Abstract
Computational network analysis has enabled researchers to investigate patterns of interactions between anatomical regions of the brain. Identification of subnetworks of the human connectome can reveal how the network manages an interplay of the seemingly competing principles of functional segregation and integration. Despite the study of subnetworks of the human structural connectome by various groups, the level of expression of these subnetworks in each subject remains for the most part largely unexplored. Thus, there is a need for methods that can extract common subnetworks that together render a network portrait of a sample and facilitate analysis of the same, such as group comparisons based on the expression of the subnetworks in each subject. In this paper, we propose a framework for quantifying the subject-specific expression of subnetworks. Our framework consists of two parts, namely subnetwork detection and reconstructive projection onto subnetworks. The first part identifies subnetworks of the connectome using multi-view spectral clustering. The second part quantifies subject specific manifestations of these subnetworks by nonnegative matrix decomposition. Positivity constraint is imposed to treat each subnetwork as a structure depicting the connectivity between specific anatomical regions. We have assessed the applicability of the framework by delineating a network portrait of a clinical sample consisting of children affected by autism spectrum disorder (ASD), and a matched group of typically developing controls (TDCs). Subsequent statistical analysis on the intra- and inter-subnetwork connections, revealed decreased connectivity in ASD group between regions of social cognition, executive functions, and emotion processing.
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Notes
- 1.
Note that X is not a square matrix; hence, we cannot speak of its eigenvalues.
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Tunç, B., Shankar, V., Parker, D., Schultz, R.T., Verma, R. (2015). Towards a Quantified Network Portrait of a Population. In: Ourselin, S., Alexander, D., Westin, CF., Cardoso, M. (eds) Information Processing in Medical Imaging. IPMI 2015. Lecture Notes in Computer Science(), vol 9123. Springer, Cham. https://doi.org/10.1007/978-3-319-19992-4_51
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