Hospital of Vipiteno (SABES-ASDAA)
Department of Neurorehabilitation
I previously worked at “San Raffaele Turro Clinic” – Milano, Italy (2002-2008) as Head of the Clinical and Research Laboratory of Neuropsychology at the Neurorehabilitation ward. My principal activities were the neuropsychological diagnosis, the neuropsychological rehabilitation and the psychotherapy. I was Professor on contract at “Vita e Salute” University (San Raffaele Hospital, Milano, Italy), from 2006 to 2009, for the following courses: “Laboratory of Neuropsychology” and “Clinical Neuropsychology”. Afterward (January 2010-June 2020), I worked at the “Department of Parkinson’s disease, Movement disorders and Brain Injury Rehabilitation” at the “Moriggia-Pelascini” Hospital (Gravedona ed Uniti, CO, Itlay). Here, I was the Head of the Neuropsychological clinical service and of the Neuropsychological research laboratory. I was also the consultant Psychologist for the ICU and the Medical and Surgical wards. During the last years, at my department, I was also the co-study coordinator of the “Parkinson’s Outcome Project” (promoted by the Parkinson Foundation). For this project I was the main responsible for data collection. At the moment, I am Researcher at the Department of Neurorehabilitation at the Vipiteno Hospital (Vipiteno-Sterzing, BZ, Italy). Currently, me and my collaborators are conducting a study aimed to evaluate the neuropsychological and neurophysiological correlates of fatigue in patients who recovered from COVID19. Further, I’m working at the design of a brief, time-saving cognitive, learning machine based assessment for rising the accuracy of the neuropsychological diagnosis of patients with movement disorder. My principal clinical interests entail the neuropsychology of movement disorders and of the other neurodegenerative diseases, the pathology and the pathophysiology of the reward system and the emotional and cognitive aspects related with learning and action performing. I’m actually involved also in different research activities in the following fields: i) movement disorders rehabilitation, ii) motor learning, iii) interplay between motor and cognitive aspects in motor learning and rehabilitation, iv) effects of the dopaminergic therapy on reward system and learning capacities, v) clinical, neuropsychological, molecular and neurophysiological markers of neural plasticity and learning.

Presenter of 1 Presentation

COGNITIVE SCREENING IN PATIENTS WITH PARKINSONISM: PROPOSAL FOR A NEW, MACHINE LEARNING-BASED DIAGNOSTIC TOOL

Session Type
SYMPOSIUM
Date
Fri, 18.03.2022
Session Time
02:45 PM - 04:45 PM
Room
ONSITE: 133-134
Lecture Time
03:15 PM - 03:30 PM

Abstract

Aims

The assessment of cognitive deficits is crucial in the field of parkinsonisms diagnosis and management. To evaluate cognitive profile, screening tests are generally preferred, since in-depth neuropsychological batteries are reliable but time-consuming. Otherwise, low-level of correspondence is observed, when the two evaluations are compared.

A new tool, we named CoMDA (Cognition in Movement Disorders Assessment), was composed by merging all items, without repetition, of Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) and Frontal Assessment Battery (FAB). Moreover, a machine learning was developed to classify the CoMDA score and to reach accurate prediction of risk of dementia.

Methods

500 patients (400 with Parkinson’s disease, 41 with vascular parkinsonism, 31 with progressive supranuclear palsy, 28 with multisystem atrophy) underwent CoMDA (Level 1) and in-depth neuropsychological battery (Level 2), considered as the gold standard for classification of cognition

Results

Referring on Level 2, the rate of false negatives was 55,7% for MMSE, 45,6% for FAB and 57,4% for MoCA. Otherwise, the classification obtained with CoMDA dropped to 31,7% the false negative rate. Considering Level 2 as a 3-level continuous feature, a Machine Learning model was developed to classify L2 feature accurately and with adequate cross-generalization capacity. CoMDA-ML produce the accurate and generalizable L2 predictions (micro average ROC curve, AUC = .81; and AUC=.85, .67 and .83 for L2 individual classes respectively).

Conclusions

CoMDA and COMDA-ML are reliable and time-sparing instruments, accurate in distinguishing cognitively impaired patients from those with normal cognition.

This study has been registered on ClinicalTrials.gov (NCT04858893).

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