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Interindividual Covariations of Brain Functional and Structural Connectivities Are Decomposed Blindly to Subnetworks: A Fusion‐Based Approach
Author(s) -
Keyvanfard Farzaneh,
NasiraeiMoghaddam Abbas,
Hagmann Patric
Publication year - 2020
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.26988
Subject(s) - consistency (knowledge bases) , dynamic functional connectivity , pattern recognition (psychology) , permutation (music) , sample size determination , computer science , correlation , resting state fmri , neuroscience , artificial intelligence , psychology , mathematics , statistics , physics , acoustics , geometry
Background Studying brain interindividual variations has recently gained interest to understand different human behaviors. It is particularly important to investigate how a variety of functional differences can be associated with a few differences in brain structure. It would be more meaningful if such an investigation is performed jointly at the network level to connect structural building blocks to functional variations modules. Purpose To decompose the interindividual variations of brain in the form of mutual functional and structural subnetworks based on a data‐driven approach. Study Type Retrospective. Population In all, 92 healthy subjects. Field Strength/Sequence 3T Siemens/MPRAGE, diffusion spectrum imaging (DSI) acquisition protocol, gradient echo sequence. Assessment The proposed approach was quantitatively assessed by examining the consistency of the networks against the number of subjects. Distribution of the obtained components across brain regions was studied and their relevance was qualitatively evaluated by comparison to variations that had been independently reported previously. Statistical Tests Permutation test, two‐sample t ‐test, Pearson correlation coefficient. Results Ten pairs of components including functional and structural subnetworks were obtained. Assessing the reproducibility of the proposed method with respect to the sample size indicated reliable detection of connections (above 70%) for all components by reducing the number of subjects to 70. Specifically, one of the functional subnetworks can be used to distinguish left‐handed from right‐handed people ( P = 2.6 × 10 ‐8 ) as the basic interindividual variation. This functional subnetwork has a main overlap (40.18%) with the somatomotor system and the Broca part was captured in its corresponding structural subnetwork. Data Conclusion These results show that the proposed method can reveal intersubject variations systematically through a mathematical algorithm of joint independent component analysis. They confirm that intersubject variations can be expressed in the form of building blocks. In contrast to the functional subnetworks that were discoverable independently, their structural counterparts were found and interpreted only in conjunction with the functional subnetworks. Level of Evidence: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:1779–1788.