Stable between‐subject statistical inference from unstable within‐subject functional connectivity estimates

• Published in: Human brain mapping Vol. 40; no. 4; pp. 1234 - 1243
• Main Authors: Vidaurre, Diego, Woolrich, Mark W., Winkler, Anderson M., Karapanagiotidis, Theodoros, Smallwood, Jonathan, Nichols, Thomas E.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.03.2019
• Subjects: Algorithms; Brain - physiology; Cognition; Cognition - physiology; Computer applications; Computer Simulation; Connectome - methods; Data acquisition; dynamic functional connectivity; functional connectivity; hidden Markov model; Humans; hypothesis testing; Image Processing, Computer-Assisted - methods; multiple replications; Neural Pathways - physiology; permutation testing; Permutations; Statistical analysis; Statistical inference; statistical testing; Statistics; test combination; multiple replications; dynamic functional connectivity; functional connectivity; hidden Markov model; hypothesis testing; permutation testing; statistical testing; test combination
• DOI: 10.1002/hbm.24442
• ISSN: 1065-9471; 1097-0193

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 repeated multiple times, referred to as replications. Focusing on functional connectivity, this noisy process can be: (a) computational, for example, when using an approximate inference algorithm for which different runs can produce different results or (b) observational, if we have the capacity to acquire data multiple times, and the different acquired data sets can be considered noisy examples of some underlying truth. In both cases, we are not interested in the individual replications but on the unobserved process generating each replication. In this note, we show how results can be combined instead of choosing just one of the estimated models. Using both simulations and real data, we show the benefits of this approach in practice.