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Functional Connectivity Changes and Executive and Social Problems in Neurofibromatosis Type I.

, , , , , , and . Brain Connectivity, 5 (5): 312-320 (2015)

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Functional Connectivity Analyses Using Emulated and Conventional Resting-State Data: Parts Versus the Whole Story., , , , , , , and . Brain Connectivity, 4 (10): 842-848 (2014)Association between increased magnetic susceptibility of deep gray matter nuclei and decreased motor function in healthy adults., , , , , , , , and . NeuroImage, (2015)Electroencephalographic complexity markers explain neuropsychological test scores in Alzheimer's disease., , , , , , , , , and 4 other author(s). BHI, page 496-499. IEEE, (2014)Bayesian deep neural networks for low-cost neurophysiological markers of Alzheimer's disease severity., , , , , , , , , and 4 other author(s). CoRR, (2018)Functional Connectivity Changes and Executive and Social Problems in Neurofibromatosis Type I., , , , , , and . Brain Connectivity, 5 (5): 312-320 (2015)A comprehensive analysis of resting state fMRI measures to classify individual patients with Alzheimer's disease., , , , , , , , and . NeuroImage, (2018)Bayesian Gaussian Process Classification from Event-Related Brain Potentials in Alzheimer's Disease., , , , , , , , , and 3 other author(s). AIME, volume 10259 of Lecture Notes in Computer Science, page 65-75. Springer, (2017)A flexible method for the automated offline-detection of artifacts in multi-channel electroencephalogram recordings., , , , , , , and . EMBC, page 3793-3796. IEEE, (2017)Individual classification of Alzheimer's disease with diffusion magnetic resonance imaging., , , , , , , , , and . NeuroImage, (2017)Segmentation of age-related white matter changes in a clinical multi-center study., , , , , , , , , and 5 other author(s). NeuroImage, 41 (2): 335-345 (2008)