| Ամփոփում: | <p>Resting-state fMRI (rs-fMRI) dysfunction within the basal ganglia network (BGN) is a feature of early Parkinson’s disease (Szewczyk-Krolikowski et al., 2014, Rolinski et al., 2015), and may be a diagnostic biomarker of basal ganglia dysfunction. Currently, it is unclear whether these changes are present in so-called idiopathic rapid eye movement sleep behaviour disorder (RBD), a condition associated with a high rate of future conversion to Parkinson’s. In this study, we explore the utility of rs-fMRI to detect BGN dysfunction in RBD. We compare these data to a set of healthy controls, and to a set of patients with established early Parkinson’s. Furthermore, we explore the relationship between rs-fMRI BGN dysfunction and loss of dopaminergic neurons assessed with dopamine transporter single photon emission computerised tomography (SPECT), and perform morphometric analyses to assess grey matter loss.</p> <p>26 patients with polysomnographically established RBD, 48 Parkinson’s patients and 23 healthy controls were included in this study. Resting-state networks were isolated from task-free fMRI data using dual regression with a template was derived from a separate cohort of 80 elderly HC participants. Rs-fMRI parameter estimates were extracted from the study subjects in the BGN. In addition, 8 RBD, 10 Parkinson’s and 10 control subjects received 123I-ioflupane SPECT. We tested for reduction of BGN connectivity, and for loss of tracer uptake in RBD and Parkinson’s relative to each other and to controls.</p> <p>Connectivity measures of BGN network dysfunction differentiated both RBD and Parkinson’s from controls with high sensitivity (96%) and specificity (74% for RBD, 78% for PD), indicating its potential as an indicator of early basal ganglia dysfunction. RBD was indistinguishable from Parkinson’s on rs-fMRI despite obvious differences on dopamine transported SPECT.</p> <p>Basal ganglia connectivity is a promising biomarker for the detection of early BGN dysfunction, and may help to identify patients at risk of developing Parkinson’s in the future. Future risk stratification using a polymodal approach could combine BGN connectivity with clinical and other imaging measures, with important implications for future neuroprotective trials in RBD.</p>
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