, J.A., Balota, D.A., Raichle, M.E., & Petersen, S.E. (1999). Effects of lexicality,

frequency, and spelling-to-sound consistency on the functional anatomy of

reading. Neuron, 24, 205-218.

Fischer-Baum, S., Bruggemann, D., Gallego, I. F., Li, D. S. P., & Tamez, E. R. (2017).

Decoding levels of representation in reading: A representational similarity

approach. Cortex: A Journal Devoted to the Study of the Nervous System and

Behavior, 90, 88–102.

Fletcher, J. M., Stuebing, K. K., Barth, A. E., Denton, C. A., Cirino, P. T., Francis, D. J.,

& Vaughn, S. (2011). Cognitive correlates of inadequate response to reading

intervention. School Psychology Review, 40 (1), 3–22.

Friederici A. D., Meyer M., von Cramon D. Y. (2000a). Auditory language

comprehension: an event-related fMRI study on the processing of syntactic and

lexical information. Brain and Language. 75, 289–300.

Friederici A. D., Opitz B., von Cramon D. Y. (2000b). Segregating semantic and

syntactic aspects of processing in the human brain: an fMRI investigation of

different word types. Cerebral Cortex 10, 698–705.

Friederici, A. D. (2002). Towards a neural basis of auditory sentence processing. Trends

in Cognitive Sciences, 6(2), 78-84.

76

Friederici, A. D., & Gierhan, S. M. (2013). The language network. Current opinion in

neurobiology, 23(2), 250–254.

Glezer, L. S., Eden, G., Jiang, X., Luetje, M., Napoliello, E., Kim, J., & Riesenhuber, M.

(2016). Uncovering phonological and orthographic selectivity across the reading

network using fMRI-RA. NeuroImage, 138, 248–256.

González-Garrido, A. A., Gómez-Velázquez, F. R., Salido-Ruiz, R. A., Espinoza-Valdez,

A., Vélez-Pérez, H., Romo-Vazquez, R., … Berumen, G. (2018). The analysis of

EEG coherence reflects middle childhood differences in mathematical

achievement. Brain and Cognition, 124, 57–63.

Gorsuch, R. L. (1990). Common factor analysis versus component analysis: Some well

and little known facts. Multivariate Behavioral Research, 25(1), 33–39.

Grech, R., Cassar, T., Muscat, J., Camilleri, K. P., Fabri, S. G., Zervakis, M., …

Vanrumste, B. (2008). Review on solving the inverse problem in EEG source

analysis. Journal of Neuroengineering and Rehabilitation, 5(1), 25.

Hagler, D. J., & Sereno, M. I. (2006). Spatial maps in frontal and prefrontal cortex.

Neuroimage 29, 567– 577.

Hagler, D. J., Riecke, L., & Sereno, M. I. (2007). Parietal and superior frontal

visuospatial maps activated by pointing and saccades. NeuroImage 35, 1562–

1577.

Hagoort, P., & Brown, C. (1999). The Neurocognition of Language. Oxford: Oxford

University Press.

Heim, S., Alter, K., Ischebeck, A. K., Amunts, K., Eickhoff, S. B., Mohlberg, H., Zilles,

K., von Cramon, D. Y., & Friederici, A. D. (2005). The role of the left

Brodmann’s areas 44 and 45 in reading words and pseudowords. Cognitive Brain

Research, 25(3), 982–993.

Herbster, A.N., Mintun, M.A., Nebes, R.D., & Becker, J.T. (1997). Regional cerebral

blood flow during word and nonword reading. Human Brain Mapping 5, 84-92.

Honey, C. J., Sporns, O., Cammoun, L., Gigandet, X., Thiran, J. P., Meuli, R., &

Hagmann, P. (2009). Predicting human resting-state functional connectivity from

structural connectivity. Proceedings of the National Academy of Sciences, 106(6),

2035–2040.

Horowitz-Kraus, T., Toro-Serey, C., & DiFrancesco, M. (2015). Increased resting-state

functional connectivity in the cingulo-opercular cognitive-control network after

intervention in children with reading difficulties. PLoS ONE, 10(7), 1-21.

Hruby, G. G. (2012). Three requirements for justifying an educational

neuroscience. British Journal of Educational Psychology 82, 1–23.

77

Hruby, G.G., & Goswami, U. (2019). Educational neuroscience for reading educators. In

D.E. Alvermann, N.J. Unrau, M. Sailors, & R.B. Ruddell (Eds.) Theoretical

Models and Processes of Literacy. New York: Routledge.

Huang, R‐S., & Sereno, M. I. (2013). Bottom‐up retinotopic organization supports top‐

down mental imagery. Open Neuroimaging Journal 7, 58– 67.

Huk, A. C., Dougherty, R. F., & Heeger, D. J. (2002). Retinotopy and functional

subdivision of human areas MT and MST. Journal of Neuroscience 22, 7195–

7205.

IBM SPSS Statistics for Windows. (2017). Armonk, NY: IBM Corp: IBM Corp.

Jäncke, Lutz, Saka, M. Y., Badawood, O., & Alhamadi, N. (2019). Resting-state

electroencephalogram in learning-disabled children: Power and connectivity

analyses. NeuroReport, 30(2), 95–101.

Janzen, T., Graap, K., Stephanson, S., Marshall, W., & Fitzsimmons, G. (1995).

Differences in baseline EEG measures for ADD and normally achieving

preadolescent males. Biofeedback and Self-Regulation, 20(1), 65–82.

Jobard, G., Crivello, F. & Tzourio-Magoyer, N. (2003). Evaluation of the dual route

theory of reading: A metanalysis of 35 neuroimaging studies. Neuroimage, 20 (2),

693– 712.

Jobard, G., Vigneau, M., Simon, G., & Tzourio-Mazoyer, N. (2011). The weight of skill:

Interindividual variability of reading related brain activation patterns in fluent

readers. Journal of Neurolinguistics, 24(1), 113–132.

John, E. R., Prichep, L., Fridman, J., & Easton, P. (1988). Neurometrics: Computer-

assisted differential diagnosis of brain dysfunctions. Science, 239(4836), 162–

169.

Johnstone, J., & Gunkelman, J. (2003). Use of databases in QEEG evaluation. Journal of

Neurotherapy, 7(3–4), 31–52.

Kastner, S., DeSimone, K., Konen, C. S., Szczepanski, S. M., Weiner, K. S., &

Schneider, K. A. (2007). Topographic maps in human frontal cortex revealed in

memory‐guided saccade and spatial working‐memory tasks. Journal of

Neurophysiology 97, 3494– 3507.

Klein, M., Grainger, J., Wheat, K. L., Millman, R. E., Simpson, M. I. G., Hansen, P. C.,

& Cornelissen, P. L. (2015). Early activity in Broca’s area during reading reflects

fast access to articulatory codes from print. Cerebral Cortex, 25(7), 1715–1723.

Kock, N., & Lynn, G. (2012). Lateral Collinearity and Misleading Results in Variance-

Based SEM: An Illustration and Recommendations. Econometrics: Multiple

Equation Models eJournal.

78

Lehne, M., Engel, P., Rohrmeier, M., Menninghaus, W., Jacobs, A.M., & Koelsch, S.

(2015). Reading a suspenseful literary text activates brain areas related to social

cognition and predictive inference. PLoS ONE, 10(5), 1–18.

Lehongre, K., Morillon, B., Giraud, A.-L., & Ramus, F. (2013). Impaired auditory

sampling in dyslexia: Further evidence from combined fMRI and EEG. Frontiers

in Human Neuroscience, 7, 454.

Ludersdorfer, P., Kronbichler, M., & Wimmer, H. (2015). Accessing orthographic

representations from speech: The role of left ventral occipitotemporal cortex in

spelling. Human Brain Mapping, 36(4), 1393–1406.

Marosi, E., Harmony, T., Reyes, A., Bernal, J., Fernandez, T., Guerrero, V., Rodriguez,

M., Silva, J., Yanez, G., & Rodriguez, H. (1997). A follow-up study of EEG

coherences in children with different pedagogical evaluations. International

Journal of Psychophysiology, 25, 227-235.

Martin, A., Schurz, M., Kronbichler, M., & Richlan, F. (2015). Reading in the brain of

children and adults: a meta-analysis of 40 functional magnetic resonance imaging

studies. Human Brain Mapping, 36, 1963– 1981.

MATLAB (Version 2018a). (2018). Natick, Massachusetts, United States: The

MathWorks, Inc.

McCandliss, B. D., Cohen, L., & Dehaene, S. (2003). The visual word form area:

expertise for reading in the fusiform gyrus. Trends in Cognitive Sciences, 7, 293–

299.

McNorgan, C., Chabal, S., O’Young, D., Lukic, S., & Booth, J. R. (2015). Task

dependent lexicality effects support interactive models of reading: A meta-

analytic neuroimaging review. Neuropsychologia, 67, 148–158.

Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009). Preferred

Reporting Items for Systematic Reviews and MetaAnalyses: The PRISMA

Statement. PLoS Med 6(7): e1000097.

Noble, K. G., Farah, M. J., & McCandliss, B. D. (2006). Socioeconomic background

modulates cognition-achievement relationships in reading. Cognitive

development, 21(3), 349–368.

Norton, E. S., Kovelman, I., & Petitto, L.-A. (2007). Are there separate neural systems

for spelling? New insights into the role of rules and memory in spelling from

functional magnetic resonance imaging. Mind, Brain, and Education, 1(1), 48–59.

Oberhuber, M., Jones, Ō. P., Hope, T. M. H., Prejawa, S., Seghier, M. L., Green, D. W.,

& Price, C. J. (2013). Functionally distinct contributions of the anterior and

posterior putamen during sublexical and lexical reading. Frontiers in Human

Neuroscience, 7, 1-10.

79

Pascual-Marqui, R. D., Michel, C. M., & Lehmann, D. (1994). Low resolution

electromagnetic tomography: A new method for localizing electrical activity in

the brain. International Journal of Psychophysiology, 18(1), 49–65.

Patael, S. Z., Farris, E. A., Black, J. M., Hancock, R., Gabrieli, J., Cutting, L. E., &

Hoeft, F. (2018). Brain basis of cognitive resilience: Prefrontal cortex predicts

better reading comprehension in relation to decoding. PloS ONE, 13(6),

e0198791.

Peelen, M. V., & Kastner, S. (2014). Attention in the real world: Toward understanding

its neural basis. Trends in Cognitive Science 18, 242– 250.

Pleisch, G., Karipidis, I. I., Brauchli, C., Rothlisberger, M., Hofstetter, C., Stampfli, P.,

Walitza, S., & Brem, S. (2019). Emerging neural specialization of the ventral

occipitotemporal cortex to characters through phonological association learning in

preschool children. NeuroImage 189, 813-831.

Preston, J. L., Molfese, P. J., Frost, S. J., Mencl, W. E., Fulbright, R. K., Hoeft, F., Landi,

N., Shankweiler, D., & Pugh, K. R. (2016). Print-speech convergence predicts

future reading outcomes in early readers. Psychological Science, 27(1), 75–84.

Price, C. J., Winterburn, D., Giraud, A. L., Moore, C. J., & Noppeney, U. (2003).

Cortical localisation of the visual and auditory word form areas: A

reconsideration of the evidence. Brain and Language, 86(2), 272–286.

Pugh, K., Shaywitz, B., Shaywitz, S., Constable, T., Skudlarski, P., Fulbright, R., Bronen,

R., Shankweiler, D., Katz, L., Fletcher, J., & Gore, J. (1996). Cerebral

organization of component processes of reading. Brain, 119, 1221-1238.

Pugh, K.R., Mencl, W.E., Jenner, A.R., Katz, L., Frost, S.J., Lee, J.R., Shaywitz, S.E., &

Shaywitz, B.A. (2001). Neurobiological studies of reading and reading disability.

Journal of Communication Disorders, 34(6), 479-92.

Pugh, R, K., Shaywitz, B., Shaywitz, S., Shankweiler, D., Katz, L., Fletcher, J.,

Skudlarski, P., Fulbright, R., Constable, T., Bronen, R., Lacadie, C., & Gore, J.

(1997). Predicting reading performance from neuroimaging profiles: The cerebral

basis of phonological effects in printed work identification. Journal of

Experimental Psychology: Human Perception and Performance, 23, 299-318.

Rippon, G., & Brunswick, N. (2000). Trait and state EEG indices of information

processing in developmental dyslexia. International Journal of Psychophysiology,

36(3), 251–265.

Rumsey, J. M., Horwitz, B., Donohue, B. C., Nace, K., Maisog, J. M., & Andreason, P.

(1997). Phonological and orthographic components of word recognition: A PET-

rCBF study. Brain, 120(5), 739–759.

80

Sandak, R., Mencl, W. E., Frost, S. J., & Pugh, K. R. (2004): The neurobiological basis

of skilled and impaired reading: Recent findings and new directions. Scientific

Studies of Reading, 8, 273– 292.

Satterfield, J. H., Cantwell, D. P., Lesser, L. I., & PODOSIN, R. L. (1972). Physiological

studies of the hyperkinetic child: I. American Journal of Psychiatry, 128(11),

1418–1424.

Saygin, Z. M., Norton, E. S., Osher, D. E., Beach, S. D., Cyr, A. B., Ozernov-Palchik, O.,

Yendiki, A., Fischl, B., Gaab, N., & Gabrieli, J. D. E. (2013). Tracking the Roots

of Reading Ability: White Matter Volume and Integrity Correlate with

Phonological Awareness in Prereading and Early-Reading Kindergarten Children.

Journal of Neuroscience, 33(33), 13251–13258.

Schlaggar, B. L., & McCandliss, B. D. (2007). Development of neural systems for

reading. Annual Review of Neuroscience, 30, 475–503.

Schluppeck, D., Glimcher, P., & Heeger, D. J. (2005). Topographic organization for

delayed saccades in human posterior parietal cortex. Journal of Neurophysiology

94, 1372– 1384.

Sela, I., Izzetoglu, M., Izzetoglu, K., & Onaral, B. (2014). A functional near-infrared

spectroscopy study of lexical decision task supports the dual route model and the

phonological deficit theory of dyslexia. Journal of Learning Disabilities, 47(3),

279–288.

Sereno, M. I., & Huang, R‐S. (2006). A human parietal face area contains aligned head‐

centered visual and tactile maps. Nature Neuroscience 9, 1337– 1343.

Shaywitz, B. A., Skudlarski, P., Holahan, J. M., Marchione, K. E., Constable, R. T.,

Fulbright, R. K., Zelterman, D., Lacadie, C., & Shaywitz, S. E. (2007). Age-

related changes in reading systems of dyslexic children. Annals of Neurology, 61,

363–370.

Simos, P. G., Fletcher, J. M., Sarkari, S., Billingsley, R. L., Francis, D. J., Castillo, E. M.,

Pataraia, E., Denton, C., & Papanicolaou, A. C. (2005). Early development of

neurophysiological processes involved in normal reading and reading disability:

A magnetic source imaging study. Neuropsychology, 19(6), 787–798.

Sood, M.R., & Sereno, M. (2016). Areas activated during naturalistic reading

comprehension overlap topological visual, auditory, and somatomotor maps.

Human Brain Mapping, 37, 2784 - 2810.

Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4th ed.).

Needham Heights, MA: Allyn and Bacon.

81

Takamura, T., & Hanakawa, T. (2017). Clinical utility of resting-state functional

connectivity magnetic resonance imaging for mood and cognitive disorders.

Journal of Neural Transmission, 124(7), 821–839.

Thatcher, R. W. (2011). Neuropsychiatry and quantitative EEG in the 21st Century.

Neuropsychiatry, 1(5), 495–514.

Thatcher, R. W., North, D. M., Curtin, R. T., Walker, R. A., Biver, C. J., Gomez, J. F., &

Salazar, A. M. (2001a). An EEG severity index of traumatic brain injury. The

Journal of Neuropsychiatry and Clinical Neurosciences, 13(1), 77–87.

Thatcher, R. W., North, D. M., Curtin, R. T., Walker, R. A., Biver, C. J., Gomez, J. F., &

Salazar, A. M. (2001b). An EEG severity index of traumatic brain injury. The

Journal of Neuropsychiatry and Clinical Neurosciences, 13(1), 77–87.

Thatcher, R. W., North, D., & Biver, C. (2005a). EEG and intelligence: Relations

between EEG coherence, EEG phase delay and power. Clinical Neurophysiology,

116(9), 2129–2141.

Thatcher, R. W., Walker, R., Gerson, I., & Geisler, F. (1989). EEG discriminant analyses

of mild head trauma. Electroencephalography and Clinical Neurophysiology,

73(2), 94–106

Thornton, K. E., & Carmody, D. P. (2013). The Relation Between Memory Improvement

and QEEG Changes in Three Clinical Groups as a Result of EEG Biofeedback

Treatment. Journal of Neurotherapy, 17(2), 116–131.

Turkeltaub, P. E., Eden, G. F., Jones, K. M., & Zeffiro, T. A. (2002). Meta-analysis of the

functional neuroanatomy of single word reading: method and validation.

Neuroimage, 16, 765–780.

Twomey, T., Duncan, K. J. K., Price, C. J., & Devlin, J. T. (2011). Top-down modulation

of ventral occipito-temporal responses during visual word recognition.

NeuroImage, 55(3), 1242–1251.

Twomey, T., Waters, D., Price, C. J., Kherif, F., Woll, B., & MacSweeney, M. (2015).

Identification of the regions involved in phonological assembly using a novel

paradigm. Brain and Language, 150, 45–53.

Vanderauwera, J., De Vos, A., Forkel, S. J., Catani, M., Wouters, J., Vandermosten, M.,

& Ghesquière, P. (2018). Neural organization of ventral white matter tracts

parallels the initial steps of reading development: A DTI tractography study.

Brain and Language, 183, 32–40.

Vigário, R., Sarela, J., Jousmiki, V., Hamalainen, M., & Oja, E. (2000). Independent

component approach to the analysis of EEG and MEG recordings. IEEE

Transactions on Biomedical Engineering, 47(5), 589–593.

82

Wood, F. B., Hill, D. F., Meyer, M. S., & Flowers, D. L. (2005). Predictive Assessment

of Reading. Annals of Dyslexia, 55(2), 193–216.

Woodcock, R. W., McGrew, K. S., & Mather, N. (2001b). Woodcock-Johnson III Tests of

Achievement. Itasca, IL: Riverside Publishing.

Wu, J., Wang, B., Yan, T., Li, X., Bao, X., & Guo, Q. (2012). Different roles of the

posterior inferior frontal gyrus in Chinese character form judgment differences

between literate and illiterate individuals. Brain Research, 1431, 69–76.

Wu, W., West, S. G., & Hughes, J. N. (2008). Short-term effects of grade retention on the

growth rate of Woodcock–Johnson III broad math and reading scores. Journal of

School Psychology, 46(1), 85–105.

Yu, X., Raney, T., Perdue, M. V., Zuk, J., Ozernov, P. O., Becker, B. L. C., Raschle, N.

M., & Gaab, N. (2018). Emergence of the neural network underlying

phonological processing from the prereading to the emergent reading stage: A

longitudinal study. Human Brain Mapping, 39(5), 2047–2063.

83

APPENDIX A

ADAPTED PRISMA FLOW DIAGRAM

s

Figure A.1 Adapted Prisma Flow Diagram

Records identified through

database searching

(n =152)

Scr

een

ing

In

clu

ded

E

ligib

ilit

y

Iden

tifi

cati

on

Additional records identified

through other sources

(n = 0)

Records after duplicates removed

(n =141)

Records screened

(n = 141)

Records excluded

(n = 63)

Articles on reading

disability/disorder

were excluded.

Full-text articles

assessed for eligibility

(n =78)

Further close

reviewing of the 78

articles led to the

addition of book

chapters and articles

from references.

n=18

(n =10)

Studies included in

qualitative synthesis

(n =96)

84

APPENDIX B

PRINCIPAL COMPONENT ANALYSIS LOADING WEIGHTS

Table B.1 Rotated Delta component # 2 matrix in the left hemisphere (BA 44)

Brodmann Area 44-Delta (LH)

Principal Component 2

Loading Weights

Brodmann Area 36L-44L_Delta .910

Brodmann Area 44L-AmyL Delta .898

Brodmann Area 28L-44L Delta .888

Brodmann Area 34L-44L_Delta .874

Brodmann Area 44L-HipL_Delta .864

Brodmann Area 21L-44L Delta .810

Brodmann Area 20L-44L_Delta .800

Brodmann Area 35L-44L_Delta .777

Brodmann Area 13L-44L Delta .753

Brodmann Area 38L-44L_Delta .749

Brodmann Area 27L-44L_Delta .652

Brodmann Area 4L-44L_Delta .484

Brodmann Area 44L-47L Delta .425

Brodmann Area 24L-44L_Delta .524

Brodmann Area 25L-44L_Delta .559

Brodmann Area 11L-44L_Delta .529

Brodmann Area 32L-44L Delta .444

Brodmann Area 10L-44L_Delta .518

Brodmann Area 37L-44L_Delta .412

Brodmann Area 30L-44L_Delta .443

85

Table B.2 Rotated Theta component # 2 matrix in the right hemisphere (BA 44)

Brodmann Area 44-Theta (RH)

Principal Component 2

Loading Weights

Brodmann Area _27R-44R_Theta .577

Brodmann Area _31R-44R_Theta .938

Brodmann Area_23R-44R_Theta .933

Brodmann Area_17R-44R_Theta .931

Brodmann Area_30R-44R_Theta .923

Brodmann Area_19R-44R_Theta .872

Brodmann Area_39R-44R_Theta .839

Brodmann Area_7R-44R_Theta .730

Brodmann Area _18R-44R_Theta .707

Brodmann Area _37R-44R_Theta .680

Table B.3 Rotated Beta 2 component # 3 matrix in the right hemisphere (BA 44)

Brodmann Area 44-Beta 2 (RH)

Principal Component 3

Loading Weights

Brodmann Area _39R-44R_Beta2 .574

Brodmann Area _31R-44R_Beta2 .936

Brodmann Area _23R-44R_Beta2 .921

Brodmann Area 17R-44R_Beta2 .910

Brodmann Area _30R-44R_Beta2 .908

Brodmann Area _7R-45R_Beta2 .640

Brodmann Area _19R-44R_Beta2 .606

Brodmann Area _27R-44R_Beta2 .440

86

Table B.4 Rotated Alpha 1 component # 3 matrix in the left hemisphere (BA 45)

Brodmann Area 45-Alpha 1(LH)

Principal Component 3

Loading Weights

Brodmann Area _40L-45L_Alpha1 .841

Brodmann Area _41L-45L_Alpha1 .787

Brodmann Area _1L-45L_Alpha1 .784

Brodmann Area _43L-45L_Alpha1 .771

Brodmann Area 42L-45L_Alpha1 .760

Brodmann Area _29L-45L_Alpha1 .734

Brodmann Area _22L-45L_Alpha1 .720

Brodmann Area _3L-45L_Alpha1 .667

Brodmann Area _2L-45L_Alpha1 .649

Table B.5 Rotated Beta 2 component # 3 matrix in the left hemisphere (BA 45)

Brodmann Area 45-Beta 2 (RH)

Principal Component 3

Loading Weights

Brodmann Area_23R-45R_Beta2 .912

Brodmann Area_17R-45R_Beta2 .906

Brodmann Area_39R-45R_Beta2 .764

Brodmann Area_19R-45R_Beta2 .761

Brodmann Area_7R-45R_Beta2 .717

Brodmann Area_18R-45R_Beta2 .604