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Copy of multimodality

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Junghoon Kim

on 23 March 2013

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Transcript of Copy of multimodality

Multimodal, multivariate understanding of TBI brains*

Brian Avants & Junghoon Kim 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:
Multimodality markers of
processing speed deficits Network=
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
variance Hongzhi Wang, Paul Yushkevich Thalamus Related White Matter
Integrity & Cortical thickness in TBI i.e. processing speed Cortex (T1) Thalamus Cerebral
(ASL) White
(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 UPENN Dept of Radiology
Moss Rehab 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

q < 0.05
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