105 Temporal Features

signal metrics, peaks,

kurtosis, skewedness, entropy,

amplitudes, power bands,

HPSD, auto correlation etc.

Preprocessing

Functional MRI

Spatial and Temporal Features of fMRI Networks to Distinguish Real Networks from Noise Preliminary work to use patterns of functional networks to classify neuropsychiatric disorder

V. Sochat, Rubin Lab, Stanford University School of Medicine, Stanford CA

Introduction

• Independent Component Analysis ICA is a data-driven method to decompose functional

neuroimaging data into brain networks.

• The decomposed independent components encompass a mix of true neural signal,

machine artifact, motion, and physiological noise that are typically visually distinguished.

• Neurological disorders are beginning to be understood based on aberrant brain structure

and function on the single network level.

• No methods exist for identifying patterns across all networks to distinguish disorder.

What Does the Data Look Like?

How do we Define a Standard?

Lasso L1 Constrained linear regression selects 124 features to distinguish real from

noisy components (N=1518) with a cross validation accuracy of .8675.

Supported by Microsoft Research, NSF

Stanford Graduate Fellowship

Can We Predict Component Type from Features?

Meeting with MIND Institute 9/24/2012 to finalize

standard development to allow for robust computation

of functional network fingerprints.

Why Should I Care?

What is a network?

There are no standards or

definitions of functional

brain networks beyond

expert opinion.

What is a subnetwork?

Overdetermined ICA

produces “subnetworks,”

but we do not completely

understand how they

match to main networks.

How to Rx Disorder?

Diagnosis of neuro-

psychiatric disorder with

the DSM is categorical,

checklist based, and

terrible.

We need methods to

establish standards for

networks and noise.

Computational definition

of subnetworks matching

to main networks will

allow for investigation of

patterns of neural activity

within main networks.

Biomarkers from

functional imaging can

drive diagnosis of disorder

and subtyping.

1. Standard and Features 2. Subnetwork Definition 3. Classify Disorder

This Project:

Spatial and temporal

features to distinguish real

from noisy components

Next Stage of Work:

Features of subnetworks

and matching to main

networks

Big Picture:

Patterns of functional brain

networks for classification

of neuropsychiatric disorder u

ns

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pro

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ms

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bje

ctiv

e

Realign / Reslice

Motion Correction

Segmentation Smoothing Filtering Normalization IC

A

n x m n x n n x m

Contact vsochat@stanford.edu

What Features Define the Networks?

1. Outline: database of “known” networks scattered in

literature. Currently identification is done manually.

1. component type (real, noise, etc.)

2. network type (motor, visual, default mode network)

3. network name (“precuneus posterior cingulate”)

4. intuitive name (“the tie fighter”)

135 Spatial Features

Regional activation,

matter types, kurtosis,

entropy, skewedness,

degree of clustering

Selected Features “Eyeballs” Component

Perfect_total_activation_in_GM

Percent_total_activation_in_WM

Olfactory_R

Skewedness of IC distribution

Avg_distance_btw_10_local_max

Spatial Entropy of IC distribution

Percent_total_activation_in_eyeballs

LASSO cva: 0.9841

2. Teach send database and networks to experts

3. Label: experts use annotation tool to label networks

4. Fingerprint: define networks based on pattern of features

Good / Bad

Network X / Not Network X

42 Networks

1 1

2

3

4

3 4

Thank You Rebecca Sawyer and Kaustubph Supekar, Signal Processing

Daniel Rubin: Best Advisor Ever!

24 healthy control

29 schizophrenia

Network fingerprints will allow for automatic labeling

and filtering of single subject ICA. For the first time,

subnetworks can be assigned meaningful labels, the

main networks they match to.

Different patterns of these subnetworks within the

space of a main network will distinguish disorder.

These methods will be extended to other imaging data

Bad Good

Bad 740 121

Good 78 579 N = 1518 Networks

spatial maps

and timecourses Please see research journal for preliminary match work