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Neuroimage. 2013 Nov 1;81:400-411. doi: 10.1016/j.neuroimage.2013.05.009. Epub 2013 May 14.

Inter-subject alignment of human cortical anatomy using functional connectivity.

Author information

1
Department of Electrical Engineering, Princeton University, Princeton, NJ, USA; Department of Biomedical Engineering, Columbia University, New York, NY, USA. Electronic address: bc2468@columbia.edu.
2
Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
3
Department of Psychological & Brain Sciences, Dartmouth College, Hanover, NH, USA.
4
Department of Electrical Engineering, Princeton University, Princeton, NJ, USA.
5
Department of Psychological & Brain Sciences, Dartmouth College, Hanover, NH, USA; Center for Mind/Brain Sciences (CIMeC), Universitá degli studi di Trento, Rovereto, Italy.

Abstract

Inter-subject alignment of functional MRI (fMRI) data is necessary for group analyses. The standard approach to this problem matches anatomical features of the brain, such as major anatomical landmarks or cortical curvature. Precise alignment of functional cortical topographies, however, cannot be derived using only anatomical features. We propose a new inter-subject registration algorithm that aligns intra-subject patterns of functional connectivity across subjects. We derive functional connectivity patterns by correlating fMRI BOLD time-series, measured during movie viewing, between spatially remote cortical regions. We validate our technique extensively on real fMRI experimental data and compare our method to two state-of-the-art inter-subject registration algorithms. By cross-validating our method on independent datasets, we show that the derived alignment generalizes well to other experimental paradigms.

KEYWORDS:

Functional connectivity; Inter-subject registration; Surface-based methods

PMID:
23685161
PMCID:
PMC3729877
DOI:
10.1016/j.neuroimage.2013.05.009
[Indexed for MEDLINE]
Free PMC Article

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