AUTHOR=Amboni Marianna , Ricciardi Carlo , Adamo Sarah , Nicolai Emanuele , Volzone Antonio , Erro Roberto , Cuoco Sofia , Cesarelli Giuseppe , Basso Luca , D'Addio Giovanni , Salvatore Marco , Pace Leonardo , Barone Paolo 

TITLE=Machine learning can predict mild cognitive impairment in Parkinson's disease

JOURNAL=Frontiers in Neurology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2022.1010147

DOI=10.3389/fneur.2022.1010147

ISSN=1664-2295

ABSTRACT=Background:  Clinical markers of cognitive decline in Parkinson disease (PD) encompass a number of mental non-motor symptoms like 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)
Aim: Finding significant features associated with PD-MCI through a machine learning approach.
Patients and Methods: Patients 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 neuropsychological examination patients were classified in 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 twice: 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-5 features of the former model.
Results: Seventy-five PD patients were enrolled (33 PD-MCI and 42 noPD-MCI). PD-MCI vs. noPD-MCI resulted older and showed worse gait pattern, 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.
Conclusions:  This 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.