An Overview, Challenges, and Future Directions - SAMSI

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Experiments Models Connectivity Prediction Summary Anatomically Weighted Functional Distance ( d ij = 1 − π ij λ ) f ij f ij is a measure of functional dissimilarity between regions i and j E.g. 1 − ρ ij We allow lag-u associations, u ∈ [−2, 2] π ij is the probability of structural connectivity π ij ∈ [0, 1) We consider second-order connections Adjust for physical distances between regions λ is a parameter used to potentially attenuate the impact of anatomical weighting λ determined empirically λ ∈ [1, ∞) d ij → f ij , as λ → ∞ F. D. Bowman (Emory University) SAMSI: NDA RTP, NC 45 / 77
Experiments Models Connectivity Prediction Summary awFC Anatomically weighted Functional Connectivity (awFC): Perform subject-specific clustering using average linkage method applied to distances d ij Empirically optimize λ using objective function based solely on the functional data h(λ, G) = log ( FCwi FC tot ) Generate group cluster solution from subject-specific networks Conduct hypothesis testing to determine the statistical significance of the correlation between each pair of regions within the identified networks F. D. Bowman (Emory University) SAMSI: NDA RTP, NC 46 / 77
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An Overview, Challenges, and Future Directions - SAMSI

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