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.

Imaging biomarkers in Parkinson disease (PD) are increasingly important for monitoring progression in clinical trials and also have the potential to improve clinical care and management. This Review addresses a critical need to make clear the temporal relevance for diagnostic and progression imaging biomarkers to be used by clinicians and researchers over the clinical course of PD. Magnetic resonance imaging (diffusion imaging, neuromelanin-sensitive imaging, iron-sensitive imaging, T1-weighted imaging), positron emission tomography/single-photon emission computed tomography dopaminergic, serotonergic, and cholinergic imaging as well as metabolic and cerebral blood flow network neuroimaging biomarkers in the preclinical, prodromal, early, and moderate to late stages are characterized.OBSERVATIONS If a clinical trial is being carried out in the preclinical and prodromal stages, potentially useful disease-state biomarkers include dopaminergic imaging of the striatum; metabolic imaging; free-water, neuromelanin-sensitive, and iron-sensitive imaging in the substantia nigra; and T1-weighted structural magnetic resonance imaging. Disease-state biomarkers that can distinguish atypical parkinsonisms are metabolic imaging, free-water imaging, and T1-weighted imaging; dopaminergic imaging and other molecular imaging track progression in prodromal patients, whereas other established progression biomarkers need to be evaluated in prodromal cohorts. Progression in early-stage PD can be monitored using dopaminergic imaging in the striatum, metabolic imaging, and free-water and neuromelanin-sensitive imaging in the posterior substantia nigra. Progression in patients with moderate to late-stage PD can be monitored using free-water imaging in the anterior substantia nigra, R2* of substantia nigra, and metabolic imaging. Cortical thickness and gyrification might also be useful markers or predictors of progression. Dopaminergic imaging and free-water imaging detect progression over 1 year, whereas other modalities detect progression over 18 months or longer. The reliability of progression biomarkers varies with disease stage, whereas disease-state biomarkers are relatively consistent in individuals with preclinical, prodromal, early, and moderate to late-stage PD.CONCLUSIONS AND RELEVANCE Imaging biomarkers for various stages of PD are readily available to be used as outcome measures in clinical trials and are potentially useful in multimodal combination with routine clinical assessment. This Review provides a critically important template for considering disease stage when implementing diagnostic and progression biomarkers in both clinical trials and clinical care settings.

A BS TRACT: Objective: Accurate diagnosis is particularly challenging in Parkinson's disease (PD), multiple system atrophy (MSAp), and progressive supranuclear palsy (PSP). We compare the utility of 3 promising biomarkers to differentiate disease state and explain disease severity in parkinsonism: the Automated Imaging Differentiation in Parkinsonism (AID-P), the Magnetic Resonance Parkinsonism Index (MRPI), and plasma-based neurofilament light chain protein (NfL). Methods: For each biomarker, the area under the curve (AUC) of receiver operating characteristic curves were quantified for PD versus MSAp/PSP and MSAp versus PSP and statistically compared. Unique combinations of variables were also assessed. Furthermore, each measures association with disease severity was determined using stepwise multiple regression. Results: For PD versus MSAp/PSP, AID-P (AUC, 0.900) measures had higher AUC compared with NfL (AUC, 0.747) and MRPI (AUC, 0.669), P < 0.05. For MSAp versus PSP, AID-P (AUC, 0.889), and MRPI (AUC, 0.824) measures were greater than NfL (AUC, 0.537), P < 0.05. We then combined measures to determine if any unique combination provided enhanced accuracy and found that no combination performed better than the AID-P alone in differentiating parkinsonisms. Furthermore, we found that the AID-P demonstrated the highest association with the MDS-UPDRS (R adj 2-AID-P, 26.58%; NfL,15.12%; MRPI, 12.90%). Conclusions: Compared with MRPI and NfL, AID-P provides the best overall differentiation of PD versus MSAp/ PSP. Both AID-P and MRPI are effective in differentiating MSAp versus PSP. Furthermore, combining biomarkers did not improve classification of disease state compared with using AID-P alone. The findings demonstrate in the current sample that the AID-P and MRPI are robust biomarkers for PD, MSAp, and PSP.

Gait impairment during complex walking in older adults is thought to result from a progressive failure to compensate for deteriorating peripheral inputs by central neural processes. It is the primary hypothesis of this article that failure of higher cerebral adaptations may already be present in middle-aged adults who do not present observable gait impairments. We, therefore, compared metabolic brain activity during steering of gait (ie, complex locomotion) and straight walking (ie, simple locomotion) in young and middle-aged individuals. Cerebral distribution of [18F]-fluorodeoxyglucose, a marker of brain synaptic activity, was assessed during over ground straight walking and steering of gait using positron emission tomography in seven young adults (aged 24 ± 3) and seven middle-aged adults (aged 59 ± 3). Brain regions involved in steering of gait (posterior parietal cortex, superior frontal gyrus, and cerebellum) are retained in middle age. However, despite similar walking performance, there are age-related differences in the distribution of [18F]-fluorodeoxyglucose during steering: middle-aged adults have (i) increased activation of precentral and fusiform gyri, (ii) reduced deactivation of multisensory cortices (inferior frontal, postcentral, and fusiform gyri), and (iii) reduced activation of the middle frontal gyrus and cuneus. Our results suggest that preclinical decline in central sensorimotor processing in middle age is observable during complex walking.