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Population level differentially expressed brain connectivity network detection and inferences

Banff International Research Station for Mathematical Innovation and Discovery

Many challenges remain for group-level whole-brain connectivity network analyses because the massive connectomics connectivity metrics are correlated and the correlation structure is constrained by the extraordinarily complex, yet highly organized, topology of the underlying neural architecture. To detect the truly differentially expressed brain connectivity, for example, between subjects with mental disorders and healthy controls using the high dimensional omics data often face with the tradeoff between false positive discoveries and the lack of power. We consider that the differentially expressed connectivity metrics/edges are not randomly distributed in the whole-brain connectivity structure but rather in an organized topological structure. We develop several novel machine learning algorithms to automatically detect topological structures, and furthermore .

Mathematics; Statistics; Game theory, economics, social and behavioral sciences

video/mp4

Author affiliation: University of Maryland

2016-08-05

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/58679

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/