Clinical applications of the functional connectome

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The Neuro Bureau ADHD-200 Preprocessed Repository (2016, background): “, 2014), which are providing new hope for using neuroimaging data to parse the heterogeneity within mental health disorders (Castellanos et al., 2013).”
Great Expectations: Using Whole-Brain Computational Connectomics for Understanding Neuropsychiatric Disorders (2014, background): “Yet, the proliferation and widespread availability of rs-MRI across multiple centers and populations mean that care has to be taken to ensure the validity, reliability, sensitivity and specificity of the data (Castellanos et al., 2013).”
Deriving reproducible biomarkers from multi‐site resting‐state data: An Autism‐based example (2016, methodology): “For example screening tools would need to favor sensitivity over accuracy or specificity; in contrast with medical diagnostic tests that require specificity over sensitivity or accuracy (Castellanos et al., 2013).”
DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging (2016, background): “, 2009; Zuo and Xing, 2014), sensitivity (Castellanos et al., 2013; Fox and Greicius, 2010; Greicius, 2008) and comparability across labs (ADHD-200-Consortium, 2012; Biswal et al.”