Digital phenotyping of Parkinson's disease via natural language processing.

Frontostriatal degeneration in Parkinson's disease (PD) is associated with language deficits, which can be identified using natural language processing, a remarkable tool for digital-phenotyping. Current evidence is mostly blind to the disorder's cognitive phenotypes. We validated an AI-driven approach to capture digital language markers of PD with and without mild cognitive impairment (PD-MCI, PD-nMCI) relative to healthy controls (HCs).

AI-based staging, causal hypothesis and progression of subjects at risk of Alzheimer's disease: a multicenter study.

Introduction: In 2024, 11 European scientific societies/organizations and one patient advocacy association have defined a patient-centered biomarker-based diagnostic workflow for memory clinics evaluating neurocognitive disorders.

Methods: We tested the performance of an artificial intelligence (AI) tool applied to neuropsychological and magnetic resonance imaging (MRI) assessment for staging and causal hypothesis, which are the two recommended workflow steps guiding the next one recommending optimal biomarkers to be used for a biological diagnosis of neurocognitive disorders, according to intersocietal recommendations. Moreover, we assessed the AI performance in predicting the progression to Alzheimer's disease (AD)-dementia.

Digital Phenotyping of Parkinson's Disease via Natural Language Processing.

Frontostriatal degeneration in Parkinson's disease (PD) is associated with language deficits, which can be identified using natural language processing, a remarkable tool for digital phenotyping. Current evidence is limited in linguistic coverage and mostly blind to the disorder's cognitive phenotypes. We validated an AI-driven approach to capture digital language markers of PD with and without mild cognitive impairment (PD-MCI, PD-nMCI) relative to healthy controls (HCs).

Effectiveness of a home-based computerized cognitive training in Parkinson's disease: a pilot randomized cross-over study.

Introduction: Cognitive symptoms are common in Parkinson's Disease (PD), and digital interventions like telerehabilitation other an accessible way to manage these symptoms. This study aimed to assess the effectiveness of a Home-Based Computerized Cognitive Training (HB-CCT) program in individuals with PD using a pilot randomized cross-over design.

Methods: Twenty-five participants (mean age 69.

Exploring the Neuropsychological Correlates of Swallowing Disorders in People with Parkinson's Disease: a Cross-Sectional Study.

Around 80% of persons with Parkinson's disease (PD) present symptoms of dysphagia. Although cognitive impairment may contribute to dysphagia, few studies have investigated the association between the PD neuropsychological profile and objective measures of swallowing dysfunction. Since the swallowing function comprises involuntary but also voluntary actions, we hypothesize that specific measures of attention and executive functions can be underlined in PD-related dysphagia.

Unveiling the Diagnostic Potential of Linguistic Markers in Identifying Individuals with Parkinson's Disease through Artificial Intelligence: A Systematic Review.

While extensive research has documented the cognitive changes associated with Parkinson's disease (PD), a relatively small portion of the empirical literature investigated the language abilities of individuals with PD. Recently, artificial intelligence applied to linguistic data has shown promising results in predicting the clinical diagnosis of neurodegenerative disorders, but a deeper investigation of the current literature available on PD is lacking. This systematic review investigates the nature of language disorders in PD by assessing the contribution of machine learning (ML) to the classification of patients with PD.

Motoric Cognitive Risk Syndrome, Subtypes and 8-Year All-Cause Mortality in Aging Phenotypes: The Salus in Apulia Study.

Background: This study aims to establish the key clinical features of different motoric cognitive risk (MCR) subtypes based on individual quantitative measures of cognitive impairment and to compare their predictive power on survival over an 8-year observation time. Methods: We analyzed data from a population-based study of 1138 subjects aged 65 years and older in south Italy. These individuals were targeted and allocated to subtypes of the MCR phenotype according to the slowness criterion plus one other different cognitive domain for each characterized phenotype (Subjective Cognitive Complaint [SCC]; Global Function [Mini Mental State Examination (MMSE) < 24]; or a combination of both).

Dietary Patterns Associated with Diabetes in an Older Population from Southern Italy Using an Unsupervised Learning Approach.

Dietary behaviour is a core element in diabetes self-management. There are no remarkable differences between nutritional guidelines for people with type 2 diabetes and healthy eating recommendations for the general public. This study aimed to evaluate dietary differences between subjects with and without diabetes and to describe any emerging dietary patterns characterizing diabetic subjects.