Automated Categorization of Parkinsonian Syndromes Using Magnetic Resonance Imaging in a Clinical Setting.

BACKGROUND: Machine learning algorithms using magnetic resonance imaging (MRI) data can accurately discriminate parkinsonian syndromes. Validation in patients recruited in routine clinical practice is missing. OBJECTIVE: The aim of this study was to assess the accuracy of a machine learning algorithm trained on a research cohort and tested on an independent clinical replication cohort for the categorization of parkinsonian syndromes. METHODS: Three hundred twenty-two subjects, including 94 healthy control subjects, 119 patients with Parkinson's disease (PD), 51 patients with progressive supranuclear palsy (PSP) with Richardson's syndrome, 35 with multiple system atrophy (MSA) of the parkinsonian variant (MSA-P), and 23 with MSA of the cerebellar variant (MSA-C), were recruited. They were divided into a training cohort (n = 179) scanned in a research environment and a replication cohort (n = 143) examined in clinical practice on different MRI systems. Volumes and diffusion tensor imaging (DTI) metrics in 13 brain regions were used as input for a supervised machine learning algorithm. To harmonize data across scanners and reduce scanner-dependent effects, we tested two types of normalizations using patient data or healthy control data. RESULTS: In the replication cohort, high accuracies were achieved using volumetry in the classification of PD-PSP, PD-MSA-C, PSP-MSA-C, and PD-atypical parkinsonism (balanced accuracies: 0.840-0.983, area under the receiver operating characteristic curves: 0.907-0.995). Performances were lower for the classification of PD-MSA-P, MSA-C-MSA-P (balanced accuracies: 0.765-0.784, area under the receiver operating characteristic curve: 0.839-0.871) and PD-PSP-MSA (balanced accuracies: 0.773). Performance using DTI was improved when normalizing by controls, but remained lower than that using volumetry alone or combined with DTI. CONCLUSIONS: A machine learning approach based on volumetry enabled accurate classification of subjects with early-stage parkinsonism, examined on different MRI systems, as part of their clinical assessment. © 2020 International Parkinson and Movement Disorder Society.

Anatomical and behavioral impact of a lentiviral tool tapping onto hippocampal serotonin reuptake in rats.

We aimed at the further characterization of rats in which SERT gene silencing was achieved by hippocampal injection of a lentiviral vector, carrying three si-RNA to block SERT mRNA at 66% of normal levels. Improved self-control and reduced restlessness were already demonstrated in these rats. Present further studies consisted of male adult rats, bilaterally inoculated within the hippocampus; control rats received lentivirus particles inactivated with heat. Both groups were maintained in isolation for 5 months, starting from inoculation. Neurochemical changes were studied by proton magnetic resonance spectroscopy (1H-MRS): we found increased hippocampal viability and bioenergetic potential; however, rats showed a behaviorally depressive pattern, also characterized by enhanced affiliation. Based on the extent of such effects, the whole lenti-SERT group was divided into two subgroups, termed intermediate- and extreme- phenotype profiles. While all rats had a widespread modification within dorsal/ventral striatum, amygdala, and hypothalamus, only the former subgroup showed an involvement of Raphé medialis, while, for the latter subgroup, an increase of SERT within hippocampus was unexpectedly caused. Within the less-affected "intermediate" rats, hippocampal 5-HT7 receptors were down-modulated, and also similarly within substantia nigra, septum, and neocortex. This picture demonstrates that additional rather than fewer neurobiological changes accompany a lower phenotypic expression. Overall, tapping hippocampal SERT affected the balance between habits versus strategies of coping by promoting morphogenetic processes indicative of a serotonergic fiber plasticity. Supplementary studies about serotonergic dynamics and neurogenesis within fronto-striatal circuits are needed.