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Objective: Evaluation of a data-driven, model-based classification approach to discriminate idiopathic Parkinson's disease (PD) patients from healthy controls (HC) based on between-network connectivity in whole-brain resting-state functional MRI (rs-fMRI)., Methods: Whole-brain rs-fMRI (EPI, TR = 2.2 s, TE = 30 ms, flip angle = 90°. resolution = 3.1 × 3.1 × 3.1 mm, acquisition time ≈ 11 min) was assessed in 42 PD patients (medical OFF) and 47 HC matched for age and gender. Between-network connectivity based on full and L2-regularized partial correlation measures were computed for each subject based on canonical functional network architectures of two cohorts at different levels of granularity (Human Connectome Project: 15/25/50/100/200 networks; 1000BRAINS: 15/25/50/70 networks). A Boosted Logistic Regression model was trained on the correlation matrices using a nested cross-validation (CV) with 10 outer and 10 inner folds for an unbiased performance estimate, treating the canonical functional network architecture and the type of correlation as hyperparameters. The number of boosting iterations was fixed at 100. The model with the highest mean accuracy over the inner folds was trained using an non-nested 10-fold 20-repeats CV over the whole dataset to determine feature importance., Results: Over the outer folds the mean accuracy was found to be 76.2% (median 77.8%, SD 18.2, IQR 69.4 - 87.1%). Mean sensitivity was 81% (median 80%, SD 21.1, IQR 75 - 100%) and mean specificity was 72.7% (median 75%, SD 20.4, IQR 66.7 - 80%). The 1000BRAINS 50-network-parcellation, using full correlations, performed best over the inner folds. The top features predominantly included sensorimotor as well as sensory networks., Conclusion: A rs-fMRI whole-brain-connectivity, data-driven, model-based approach to discriminate PD patients from healthy controls shows a very good accuracy and a high sensitivity. Given the high sensitivity of the approach, it may be of use in a screening setting., Advances in Knowledge: Resting-state functional MRI could prove to be a valuable, non-invasive neuroimaging biomarker for neurodegenerative diseases. The current model-based, data-driven approach on whole-brain between-network connectivity to discriminate Parkinson's disease patients from healthy controls shows promising results with a very good accuracy and a very high sensitivity.

Beta oscillations within motor-cortical areas have been linked to sensorimotor function. In line with this, pathologically altered beta activity in cortico-basal ganglia pathways has been suggested to contribute to the pathophysiology of Parkinson's disease (PD), a neurodegenerative disorder primarily characterized by motor impairment. Although its precise function is still discussed, beta activity might subserve an anticipatory role in preparation of future actions. By reanalyzing previously published data, we aimed at investigating the role of pre-stimulus motor-cortical beta power modulation in motor sequence learning and its alteration in PD. 20 PD patients and 20 healthy controls (HC) performed a serial reaction time task (SRTT) in which reaction time gain presumably reflects the ability to anticipate subsequent sequence items. Randomly varying patterns served as control trials. Neuromagnetic activity was recorded using magnetoencephalography (MEG) and data was reanalyzed with respect to task stimuli onset. Assuming that pre-stimulus beta power modulation is functionally related to motor sequence learning, reaction time gain due to training on the SRTT should vary depending on the amount of beta power suppression prior to stimulus onset. We hypothesized to find less pre-stimulus beta power suppression in PD patients as compared to HC associated with reduced motor sequence learning in patients. Behavioral analyses revealed that PD patients exhibited smaller reaction time gain in sequence relative to random control trials than HC indicating reduced learning in PD. This finding was indeed paralleled by reduced pre-stimulus beta power suppression in PD patients. Further strengthening its functional relevance, the amount of pre-stimulus beta power suppression during sequence training significantly predicted subsequent reaction time advantage in sequence relative to random trials in patients. In conclusion, the present data provide first evidence for the contribution of pre-stimulus motor-cortical beta power suppression to motor sequence learning and support the hypothesis that beta oscillations may subserve an anticipatory, predictive function, possibly compromised in PD., (Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.)

Huntington's disease (HD) is a hereditary neurodegenerative disorder which is associated with severe disturbances of motor function, especially choreatic movements, cognitive decline and psychiatric symptoms. Various brain stimulation methods have been used to study brain function in patients with HD. Moreover, brain stimulation has evolved as an alternative or additive treatment option, besides current symptomatic medical treatment. This article summarizes the results of brain stimulation to better understand the characteristics of cortical excitability and plasticity in HD and gives a perspective on the therapeutic role for noninvasive and invasive neuromodulatory brain stimulation methods.