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Predicting Autism Spectrum Disorder from Brain Imaging Data by Graph Convolutional Network.

, , , , and . IJCNN, page 1-8. IEEE, (2021)

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Interpretable Deep Learning for Neuroimaging-Based Diagnostic Classification., , , , and . IEEE Access, (2024)Dual pathways for haptic and visual perception of spatial and texture information., , , , , , , and . NeuroImage, 57 (2): 462-475 (2011)Functional parcellation of the hippocampus based on its layer-specific connectivity with default mode and dorsal attention networks., , and . NeuroImage, (2022)Patterns of effective connectivity during memory encoding and retrieval differ between patients with mild cognitive impairment and healthy older adults., , , , and . NeuroImage, (2016)Instantaneous and causal connectivity in resting state brain networks derived from functional MRI data., , and . NeuroImage, 54 (2): 1043-1052 (2011)Assessing and Compensating for Zero-Lag Correlation Effects in Time-Lagged Granger Causality Analysis of fMRI., , and . IEEE Trans. Biomed. Eng., 57 (6): 1446-1456 (2010)Directed Transfer Function Analysis of fMRI Data to Investigate Network Dynamics., , , and . EMBC, page 671-674. IEEE, (2006)Predicting Autism Spectrum Disorder from Brain Imaging Data by Graph Convolutional Network., , , , and . IJCNN, page 1-8. IEEE, (2021)Clustering of dynamic functional connectivity features obtained from functional Magnetic Resonance Imaging data., , , and . ICACCI, page 308-312. IEEE, (2015)Brain Networks Shaping Religious Belief., , , , and . Brain Connect., 4 (1): 70-79 (2014)