Background Development of valid, non-invasive biomarkers for parkinsonian syndromes is crucially needed. We aimed to assess whether non-invasive diffusion-weighted MRI can distinguish between parkinsonian syndromes using an automated imaging approach. MethodsWe did an international study at 17 MRI centres in Austria, Germany, and the USA. We used diffusion-weighted MRI from 1002 patients and the Movement Disorders Society Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) to develop and validate disease-specific machine learning comparisons using 60 template regions and tracts of interest in Montreal Neurological Institute space between Parkinson's disease and atypical parkinsonism (multiple system atrophy and progressive supranuclear palsy) and between multiple system atrophy and progressive supranuclear palsy. For each comparison, models were developed on a training and validation cohort and evaluated in an independent test cohort by quantifying the area under the curve (AUC) of receiving operating characteristic curves. The primary outcomes were free water and free-water-corrected fractional anisotropy across 60 different template regions. Findings In the test cohort for disease-specific comparisons, the diffusion-weighted MRI plus MDS-UPDRS III model (Parkinson's disease vs atypical parkinsonism had an AUC 0•962; multiple system atrophy vs progressive supranuclear palsy AUC 0•897) and diffusion-weighted MRI only model had high AUCs (Parkinson's disease vs atypical parkinsonism AUC 0•955; multiple system atrophy vs progressive supranuclear palsy AUC 0•926), whereas the MDS-UPDRS III only models had significantly lower AUCs (Parkinson's disease vs atypical parkinsonism 0•775; multiple system atrophy vs progressive supranuclear palsy 0•582). These results indicate that a non-invasive imaging approach is capable of differentiating forms of parkinsonism comparable to current gold standard methods.Interpretations This study provides an objective, validated, and generalisable imaging approach to distinguish different forms of parkinsonian syndromes using multisite diffusion-weighted MRI cohorts. The diffusion-weighted MRI method does not involve radioactive tracers, is completely automated, and can be collected in less than 12 min across 3T scanners worldwide. The use of this test could positively affect the clinical care of patients with Parkinson's disease and parkinsonism and reduce the number of misdiagnosed cases in clinical trials.

Neurite orientation dispersion and density imaging (NODDI) uses a three‐compartment model to probe brain tissue microstructure, whereas free‐water (FW) imaging models two‐compartments. It is unknown if NODDI detects more disease‐specific effects related to neurodegeneration in Parkinson's disease (PD) and atypical Parkinsonism. We acquired multi‐ and single‐shell diffusion imaging at 3 Tesla across two sites. NODDI (using multi‐shell; isotropic volume [Viso]; intracellular volume [Vic]; orientation dispersion [ODI]) and FW imaging (using single‐shell; FW; free‐water corrected fractional anisotropy [FAt]) were compared with 44 PD, 21 multiple system atrophy Parkinsonian variant (MSAp), 26 progressive supranuclear palsy (PSP), and 24 healthy control subjects in the basal ganglia, midbrain/thalamus, cerebellum, and corpus callosum. There was elevated Viso in posterior substantia nigra across Parkinsonisms, and Viso, Vic, and ODI were altered in MSAp and PSP in the striatum, globus pallidus, midbrain, thalamus, cerebellum, and corpus callosum relative to controls. The mean effect size across regions for Viso was 0.163, ODI 0.131, Vic 0.122, FW 0.359, and FAt 0.125, with extracellular compartments having the greatest effect size. A key question addressed was if these techniques discriminate PD and atypical Parkinsonism. Both NODDI (AUC: 0.945) and FW imaging (AUC: 0.969) had high accuracy, with no significant difference between models. This study provides new evidence that NODDI and FW imaging offer similar discriminability between PD and atypical Parkinsonism, and FW had higher effect sizes for detecting Parkinsonism within regions across the basal ganglia and cerebellum.

It is well-established that during goal-directed motor tasks, patients with essential tremor have increased oscillations in the 0-3 and 3-8 Hz bands. It remains unclear if these increased oscillations relate to activity in specific brain regions. This study used task-based functional magnetic resonance imaging to compare the brain activity associated with oscillations in grip force output between patients with essential tremor, patients with Parkinson's disease who had clinically evident tremor, and healthy controls. The findings demonstrate that patients with essential tremor have increased brain activity in the motor cortex and supplementary motor area compared with controls, and this activity correlated positively with 3-8 Hz force oscillations. Brain activity in cerebellar lobules I-V was reduced in essential tremor compared with controls and correlated negatively with 0-3 Hz force oscillations. Widespread differences in brain activity were observed between essential tremor and Parkinson's disease. Using functional connectivity analyses during the task evidenced reduced cerebellar-cortical functional connectivity in patients with essential tremor compared with controls and Parkinson's disease. This study provides new evidence that in essential tremor 3-8 Hz force oscillations relate to hyperactivity in motor cortex, 0-3 Hz force oscillations relate to the hypoactivity in the cerebellum, and cerebellar-cortical functional connectivity is impaired.

Parkinson’s disease (PD) and the parkinsonian variant of multiple system atrophy (MSAp) are neurodegenerative disorders that can be difficult to differentiate clinically. This study provides the first characterization of the patterns of task-related functional magnetic resonance imaging (fMRI) changes across the whole brain in MSAp. We used fMRI during a precision grip force task and also performed voxel-based morphometry (VBM) on T1-weighted images in MSAp patients, PD patients, and healthy controls. All groups were matched on age, and the patient groups had comparable motor symptom durations and severities. There were three main findings. First, MSAp and PD had reduced fMRI activation in motor control areas, including the basal ganglia, thalamus, insula, primary sensorimotor and prefrontal cortices, and cerebellum compared with controls. Second, there were no activation differences among the disease groups in the basal ganglia, thalamus, insula, or primary sensorimotor cortices, but PD had more extensive activation deficits throughout the cerebrum compared with MSAp and controls. Third, VBM revealed reduced volume in the basal ganglia, middle and inferior cerebellar peduncles, pons, and throughout the cerebrum in MSAp compared with controls and PD, and additionally throughout the cerebellar cortex and vermis in MSAp compared with controls. Collectively, these results provide the first evidence that fMRI activation is abnormal in the basal ganglia, cerebellum, and cerebrum in MSAp, and that a key distinguishing feature between MSAp and PD is the extensive and widespread volume loss throughout the brain in MSAp.

No abstract