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Standardizing the intrinsic brain: Towards robust measurement of inter-individual variation in 1000 functional connectomes.

, , , , and . NeuroImage, (2013)

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Reliable intrinsic connectivity networks: Test-retest evaluation using ICA and dual regression approach., , , , , and . NeuroImage, 49 (3): 2163-2177 (2010)Generative models of the human connectome., , , , , , , , , and 4 other author(s). NeuroImage, (2016)Standardizing the intrinsic brain: Towards robust measurement of inter-individual variation in 1000 functional connectomes., , , , and . NeuroImage, (2013)A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics., , , , , , , , , and . NeuroImage, (2013)Extracting information from functional connectivity maps via function-on-scalar regression., , , , , , , , and . NeuroImage, 56 (1): 140-148 (2011)Functional Connectivity Changes Across the Spectrum of Subjective Cognitive Decline, Amnestic Mild Cognitive Impairment and Alzheimer's Disease., , , , , , , , , and . Front. Neuroinform., (2019)Subject order-independent group ICA (SOI-GICA) for functional MRI data analysis., , , , , and . NeuroImage, 51 (4): 1414-1424 (2010)Fluctuations between high- and low-modularity topology in time-resolved functional connectivity., , , , , , and . NeuroImage, 180 (Part): 406-416 (2018)Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in connectomics and genomics., , , , , , , , , and 6 other author(s). PLoS Comput. Biol., (2021)DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging., , , and . Neuroinformatics, 14 (3): 339-351 (2016)