a review of neural networks & theoretical ... - about ansa

A Review of

Neural Networks

& Theoretical

ApplicationsDAVID S. CANTOR, PH.D., MS, FNAN, QEEG-D, BCN

MIND AND MOTION DEVELOPMENTAL CENTERS OF GEORGIA, LLC

WWW.MINDMOTIONCENTERS.COM

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Acknowledgments

*DICK GENARDI, PH.D. (PRESENTATION CONTRIBUTOR)

*RICHARD SOUTAR, PH.D. (PRESENTATION CONTRIBUTOR)

STEPHEN STAHL, MD, PH.D.

LUKASZ KONOPKA, PH.D.

ADRIAN VAN DEUSEN

THOMAS COLLURA, PH.D.

RON BONNSTETTER, PH.D.

ROBERT CHABOT, PH.D.

LESLIE PRICHEP, PH.D.

Chaos is the science of surprises, of the nonlinear and the unpredictable. It

teaches us to expect the unexpected. While most traditional science deals with

supposedly predictable phenomena like gravity, electricity, or chemical reactions,

Chaos Theory deals with nonlinear things that are effectively impossible to predict

or control, like turbulence, weather, the stock market, our brain states, and so

on. These phenomena are often described by fractal mathematics, which captures

the infinite complexity of nature. Many natural objects exhibit fractal properties,

including landscapes, clouds, trees, organs, rivers etc, and many of the systems in

which we live exhibit complex, chaotic behavior. Recognizing the chaotic, fractal

nature of our world can give us new insight, power, and wisdom. For example, by

understanding the complex, chaotic dynamics of the atmosphere, a balloon pilot

can “steer” a balloon to a desired location. By understanding that our ecosystems,

our social systems, and our economic systems are interconnected, we can hope

to avoid actions which may end up being detrimental to our long-term well-being.

CHAOS….

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“As far as the laws of mathematics refer to reality, they are not certain, and as far as they are certain, they do not refer to reality.”

-Albert Einstein

Day 1: Basics

of Neural

NetworkI. GENERAL CONCEPTS AND TERMS

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Brain Complexityhttps://youtu.be/OCpYdSN_kts

Brain Rhythms: Functional Brain Networks Mediated by Oscillatory Neural Coupling

Micrograph of the cell body

with synaptic inputs.

As many as 30-50K inputs per

cell in the mammalian brains

are seen.

Cerebellar cells may have as

many as 100K inputs.

Connection Basics -

Outline

◆ Explain most common network terms found in the functional imaging literature.

◆ Discuss parcellation and types of networks.

◆ Review key regions of interest

◆ Outline the Development of Networks and how they facilitate perception and cognition (adaptation and ontogenetic development of behaviors)

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Connection Basics

Current purpose in studying the connectome of the brain is to

discover

◆ The mechanisms of communication between one structural

system/network to others, assuming each operating at

different/multiple frequencies, orchestrating activity from

specific to integrative which underlie the full range of human

behavior. This transfers occurs from ventral to dorsal levels,

between areas within and across hemispheres.

◆ How successive integration allows adaptation to events both

external and internal to the organism which occur on time

scales ranging from at least milliseconds to spanning several days.

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Connection Basics 10

The Golden Ratio 11

Connection Terms

https://youtu.be/f1E2lStdrl8

Phi: Spirit Science, The Golden Ratio

Connection

Basics- Terms

Graph TheoryA graph can mathematically represent a network as a series of vertices and connecting edges.

The adjacency matrix lists each vertex and its neighbors.

Directionality, weighting-strength of connection, could be represented

Graph theory is used to describe structure and operational characteristics of a network, detect and

quantify subsystems; mapped to functions. Straaten & Stam, 2013

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M.van den Hueval & O. Sporns,2013

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Connection Basics-Terms

◆ Node or vertex is an area of the brain as small as

a neuron or much larger region such as a

Brodmann area which is connected to other

nodes which together form a network(s).

◆ A module ( many neurons) is a group of nodes

with a large number of mutual connections within

the group and few connections to nodes out of

the group.

◆ Outside connections are to other modules which

may serve different functions

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◆ The role/importance of a node is described by

various measures called centrality measures.

◆ Nodal degree- “K” is a key centrality measure

referring to the number of edges or connections

from a node to other nodes(neighbors). Can

indicate when a node is a point of relative importance in a network. Hubs are high degree

nodes. An average K can also be calculated for

a whole network.

◆ Edges are structural connections between nodes.

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◆ Degree correlation- the extent to which nodes of the same or different degree are connected to each other.

◆ Assortative- when nodes show tendency to connect with nodes of the same degree

◆ Disassortative- when nodes show tendency to connect with nodes of different degree.

◆ At macroscopic level( EEG, MEG, fMRI) brain is assortatively organized; disassortative at neuronal level.

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◆ Hubs are nodes/vertices which play a central role in the overall

function of a network. Two major hub types are connector hubs

and provincial hubs.

◆ Connector hubs are hubs which have a broad array of

connections between modules within a network or connections between networks.

◆ Provincial hubs are also high degree nodes but which connect

to other nodes within the same module.

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◆ Rich club is example of nodes with high assortativity.

◆ Level of parcellation influences this metric. Complex

modular networks are likely assortative

◆ High degree nodes serve as connector hubs for

integration of locally processed information.

◆ At macroscopic level assortativity may arise from the

way the network is formed: by modulation based on

synchronization rather than growth. ( Stam,2010)

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◆ P(k) is the degree distribution of a graph, module,

network or system. It is a measure of the

probability that a randomly chosen node will

have the probability “k.”

◆ The presence of hubs is indicated by a power law

degree distribution, i.e. some nodes have an very

high degree, and most others have a low degree.

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Connection

Basics-Terms

“L”

CPL-characteristic path length/ simply path length-“L” is number of vertices involved to get from one node to another; equals number of nodes crossed (involved?) -not actual length of axons or spatial distribution of the nodes. Shorter “L” is held as more efficient; inversely related to IQ

Shortest path from vertex 1

to 12 is 4. L = 4.

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Connection

Basics-Terms

“C”

Clustering Coefficient

Is a local connectivity measure indicating to what extent the neighboring nodes, to which a node is connected, are connected to each other. Expressed as

ratio of actual number of edges (connections) to the number of total possible connections between a nodes neighbors

Vertex 8 has neighbors 5 & 11. “C” is

calculated by number of edges between

neighbor vertices divided by the total

possible number of edges. Thus, C= 1/1= 1.

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◆ Closeness centrality is the length of the shortest paths

between a node and the rest of the network.

◆ Betweeness centrality is the number of short communication

paths a node participates in. It is defined as the number of

shortest paths going through a node or edge. The betweeness

centrality is high when the node or edge is used for many

shortest paths. This measure can be normalized by dividing it

by its maximum value (the total amount of shortest paths in

the network). A relative draw- back of this method is that

computation time can be long, especially in networks with

many nodes.

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◆ Eigenvector centrality allocates a value to each

node in the network in such a way that the node

receives a large value if it has strong connections

with many other nodes that have themselves a

central position within the network (Lohmann et

al., 2010).

◆ Thus, connections to important nodes count

more, making the nodes with relatively few edges

to very important nodes also important (maybe more important than nodes with many edges to

less important nodes).

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Connection Basics -Terms

◆ Small worldness (SW)/Sigma is the balance between network integration and differentiation; the ratio of local clustering and the characteristic path length (CPL) of a node relative to the same ratio in a randomized (hypothetical) network.

◆ Global efficiency -the average shortest path length –i.e. smaller the CPL the more efficient the network. Some authors dispute this finding.

◆ Regional efficiency is global efficiency computed for each node; related to the clustering coefficient.

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Review

Most frequently referred to:

K- degree

P(k) degree distribution

L –path length

C- clustering coefficient

Also: Sigma ( a cumulative metric) = the ratio of normalized clustering coefficient to the characteristic path length, a measure of small-world organization.

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Two modules-

groups of vertices

with high

interconnectivity

with connector

hub (black

square).

Modularity (MOD) is the degree a system can be divided into smaller networks.

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M.van den Hueval & O. Sporns,2013

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◆ Modules/motifs(brief spatio-temporal patterning) -sub grouping of nodes-edges (graph) can be momentarily accessed (microstate) for a particular function

◆ Modules/subsystems can be hierarchically organized; subsystems within subsystems allowing for functional specialization and finer differentiation.

◆ Higher order system can influence a subsystem without affecting its own intrinsic function.

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Connection Basics-

Network Types

Ordered

◆ Each node only connected to its nearest neighbors

◆ C =high- only connections to neighbors

◆ L= high, takes many steps to reach distant node

Types fr. Watts & Strogatz, 1998

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Connection Basics-

Network Types ◆ With probability p randomly

rewire a few edges

◆ L is lowered

◆ C remains high

◆ Complex adaptive system

requires Small World network

& noise or unpredictability

◆ Matches neural & social

networks more precisely

than ordered or random

◆ Human cortex is Small World

Small World

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Connection Basics-

Network Types

Random◆ Increase p and set it equal for every

combination of two nodes

◆ C & L drop to low value

◆ Hippocampus has random organization

◆ Cerebellum has parallel organization- no reenterant loops.

Erdos & Renyi, 1959

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Connection Basics-

Network Types

Scale Free◆ Probability of new

connection depends on

node degree K

◆ Higher K node has

higher p

◆ Produces a scale free

network or power law

distribution when-Few

nodes with high K, many

nodes with low K

Scale Free Network

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Connection Basics-

Structure Implications◆ For the brain to function as a complex adaptive system it must have

small world organization and noise/variance.

◆ Some neurofeedback practitioners question whether constraint of overall variance occurs by training excessive metrics with restricted criteria. Should one consider whether a dynamical system would be limited and adaptation to the training conditions degraded when a certain number of training parameters is exceeded?

◆ Question then--- Do we limit metrics and areas targeted for training whether surface or (s)Loreta?

◆ Consider the possible influence of the number of metrics/constraints employed in training for optimal outcome may relate to the degree of controllability that a ROI evidences on associated networks

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Defining Structural Connectivity

Leading to Functional Connectivity

◆ There is no standardized generally accepted anatomical definition of a node or edge (Meehan & Bressler,2012)

◆ Many “imaging” modalities in identifying nodes use different measures—metabolic (PET), blood (MRI,fMRI,rs-fcMRI), spectral power (LFP, EcoG, EEG, MEG)

◆ Time domain of MRI is several seconds, EEG –milliseconds; hence different resolution due to time dampening

◆ Correlation of heightened activity ( under task) of a node identified by one modality not fully understood how relates to node identified by different modality. Various advantages- model constructions from each ( Zalesky et. a. 2010)

◆ Network not simply defined by co-activated nodes, must also identify edge

◆ Node could be defined not only by co-activity but by increased correlation with other node while activation remains constant

◆ Use of different values for parameters results in different nodes and connectivity patterns, determines to which network(s) a nodal areas is associated

◆ Measure of single neural unit spiking identifies that 30% of neurons associated with network function are found outside of network itself ; suggests that nodal boundaries are more diffuse than defined by fMRI & function more broadly distributed

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Connection Basics◆ Modern analysis shows that functions are widely distributed via

grouping of areas from local to long distance networks (Cf. Catani & Theibaut de Shotten, 2012)

◆ The number of and variety of networks determined from structural analysis and functional analysis depend on which metrics used and inclusion parameter values set and whether the data was taken under at rest or at task conditions.

◆ Some networks are referred to as resting state networks and others called demand or at task networks. There can be significant overlap but structure does not account for all functional networks (Honey et al., 2009).

◆ Full understanding of network behavior requires an understanding of distribution, timing, and integration of information in the service of cognition, behavior and emotion

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Basics of

Neural

NetworkII. DEVELOPMENT

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Organization

◆ Localized neural circuit developmental and

organization “builds” networks which progressively

become more complex and hierarchical over

time to assimilate accumulative knowledge and

promote “precognition” to facilitate “efficiency”.

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The Basics

Brain

Development

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Timeline of Changes◆ Differentiation of the neural tube occurs from GA 4 to 12

weeks with new neurons formed in proliferative zones

◆ Between GA 12 and 20 weeks, the neurons multiply followed by migration to cortical destinations

◆ Around GA 29 weeks, the process of myelination starts at the brain stem and continues generally in an inferior-to-superior and posterior-to-anterior path

◆ Between 2 and 7 postnatal years, it is unclear whether synaptogenesis is balanced by elimination of cells and synapses

◆ Myelination of proximal pathways tends to occur first, followed by myelination of distal pathways

◆ Cortical myelination seems to mirror functional development

◆ Maturational trajectories, with sensory tracts myelinating before motor tracts accompanied by protracted myelination of association tracts

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◆ The term ‘connectivity’ encompasses several concepts in

neuroscience. Structural connectivity describes the physical

link—the long range connections formed by white matter

tracts.

◆ On the other hand, Functional connectivity describes

statistical association of functional signals between brain

areas observed through various functional imaging

approaches, including functional MRI (fMRI), electro- and

magneto-encephalography (EEG, MEG), and

fluorodeoxyglucose (18F) positron emission tomography

(FDG-PET).

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◆The differential development of

major white matter tracts is also

accompanied by a developmental

shift in their inter-tract microstructural

correlation from a more random

state to a more organized state,

suggesting refinement of white

matter organization with maturation

(Mishra et al., 2013).

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Connection Basics -

Developmental

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Connection Basics -

Developmental

◆ Brain continues to mature through the 20s(Gogtay

et al, 2004) well into the 30s (Lebel et al 2011).

◆ Developmental network remodeling shows

decreased grey mater density, due esp to short

range connection synaptic pruning and increase

myelination occuring throughout life. (Hagmann,

2010), (Bartzokis, 2004).

◆ Remodeling seen in terms of decreased path

length, small worldness, clustering, modularity,

and fiber density—specific to hemisphere; into

adulthood.

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Connection Basics -

Developmental

◆ Fiber connection, nodal degree, and nodal

efficiency in frontal cortex decreased fr 12-30 yrs;

high increase in temporal and parietal areas.

◆ Consistent with development of executive

functions and long range anterior-posterior

increased communication.

◆ Differential trends-left hemisphere shows increased clustering, modularity, and global

efficiency(shorter characteristic path length).

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Connection Basics -

Developmental

◆ Right hemisphere the opposite. Decrease small

worldness, ie less areas of differentiation.

◆ The splenium and isthmus connect the left and

right hemisphere temporal, parietal and occipital

cortices.

◆ In the splenium and isthmus (Chung,et al. 2001)

associated with age, there is an increase in the inter-hemispheric level of myelination and /or

axon count.

Brain connectivity in

normally developing

children and adolescentsBudhachandra S. Khundrakpam, John D. Lewis,

Lu Zhao, François Chouinard-Decorte, Alan C.

Evans

MCGILL CENTRE FOR INTEGRATIVE NEUROSCIENCE, MCCONNELL BRAIN IMAGING CENTRE, MONTREAL NEUROLOGICAL INSTITUTE, MCGILL UNIVERSITY, MONTREAL, QUEBEC H3A 2B4, CANADA

NEUROIMAGE (2016),

HTTP://DX.DOI.ORG/10.1016/J.NEUROIMAGE.2016.03.062

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Measuring Networks with:

1. DTI (Diffuse Tensor imaging)-

employing FA◆ Fractional anisotropy (FA) is a scalar value

between zero and one that describes the degree

of anisotropy of a diffusion process. A value of

zero means that diffusion is isotropic, i.e. it is

unrestricted (or equally restricted) in all directions.

A value of one means that diffusion occurs only

along one axis and is fully restricted along all

other directions. FA is a measure often used in

diffusion imaging where it is thought to reflect

fiber density, axonal diameter, and myelination in

white matter. The FA is an extension of the concept of eccentricity of conic sections in 3

dimensions, normalized to the unit range.

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1. DTI (Diffuse Tensor imaging)-

employing FA◆ One common approach is the use of DTI

tractography to delineate major white matter tracts, and compute measures such as FA (indicator of microstructural integrity) of the traced tracts at several developmental ages. The resulting age-related changes show the microstructural changes for the traced white matter tracts during development. In one such study, Lebel and Beaulieu used DTI based tractography and region of interest analyses on longitudinal scans of 103 healthy subjects aged 5–32 years (each volunteer scanned twice) to describe the exponential changes in the microstructural development of white matter (using FA) from childhood to adulthood (Lebel and Beaulieu, 2011).

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Research findings indicate increasing

integration and decreasing

segregation of structural connectivity

with age indicating network-level

refinement mediated by white matter

development.

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2. rs-fMRI◆ Resting state fMRI (rs-fMRI) is a functional imaging

technique that permits measurement of spontaneous, low-frequency (0.1 Hz), and high-amplitude fluctuations while subjects are ‘at rest’ (not performing any overt task)

◆ Functional connectivity as assessed by the correlation of rs-fMRI signals has often been observed among functionally associated brain areas and is present even under anesthesia (Gusnard et al., 2001; Dosenbach et al., 2007; Fair et al., 2007, 2008; Greicius et al., 2009).

◆ Both ‘data-driven’ (e.g. independent component analysis (ICA)) and ‘hypothesis-based’ (a priori seed-selection) approaches have been used to investigate developmental changes in functional connectivity.

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2. rs-fMRI◆ On the other hand, hypothesis-based approach starts with

a priori selected seed (brain region) with which functional connectivity is computed for the rest of the brain regions.

◆ Developmental trajectories from late childhood (8–12 years) through adolescence (13–17 years) to early adulthood (19–24 years) of 5 functionally distinct cingulate-based intrinsic connectivity networks (ICNs) -5 domains of self-regulatory control: i)motor control, ii) attentional/cognitive control, iii) conflict monitoring and error processing, iv) mentalizing and social processing, and v) emotional regulation.

◆ A pattern of diffuse local functional connectivity in children while more focal patterns of functional connectivity were seen in adults, consistent with developmental patterns of activation seen in functional neuroimaging studies that move from diffuse to more specific/focal patterns (Durstonand Casey, 2006; Durston et al., 2006).

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Pros and Cons to

Traditional methods◆ Pro: Both hypothesis-based (seed-based) and ICA

approaches have proved very useful in studying specific networks or modules in detail.

◆ Con: Misses the bigger picture of how specific networks or modules interact with the remaining brain regions. This becomes especially relevant considering the fact that the coordinated activity within and across modules produces large-scale brain networks that are essential for efficient functioning of the brain

◆ E.g. studying a particular brain region, say, the anterior cingulate cortex (a seed region for the salience network) within the context of only the salience network will likely mask its function in default mode activity.

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Pros and Cons- Example

Central Executive Network( Frontal Parietal) -key nodes

◆ dlPFC & PPC----memory and attention

Salience Network-key nodes

◆ frontal insular and dACC---detecting mapping internal and external events relevant to context. Connects w subcortical-limbic structures critical to reward and motivation

Cingulo-Opercular-key nodes

◆ dACC, ant insula, antPFC

DMN-key nodes

◆ PFC and PCC; self-referential, autobiographical, memory for scenario planning, moral decision making

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Graph-based connectivity:

Developmental Studies

◆ There appears to be a developmental trajectory toward increased structural connectivity with development, consistent with white matter maturation.

◆ Graph-theoretic studies based on SCNs have revealed that brain networks in early development (1month) are stable exhibiting economic/optimal small world topology and non-random modular organization and show increased global efficiency and modularity in early development (Fan et al., 2011). Khundrakpam et al. (2013) showed that this stable organization continues in childhood and adolescence (Khundrakpam et al., 2013).

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Graph-based connectivity:

Developmental Studies

◆ During late childhood, prominent changes in global topological properties, specifically a significant reduction in local efficiency and modularity and increase in global efficiency, suggesting a shift of topological organization toward a more random configuration.

◆ The studies are confounded by:

◆ fMRI studies used selected age ranges and selected brain regions

◆ Comprehensive statistical comparisons were not performed for the global topological properties

◆ Since these graph theoretic studies on development are from different imaging modalities that capture different tissue types and brain structures, different developmental trajectories of the global topological properties might be expected

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Utilization of Connectivity in

Clinical Disorders

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Heredibility

◆ Disrupted functional connectivity in frontal lobe was associated with common genetic variants implicated in several neurodevelopmental disorders including autism (Scott-Van Zeeland et al., 2010).

◆ A study using longitudinal DTI data of 162 healthy adolescent twins and their siblings showed that the efficiency measures (global and local efficiency) of structural brain networks are highly heritable (Koenis et al., 2015)

◆ The efficiency measures increase during early adolescence relate to increase in intellectual abilities.

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Conclusions from

Developmental Connectivity

◆ 1. At the cellular level, synaptogenesis and synaptic pruning act as progressive and regressive forces, beginning in primary sensorimotor regions and later in anterior regions such as prefrontal cortex (Huttenlocher, 1990; Huttenlocher and Dabholkar, 1997), thus continuously shaping the formation and evolution of neural circuits.

◆ 2. In parallel to these synaptic level changes, brain structure and function also undergo progressive and regressive events (i.e., WM myelination and GM loss, respectively) at the macroscopic level, starting earliest at primary sensorimotor areas and occurring latest in higher-order association areas.

◆ 3. Intrinsic functional connectivity exhibits a shift from diffuse local functional connectivity in children to more focal patterns of functional connectivity in adults

◆ 4. The dynamic process of synaptogenesis and pruning that rewires connectivity at the neuronal level also operates at systems level helping to refine network connectivity in the developing brain.

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Network

Communication

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Communication within

and Between Networks

◆ While the afore studies help to understand the

“hard wiring” for structural networks facilitating

communication in these networks, how is specific

information encoded in these networks to direct

information processing and adaptive responses –

in other words - learning?

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Is Learning “hard-wired”?

Hierarchical development

promotes more expansive

“learning”

◆ Introduction of stimulation on a system trying

“calibrate” or coordinate events in the ANS fundamentally limits adaptability by the CNS

Ex: Newborn study – competing reflexes

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Functional Hierarchy of Functional

System:

= Historical (Genetic)

+ Neural

= Sensory/Perceptual

= Cognition/Physical Mngmnt

= Communication/Independent Care

= Psychosocial /Occupational Success

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Definition

◆ Sensory Processing (Integration) Disorders refers to the body’s way of handling and processing inputs from the environment

◆ Jean Ayres, Ph.D. (Occupational Therapy) – Ayres, 1979.

◆ Result of an aberrant developmental process

◆ Estimated to affect 5-16% of children and this can cause long-term impairment of intellectual and social development from disrupted processes attempting to integrated “high-bandwidth” information from multiple sensory modalities – Owen et al. ,2013

◆ White matter problems

◆ Co-morbid with ADHD, ASD, and other pathologies BUT often exists in isolation (Ahn et al., 2004).

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Communication:

Backtracking from

Experience

◆ Coordination of stimulus “perception”

comes from time locked redundancies of

stimulus events (classical conditioning)

and the extent that responses to events

lead to successful adaptive functioning

(Operant conditioning)

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What is defining temporal

order in neural firing?

◆ Metabolic periodicity “coding”

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Biological Rhythms- Cellular

Processes

◆Goodwin (1967) –continuous oscillations of cellular constituents establishes relative stable states of equilibrium despite minor fluctuation in environmental events.

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◆ The production and depletion of

metabolites serves to regulate synthesis of

cellular proteins and the utilization of these

proteins in a dynamic manner.

◆ Not random but rather occur in patterns of non-

linear oscillations and can be described by a set

of differential equations.

◆ These equations describe cellular volumetric

shifts as function of the utilization curves of

cellular proteins.

◆ The time gradient in these utilization curves

describes the oscillatory mode of general

cellular activity

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◆Negative feedback control loops play critical role in regulation of number of oscillations per unit time,,i.e. concentration of argenine, CTP, pyruvate, etc that spill out into intercellular space with certain periodicities establish temporal signals for neighboring cellular activity◆The frequency and phase

relations of these signals can establish a “code word” which would have specific effects on cells or axons receiving it.

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Cellular communication

◆ It has become apparent that the complex

“biochemical” activities which underlie the

structure and function of cells and organisms do

not form a homogeneous pattern in time such

that all processes occur simultaneously at fixed

rates. Rather there is a rhythm to these activities

whereby they are ordered relative to one another

in time, first one and then another activity rising to

a maximum and then falling off again.

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Metabolic “Turnover” is

the “rate” limiting factor

One of the major determinants of how rapidly steady states can be reached in the metabolic system, is the turnover rate of substrate molecules by the enzymes of intermediary metabolism. This falls largely in the range of 10-10"* molecules 'sec. (cf. Eigen and Hammes, 1963). The detailed studies of Chance and Hess (1959), Hess and Chance (1961), on changes in the pattern of glucose metabolism in ascites tumour cells following different disturbances show that very extensive changes in metabolic steady state occur in a matter of 1 or 2 min in response to large stimuli. For example, the level of glucose-6-phosphate rises from a very low value (~005 /LtM/g cells) to a new steady state value of about 0-8 /LiM/g cells in about 1 min after the addition of 7-5 mM of glucose to the system.

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Stahl, 2002

Time course

for cellular

processes to

facilitate

neural firing

pre-

determines

firing rate

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◆Palva and Palva (2012)

◆have determined that the “UltradianRhythm” (< 0.01 hz) in EEG recordings, BOLD signals, neuronal activity levels, and behavioral time series are likely to image the same fundamental phenomenon; a superstructure of oscillatory ISFs that regulate both the excitatory level of functional networks and the integration between them.

77

◆A complex communication “field” would be set up in terms of frequency, amplitude, and phase relationships among metabolic signals over a population of similar cells [ or cells utilizing similar chemical components]

78

Coupling of higher-frequency oscillations to ISFs. In EEG recordings,

amplitudes of 1–40 Hz oscillations (colored lines) modulate according

to the phase of an ISF (0.01–0.1 Hz) and also mirror changes in

behavioral performance (black line). ISFs inhabit a similar frequency

range as that of the fluctuating BOLD signal, suggesting the latter

may bear a similar phase-relationship to higher frequency oscillations

and behavioral performance. Reproduced with permission from

Monto et al. (2008). ISF, infra-slow fluctuation.

Taken from Meehan et. al, 2012

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◆Kandel & Schwartz (1982) –changes in neural cells underlie the basis of learning and memory:◆The learning/memory of an event

resides in the time course of the chemical processes utilized in facilitating the events juxtaposed on other ongoing cyclical events

81

◆Varela, John and Schwartz (1978)

noted that photic stimulation

differentially phase locked to the

alpha rhythm determined the

perception of two contiguous flashes

as one flash, one light in motion, or

as two successive light flashes. ◆ Background alpha serves as “temporal template” . A

significant correlation between percent alpha or

frequencies which were harmonic or sub-harmonic in alpha as noted in EEG and the accuracy of time related

tasks.

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Proposed relationship between

oscillatory phase, neuronal excitability,

and stimulus events. (A) A putative

relationship between LFP phase of a

local neuronal population and action

potential firing rate, which here is high

during optimal and low during non-

optimal phases. (B) When

superimposed, traces from single trials

show random ambient phases, which

reset and align following a stimulus

presentation (time zero, arrow). (C)

Response amplitudes to sensory

events are highly variable during the

pre-stimulus period, are enhanced

during optimal phase, and are

suppressed during non-optimal phase.

(D) A stereotypical complex

waveform, when broken into its

component frequencies, reveals low-

frequency phase modulation of

higher-frequency oscillation

amplitude, or phase-amplitude

coupling, in a nested fashion.

Reproduced with permission from

Schroeder et al. (2008). LFP, local field

potential.

Taken from: Neurocognitive

networks: Findings, models, and

theory Timothy P. Meehan, Steven L.

Bressler, 2012.

83

Adaptability is achieved by

temporal coupling in networks

underlying harmonics

84

Meehan et al., 2012◆ Beta and gamma oscillations appear to have different

functional capabilities in cortex. The switch from co-existent gamma and beta2 to global beta1 oscillations implies that the cortex is able to: (1) manifest temporal memory as ongoing beta oscillations; (2) engage in long-distance coordination by phase synchronization of those oscillations across cortical regions; and (3) bind multimodal features by interregional phase synchronization without competition between inputs (Kopell, 2010). Since experimental evidence shows that beta oscillatory phase synchronization is involved in cognitive functions such as visual short-term memory (Tallon-Baudry et al., 2001), sentence comprehension (Weiss et al., 2005), and sensory-motor integration (Lalo et al., 2007), these functions appear to be dependent on the dynamic properties of cortical cell assemblies, which may represent the nodes of neurocognitive networks.

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Connection Basics –Phase

Couplings

Cross frequency PR uses same model; when 2nd derivative

between two oscillators =0, then in PL

86

Connection Basics –

Developmental, Phase Reset

87

PSD & PLD-Perceptual

Frame

◆ To distinguish events successive in time need 40

ms-auditory & 140 ms for visual stimuli,

(Varela,1995).

◆ Learning from discontinuous sequence of narrow

time windows.

◆ PSD & PLD are on going related to external events

or background emergent process of approx. 40-

80 sec (uncertainty/chaos) PSD, 150- 800ms (

certainty/stability)PLD

88

PSD & PLD-Perceptual

Frame

◆ During phase reset large assemblies of neurons

may be in a simulated “refractory” period

because locked neurons are not available for

reallocation called by a different cluster.

◆ When PLD occurs over long distance it reduces

the size of the cluster of idling neurons-

synchronous high amplitude.

◆ Seen when stimulus lock PR causes Event Related

Desynchronization (Klimesh,2007) ie spatially

distributed/differentiated micro binding= < idling neurons,= less amplitude post stimulus.

89



◆ Phase reset is comprised of phase shift duration

(PSD) and phase lock duration (PLD)

◆ PSD is a period of instability, due to minimally

coupled oscillators, on the edge of chaos/collapse.

◆ PSD allows for an energy free release of existing

(cross)frequency couplings within and between

areas, and a call out to available neuronal

resources.

◆ Longer PSDs associated w higher I.Q. ( recruitment of

sufficient neuronal resources for processing); shorter

PSDs - insufficient resources proposed to account for

perseveration in such disorders as autism.

90



◆ Phase lock duration (PLD), a time period of

stability-the binding of neuronal units within and

across frequencies for information processing.

◆ Shorter PLD intervals associated with higher I.Q.;

long PLD intervals associated with less efficient

processing.

◆ High correlation between PLD and Coherence

because COH is the consistency of phase

relationships which contributes to stability.

◆ Inverse relationship between PSD & PLD; low R2

91

Autism and EEG Phase Reset: Deficient

GABA Mediated Inhibition in Thalamo-

Cortical Circuits

◆ Thatcher et al., 2009

92

Connection Basics 97

Thatcher, 2015

(Thatcher, 2015)

98

A Role of Phase-Resetting in

Coordinating Large Scale

Neural Networks During

Attention and Goal-Directed

Behavior

Benjamin Voloh and Thilo Womelsdorf

REVIEW PUBLISHED: 08 MARCH 2016 DOI:

10.3389/FNSYS.2016.00018

99

◆ Short periods of oscillatory activation are ubiquitous signatures of neural circuits. A broad range of studies documents not only their circuit origins, but also a fundamental role for oscillatory activity in coordinating information transfer during goal directed behavior. Recent studies suggest that resetting the phase of ongoing oscillatory activity to endogenous or exogenous cues facilitates coordinated information transfer within circuits and between distributed brain areas. Phase resets: (1) set a “neural context” in terms of narrow band frequencies that uniquely characterizes the activated circuits; (2) impose coherent low frequency phases to which high frequency activations can synchronize, identifiable as cross-frequency correlations across large anatomical distances; (3) are critical for neural coding models that depend on phase, increasing the informational content of neural representations; and (4) likely originate from the dynamics of canonical E-I circuits that are anatomically ubiquitous. These multiple signatures of phase resets are directly linked to enhanced information transfer and behavioral success. Phase resets re-organize oscillations in diverse task contexts, including sensory perception, attentional stimulus selection, cross-modal integration, Pavlovian conditioning, and spatial navigation.

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0

Example: Language

Development

◆ 1. Reproducing sounds (sound over time only)

◆ 2. Reproducing words (complex sounds over time

only)

◆ 3. Reproducing phrases (series of complex sounds

without context)

◆ 4. Utilizing phrases in the right “contexts” – now

communication

◆ 5. Applying phrases to generalize across contexts

– higher order – adolescent language- creating a “new language”

10

2

Building Network Clusters

◆ Emergence from

perceptual/emotional networks to

memory networks which provide

frames of references which enable

response networks as part of the

decision making process (all of this

happens in milliseconds –

precognition?).

10

3

Cognition (Schemas)

= Historical (Genetic)

+ Neural

= Sensory/Perceptual

= Cognition/Physical Mngmnt

= Communication/Independent Care

= Psychosocial /Occupational Success

10

4

Meehan et al., 2012

◆Neurocognitive networks may have

a nested structure, whereby nodes

contain levels of complexity at

progressively more microscopic

scales, and processes at all these

lower levels may contribute to

function at the macroscopic scale.

10

5

Connection Basics

Thatcher, 2015

10

6

Recognizing sensory/temporal

redundancies is the root of

perception and learning

◆ The redundancy of two events in time allows us to

“associate” the events and allows us to

incorporate this association into or world of

schemas or knowledge. When the associated events are responses themselves and these

responses “compete” the system must either

accommodate them adaptively or if non-

adaptive response cannot be realized – we

experience distress

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7

Sensory- Perceptual

Schemas

◆ The system first develops network “schemas” by

the type of experiences we have in the real world

impinging on the neural system over and over

again so these neural systems “learn” to have

expectancies of how the world should work.

10

8

Neuroception(FROM STEPHEN PORGES)

10

9

https://youtu.be/wzP9X5Eclm8

Brain Games 1 Watch This (this is mind blowing Perception)

Piagetian Concepts

◆ Assimilation is a process that manages how we take in new information and incorporate that new information into our existing knowledge.

◆ Piaget used the term schema to refer to a category of knowledge that you currently hold that helps you understand the world you live in and provides some basic guidance for future events. A schema describes how we organize information. We store information as a particular schema until it is needed.

◆ The process of accommodation involves altering one's existing schemas, or ideas, as a result of new information or new experiences. New schemas may also be developed during this process.

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1

https://www.verywell.com/what-is-a-schema-2795873

Higher Ordered

Developmental Shift

◆ Networks the define and coordinate “perceptual”

organization and thereby define cognitive “sets”

give way to enable using learned concepts and

abstractions to “over-rule” network neuro-caption

as a decision making process about how the

world works and how to respond in such a world.

◆ This facilitates alternative response thinking –

executive functioning – problem solve

11

3

Piagetian Development11

4

Level I –

Perceptual/Emotional

Level II – Memory Matching -

Assimilation

Level III- Decision Making

Response/Accommodation

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6

Day 1 Final Conclusions

◆ There are definitively structure-function

relationships that evolve into networks optimizing

adaptability and “survivability”

◆ While methods describing the development of

gray matter and white matter in the human brain

have been useful, the details of “temporal

coding” and directionality within and across

networks is still in infancy.

◆ The ability to refine optimized interventions is

confounded by dynamic nature across age and across individuals including factors of metabolic

variance due to dietary/chemical intrusions

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7

Day 1: Final Conclusions (2)

◆ Behavior adaptation and response is a result of our histories and experiences represented in nested networks

◆ ALL of our constructs are a function of the brain

◆ We define our reality by experiencing things in context- we have expectations

◆ We “feel” things that also based on these expectations

◆ Common Experiences result in common behaviors and ways we come to expect how people will respond and react to a given situation –acceptable “social” behavior

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8

Problem for EEG

Neurofeedback Therapy

Protocols:◆ The structure-function landscape keeps changing in course of

development when training a specific structure-function relationship in a child, we may be assuming certain maturational “states” of these networks that simply do not exist

◆ If genetics plays a significant role in developmental connections at a given age and the “efficiency” of these networks, what are the “limits” in an individual to which we can expect to alter these intrinsic networks and their efficiency

◆ An important limitation of the available fMRI studies is that hemodynamic signals only provide an indirect measure of neuronal activity. In the contrast, electroencephalography (EEG) directly measures electrophysiological activity of the brain. Little is known about the brain-wide organization of such spontaneous neuronal population signals at resting state. It is not entirely clear if or how the network structure built upon slowly fluctuating hemodynamic signals is represented in terms of fast, dynamic and spontaneous neuronal activity.

Day 2: Review

and challenges

with regard to

training

Single Cell to Networks

Single Channel Training:

The Basis

◆ Single channel training has been the basis for the

majority of research showing efficacy of NFB to

date.

◆ Over two hundred fifty studies.

◆ Efficacy level is very high.

◆ Can we justify more complex approaches?

◆ Does our knowledge base support such

approaches?

◆ Do we have evidence of comparable efficacy?

◆ Do we have evidence of enhanced efficacy?

Is Training Networks or ROIs

A Reasonable Approach?

◆ Can we identify Networks at the right level?

◆ Does EEG have the spatial resolution we need?

◆ Can we isolate specific network structures related to specific

functions?

◆ Do we know how networks interface?

◆ Do we understand how their spatial and temporal function?

◆ Do we understand up and downstream influences?

◆ Can we predict the impact of a protocol?

Micro Structural Considerations

The Present Structural

Theory Sporns & van den Heuval,

2013

Limitations of Graph TheorySporns & van den Heuval, 2013

“it should be noted that current graph-based

analyses of communication cannot fully predict

dynamic (i.e., time-varying) patterns of

communication. Factors influencing the dynamics of

neuronal time series such as local firing rates of

neurons and/or level of activity, external inputs or

task demands, coherent phase relationships, or

synaptic efficacy are generally not incorporated into

current graph analyses.”

Limitations of Anatomy

“knowledge of anatomical connectivity in the

human brain is still embryonic”

(Sporns et al., 2005)

“At NCN 2010, Sporns compared the current state of

the human connectome to a map of the world circa

1570 (Sporns, 2010)”

Meehan & Bressler (2012)

Can We Use DTI Fiber

Pathways

▪ We don’t know how the networks map to the

fiber pathways of the brain.

▪ There is no standard definition of an edge or

node.

▪ Anatomical connectivity constrains functional

connectivity, but cannot account for it fully.

Meehan & Bressler (2012)

The Hub System-Fiber Path vs Synaptic

MapThe DMN Cortical Network

8 Anatomical Subregions

◆ Posterior Cingulate

◆ Precuneus

◆ Cuneus

◆ Paracentral Lobule

◆ Isthmus of the Cingulate

◆ Superior Temporal Sulcus

◆ Inferior Parietal Cortex

◆ Superior Parietal Cortex

Highest elevated fiber counts &

densities(node degree and

strength)

“To fully understand the basis for such cases

will require a precise synaptic connectivity

map of the human brain; however, such

a map does not yet exist, and fiber-tract

imaging (DTI and DSI) may never be able

to produce one.” (Freeman, 2005)

ANATOMICAL AND FUNCITIONAL

CONNECTIVITY RELATIONSHIP: STILL

UNCLEAR

Honey et al. (2009) found only a partial agreement

between anatomical and functional connectivity, with significant functional connections occurring

between areas where no underlying direct

anatomical connection was detected.

Micrograph of the cell body

with synaptic inputs.

As many as 30-50K inputs per

cell in the mammalian brains

are seen.

Cerebellar cells may have as

many as 100K inputs.

Parcellation Limitations of

Brodmann

Different

methods

provide us with

different

configurations

Parcellation Issues-

Anatomical or Functional◆ Many “imaging” modalities in identifying nodes use different measures—

metabolic (PET), blood (MRI,fMRI,rs-fcMRI), spectral power (LFP, EcoG, EEG, MEG)

◆ Time domain of MRI is several seconds, EEG –milliseconds; hence different resolution due to time dampening (does MRI translate to qEEG?)

◆ Correlation of heightened activity ( under task) of a node identified by one modality not fully understood how relates to node identified by different modality. Various advantages- model constructions from each ( Zalesky et. a. 2010)

◆ Network not simply defined by co-activated nodes, must also identify edge

◆ Node could be defined not only by co-activity but by increased correlation with other node while activation remains constant

◆ Use of different values for parameters results in different nodes and connectivity patterns, determines to which network(s) a nodal areas is associated

◆ Measure of single neural unit spiking identifies that 30% of neurons

133

Limbic & Brainstem Study

Deficits

◆ Most anatomical parcellation studies have

focused on the cerebral cortex. Less attention has

been paid to subcortical structures such as the

basal ganglia and the thalamus, which have only

been demarcated at a coarse level using sMRI.

Brainstem systems mediating motivation,

autonomic function and arousal have been

poorly studied because they are notoriously

difficult to identify using in vivo techniques.

Nonetheless, it is important to identify these

structures because they significantly influence cortical signaling and thus affect cognitive

function.

ROI’s Have Weak

Identities

More recently, fMRI activation studies have been

used to more precisely demarcate nodes of specific

functional circuits associated with such dedicated

networks. However, regions of interest (ROIs)

identified in this manner tend to vary considerably

with task demands, patient groups used, and the

specific control or baseline conditions used to

identify them. As a result, uncovering the nodes of

neurocognitive networks in a principled and reliable

manner has turned out to be elusive.

Menon, 2011

Harmonic Interface?

There may

be a whole

level of

network

interface

that is not

fiber

based.

Functional Networks- Hubs

From Structure To Function

◆ Functional nodes were first determined by lesion studies, then fMRI- Localizationist Perspective. (discrete regions showing elevated metabolic activity (in PET), blood flow (in fMRI), spectral power (in LFP, ECoG, or MEG), or elevated firing rate (in single-unit or multi-unit recording) in correlation with performance of a cognitive function.

• Structural networks provide a complex architecture that promotes the dynamic interactions between nodes that give rise to functional networks- Network Perspective.(changes in spectral coherence between distributed neuronal assemblies may occur without changes in spectral power.)

A meso level hub can be a macro level node.

Freeman, 2014

Sources & Sinks

Studies examining the total sum of afferent and efferent connections of hub regions in such data sets have suggested that some cortical hub regions maintain an unequal balance of incoming and outgoing projections. This imbalance suggests a potential role for these cortical hub regions as neural communication ‘sources’ and ‘sinks’.

Source hubs include central brain ROIs of attentional networks

◆ dorsal prefrontal, posterior parietal, visual and insular cortex

Sink hubs (driven/receptor) include

◆ posterior cingulate, precuneus, and medial frontal

Driven vs Driving Hubs Directionality: Incoming vs

outgoing projections

◆ Posterior cingulate, precuneus, and medial

prefrontal cortex are driven hubs or sinks.

◆ Dorsal prefrontal, posterior parietal, visual and

insular hubs as driving hubs or sources.

Some areas act as controllers such as pre frontal and parietal

control areas which channel the flow of sensory and motor

actvities.

van den Hueval & Sporns

Should we be driving either category in particular with NFB?

Seed RegionsAlternative Approach To ICN

Delineation◆ Based on functional interdependence analysis

◆ First, an fMRI seed region associated with a

cognitive function is identified (active on task).

◆ Then a map is constructed of brain voxels showing

significant functional connectivity with the seed

region.

Structural Hubs & Node

ComponentsExamples In Brodmann Terms◆ Precuneus : BA-7,19,31,39

◆ ACC & BA-25,24,32,33,10

◆ PCC: BA-23,29,30,31

◆ Insular cortex BA-13

◆ Frontal cortex :BA-6,8,9,10,11

◆ Temp cortex:

◆ Lat- BA 38,20,21,22,37,40,41, 42

◆ Inf-BA 20,21, 22

◆ Sup-BA 13,21,22,38,39,41,42

◆ Transverse- BA 41, 42

◆ Middle-BA 20,21,22,37,40,6

◆ Lateral parietal cortex -BA 5, 7

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Genardi

In Summary

Area Of Direct

Conduction T3-T4

60,000,00 Neurons

10-14k Connections

Each

Is our spatial

resolution

sufficiently

parsimonious ?

MRI Location Functions

Fiber Pathways & Metastable Networks

Timescale Conundrums

Cognition, and the neurocognitive networks that support it,

evolve continuously in real time on the timescale of milliseconds.

They constitute “a rapid sequence of spatial distributions of

electric potential, known as ‘microstates’ (Lehmann et al., 1998)

or ‘frames’ (Freeman,2006), which remain stable for around 100

ms and then transition abruptly to the next state (Freeman,

2006; Lehmann and Michel, 2011; Lehmann et al., 2009;

Lehmann et al., 1998) (Freeman, 2005).

SLOW TIMESCALE CONSTRAIN FAST TIME SCALES

Where neurocognitive network interactions undergo the

instantaneous transformations that manifest cognitive function

under constraint. (The constraint is provided by longer and

slower networks.)

Large Scale Networks

Network Paradigms

◆ Various authors from different research

paradigms, using different analytic and

conceptual approaches, arrive at different

network identities and configurations.

◆ These reflect different levels and methods of

analysis.

◆ They have considerable overlap and converge

over time.

Menon, 2015

Large Scale Connectivity

Short path = physical fiber (not graph)

Six Major Principles of

Large Scale Studies

First, large-scale functional organization is characterized by a non-random, small-world, modular global brain architecture with strategic hub regions that regulate communication among different functional systems (Sporns, 2011a, 2011b).

Second, strong interhemispheric connectivity between homotopic regions, with a gradient of decreasing left–right connectivity from sensory to association and heteromodal cortices, is a prominent feature of large-scale functional brain organization (Ryali, Chen, Supekar, & Menon, 2012; Stark et al., 2008).

Third, the human brain is intrinsically organized into coherent functional networks (Bressler & Menon, 2010), with brain areas that are commonly engaged during cognitive tasks forming brain networks that can be readily identified using intrinsic functional connectivity (Damoiseaux et al., 2006).

Principles Continued◆ Fourth, functional brain organization is characterized by task-

and context-dependent activated and deactivated brain systems, pointing to bottlenecks in parallel processing and temporally restricted access to neural resources (Fox et al., 2005; Greicius et al., 2003; Greicius & Menon, 2004; Honey, Kotter, Breakspear, & Sporns, 2007).

◆ Fifth, the most widely deactivated regions form a coherent large-scale network, termed the default-mode network, which is a tightly functionally and structurally connected system important for self- referential information processing and monitoring of the internal mental landscape (Greicius et al., 2003; Greicius, Srivastava, Reiss, & Menon, 2004; Qin & Northoff, 2011).

◆ Sixth, core prefrontal–parietal control systems can be dissoci-ated into distinct brain networks with distinct roles in cognition. Notably, the salience network, anchored in the insula and anterior cingulate cortex, is a system that plays an important role in attentional capture of biologically and cognitively relevant events while the lateral frontoparietalcentral executive network, anchored in the dorsolateral prefrontal cortex and supramarginal gyrus, is important for the working memory and higher-order cognitive processes (Menon & Uddin, 2010; Seeley et al., 2007; Sridharan, Levitin, & Menon, 2008).

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Menon,2011

Leech- 14

ICNs

Laird Interconnectivity

Networks

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Genardi

Laird Networks Identified

Task Based Co-operation of

Primary Networks & Hubs

In task-based functional

imaging, coactivations of

the anterior insula, anterior

cingulate cortex, and the

dorsolateral and the

ventrolateral prefrontal

cortices, as well as the

supramarginal gyrus,

intraparietal sulcus, and

superior parietal lobules of

the lateral parietal cortex

are common across a wide

range of cognitive tasks.

Network SelectionEach Node/Hub Has

Complex Network Interface

Do we have the norms to correctly adjust each network node?

Do we need to adjust micro level nodes?

Metastable Network

Competition“findings appear to confirm that distributed networks

underlie

cognitively demanding or executive capabilities, and

that these networks arise dynamically with task

demands.”

“transitions between metastable states are facilitated by

competition. Different states of neurocognitive networks

are conceived to be in continuous competition.”

(Rabinovich et al., 2008)

Convergence Zones

Van den Heuval, M., Sporns, O. (2013)

“the connective core hypothesis’suggests that

interconnected hub regions that are topologically

central offer an important substrate for cognitive

integration, not only for broadcasting and dynamic

coupling of neural signals but also by offering an

‘arena for dynamic cooperation and competition’

among otherwise segregated information “

Convergence Zone Issues

“hub regions across areas of the cortex in which

multiple functional domains overlap, forming

‘confluence zones’ or ‘convergence zones’ of neural

interactions” (van den Hueval & Sporns)

Network Exclusivity:

Multimodality

If we correctly adjust a network will it

adversely influence one of its alternate

functions?

Resting State Networks (ICN): 6 out of 7 Use Temporal Lobe Nodes

Can I separate just one of the networks out to train?

The 01-02 Anxiety SurpriseUnanticipated Consequences of

Training◆ For example, BA area 19 is not just “a” visual region

but actually a mosaic of different regions belonging to the extrastriate visual cortex (Orbanet al., 2004)

◆ This region has inputs to limbic regions as well as the amygdala.(Wang et al 2012)

◆ Soutar trained this area and documented that very robust reductions occurred in anxiety across clients and clinics as measured with a a normed anxiety instrument.

ICNs- Large Scale NetworksCo-ordinating Specific Functional

Nodes◆ AI- Anterior Insula Network- Self Awareness, Switching

◆ ACC- Anterior Cingulate Network- Attention, Reward Anticipation, Impulse Control

◆ DLPFC- Dorsolateral Prefrontal Network- Working Memory, Planning, Abstract Reasoning

◆ CEN- Central Executive Network- Memory & Attention

◆ PPC Posterior Parietal Cortex Network- Emotional and self-Referential Processing

◆ MTL-Medial Temporal Lobe Network- Episodic Memory, language

◆ DMN- Default Mode Network

◆ FPN-Fronto-Parietal- Rule based problem solving, Sensory contents of attention. Select spatil & category. Select behavioral relevant info-control network.

◆ PCC –Posterior Cingulate Gyrus- Lnguage, Memory, ST Memory. Attention Release (Peterson & Posner)

EEG

Correlate

Networks

Chen et al 2012

Derived from ICA,

sLORETA and

Coherence Analysis

combination.

Homologous networks

highly dependent on

corpus callosum with

increased FP

intrahemispheric

activity in the EO state.

Match MRI findings.

3 Major ICNsFunctional Hubs

◆ Central Executive- Memory & Attention DLPFC, PPC

◆ Default Mode- Autobiographical, self-monitoring and social cognitive

functions AI, DACC, PFC, PPC, MTL

◆ Salience- Modulates autonomic reactivity to salient stimuli AI,ACC,

DACC

◆ Note different nodes/hubs based on different imaging anaysis.

Subcortical Connections

CEN & SN have subcortical connectivity in the

◆ anterior thalamus (antTHAL),

◆ dorsal caudate nucleus (dCN),

◆ dorsomedial thalamus (dmTHAL),

◆ hypothalamus (HT),

◆ periaqueductal gray(PAG),

◆ putamen (Put),

◆ sublenticular extended amygdala (SLEA),

◆ SuN/VTA

◆ temporal pole (TP).

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Genardi

Subcortical InputsMemory and Emotion

If we choose a network for a Brodmann function will it

adversely affect a related but unacknowledged

subcortical function?

Modular vs Large Scale

ParadigmsBressler & Mennon, 2010

◆ Modular perspective expected functions to be

isolated to specific ROIs

◆ New Data indicates cojoint function of brain areas

is more accurate portrayal

◆ Cognitive function is emergent and constrained by

core structural and functional aspects of networks.

◆ A process of excitatory vs Inhibitory subnetworks-

nodes

Process vs Dynamic

Networks

◆ Processing-type networks are considered more

modular and static.

◆ Control-type networks are hypothesized to be dynamic and flexible, with an ability to adapt to a

wide variety of tasks.

Information Processing: Intro into

what we need to do to learn

Global Workspace

With hub nodes and their connections attracting and disseminating a large number of all neural communication paths, brain hubs and their connections, as a system, have been hypothesized as a convergent structure for integration of information, together forming a putative anatomical substrate for a functional ‘global workspace’.

Such a workspace is hypothesized as a cognitive architecture in which segregated functional systems can share and integrate information by means of neuronal interactions, with an important role for pathways that link central regions and constitute a global workspace.

Salience & Switching

Field Theory Perspective

The critical inference to be drawn from the

multichannel EEG data is that phase transitions occur

over large fractions of each cerebral hemisphere, by

which the oscillations of populations of neurons in the

beta and gamma ranges are repeatedly reinitialized, resynchronized within very few milliseconds [Freeman,

2005], and then restabilized with a new AM

(amplitude modulation) pattern for 3-5 cycles of the

center frequency of the shared carrier wave, while

the intensity of pattern transmission rises to a

maximum [Freeman,2004, 2005].

Switching Internal To

External

◆ AI switches between CEN for external processing

and DMN for internal processing

◆ Leech et al. ( 2013) found that the ventral PCC

has high connectivity with the DMN when

attention is focused internally; whereas the dorsal

PCC shows increased functional connection with

the DMN and concomitant anti-correlation with

the DAT for external attention task

DMN & SwitchingEC vs EO Maps

Eyes closed main cortical generator in the posterior cingulate. alpha oscillations generated to all other regions.

Eyes open: anterior cingulate activates with delta and beta dominating-as a sender of mainly theta-alpha oscillations to the dorsolateral pre-frontal cortices?

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5

Phase Shift and Phase

LockSwitching dynamics referred to as "Phase Shift" and "Phase Locking"

also called "Phase Reset" are a macro-to-meso level transition metric

in which phase shift recruits available neurons

Phase lock is the binding or synchrony of groups of neurons that

simultaneously mediate different functions in different brain regions.

Short distances are exponentially related to long phase lock

durations and short phase shift duration

Long distances are exponentially related to long phase shift durations

and short phase lock dura-tion. (local networks are likely more

devoted to a small set of calculations whereas long distance

connections synchronize large scale networks (rich club) that draw on local resources.

(Ermentrout and Kopell, 1994; Ko and Ermentrout, 2007; Thatcher et al, 2014)

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Genardi

Salience Afferents &

Efferents

LORETA Version DMN

DMN

Its key nodes have been variously linked to: ◆ Episodic memory retrieval (Sestieri, Corbetta, Romani, &

Shulman, 2011; Vannini et al., 2011),

◆ Autobiographical memory (Dastjerdi et al., 2011; Spreng et al., 2009), and

◆ Internal speech (Binder, Desai, Graves, & Conant, 2009),

◆ Specific nodes in the medial prefrontal cortex have been differentially associated with self- related and social cognitive processes (Amodio & Frith, 2006; Spreng et al., 2009),

◆ Value-based decision making (Rangel, Camerer, & Montague, 2008), and

◆ Emotion regulation (Etkin, Egner, & Kalisch, 2011). Genardi

FPN-Central Executive Network-CENComposed of approximately 18 independent subregions involving thalamus, occipital, temporal and parietal regions;

Subdivisions: Into control regions in fronto-temporal and thalamus- and-sensory processing areas of the occipital and temporal cortex.

Switches objects based on Interhemispheric Competition

Frontoparietal is an important network (FPN)( Central Executive ( CEN). It is essential in cognitive processing. Anchored in dlPFC & PPC, has strong intrinsic functional coupling.

Select objects of attentional interst based on PPC coding of feature, object quality and category type.

Rule based problem solving, Sensory contents of attention. Select spatial & category. Select behavioral relevant info-control network. The central executive network is critical for actively maintaining and manipulating information in the working memory, for rule-based problem solving, and for decision- making in the context of goal-directed behavior (Koechlin & Summerfield, 2007; Miller & Cohen, 2001; Muller & Knight, 2006; Petrides, 2005)

Maintains and manipulates WM info, involved in rule based problem solving, decisions related to goal attainment. Dysregularities of FPN found in most psych disorders.

It has important sub-functions . The FPN is involved in the top down control of the dorsal attention network, DAT, which is devoted to visual attention and eye movements. DAT includes

◆ intraparietal sulcus

◆ parts of the superior parietal lobes

◆ the medial temporal lobe complex

◆ frontal eye fields

Scolari et al, 2015

Paradigm Alternative

Example FPCN

Another important network is the frontoparietal control network ( FPCN) which is involved in executive control & becomes more active duringTask Positive activity. During Internal Attention as well. It includes

• parts of the frontal pole

• dorsolateral prefrontal cortex

• the rostral anterior cingulate cortex

• supplemental motor area

• the anterior insula

• and parts of the inferior parietal lobe.

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Subcortical Affective

Contributions

Psychopathology & Salience

Autism

Depression

Anxiety

Schizophrenia

Genardi

Emotion & Memory--PCC

According to functional studies the posterior cingulate gyrus/cortex PCC has 14 independent subregions

◆ Not as directly involved in motor initiation as the anterior cingulate gyrus is, still active when learning a complex motor skill.

◆ More frequently activated during language tasks (e.g., lexico-semantic processing) than its anterior segment, but its role in emotion is obvious (e.g., fear conditioning) as well as its participation in different types of memory (e.g., topographic memory, episodic memory, etc.).

◆ The brain areas involved in emotion, mainly the limbic system, including the MTL & cingulate gyrus, are the very same areas involved in memory. Indicating there is a close association between emotion/motivation and memory: only information that is significant from the emotional/motivational point of view is memorized. Emotionally neutral information is usually ignored.

Genardi

MTL Network

◆ Includes:

◆ the hippocampal formation

◆ extra-hippocampal cortical regions

◆ entorhinal cortex (ERC),

◆ perirhinal cortex (PRC), and

◆ parahippocampal cortex (PHC)

◆ Part of DMN

◆ Connects to Orbital Frontal and Posterior Cingulate

◆ Participtes in several cognitive domains, including episodic memory, perception, social cognition, and semantic cognition.

Seed Based Identity Das et al, 2016

MTL Subcortical Inputs

Chen at al,

2015

Cognitive Control

Network Control Paradigm Gu

et al 2015Specific nodes “drive” the

networks into modes or states

of function.

Processes:

• Link multiple sources of

information to solve problems

• Selective retrieval of

information from memory

• Inhibition of inappropriate

behavioural responses

Outcomes: Transitory changes in

patterns of cooperation and

competition between distributed

neural systems, including regions

in attention, default mode,

frontoparietal and cingulo-

opercular networks.

Control Systems

Profiles

◆ Average Controllability Areas-Hubs that require little energy to shift state.

◆ Modal Controllability Areas-Nodes with lower degree of connectivity require more energy to shift to harder to achieve cognitive states, EspExecutive and Attention states.

◆ Boundary Controllability Areas-Manage coupling and decoupling of networks.

Modular structure has been

reported in structural, functional

and dynamic brain networks.

Key Networks By Control

State

Category B is correlated with IQ: Higher B = Higher IQ

Implications of Paradigm

“moving any diseased state to a healthy state is

difficult, even with a complex combinations of drugs,

brain stimulation and cognitive therapies.”

Implication:

Loss of mitochondrial efficiency would lower brains

ability to reach higher energy state and

inflammation and lesions could do the same.

Implications 2

“Moreover, the default mode is the state to which

the brain relaxes back after the task has been

performed, readying the brain to move to new task

states, when the cycle will repeat.”

Implication:

Stermans Aviation Research predicted this resulting

in his theory of required Synchronizaiton between

tasks.

Gyorgy Buzsaki

◆ Network hierarchy is determined by

computational solution- ie no real top and

bottom solution.

◆ Function is highly distributive

◆ Synaptic weighting emerges from auto-

associative attractor networks.

◆ All circuits can sustain autonomous self-organizing

activity.

Self-Organization of

Critical States Issue

◆ The brain operates near the edge of chaos

according to Power Law observations of EEG.

◆ Self organization requires a high degree of error, error detection and error correction.

◆ Cortical networks must be free to engage in

activity outside a normative range in order to self

correct.

Pettersen et al, 2014

Systems Theory & The Brain

We are dealing with a system of micro, meso and macro network structures of profound complexity that operate more like a nested system that generates shifting attractor states defining a field of “network state space.”

Freeman, 2005

A Demo of A Network –

Decision Making

NFB Training StrategiesConsiderations & Conundrums

Goals of Assessment &

Training NFBCan We Do This?

◆ To identify key hubs (or nodes)

◆ To stimulate key hubs (or nodes)

◆ To exercise associated networks to improve function.

◆ Stimulate dendritic growth.

◆ Enhance network growth.

◆ Enhance network connectivity with associated

networks (1 electrode local, 2 electrode distant).

◆ Encourage capillary density & increased perfusion.

◆ Enhance delivery of metabolic resources.

◆ Increase network activity-beta/gamma sympathetic

◆ Decrease activity-alpha parasympathetic

◆ Increase Limbic Input- theta affective-memory

Genardi

Targeting Theories

Select Regions of Interest based on symptoms.

◆ ROIs are defined based on BA research.

◆ Select network paradigm (Hagmann, Laird,

Alternative DTI, etc).

◆ Select networks based on general symptoms.

◆ Train network hubs or nodes.

◆ No maximum limit to number of target variables

trained (one vendor).

Using Symptom- Function

Mapping◆ In neurofeedback we choose training targets/strategies by

compiling convergent data of symptom expression/dysfunction which can be mapped to a specific ROI/network

◆ Variety of simple ( at times laborious) to sophisticated and streamlined mapping of training goal to training target. Inputs to treatment plan include analysis of raw EEG dynamics, qEEG analysis, neuropsychological testing, variety of behavioral, cognitive and emotional assessments.

◆ Excellent examples Neuroguide Sx Checklist w integrated protocol recommendations, Loreta Progress Report, CNC with qEEGPro analysis, NewMind checklists & cognitive assessments w integrated protocol recommendations, BrainDxanalysis and report, Brainmaster live sLoreta analysis and data summary table.

Genardi

Training Strategy Options

◆ Train an ROI or group of ROIs (network) known to support a specific function; train associated parameters to a defined value- Remediation

◆ Train an essential/ area(s) subserving multiple functions/networks. Optimization (Generalized)

Rich Club

DMN

PCC

◆ Train dynamic relationship among key Large Scale networks

DMN, CEN, SN, DAT

◆ Train along some key attribute

Primary, secondary, tertiary cortex

Sources and sinks

Controllability

Genardi

Metabolic Aspects of

TrainingBressler & Menon, 2010

“the elevated excitability of neurons within an area leads to elevated metabolic activity, which in turn causes an increase in local blood oxygen availability. The elevated excitability could also cause increased interactions between neurons within the area. Interactions between different populations can produce oscillatory activity and can have important functional consequences if, for example, the interactions lead to increased sensitivity of neurons within the area to the inputs that they receive. “

Stimulate dendritic growth.Enhance network growth.Encourage capillary density & increased perfusion.Enhance delivery of metabolic resources.

Physiological Accuracy

Issues & LORETA

◆ THERE ARE MICROSTRUCTURAL SUBREGIONS IN BRODMANN AREAS-THEY ARE NOT HOMGENEOUS IN FUNCTION AND SERVE A MULTIPICITY OF EXTENDED NETWORK FUNCTIONS.

◆ FMRI SPATIAL RESOLUTION ONLY RECOGNIZES MACROANATOMY (BA/fMRI findings only match macro).

◆ STRUCTURAL FUNCTIONAL CORRELATIONS BASED SOLEY ON MACROANATOMY ARE QUESITONABLE.

◆ MICROANATOMY IS HIGHLY VARIABLE ACROSS BRAINS

◆ “Talairach and Tournoux’s (1988, 1993) atlas is of limited value as well.”(Geyer et al, 2011)

Select ROI: Brodmann LimitationsSelection & Cell Columns

Limitations of Spatial Resolution

◆ ROIs are dependent on the generation of current from large Dipole Layers for detection.

◆ ROIs may contain millions of cells and network components. Which one and how do we select it and train it with Limited Resolution?

“in vitro transmitter receptor

autoradiography (Zilles et al., 2002) have

revealed functionally relevant subregions

within many of the areas considered by

Brodmann to be homogeneous.”

Anatomical Accuracy &

10-20 System

Homan et al (1987) reports,

based on cadaver studies, that there is a 10% variance

between the 10-20 system and

actual gross anatomical

locations (Homan et al, 1987).

Which Symptoms Relate To DMN: Laird

#18 ?

◆ Self-reflective thoughts

and judgments that

depend on inferred

social and emotional

content. dMPFC

◆ Episodic memory HF

◆ Fantasy, daydreams,

envisioning the future,

past ruminations, moral

judgments, inferring

thoughts of others PCC.

Selectivity Challenge

Network Sequencing

Effective Connectivity PatternsNetworks have dynamic, not

static, patterns of information

flow.

Some network nodes may

need to be more active while

others need to be less active.

These patterns of activity may

need to unfold in a specific

temporal sequence

Can we identify the correct or

most efficient pattern of

activity?

Which Frequencies at what

time sequence is optimal for

training?

Upstream vs Downstream

IssuesWhere Do We Start Training?

Spectral Compensation During

Training

The Brain Changes In

Iterative Sequences of Error

Correction“Implicit to the commonly held notion

of plasticity is the concept that there is a definable

starting point after which one may be able

to record and measure change. In fact, there

is no such beginning point because any event

falls upon a moving target, i.e., a brain undergoing

constant change triggered by previous

events or resulting from intrinsic remodeling

activity.” (Pascual-Leone)

Theta Amplitude

Compensates for Alpha &

Beta Asymmetry Training.

Training Symmetry vs Z

Score

Amplitude AsymmetryZ Score

Implications For Training

Approaches

Using Z Score To Train

Networks?

Global Constraint Theory:

Z = 0◆ More target variables contrained result in a better

outcome?

◆ Reduce all outliers equally so they shift from the tails of the distribution to within 2 standard deviations or less.

◆ Key Strategy: minimize efforts to train targets with values closer to the norm.

◆ Evidence suggests that more normed targets move outside the arbitrary thresholds and require that movement for maximum plasticity required to reorganize.

◆ Each individual may require a different definition of threshold boundaries for maximal function.

Averaged vs Dynamic Movement

Actual EEG

Variance is

greater than

mean values

and often

outside normal

range.

Alternative Z score

Strategy

◆ Train midrange targets and capture extreme

outliers as they progressively move into range.

◆ Z momentum, ZMO or Z mean

◆ Train extreme outliers first and then progressively

incorporate midrange targets.

◆ Use Min and max or Z bars to evaluate targets.

◆ Should a hub be weighted differently than a node

with respect to deviance?

The sLORETA Z Score

Method

Select all locations and values in all neurometric

dimensions and constrain to within 2 SD using upper

and lower thresholds.

◆ Select key ROIs and train all values

◆ Select key ROIs and train some values.

◆ Select values based on approximation to mean.

◆ When average of all means remain within 2sd

provide reinforcement.

Strengths & Weaknesses

◆ Captures all measured deviations.

◆ May have more specific local effect.

◆ Some values may need room to deviate outside

the norm.

◆ Deviance may be dynamic requiring moving

targets rather than a static issue.

◆ Training to a Static Norm while on task.

A Model for QEEG and sLORETA

Correlates to Predicting and

Enhancing Human

Performance: A Multivariate

ApproachDAVID S. CANTOR, PH.D., DICK GENARDI, PH.D.,

BARBARA MINTON, PH.D., ROBERT CHABOT, PH.D.

QEEG and LORETA Correlates

to Neuropsychological

Performance

Subjects:

• 128 cases were drawn from a clinical population from the private practice of first author

• Age range = 5 – 77 (M = 18.45, SD=16.5)

• Scores from approximately 27 test instruments have been implemented into this database with varying N subjects in each of the cells

Measures: EEG

• Data collected either on Cadwell Easy II or Deymed TruScan-32– Minimum of 15 minutes of eye closed data

collected- 0-70 Hz; 256 time points/sec; 10-20 System minus Fpz, Oz

– Minimum of 60 seconds of artifact free data processed off-line

– All data translated for use with NYU database

– A total of 1614 bipolar and monopolar, and various multivariate measures were derived with age regressed Z-score transformations

Methods:

• Step-wise regression analyses for 1300 of the qEEg variable set were used to establish which variables were highest in predicting various performance scores

• Regression fitting was used to establish equations including intercept values that can be used to predict such scores.

• Group average QEEG maps were derived to characterize some of general QEEG profile correlated for these measures

• Source localization methods are being used to identify key neuro-anatomic structures associated with deviations in the QEEG spectrum that are then correlated with performance

Results -1:

Results – 2:

Case Example – FSIQ = 49

Case Sample – FSIQ = 49

Results -3: Sample Equations

• FSIQ = 99.28 – 6.25(MCoLatD) – 3(MReO2L) + 5.46

(MAsF7F8C) + 7.0(MCoT5T6D) – 6.40 (MCo0102B) –

3.52 (BAsHeadD) – 4.22 (MCoMedT)

• VisMem = 88.1 + 7.87(MMFC3S) – 5.95(MCoPostT) –

11.0 (MAsF7F8S) + 6.35(MAsPostA) – 7.48(BAsFTA) -

5.50(MCoT5T6C) – 5.21(BReC4CzA) +

5.94(MIcFp2F4D) – 4.86(MMFRPosB)

Further Analyses (1)

◆ FSIQ = 99.28 – 6.25(MCoLatD) – 3(MReO2L) + 5.46

(MAsF7F8C) + 7.0(MCoT5T6D) – 6.40 (MCo0102B) –

3.52 (BAsHeadD) – 4.22 (MCoMedT)

◆ Case 1 – Mitochondrial Disorder

◆ Clinical Findings = IQ = 10-20

◆ Equation = 14.98

◆ Case – Spinal Muscular Dystrophy

◆ EQ: IQ – 81

RESTING STATE CORTICAL

RHYTHMS IN ATHLETES: A HIGH-

RESOLUTION EEG STUDYCLAUDIO DEL PERCIO, PH.D.,

Abstract◆ The present electroencephalographic (EEG) study tested the

working hypothesis that the amplitude of resting state cortical EEG rhythms (especially alpha, 8-12 Hz) was higher in elite athletes compared with amateur athletes and non-athletes, as a reflection of the efficiency of underlying back-ground neural synchronization mechanisms. Eyes-closed resting state EEG data were recorded in 16 elite karate athletes, 20 amateur karate athletes, and 25 non-athletes. The EEG rhythms of interest were delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.513 Hz), beta 1 (13-20 Hz), and beta 2 (20-30 Hz). EEG cortical sources were estimated by low resolution brain electromagnetic tomography (LORETA). Statistical results showed that the amplitude of parietal and occipital alpha 1 sources was significantly higher in the elite karate athletes than in the non-athletes and karate amateur athletes. Similar results were observed in parietal and occipital delta sources as well as in occipital theta sources. Finally, a control confirmatory experiment showed that the amplitude of parietal and occipital delta and alpha 1 sources was stronger in 8 elite rhythmic gymnasts compared with 14 non-athletes. These results support the hypothesis that cortical neural synchronization at the basis of eyes-closed resting state EEG rhythms is enhanced in elite athletes than in control subjects.

Conclusions

◆ Neurometric Multivariate Approaches are to date

the best way to define Normal Profiles from

Disorders suggesting that there is range of

homeostatic mechanism that likely “ready” an

individual to be adaptive

◆ Similar Multivariate Approaches can be used to

specify Psychological Constructs and

Performance Capabilities

◆ The future of Neurofeedback Protocols will

depend on the refinement and ability to use multivariate equations that are the training

protocols

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