Connectome Classification: Statistical Connectomics for Analysis of Connectome DataPresentation Transcript
Connectome Classiﬁcation: Statistical Connectomics for Analysis of Connectome Data Joshua T. Vogelstein, PhD d: Applied Math. & Stats u: Johns Hopkins w: jovo.me e: email@example.com
Statistical Connectomics Statistics “the art of data collection and analysis”Connectomics “the study of connectomes” Statistical “the art of connectome data collectionConnectomics and analysis”
Contributors Stats Carey E. Priebe Data Collection Glen A. Coppersmith Susan Resnick Mark DredzeConnectome Inference Will R. Gray Wisdom John Bogovic R. Jacob Vogelstein Jerry Prince Support: various grants
Simplest. Example. Ever.Blind People Deaf People V1 V1 A1 M1 A1 M1
Simplest. Example. Ever.Blind People Deaf People V1 No possible classiﬁer V1 based on graph invariants can perform this insanely simple classiﬁcation A1 M1 A1 M1 problem!!!
Discussion• 83% > 75%• yay statistical modeling!
4. Construct a Decision Rule: Signal Subgraph Estimation • for each edge, we compute the signiﬁcance of the difference between the two classes using Fisher’s exact test • the incoherent signal subgraph estimator ﬁnds the s edges that are most signiﬁcant • the coherent signal subgraph estimator ﬁnds the s edges that are most signiﬁcant incident to m vertices