neuroimaging introduction
TRANSCRIPT
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!