computational methods in neuroimaging
DESCRIPTION
Computational methods in NeuroimagingTRANSCRIPT
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Krishna Prasad MiyapuramCognitive Science & Computer Science
Indian Institute of Technology Gandhinagar
Computational Methods in Neuroimaging
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19982000
20022004
20082011
2012
ElectronicsArtificial Intelligence
Cognitive Neuroscience
Neuroeconomics
Predictive coding
M.Tech.
M.Sc.
Journey of a thousand miles begins with a single step
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Outline
• Imaging the Human Brain
• Physics of Functional MRI
• Classical analysis: Statistical Parametric Mapping
• Data Visualization
• Beyond Blobs: Functional Connectivity
• Machine Learning Methods
• Data Mining Techniques
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The BIG Question
Cognitive Science
Psychology
Artificial Intelligence
Neuroscience
EducationLinguistics
Anthropology
Philosophy
What is the nature of human MIND?
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The small Answer
Study the human BRAIN!
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Neuroimaging Techniques
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Frontal LobeParietal Lobe
Occipital Lobe
Temporal Lobe
Cerebellum
Basal ganglia
The Human Brain
Trees do not have an organ called “brain”.
AnteriorPosterior
Superior(Dorsal)
Inferior(Ventral)
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Parts of the Brain
Temporal LobeOccipital Lobe
Frontal LobeParietal Lobe
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Terminology – Planes and Sections
Coronal
Saggital
Axial
Axial / Horizontal Plane
Saggital Plane
Coronal Plane
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3D imaging
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The Nobel Prize in Physiology or Medicine for2003 jointly to
Paul C. Lauterbur Sir Peter Mansfield
"for their discoveries concerning magnetic resonance imaging“
http://www.nobel.se/medicine/laureates/2003/press.html
Sectional view of an MRI Scanner
Scanner room
Console room
Patient Table
RF (Head) coil
Gradient coil
Static magnetic field
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Physics of MRI
Brain activity
Oxyhaemoglobin
Deoxyhaemoglobin
MRI signal intensity
Oxygen consumption
Cerebral blood flow
Rest (Normal blood flow)
Activation (High blood flow)
(A) (B)
(C)
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Experimental Design
A B A B B A B A A B A
B BA A
BA
BA
B BA
BA
B
Input
Output
Input
Output
Process
BaselineTask condition
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Data Analysis
fMRI time series
Statistical Parametric Map
Within-subject registrationslice-timing correction
RealignmentCoregistration
(structural to functional)
Between-subject registrationspatial normalization
Spatial smoothing
General Linear ModelDesign matrix
Parameter estimation
Statistical InferenceLinear Contrasts
Thresholding
Random Effects Analysis(Group analysis only)
Preprocessing
Statistical Analysis
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Softwares for fMRI Analysis
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Statistical Parametric Mapping
• SPM is a form of data reduction, condensing information (in a statistically meaningful way) from a number of individual scans into a single image volume that can be more easily viewed and interpreted.
SPM has an extensive web site at:http://www.fil.ion.ucl.ac.uk/spm
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Image Processing
Within-subject registrationslice-timing correction
RealignmentCoregistration
(structural to functional)
Between-subject registrationspatial normalization
Spatial smoothing
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Need for motion correction
• People move, even if they don’t realize!
(A) (B)
(D)(C)roll
pitch
yaw
x
y
z
Rigid body movement: 3 translation parameters
3 rotation parameters
Same location in the grid
Same location in the brain
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Statistical Analysis
General Linear ModelDesign matrix
Parameter estimation
Statistical InferenceLinear Contrasts
Thresholding
Random Effects Analysis(Group analysis only)
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Statistics: How?fMRI model setup
• A General Linear Model (GLM) is setup modellingthe control and test conditions as effects of interest.
y = Xb + e• The GLM is used to specify
the conditions in the form of a design matrix, which defines the experimental design and the nature of hypothesis testing to be implemented.
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Specifying Contrasts– A contrast can be used to compare different
conditions in the study.
– The conditions that are of interest are given a positive value, such as 1, and conditions that are subtracted from the conditions of interest are given a negative value, such as -1.
Thresholding:During the assessment of
Results, height and extent thresholds are applied to determine significant activations.
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Visualization
Glass Brain for Active-Rest Brain Slice picture for Active-Rest
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Rendering onto Subject’s Anatomical Brain
• A High resolution anatomical image (dimensions: 128x128x160 , resolution 1.95 x 1.95 x 1 mm) is acquired.
• This image is Segmented into Grey, White and CSF images.
• The subjects brain is extracted from the Grey and White matter images.
• The activations can now be rendered onto 3D anatomical image of the subject
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Psychophysiological Interactions
• Slides from Roland Benoit, MfD 2007/8• Data from
– C. Buchel and K. Friston. Modulation of connectivity in visual pathways by attention: Cortical interactions evaluated with structural equation modelling and fMRI, Cerebral Cortex, 7: 768-778, 1997
• Figures from – K.J. Friston, C. Buchel, G.R. Fink, J. Morris, E. Rolls, and
R. Dolan. Psychophysiological and modulatory interactions in Neuroimaging. NeuroImage, 6:218-229, 1997
– Christian Ruff’s ppt “Experimental Design”
• Tutorial: http://www.fil.ion.ucl.ac.uk/spm/data/
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Functional Connectivity
Functional IntegrationFunctional Segregation
Effective ConnectivityFunctional Connectivity
Attention
V1
V5
An Example
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Set
source
target
stimuli
source
target
Two Interpretations
Context-sensitive connectivity Modulation of stimulus-specific responses
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How it works: Interactions
V1 X Attention
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How it is done: PPI & SPM5
• Estimate GLM
• Extract time series at Region of Interest
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How it is done: PPI & SPM5
3. Deconvolve, Calculate Interaction, Reconvolve
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How it is done: PPI & SPM5
3. Estimate new GLM
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How it works: GLM
0 0 1
V1 Att V1XAtt
z = -9 mm
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Multi Voxel Pattern Analysis
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Problem Statement
• Over the past decade functional Magnetic Resonance Imaging (fMRI) has emerged as a powerful technique to locate activity of human brain while engaged in a particular task or cognitive state.
• We consider the inverse problem of detecting the cognitive state of a human subject based on the fMRI data.
• f : fMRI-sequence(t1,tN) CognitiveState
Dominant Cognitive StatefMRI SCAN 1 fMRI SCAN 2 fMRI SCAN N
fMRI time series (N >= 1)
f
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Definition and Motivation
• What is Cognitive State?
• It is the state of a process/operation within the human brain that affects it’s mental contents.
• Motivation :
– Such functions could provide the basis for a new approach– to study human reasoning processes.
– Also deeper understanding of the functioning of human – brain could help us build more advanced AI systems.
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Visuo Motor Sequence Learning
Visuo-Motor Mapping:• Association of various visual
instructions to appropriate actions.
• -Stopping at red traffic signal• -Driving slowly at speed• breaker
Sequence Learning:• Learning a task that requires
sequencing a number of actions to achieve a goal.
• -Driving a car• -Lacing a shoe
Visual
Instruction 1Motor Action 1
Visual
Instruction 2
Visual
Instruction 3
Visual
Instruction N
Motor Action 2
Motor Action 3
Motor Action N
Visuo-Motor Mappings
S
e
q
u
e
n
c
e
L
e
a
r
n
i
n
g
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Position-to-Position Mapping
P
o
s
i
t
i
o
n
S
e
q
u
e
n
c
e
1
2
1
2
1
2
1 2
1
2
1
2
Visual Display Keypad Response
Position-to-Color Mapping
P
o
s
i
t
i
o
n
S
e
q
u
e
n
c
e
2
1
1
2
2
1
1
2
2 1
2
1
Visual Display Keypad Response
Visuo-Motor Tasks
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VisualStimuli
ArbitraryMapping
(Position-to-Color)
Response
P2C
P2P
VisualStimuli
Trial & Error
Response
EarlyLearningLate
Learning
Classification Problem
P2P Vs P2C: Detect the following cognitive states
– “subject is paying attention only towards the position of the visual stimuli”
– “subject is paying attention towards the position and color of the visual stimuli”
Early Vs Late Learning: Detect– the following cognitive
states– “subject has learnt the V-M
sequence”– “subject is in the early
process of learning the V-M sequence”
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Machine Learning Approach
• To estimate the function f: fMRI-sequence(t1, tN) -> CognitiveState we have explored the following machine learning techniques:
Gaussian Naïve Bayes (GNB) Classifier k-Nearest Neighbor (kNN) Algorithm Support Vector Machines (SVM)
• Single-Subject Classifier: Classifiers that are trained and tested with a single subject’s fMRI data.
• Multiple-Subject Classifier: Classifiers that are trained with fMRI data of multiple subjects and tested with data of a new subject,
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1. Very high dimensional data (184707 voxels/features).
2. Variation in shapes and sizes of brain across human subjects.
3. Variation in the level of fMRI activity across subjects.
Major Challenges
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Feature Selection
• Select the n most discriminating voxels (Discrim) : Voxels are selected based on their ability to distinguish one target class (Cognitive States) from the other.
• – Select the n most active voxels (Active) : Voxels are selected based on their ability to distinguish either target class (Cognitive States) from the baseline condition.
• – Select the n feature pairs whose correlation discriminates the target classes (CorrPair) : Voxel pairs are selected based on the ability of their correlation to discriminate the target classes.
• We observed Poor performance of Discrim and Active features and relatively better performance of CorrPair features for multiple-subject classifiers.
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20
40
60
80
t=12 t=36 t=72 t=12 t=36 t=72 t=12 t=36 t=72 t=12 t=36 t=72
GNB KNN (k=5) KNN (k=9) SVM
200
474
88
92
96
100
50 100 200 50 100 200 50 100 200 50 100 200
GNB KNN (k=5) KNN (k=9) SVM
Discrim
Active
Sin
gle
Su
bje
ctM
ult
iple
Su
bje
ct
No. of features
Classification Accuracy (%)
No. of features
Time Interval
CorrPair Feature Selection
Feature Selection
P2P Vs P2C Classification Study
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97
100
40
55
70
85
50 100 200 50 100 200 50 100 200 50 100 200
GNB KNN (k=5) KNN (k=9) SVM
Discrim
Active
20
40
60
80
t=12 t=36 t=72 t=12 t=36 t=72 t=12 t=36 t=72 t=12 t=36 t=72
GNB KNN (k=5) KNN (k=9) SVM
200
445
Sin
gle
Su
bje
ctM
ult
iple
Su
bje
ct
No. of features
Classification Accuracy (%)
No. of features
Time Interval
CorrPair Feature Selection
Feature Selection
Early Vs Late Learning Study
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Interim Conclusions
• The problem of detection of cognitive states in such a high dimensional feature space is feasible when right choice of features is made along with suitable methods for representation of data.
• Overall much better performance of single-subject classifiers over the multiple-subject classifiers.
• We were unsuccessful in learning a classifier function for “Four-Way Classification Study", the question that we can detect all the cognitive states is yet to be answered.
P2P
P2C
Early Late
P2P Early P2P Late
P2C Early P2C Late
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Imagery Conditioning
• Imagery: Mental States like those that arise during perception but occur in the absence of immediate sensory input.
• What occurs in your mind when you see the following word
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Imagery Conditioning
• Were your mental contents like this
– Has shape (round)
– Has colour (brown)
– Is type (cake)
•
Neither of the above …
Umm! It’s yummy!
Pylyshyn
Kosslyn
OR
OR
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•
Experimental Paradigm
Perception Imagination
Reward
No Reward
Poisson ITI (mean 4 sec)
++
2 sec1 sec
3 sec
NothingScrambled
PictureMoney
BillNothing
Scrambled Picture
Money Bill
What did you See?
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Reward Predicting Responses
Activation in Midbrain is greater for stimuli predicting reward than control stimuli irrespective of the reward being Perceived or Imagined
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Predicting the imagined contents
• Support Vector Machine classifier trained on midbrain activation from visually presented trials successfully predicts whether the participant is imagining a reward or control picture
Participants
Mid
bra
in a
ctiv
atio
n (
clu
ster
ave
rage
)
Visual presentation CS+ CS-Imagination CS+ CS-
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Data mining: Meta Analysis
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Summary
• Computational Tools are indispensible for neuroimaging
• Classical Analysis uses a standard framework for functional localization
• We can ask questions about functional Integration (a.k.a. effective connectivity)
• Machine Learning Methods have made the reverse inference of cognitive states possible
• Further advances in Computational data mining techniques are to bring in a revolution in Neuroinformatics
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Thank you
http://cogs.iitgn.ac.in
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The Nobel Prize in Physiology or Medicine for2003 jointly to
Paul C. Lauterbur Sir Peter Mansfield
"for their discoveries concerning magnetic resonance imaging“
http://www.nobel.se/medicine/laureates/2003/press.html
The small Answer
Study the human BRAIN!