Gastrointestinal (GI) dysfunction is the most common non-motor symptom of Parkinson's disease (PD). Symptoms of GI dysmotility include early satiety and nausea from delayed gastric emptying, bloating from poor small bowel coordination, and constipation and defecatory dysfunction from impaired colonic transit. Understanding the pathophysiology and treatment of these symptoms in PD patients has been hampered by the lack of investigation into GI symptoms and pathology in PD animal models. We report that the prototypical parkinsonian neurotoxin, MPTP (1-methyl 4-phenyl 1,2,3,6-tetrahydropyridine), is a selective dopamine neuron toxin in the enteric nervous system (ENS). When examined 10 days after treatment, there was a 40% reduction of dopamine neurons in the ENS of C57Bl/6 mice administered MPTP (60 mg/kg). There were no differences in the density of cholinergic or nitric oxide neurons. Electrophysiological recording of neural-mediated muscle contraction in isolated colon from MPTP-treated animals confirmed a relaxation defect associated with dopaminergic degeneration. Behaviorally, MPTP induced a transient increase in colon motility, but no changes in gastric emptying or small intestine transit. These results provide the first comprehensive assessment of gastrointestinal pathophysiology in an animal model of PD. They provide insight into the impact of dopaminergic dysfunction on gastrointestinal motility and a benchmark for assessment of other PD model systems.

The dystonias are a group of disorders characterized by involuntary twisting and repetitive movements. DYT1 dystonia is an inherited form of dystonia caused by a mutation in the TOR1A gene, which encodes torsinA. TorsinA is expressed in many regions of the nervous system, and the regions responsible for causing dystonic movements remain uncertain. Most prior studies have focused on the basal ganglia, although there is emerging evidence for abnormalities in the cerebellum too. In the current studies, we examined the cerebellum for structural abnormalities in a knock-in mouse model of DYT1 dystonia. The gross appearance of the cerebellum appeared normal in the mutant mice, but stereological measures revealed the cerebellum to be 5% larger in mutant compared to control mice. There were no changes in the numbers of Purkinje cells, granule cells, or neurons of the deep cerebellar nuclei. However, Golgi histochemical studies revealed Purkinje cells to have thinner dendrites, and fewer and less complex dendritic spines. There also was a higher frequency of heterotopic Purkinje cells displaced into the molecular layer. These results reveal subtle structural abnormalities of the cerebellum that are similar to those reported for the basal ganglia in the DYT1 knock-in mouse model.

Freezing of gait (FOG) is a poorly understood heterogeneous gait disorder seen in patients with parkinsonism which contributes to significant morbidity and social isolation. FOG is currently measured with scales that are typically performed by movement disorders specialists (i.e., MDS-UPDRS), or through patient completed questionnaires (N-FOG-Q) both of which are inadequate in addressing the heterogeneous nature of the disorder and are unsuitable for use in clinical trials The purpose of this study was to devise a method to measure FOG objectively, hence improving our ability to identify it and accurately evaluate new therapies. A major innovation of our study is that it is the first study of its kind that uses the largest sample size (>30 h, N = 57) in order to apply explainable, multi-task deep learning models for quantifying FOG over the course of the medication cycle and at varying levels of parkinsonism severity. We trained interpretable deep learning models with multi-task learning to simultaneously score FOG (cross-validated F1 score 97.6%), identify medication state (OFF vs. ON levodopa; cross-validated F1 score 96.8%), and measure total PD severity (MDS-UPDRS-III score prediction error ≤ 2.7 points) using kinematic data of a well-characterized sample of N = 57 patients during levodopa challenge tests. The proposed model was able to explain how kinematic movements are associated with each FOG severity level that were highly consistent with the features, in which movement disorders specialists are trained to identify as characteristics of freezing. Overall, we demonstrate that deep learning models’ capability to capture complex movement patterns in kinematic data can automatically and objectively score FOG with high accuracy. These models have the potential to discover novel kinematic biomarkers for FOG that can be used for hypothesis generation and potentially as clinical trial outcome measures.

Freezing of gait (FOG) is a poorly understood heterogeneous gait disorder seen in patients with parkinsonism which contributes to significant morbidity and social isolation. FOG is currently measured with scales that are typically performed by movement disorders specialists (ie. MDS-UPDRS), or through patient completed questionnaires (N-FOG-Q) both of which are inadequate in addressing the heterogeneous nature of the disorder and are unsuitable for use in clinical trials The purpose of this study was to devise a method to measure FOG objectively, hence improving our ability to identify it and accurately evaluate new therapies. We trained interpretable deep learning models with multi-task learning to simultaneously score FOG (cross-validated F1 score 97.6%), identify medication state (OFF vs. ON levodopa; cross-validated F1 score 96.8%), and measure total PD severity (MDS-UPDRS-III score prediction error ≤ 2.7 points) using kinematic data of a well-characterized sample of N=57 patients during levodopa challenge tests. The proposed model was able to identify kinematic features associated with each FOG severity level that were highly consistent with the features that movement disorders specialists are trained to identify as characteristic of freezing. In this work, we demonstrate that deep learning models' capability to capture complex movement patterns in kinematic data can automatically and objectively score FOG with high accuracy. These models have the potential to discover novel kinematic biomarkers for FOG that can be used for hypothesis generation and potentially as clinical trial outcome measures.