Methodological aspects of analyzing high resolved brain connectivity for multiple subjects

Britta Pester 1 , Christoph Schmidt 2 , Karl-Jürgen Bär 3  and Lutz Leistritz 2
  • 1 Institute of Medical Statistics, Computer Sciences and Documentation, Jena University Hospital, Friedrich Schiller University, Jena, Germany
  • 2 Institute of Medical Statistics, Computer Sciences and Documentation, Jena University Hospital, Friedrich Schiller University, Jena, Germany
  • 3 Department of Psychiatry, Jena University Hospital, Friedrich Schiller University, Jena, Germany

Abstract

Analyzing directed interactions within brain networks of high spatial resolution is always associated with a limited interpretability due to the high amount of possible connections. Here, module detection algorithms have proven to helpfully subsume the information of the resulting networks for each proband. However, the between-subject comparison of clusters is not straightforward since identified modules are not matched to each other across different subjects. Tensor decomposition has successfully been applied for the detection of group-wide connectivity patterns. Yet, a thorough investigation of the effect of the involved analysis parameters and data properties on decomposition results has still been missing. In this study we filled this gap and found that - given appropriate parameter choices - tensor decomposition of functional connectivity data reveals meaningful, group-specific insights into the brain's information processing.

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Current Directions in Biomedical Engineering is an open access journal and closely related to the journal Biomedical Engineering - Biomedizinische Technik. CDBME is a forum for the exchange of knowledge in the fields of biomedical engineering, medical information technology and biotechnology/bioengineering for medicine and addresses engineers, natural scientists, and clinicians working in research, industry, or clinical practice.

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