Date post: | 11-Apr-2017 |
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Science |
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• Research Objective
• What has been done
– Problems
• Current Research
– Diagnosis and Subtyping with fMRI
– Standards and Features
– “Sub-network” Analysis
What will I talk about?
rsMRI
Network Patterns
Rx
HC
Objective Identify patterns of brain networks for diagnosis of neuropsychiatric disorder and better definition of subtypes.
Rx1
Rx2
Rx3
• ICA multisession temporal-concatenation – Kim et. al, 2010: CCICA uses group information as prior, applied to SZ – Sohn et. al, 2012: “localized ICA” to identify motor network components
• Using templates to ID networks / group diff – Pamilo et. al, 2012: different dimensionalities of ICA – Using templates to ID networks / group diff
• ICA for noise reduction / artifact detection – Tohka et. al, 2008: decision tree to identify noisy components – Thomas et. al, 2002: ICA is good for finding structured noise
• Network Classification – De Martino et. al, 2007: SVM to classify IC components – Ciuciu et. al, 2012: wavelet transform (WLMF) to compare rest/task – Beckman, 2012: Good review of ICA for modeling
What Has Been Done?
Disorder Rx
Data Rx
• Lack of gold standard for networks
– Infeasible for human to annotate every network
• Reliant on group derived templates
– Groups constantly changing
• Number of dimensions?
• Approach does not scale to large datasets!
Problems?
rsMRI
Network Patterns
Rx
HC
Objective Identify patterns of brain networks for diagnosis of neuropsychiatric disorder and better definition of subtypes.
Rx1
Rx2
Rx3
(PART I TALK)
Objective Identify patterns of brain networks for diagnosis of neuropsychiatric disorder and better definition of subtypes.
Rx and subtyping with fMRI
(PART I TALK) Spatial and temporal features to distinguish components (PART 2 TALK)
Objective Identify patterns of brain networks for diagnosis of neuropsychiatric disorder and better definition of subtypes.
Rx and subtyping with fMRI
Features and Standards
(PART I TALK) Spatial and temporal features to distinguish components (PART 2 TALK) Group networks based on features to create a computational probability atlas, network “fingerprints” (PART 3 TALK)
Objective Identify patterns of brain networks for diagnosis of neuropsychiatric disorder and better definition of subtypes.
Rx and subtyping with fMRI
Features and Standards
Functional Network Fingerprints
Raw Data
Rx and Subtyping with fMRI
Main Networks
Sub Networks
Match and Label
Featurize
Derive main and subnetworks with ICA Spatial overlap
Spatial Temporal Fingerprint
u
Network Patterns
SZ HC
1 2 3 4 5
Rx and subtyping with fMRI Features and Standards Functional Network Fingerprints
Raw Data
Rx and Subtyping with fMRI
Main Networks
Sub Networks
Match and Label
Featurize
Derive main and subnetworks with ICA Spatial overlap
Spatial Temporal Fingerprint
u
Network Patterns
SZ HC
Meaningful differences between disorder and subtypes might be found in the patterns of sub-networks
1 2 3 4 5
Rx and subtyping with fMRI Features and Standards Functional Network Fingerprints
Derive Main and Sub-networks with ICA
Rx and subtyping with fMRI Features and Standards Functional Network Fingerprints
MIND Data 24 healthy control 29 SZ
Preprocessing
fMRI
Realign / Reslice
Motion Correction
Segmentation Smoothing Filtering Normalization IC
A
n x m n x n n x m
How many dimensions? ? Infomax “correct” networks High-dim subnetworks?
Derive Main and Sub-networks with ICA
Rx and subtyping with fMRI Features and Standards Functional Network Fingerprints
MIND Data 24 healthy control 29 SZ
Preprocessing
Realign / Reslice
Motion Correction
Segmentation Smoothing Filtering Normalization IC
A
n x m n x n n x m
How many dimensions? ? Infomax “correct” networks High-dim subnetworks?
Main Networks: Infomax Subnetworks: Highest forced dimensionality
fMRI
Rx and subtyping with fMRI Features and Standards Functional Network Fingerprints
What is a Functional Network?
• Lack of gold standard for networks
– Infeasible for human to annotate every network
• Reliant on group derived templates
– Groups constantly changing
• Approach does not scale to large datasets!
Rx and subtyping with fMRI Features and Standards Functional Network Fingerprints
Define Networks with Spatial and Temporal Features
Components Features Labels
Network A Network B Scanner Noise Network C . . . Network N
• 1518 Components from 53 HC/SZ
• Labeled with “good” vs. “bad”
Rx and subtyping with fMRI Features and Standards Functional Network Fingerprints
The “Vanessa” Standard
GOOD Looks like a known network “Softer” timecourse BAD Eyeballs White Matter / CSF Rings (motion) High frequency Blood Vessel Network
42 common components
Rx and subtyping with fMRI Features and Standards Functional Network Fingerprints
Functional Network Database
Ciuciu et al., 2012
• 135 spatial – 116 AAL Regional Voxel Counts
– Matter masks
– Degree of clustering
• 105 temporal – Frequency and signal metrics
Can be used to both:
– Distinguish good from bad
– Classify networks and associated subnetworks
Rx and subtyping with fMRI Features and Standards Functional Network Fingerprints
What are the Features?
Rx and subtyping with fMRI Features and Standards Functional Network Fingerprints
Can features distinguish “Good” vs. “Bad” Components?
Lasso L1 Constrained linear regression selects 124 features to distinguish real from noisy components (N=1518) with a cross validation accuracy of .8675.
Bad Good
Bad 740 121
Good 78 579
PREDICTED
AC
TUA
L
Which features distinguish “Eyeballs” Component?
Lasso cross validation accuracy: 0.9841
Eye Other
Eye 22 7
Other 24 1465
PREDICTED
AC
TUA
L
Selected Features “Eyeballs” Component
Perfect_total_activation_in_GM
Percent_total_activation_in_WM
Olfactory_R
Skewness of IC distribution
Avg_distance_btw_10_local_max
Spatial Entropy of IC distribution
Percent_total_activation_in_eyeballs
Rx and subtyping with fMRI Features and Standards Functional Network Fingerprints
Raw Data
Main Networks
Sub Networks
Match and Label
Featurize
Derive main and subnetworks with ICA Spatial overlap
Spatial Temporal Fingerprint
u
Network Patterns
SZ HC
1 2 3 4 5
Rx and subtyping with fMRI Features and Standards Functional Network Fingerprints
Rx and Subtyping with fMRI
Big Picture: Rx and Subtyping with fMRI
Rx and subtyping with fMRI Features and Standards Functional Network Fingerprints
Pamino et. Al, 2012
Main Networks
Sub Networks
A Z
Label
A Z
Match
A
Z
SZ HC
.73 HC
.
.
.3 SZ
SCORE
Rx and subtyping with fMRI Features and Standards Functional Network Fingerprints
Big Picture: Rx and Subtyping with fMRI
Rx and subtyping with fMRI Features and Standards Functional Network Fingerprints
Big Picture: Rx and Subtyping with fMRI
Rx and subtyping with fMRI Features and Standards Functional Network Fingerprints
Matching Subnetworks to Main Networks
How do we assign? What about unassigned? How do features compare? What are subnetwork features?
Rx and subtyping with fMRI Features and Standards Functional Network Fingerprints
Example Dynamic Feature: “Hubbiness”
Hub: A piece of a network that doesn’t get torn apart Small world networks? Can hubs define groups? Task related?
What should we talk about?
• Collaboration on “standard” to send out
– Which data?
– What software?
– What interface? (Matlab vs. web based)
• Features
• Your Thoughts!