Stable between-subject statistical inference from unstable within-subject functional connectivity estimates.

ABSTRACT: Spatial or temporal aspects of neural organization are known to be important indices of how cognition is organized. However, measurements and estimations are often noisy and many of the algorithms used are probabilistic, which in combination have been argued to limit studies exploring the neural basis of specific aspects of cognition. Focusing on static and dynamic functional connectivity estimations, we propose to leverage this variability to improve statistical efficiency in relating these estimations to behavior. To achieve this goal, we use a procedure based on permutation testing that provides a way of combining the results from many individual tests that refer to the same hypothesis. This is needed when testing a measure whose value is obtained from a noisy process, which can be repe

SUBMITTER: Vidaurre D 

PROVIDER: S-EPMC6492297 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature