The clinical connectome fingerprint (CCF) was recently introduced as a way to assess brain dynamics. It is an approach able to recognize individuals, based on the brain network. It showed its applicability providing network features used to predict the cognitive decline in preclinical Alzheimer's disease. In this article, we explore the performance of CCF in 47 Parkinson's disease (PD) patients and 47 healthy controls, under the hypothesis that patients would show reduced identifiability as compared to controls, and that such reduction could be used to predict motor impairment. We used source-reconstructed magnetoencephalography signals to build two functional connectomes for 47 patients with PD and 47 healthy controls. Then, exploiting the two connectomes per individual, we investigated the identifiability characteristics of each subject in each group. We observed reduced identifiability in patients compared to healthy individuals in the beta band. Furthermore, we found that the reduction in identifiability was proportional to the motor impairment, assessed through the Unified Parkinson's Disease Rating Scale, and, interestingly, able to predict it (at the subject level), through a cross-validated regression model. Along with previous evidence, this article shows that CCF captures disrupted dynamics in neurodegenerative diseases and is particularly effective in predicting motor clinical impairment in PD.

Fading of brain network fingerprint in Parkinson’s disease predicts motor clinical impairment / Troisi Lopez, E; Minino, R; Liparoti, M; Polverino, A; Romano, A; De Micco, R; Lucidi, F; Tessitore, A; Amico, E; Sorrentino, G; Jirsa, V; Sorrentino, P. - In: HUMAN BRAIN MAPPING. - ISSN 1065-9471. - (2022). [10.1101/2022.02.03.22270343]

Fading of brain network fingerprint in Parkinson’s disease predicts motor clinical impairment

M Liparoti;F Lucidi;
2022

Abstract

The clinical connectome fingerprint (CCF) was recently introduced as a way to assess brain dynamics. It is an approach able to recognize individuals, based on the brain network. It showed its applicability providing network features used to predict the cognitive decline in preclinical Alzheimer's disease. In this article, we explore the performance of CCF in 47 Parkinson's disease (PD) patients and 47 healthy controls, under the hypothesis that patients would show reduced identifiability as compared to controls, and that such reduction could be used to predict motor impairment. We used source-reconstructed magnetoencephalography signals to build two functional connectomes for 47 patients with PD and 47 healthy controls. Then, exploiting the two connectomes per individual, we investigated the identifiability characteristics of each subject in each group. We observed reduced identifiability in patients compared to healthy individuals in the beta band. Furthermore, we found that the reduction in identifiability was proportional to the motor impairment, assessed through the Unified Parkinson's Disease Rating Scale, and, interestingly, able to predict it (at the subject level), through a cross-validated regression model. Along with previous evidence, this article shows that CCF captures disrupted dynamics in neurodegenerative diseases and is particularly effective in predicting motor clinical impairment in PD.
2022
Parkinson's disease; brain fingerprint; brain network; clinical connectome fingerprint; magnetoencephalography; motor impairment; neurodegenerative disease.
01 Pubblicazione su rivista::01a Articolo in rivista
Fading of brain network fingerprint in Parkinson’s disease predicts motor clinical impairment / Troisi Lopez, E; Minino, R; Liparoti, M; Polverino, A; Romano, A; De Micco, R; Lucidi, F; Tessitore, A; Amico, E; Sorrentino, G; Jirsa, V; Sorrentino, P. - In: HUMAN BRAIN MAPPING. - ISSN 1065-9471. - (2022). [10.1101/2022.02.03.22270343]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1695616
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