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Previous whole-brain functional connectivity studies achieved successful classifications of patients and healthy controls but only offered limited specificity as to affected brain systems. Here, we examined whether the connectivity patterns of functional systems affected in schizophrenia (SCZ), Parkinson's disease (PD), or normal aging equally translate into high classification accuracies for these conditions. We compared classification performance between pre-defined networks for each group and, for any given network, between groups. Separate support vector machine classifications of 86 SCZ patients, 80 PD patients, and 95 older adults relative to their matched healthy/young controls, respectively, were performed on functional connectivity in 12 task-based, meta-analytically defined networks using 25 replications of a nested 10-fold cross-validation scheme. Classification performance of the various networks clearly differed between conditions, as those networks that best classified one disease were usually non-informative for the other. For SCZ, but not PD, emotion-processing, empathy, and cognitive action control networks distinguished patients most accurately from controls. For PD, but not SCZ, networks subserving autobiographical or semantic memory, motor execution, and theory-of-mind cognition yielded the best classifications. In contrast, young-old classification was excellent based on all networks and outperformed both clinical classifications. Our pattern-classification approach captured associations between clinical and developmental conditions and functional network integrity with a higher level of specificity than did previous whole-brain analyses. Taken together, our results support resting-state connectivity as a marker of functional dysregulation in specific networks known to be affected by SCZ and PD, while suggesting that aging affects network integrity in a more global way. Hum Brain Mapp 38:5845-5858, 2017. © 2017 Wiley Periodicals, Inc., (© 2017 Wiley Periodicals, Inc.)

Idiopathic rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia characterized by the loss of physiological atonia of skeletal muscles with abnormal behavior during dream sleep. RBD may be the initial manifestation of neurodegenerative diseases, particularly of α-synucleinopathies such as Parkinson's disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA). However, gauging the individual risk of subsequent phenoconversion and making assumptions on the type of disease that may subsequently follow RBD is challenging. Over the past years, a growing number of studies have sought to establish reliable neuroimaging markers to detect neurodegenerative brain changes in RBD subjects at the earliest possible stage. The present review summarizes recent advances in brain imaging in RBD and provides recommendations for the application of currently available structural and functional neuroimaging modalities to monitor disease progression and risk of subsequent phenoconversion. Further imaging research applying multimodal approaches is encouraged to enhance accuracy of prognoses. Additionally, more longitudinal studies are warranted to validate findings from cross-sectional studies on RBD progression and risk of subsequent phenoconversion. Aside from enabling reliable prognoses on a single-subject-level in the near future, this might give further insight into RBD pathophysiology, and finally augment the development of intervention strategies and disease-modifying therapies., (Copyright © 2016 Elsevier Ltd. All rights reserved.)