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BackgroundClinical markers of cognitive decline in Parkinson's disease (PD) encompass several mental non-motor symptoms such as hallucinations, apathy, anxiety, and depression. Furthermore, freezing of gait (FOG) and specific gait alterations have been associated with cognitive dysfunction in PD. Finally, although low cerebrospinal fluid levels of amyloid-β42 have been found to predict cognitive decline in PD, hitherto PET imaging of amyloid-β (Aβ) failed to consistently demonstrate the association between Aβ plaques deposition and mild cognitive impairment in PD (PD-MCI).AimFinding significant features associated with PD-MCI through a machine learning approach.Patients and methodsPatients were assessed with an extensive clinical and neuropsychological examination. Clinical evaluation included the assessment of mental non-motor symptoms and FOG using the specific items of the MDS-UPDRS I and II. Based on the neuropsychological examination, patients were classified as subjects without and with MCI (noPD-MCI, PD-MCI). All patients were evaluated using a motion analysis system. A subgroup of PD patients also underwent amyloid PET imaging. PD-MCI and noPD-MCI subjects were compared with a univariate statistical analysis on demographic data, clinical features, gait analysis variables, and amyloid PET data. Then, machine learning analysis was performed two times: Model 1 was implemented with age, clinical variables (hallucinations/psychosis, depression, anxiety, apathy, sleep problems, FOG), and gait features, while Model 2, including only the subgroup performing PET, was implemented with PET variables combined with the top five features of the former model.ResultsSeventy-five PD patients were enrolled (33 PD-MCI and 42 noPD-MCI). PD-MCI vs. noPD-MCI resulted in older and showed worse gait patterns, mainly characterized by increased dynamic instability and reduced step length; when comparing amyloid PET data, the two groups did not differ. Regarding the machine learning analyses, evaluation metrics were satisfactory for Model 1 overcoming 80% for accuracy and specificity, whereas they were disappointing for Model 2.ConclusionsThis study demonstrates that machine learning implemented with specific clinical features and gait variables exhibits high accuracy in predicting PD-MCI, whereas amyloid PET imaging is not able to increase prediction. Additionally, our results prompt that a data mining approach on certain gait parameters might represent a reliable surrogate biomarker of PD-MCI.

Several MRI techniques have become available to support the early diagnosis of multiple system atrophy (MSA), but few longitudinal studies on both MSA variants have been performed, and there are no established MRI markers of disease progression. We aimed to characterize longitudinal brain changes in 26 patients with MSA (14 MSA-P and 12 MSA-C) over a 1-year follow-up period in terms of local tissue density and T1w/T2w ratio in a-priori regions, namely, bilateral putamen, cerebellar gray matter (GM), white matter (WM), and substantia nigra (SN). A significant GM density decrease was found in cerebellum and left putamen in the entire group (10.7 and 33.1% variation, respectively) and both MSA subtypes (MSA-C: 15.4 and 33.0% variation; MSA-P: 7.7 and 33.2%) and in right putamen in the entire group (19.8% variation) and patients with MSA-C (20.9% variation). A WM density decrease was found in the entire group (9.3% variation) and both subtypes in cerebellum-brainstem (MSA-C: 18.0% variation; MSA-P: 5% variation). The T1w/T2w ratio increase was found in the cerebellar and left putamen GM (6.6 and 24.9% variation), while a significant T1w/T2w ratio decrease was detected in SN in the entire MSA group (31% variation). We found a more progressive atrophy of the cerebellum in MSA-C with a similar progression of putaminal atrophy in the two variants. T1w/T2w ratio can be further studied as a potential marker of disease progression, possibly reflecting decreased neuronal density or iron accumulation.

BackgroundMild cognitive impairment (MCI) is frequent in Parkinson’s disease (PD) and represents a risk factor for the development of dementia associated with PD (PDD). Since PDD has been associated with disability, caregiver burden, and an increase in health-related costs, early detection of MCI associated with PD (PD-MCI) and its biomarkers is crucial.ObjectiveGiven that gait is considered a surrogate marker for cognitive decline in PD, the aim of this study was to compare gait patterns in PD-MCI subtypes in order to verify the existence of an association between specific gait features and particular MCI subtypes.MethodsA total of 67 patients with PD were consecutively enrolled and assessed by an extensive clinical and cognitive examination. Based on the neuropsychological examination, patients were diagnosed as patients with MCI (PD-MCI) and without MCI (no-PD-MCI) and categorized in MCI subtypes. All patients were evaluated using a motion capture system of a BTS Bioengineering equipped with six IR digital cameras. Gait of the patients was assessed in the ON-state under three different tasks (a single task and two dual tasks). Statistical analysis included the t-test, the Kruskal–Wallis test with post hoc analysis, and the exploratory correlation analysis.ResultsGait pattern was poorer in PD-MCI vs. no-PD-MCI in all tasks. Among PD-MCI subtypes, multiple-domain PD-MCI and amnestic PD-MCI were coupled with worse gait patterns, notably in the dual task.ConclusionBoth the magnitude of cognitive impairment and the presence of memory dysfunction are associated with increased measures of dynamic unbalance, especially in dual-task conditions, likely mirroring the progressive involvement of posterior cortical networks.

Introduction: The aim of this study is to investigate the impact of orthostatic hypotension (OH) on cognitive functions in patients with multiple system atrophy (MSA) followed over time.Methods: Thirty-two MSA patients were enrolled and underwent a comprehensive neuropsychological battery; at baseline (T0) 15 out of 32 patients presented OH, assessed by means of orthostatic standing test. All patients underwent a follow-up (T1) evaluation 12 months after baseline. Thirteen out of 32 patients also underwent a second follow-up (T2) evaluation at 24 months. Changes over time on different neuropsychological tasks were compared between patients with and without OH by means of Mann-Whitney's U-test. Moreover, clinical categories of normal cognition, mild cognitive impairment, and dementia were determined, and changes at T1 and T2 in global cognitive status were compared between patients with and without OH.Results: At T0, patients with OH had better performance on words/non-words repetition task (p = 0.02) compared to patients without OH. Compared to patients without OH, patients with OH performed worse on semantic association task (p < 0.01) at T1 and on Stroop test-error effect (p = 0.04) at T2. The percentage of patients with worsened cognitive status at T1 was higher among patients with OH than among patients without OH (93 vs. 59%, p = 0.03). OH (β = −4.67, p = 0.01), education (β = 0.45, p = 0.02), age (β = 0.19, p = 0.03), and Montreal Cognitive Assessment battery (MOCA) score at T0 (β = −0.26, p = 0.04) were significant predictors of global cognitive status worsening at T1.Discussion: We found that global cognitive status worsened at 1-year follow-up in 93% of patients with OH, and OH, along with age, education, and MOCA score, predicted cognitive worsening over time. To clarify the relationship between OH and cognitive dysfunction in MSA, we suggest the use of clinical categories of normal cognition, mild cognitive impairment, and dementia in further longitudinal studies on MSA patients with and without OH.

The objective of the present study was to describe gait parameters of progressive supranuclear palsy (PSP) phenotypes at early stage verifying the ability of gait analysis in discriminating between disease phenotypes and between the other variant syndromes of PSP (vPSP) and Parkinson's disease (PD). Nineteen PSP (10 PSP-Richardson's syndrome, five PSP-parkinsonism, and four PSP-progressive gait freezing) and nine PD patients performed gait analysis in single and dual tasks. Although phenotypes showed similar demographic and clinical variables, Richardson's syndrome presented worse cognitive functions. Gait analysis demonstrated worse parameters in Richardson's syndrome compared with the vPSP. The overall diagnostic accuracy of the statistical model during dual task was almost 90%. The correlation analysis showed a significant relationship between gait parameters and visuo-spatial, praxic, and attention abilities in PSP-Richardson's syndrome only. vPSP presented worse gait parameters than PD. Richardson's syndrome presents greater gait dynamic instability since the earliest stages than other phenotypes. Computerized gait analysis can differentiate between PSP phenotypes and between vPSP and PD.