On testing for spatial correspondence between maps of human brain structure and function.

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TitleOn testing for spatial correspondence between maps of human brain structure and function.
Publication TypeJournal Article
Year of Publication2018
AuthorsAlexander-Bloch, AF, Shou, H, Liu, S, Satterthwaite, TD, Glahn, DC, Shinohara, RT, Vandekar, SN, Raznahan, A
JournalNeuroimage
Volume178
Pagination540-551
Date Published2018 09
ISSN1095-9572
KeywordsBrain, Brain Mapping, Humans, Image Processing, Computer-Assisted, Models, Neurological, Neural Pathways
Abstract

A critical issue in many neuroimaging studies is the comparison between brain maps. Nonetheless, it remains unclear how one should test hypotheses focused on the overlap or spatial correspondence between two or more brain maps. This "correspondence problem" affects, for example, the interpretation of comparisons between task-based patterns of functional activation, resting-state networks or modules, and neuroanatomical landmarks. To date, this problem has been addressed with remarkable variability in terms of methodological approaches and statistical rigor. In this paper, we address the correspondence problem using a spatial permutation framework to generate null models of overlap by applying random rotations to spherical representations of the cortical surface, an approach for which we also provide a theoretical statistical foundation. We use this method to derive clusters of cognitive functions that are correlated in terms of their functional neuroatomical substrates. In addition, using publicly available data, we formally demonstrate the correspondence between maps of task-based functional activity, resting-state fMRI networks and gyral-based anatomical landmarks. We provide open-access code to implement the methods presented for two commonly-used tools for surface based cortical analysis (https://www.github.com/spin-test). This spatial permutation approach constitutes a useful advance over widely-used methods for the comparison of cortical maps, thereby opening new possibilities for the integration of diverse neuroimaging data.

DOI10.1016/j.neuroimage.2018.05.070
Alternate JournalNeuroimage
PubMed ID29860082
PubMed Central IDPMC6095687
Grant ListR01 MH112847 / MH / NIMH NIH HHS / United States
ZIA MH002949-01 / / Intramural NIH HHS / United States
T32 MH065218 / MH / NIMH NIH HHS / United States
T32 MH019961 / MH / NIMH NIH HHS / United States
R01 NS085211 / NS / NINDS NIH HHS / United States
R01 MH107703 / MH / NIMH NIH HHS / United States
R01 NS060910 / NS / NINDS NIH HHS / United States