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Statistical inference in brain graphs using threshold-free network-based statistics.


ABSTRACT: The description of brain networks as graphs where nodes represent different brain regions and edges represent a measure of connectivity between a pair of nodes is an increasingly used approach in neuroimaging research. The development of powerful methods for edge-wise group-level statistical inference in brain graphs while controlling for multiple-testing associated false-positive rates, however, remains a difficult task. In this study, we use simulated data to assess the properties of threshold-free network-based statistics (TFNBS). The TFNBS combines threshold-free cluster enhancement, a method commonly used in voxel-wise statistical inference, and network-based statistic (NBS), which is frequently used for statistical analysis of brain graphs. Unlike the NBS, TFNBS generates edge-wise significance values and does not require the a priori definition of a hard cluster-defining threshold. Other test parameters, nonetheless, need to be set. We show that it is possible to find parameters that make TFNBS sensitive to strong and topologically clustered effects, while appropriately controlling false-positive rates. Our results show that the TFNBS is an adequate technique for the statistical assessment of brain graphs.

SUBMITTER: Baggio HC 

PROVIDER: S-EPMC6619254 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Statistical inference in brain graphs using threshold-free network-based statistics.

Baggio Hugo C HC   Abos Alexandra A   Segura Barbara B   Campabadal Anna A   Garcia-Diaz Anna A   Uribe Carme C   Compta Yaroslau Y   Marti Maria Jose MJ   Valldeoriola Francesc F   Junque Carme C  

Human brain mapping 20180215 6


The description of brain networks as graphs where nodes represent different brain regions and edges represent a measure of connectivity between a pair of nodes is an increasingly used approach in neuroimaging research. The development of powerful methods for edge-wise group-level statistical inference in brain graphs while controlling for multiple-testing associated false-positive rates, however, remains a difficult task. In this study, we use simulated data to assess the properties of threshold  ...[more]

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