Titlebook: Connectomics in NeuroImaging; Second International Guorong Wu,Islem Rekik,Brent Munsell Conference proceedings 2018 Springer Nature Switzer

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Riemannian Regression and Classification Models of Brain Networks Applied to Autism,thods that exploit the Riemannian geometry of SPD matrices appropriately adhere to the positive definite constraint, unlike Euclidean methods. Recently proposed approaches for rsfMRI analysis have achieved high accuracy on public datasets, but are computationally intensive and difficult to interpret

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Defining Patient Specific Functional Parcellations in Lesional Cohorts via Markov Random Fields, initial parcellation and then iteratively reassigns the voxel memberships at the subject level. Our algorithm uses a maximum . inference strategy based on the neighboring voxel assignments and the Pearson correlation coefficients between the voxel time series and the parcel reference signals. Our m

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Data-Specific Feature Selection Method Identification for Most Reproducible Connectomic Feature Dison of extremely high-dimensional connectomic data drawn from multiple neuroimaging sources (e.g., functional and structural MRIs), effective feature selection (FS) methods have become indispensable components for (i) disentangling brain states (e.g., early vs late mild cognitive impairment) and (ii)

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Connectivity-Driven Brain Parcellation via Consensus Clustering,oposed dense connectivity representation, termed continuous connectivity, by first performing graph-based hierarchical clustering of individual brains, and subsequently aggregating the individual parcellations into a consensus parcellation. The search for consensus minimizes the sum of cluster membe

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