Content area

Abstract

Aligning brain structures across individuals is a central prerequisite for comparative neuroimaging studies. Typically, registration approaches assume a strong association between the features used for alignment, such as macro-anatomy, and the variable observed, such as functional activation or connectivity. Here, we propose to use the structure of intrinsic resting state fMRI signal correlation patterns as a basis for alignment of the cortex in functional studies. Rather than assuming the spatial correspondence of functional structures between subjects, we have identified locations with similar connectivity profiles across subjects. We mapped functional connectivity relationships within the brain into an embedding space, and aligned the resulting maps of multiple subjects. We then performed a diffeomorphic alignment of the cortical surfaces, driven by the corresponding features in the joint embedding space. Results show that functional alignment based on resting state fMRI identifies functionally homologous regions across individuals with higher accuracy than alignment based on the spatial correspondence of anatomy. Further, functional alignment enables measurement of the strength of the anatomo-functional link across the cortex, and reveals the uneven distribution of this link. Stronger anatomo-functional dissociation was found in higher association areas compared to primary sensory- and motor areas. Functional alignment based on resting state features improves group analysis of task based functional MRI data, increasing statistical power and improving the delineation of task-specific core regions. Finally, a comparison of the anatomo-functional dissociation between cohorts is demonstrated with a group of left and right handed subjects.

Details

Title
Diffeomorphic functional brain surface alignment: Functional demons
Author
Karl-Heinz Nenning 1 ; Liu, Hesheng 2 ; Ghosh, Satrajit S 3 ; Sabuncu, Mert R 4 ; Schwartz, Ernst 1 ; Langs, Georg 5 

 Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna, Austria 
 A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA 
 McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, USA; Department of Otolaryngology, Harvard Medical School, USA 
 A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA; School of Electrical and Computer Engineering, Cornell University, USA; Meinig School of Biomedical Engineering, Cornell University, USA 
 Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna, Austria; Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, USA 
Pages
456-465
Publication year
2017
Publication date
Aug 1, 2017
Publisher
Elsevier Limited
ISSN
10538119
e-ISSN
10959572
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
1927872758
Copyright
Copyright Elsevier Limited Aug 1, 2017