Subnetworks in Schizophrenia, fMRI

Patterns of fMRI Networks to Diagnose Rx and Disease Subtypes

Presentation for MIND Institute

• 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


Rx1

Rx2

Rx3

(PART I TALK)


Rx and subtyping with fMRI

(PART I TALK) Spatial and temporal features to distinguish components (PART 2 TALK)



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)



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


Main Networks

Sub Networks

Match and Label

Featurize



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


Derive Main and Sub-networks with ICA


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


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


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!


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”


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


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


What are the Features?


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


Raw Data

Main Networks

Sub Networks

Match and Label

Featurize



u

Network Patterns

SZ HC

1 2 3 4 5



Big Picture: Rx and Subtyping with fMRI


Pamino et. Al, 2012

Main Networks

Sub Networks

A Z

Label

A Z

Match

A

Z

SZ HC

.73 HC

.

.

.3 SZ

SCORE


Big Picture: Rx and Subtyping with fMRI


Matching Subnetworks to Main Networks

How do we assign? What about unassigned? How do features compare? What are subnetwork features?


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!

Thank you

[email protected]

Subnetworks in Schizophrenia, fMRI

Science

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