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multimodality

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Brainiac Stnava

on 8 November 2013

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Transcript of multimodality

Learning about the brain from multiple
imaging modalities: TBI*

Brian Avants
Why multiple modalities?
Medical imaging analytics @ PICSL
#1 in 3 independently
evaluated anatomical mapping challenges
(
Klein 2009
,
Murphy 2011 CT/Lung
,
Multi-atlas challenge 2012
)
ANTs is optimized
for integrating modalities
by spatial alignment
Traumatic Brain Injury:

Multi
modality
markers of
processing speed deficits

Network=
Structure
Function
Cognition

structure
function
cognition
Thalamus:
known effects
Connectivity
Cortex
Multivariate Characterization of Thalamus in TBI
Four modalities
volume of each sub-region of the thalamus ( T1 )
fractional anisotropy in each region ( DTI )
Cerebral blood flow (ASL)
Processing speed (psychometrics)
The 3429 sessions are from
32 different international sites
,
13 years of data collection, for both 1.5T and 3.0T data, 3 different scanner manufacturers, and
50+ scanners.
2/3 of these scans are HD subjects, 1/3 are normal controls.
Multivariate
segmentation:
>95%
variance
Hongzhi Wang, Paul Yushkevich
Thalamus Related White Matter
Integrity & Cortical thickness in TBI

i.e. processing speed
Cortex (T1)
Thalamus
Cerebral
Blood
Flow
(ASL)
White
Matter
(DTI)
Identify relationships between thalamus & other modalities (via SCCAN) to reduce dimensionality &
identify a network predictive of processing speed.
Courtesy:
Prof Hans I Johnson
University of Iowa
Identify structures
related to TBI

Within TBI sub-group, what
predicts variation in processing speed?

Employ a TBI-specific hypothesis to filter a large multimodality dataset down to a manageable and interpretable scale (
<50 tests total
)
Quantified thalamus variation with 3 modalities.
Show thalamic projections and thalamo-cortical regions affected by TBI.
Within TBI subjects, we further identify a cortico-connective network that is related to processing speed.
Multivariate processing.
Framework is extensible to numerous related analysis problems.
N=40, 22 TBI
matched age, gender

22 male

mean age 30+/-10

educ = 14y +/- 2
* joint work with
Junghoon Kim of
Moss Rehab & Ben Kandel,
Paramveer Dhillon,
Jeff Duda of PICSL
Penn Image Computing
& Science Laboratory
UPENN Dept of Radiology
BrainMeasure ~ 1 + Age + Gender + Education + TBI
all 40 subjects
ProcessingSpeed ~ Age + Gender + Education + Duration + Brain
Supported by: 5R01EB006266-04, P01-AG032953-02,
NLM-HHSN276201000492P, 5R01DA014129-08
http://brianavants.wordpress.com
White matter integrity
TBI ~ cortical thickness
GM & WM Eigenanatomy
SCCAN uses thalamus variation to extract white matter regions.
Also use thalamus variation to extract cortical regions.
SCCAN uses thalamus variation to extract white matter regions related to TBI.
WM
GM
Thal.

q < 0.05
Full transcript