Effective connectivity and intersubject variability: using a multisubject network to test differences and commonalities.
This article is about intersubject variability in the functional integration of activity in different brain regions. Previous studies of functional and effective connectivity have dealt with intersubject variability by analyzing data from different subjects separately or pretending the data came from the same subject. These approaches do not allow one to test for differences among subjects. The aim of this work was to illustrate how differences in connectivity among subjects can be addressed explicitly using structural equation modeling. This is enabled by constructing a multisubject network that comprises m regions of interest for each of the n subjects studied, resulting in a total of m x n nodes. Constructing a network of regions from different subjects may seem counterintuitive but embodies two key advantages. First, it allows one to test directly for differences among subjects by comparing models that do and do not allow a particular connectivity parameter to vary over subjects. Second, a multisubject network provides additional degrees of freedom to estimate the model's free parameters. Any neurobiological hypothesis normally addressed by single-subject or group analyses can still be tested, but with greater sensitivity. The common influence of experimental variables is modeled by connecting a virtual node, whose time course reflects stimulus onsets, to the sensory or "input" region in all subjects. Further experimental changes in task or cognitive set enter through modulation of the connections. This approach allows one to model both endogenous (or intrinsic) variance and exogenous effects induced by experimental design. We present a functional magnetic resonance imaging study that uses a multisubject network to investigate intersubject variability in functional integration in the context of single word and pseudoword reading. We tested whether the effect of word type on the reading-related coupling differed significantly among subjects. Our results showed that a number of forward and backward connections were stronger for reading pseudowords than words, and, in one case, connectivity showed significant intersubject variability. The discussion focuses on the implications of our findings and on further applications of the multisubject network analysis.
Mechelli
Andrea
A
Wellcome Department of Imaging Neuroscience, Institute of Neurology, 12 Queen Square, London, WCIN 3BG, United Kingdom. andream@fil.ion.ucl.ac.uk
Penny
Will D
WD
Price
Cathy J
CJ
Gitelman
Darren R
DR
Friston
Karl J
KJ
D000328:Adult; D000465:Algorithms; D001931:Brain Mapping / Q000379:methods; D002540:Cerebral Cortex / Q000502:physiology*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D021621:Imaging, Three-Dimensional / Q000706:statistics & numerical data*; D007206:Individuality; D016013:Likelihood Functions; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D008297:Male; D011939:Mental Recall / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology*; D010700:Phonetics; D011932:Reading; D012016:Reference Values; D012660:Semantics; D013702:Temporal Lobe / Q000502:physiology