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Neuroimage. 2018 Sep;178:540-551. doi: 10.1016/j.neuroimage.2018.05.070. Epub 2018 Jun 1.

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

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Yale University School of Medicine, Department of Psychiatry, USA. Electronic address:
University of Pennsylvania Perelman School of Medicine, Department of Biostatistics, Epidemiology, and Informatics, USA.
Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Intramural Program, USA.
University of Pennsylvania School of Medicine, Department of Psychiatry, USA.
Yale University School of Medicine, Department of Psychiatry, USA; Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital, USA.


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 ( 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.

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