neuroimaging introduction

Neuroimaging Introduction

Feature Group MeetingAugust 16, 2012

Introduction

• The Human Brain– What are we trying to look at?

• Modalities– How do we measure?

• Data• The Informatics Landscape– Processing Pipeline– Why?

The Human Brain

3 lbs109 neurons1015 synaptic connections

Measuring Structure and Function

Invasive Non-invasive

Structure

sMRI

CT

DTI

Function

fMRI

PET

EEG

MEGMODALITIES

Measuring Structure and Function

? Population Protocol Data

What happens to the structure of region X as we get older?What is my brain doing when I see pictures of cats?Which regions are working together?

? Public Repository

MODALITIES

Measuring Structure and Function

Invasive Non-invasive

Structure

sMRI

CT

DTI

Function

fMRI

PET

EEG

MEGMODALITIES

Measuring Structure and Function

Invasive Non-invasive

Structure

sMRI

CT

DTI

Function

fMRI

PET

EEG

MEGMODALITIES

MODALITIES © 2008 HowStuffWorks.com

What does an image look like?

DATA

SLICE

VOXEL

AXIAL SAGGITAL CORONAL

Structural Data• T1 weighted

– TR: short– TE: short– Fat: bright– Fluid: dark

• T2 weighted– TR: long– TE: long– Fat: intermediate-bright– Fluid: bright

DATA

Functional Data

DATA

What do the files look like?

DATA

P FilesImaging Data

HeaderNifti

• .nii (one file)• .img / .hdr combo

3D

• .nii.gz (compressed file)• .nii (uncompressed)• .img/.hdr combos

4D

Segmentation

Realign / Reslice

Motion Correction

Segmentation Smoothing Filtering

fMRI Processing Pipeline

ANALYSIS

Registration

Normalization

Statistical Test

Segmentation

Realign / Reslice

Motion Correction

Segmentation Smoothing Filtering

Data Driven Approaches?

ANALYSIS

Registration

Normalization

?

Data Driven Approaches?

ANALYSIS

• Connectivity Analysis– Seed-based– Matrix Decomposition (ICA)

Independent Component Analysis (ICA)

ANALYSIS

• One 3D image [ v1 v2 v3 v4… v4 ]• 4D Image Matrix, M

v1 v2 v3 v4 v5 v6 v7 . . . vn

Voxels

Time

Independent Component Analysis (ICA)

http://www.fmrib.ox.ac.uk/fsl/melodic/index.htmlANALYSIS

n x m n x n n x m

n time pointsm voxels

3D image flattened, all voxels at T =1 Components spatial map

Independent Component Analysis (ICA)

http://www.fmrib.ox.ac.uk/fsl/melodic/index.htmlANALYSIS

n x m n x n n x m

Independent Component Analysis (ICA)

http://www.fmrib.ox.ac.uk/fsl/melodic/index.htmlANALYSIS

• Features• Classification

– Noise vs. “real”– Network X vs Y– ADHD vs control

SPATIAL

TIMECOURSEPATTERNS OF NETWORKS

Informatics Landscape

INFORMATICS LANDSCAPE

Analysis Method

Public Data Process Machine

Learning

Disorder diagnosisClassification of subtypes of diseaseImproved filtering methodsUnderstanding human connectome

Why?

Thank You!

vsochat@stanford.edu