Collaborations with the Collaborations with the UCLA Laboratory of Neuro Imaging UCLA Laboratory of Neuro Imaging (LONI) (LONI) Paul Thompson, Ph.D. Professor of Neurology & Psychiatry Director, Imaging Genetics Center Laboratory of Neuro Imaging UCLA School of Medicine, Los Angeles, CA
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Collaborations with the UCLA Laboratory of Neuro Imaging (LONI)
Collaborations with the UCLA Laboratory of Neuro Imaging (LONI). Paul Thompson, Ph.D. Professor of Neurology & Psychiatry Director, Imaging Genetics Center Laboratory of Neuro Imaging UCLA School of Medicine, Los Angeles, CA. - PowerPoint PPT Presentation
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Collaborations with theCollaborations with theUCLA Laboratory of Neuro UCLA Laboratory of Neuro
Imaging (LONI) Imaging (LONI)
Paul Thompson, Ph.D.
Professor of Neurology & Psychiatry
Director, Imaging Genetics Center
Laboratory of Neuro Imaging
UCLA School of Medicine, Los Angeles, CA
UCLA-CMRR were awarded 2 UCLA-CMRR were awarded 2 Collaborative R01s – only possible Collaborative R01s – only possible
because of the P41 – expertise, because of the P41 – expertise, infrastructure infrastructure
CMRR provides UCLA with novel scans, algorithms, analyses that we work on together (>30 papers in 4 years):
R01 NIBIB, HARDI Mapping of Disease Effects on the Brain - $549,204/year How can HARDI advance the study of Alzheimer’s disease?
- develop new algorithms to handle HARDI, multi-shell DWI (compute ODFs, tracts, networks)
- do better diffusion models help? (HARDI, TDF, CSA-ODF) and connectivity – CMRR expertise in mathematics as well as acquisition
Joint papers – with G. Sapiro (30 papers); C. Lenglet (26); Bryon Mueller (8), N. Harel (8); E. Yacoub (2), etc.
22ndnd R01 (UCLA/CMRR) – R01 (UCLA/CMRR) – Compare 7T and 3T Diffusion Compare 7T and 3T Diffusion
Imaging Imaging in Alzheimer’s disease, MCIin Alzheimer’s disease, MCI
• Scan 80 people at both 7T and 3T (Kelvin Lim and Bryon Mueller) – highly valuable dataset for the field
1. How can 7T scanning advance the study of AD and MCI?
2. How do DTI measures change at higher field? (FA/ODFs, networks, connectivity measures)
CMRR 7T DWI - Noam Harel; UCLA tractography
CMRR 7T DWI - Noam Harel; UCLA tractography
CMRR 7T DWI; Thompson-Lenglet-Sapiro R01
7T picks up thinner tracts; reconstruction method also matters
Connectivity matrices
10 people scannedat both 7 and 3T
Data reconstructed
with sum-of-squares,adaptive recombine,
SENSE1, etc.
Structural Network Structural Network Hubs Damaged by HIVHubs Damaged by HIV
Hough Transform Tractography AlgorithmAganj I, Lenglet C, Jahanshad N, Yacoub E, Harel N, Thompson PM, Sapiro G (2011).
A Hough Transform Global Probabilistic Approach to Multiple-Subject Diffusion MRI Tractography, Medical Image Analysis, 2011 Aug;15(4):414-25.
Whole Brain HARDI Tractography (CMRR, Aganj et al.)
This is the most robust tractography algorithm we have ever used (avoids ‘spaghetti’)
Trying it now in many clinical projects (HIV, 22q, children)
Collaborating on better mathematical models of Diffusion - critical to infer FA, fiber directions, connectivity
To capture diffusion at multiple b-values, To capture diffusion at multiple b-values, we studied a fast q-space sampling schemewe studied a fast q-space sampling scheme