Basal ganglia dysfunction in idiopathic REM sleep behaviour disorder parallels that in early Parkinson’s disease

Abstract

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. 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. 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. 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.