Graph Theory on Brain Cortical Sources in Parkinson’s Disease: The Analysis of ‘Small World’ Organization from EEG

Parkinson’s disease (PD) is the second most common neurodegenerative disease in the elderly population. Similarly to other neurodegenerative diseases, the early diagnosis of PD is quite difficult. The current pilot study aimed to explore the differences in brain connectivity between PD and NOrmal eL...

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Autores principales:	Fabrizio Vecchio, Chiara Pappalettera, Francesca Miraglia, Francesca Alù, Alessandro Orticoni, Elda Judica, Maria Cotelli, Francesca Pistoia, Paolo Maria Rossini
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	EEG graph theory functional connectivity eLORETA Parkinson’s disease small world Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/503ddbd0495441a2a79b2ff3bcc61622
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spelling	oai:doaj.org-article:503ddbd0495441a2a79b2ff3bcc616222021-11-11T19:13:42ZGraph Theory on Brain Cortical Sources in Parkinson’s Disease: The Analysis of ‘Small World’ Organization from EEG10.3390/s212172661424-8220https://doaj.org/article/503ddbd0495441a2a79b2ff3bcc616222021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7266https://doaj.org/toc/1424-8220Parkinson’s disease (PD) is the second most common neurodegenerative disease in the elderly population. Similarly to other neurodegenerative diseases, the early diagnosis of PD is quite difficult. The current pilot study aimed to explore the differences in brain connectivity between PD and NOrmal eLDerly (Nold) subjects to evaluate whether connectivity analysis may speed up and support early diagnosis. A total of 26 resting state EEGs were analyzed from 13 PD patients and 13 age-matched Nold subjects, applying to cortical reconstructions the graph theory analyses, a mathematical representation of brain architecture. Results showed that PD patients presented a more ordered structure at slow-frequency EEG rhythms (lower value of SW) than Nold subjects, particularly in the theta band, whereas in the high-frequency alpha, PD patients presented more random organization (higher SW) than Nold subjects. The current results suggest that PD could globally modulate the cortical connectivity of the brain, modifying the functional network organization and resulting in motor and non-motor signs. Future studies could validate whether such an approach, based on a low-cost and non-invasive technique, could be useful for early diagnosis, for the follow-up of PD progression, as well as for evaluating pharmacological and neurorehabilitation treatments.Fabrizio VecchioChiara PappaletteraFrancesca MiragliaFrancesca AlùAlessandro OrticoniElda JudicaMaria CotelliFrancesca PistoiaPaolo Maria RossiniMDPI AGarticleEEGgraph theoryfunctional connectivityeLORETAParkinson’s diseasesmall worldChemical technologyTP1-1185ENSensors, Vol 21, Iss 7266, p 7266 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	EEG graph theory functional connectivity eLORETA Parkinson’s disease small world Chemical technology TP1-1185
spellingShingle	EEG graph theory functional connectivity eLORETA Parkinson’s disease small world Chemical technology TP1-1185 Fabrizio Vecchio Chiara Pappalettera Francesca Miraglia Francesca Alù Alessandro Orticoni Elda Judica Maria Cotelli Francesca Pistoia Paolo Maria Rossini Graph Theory on Brain Cortical Sources in Parkinson’s Disease: The Analysis of ‘Small World’ Organization from EEG
description	Parkinson’s disease (PD) is the second most common neurodegenerative disease in the elderly population. Similarly to other neurodegenerative diseases, the early diagnosis of PD is quite difficult. The current pilot study aimed to explore the differences in brain connectivity between PD and NOrmal eLDerly (Nold) subjects to evaluate whether connectivity analysis may speed up and support early diagnosis. A total of 26 resting state EEGs were analyzed from 13 PD patients and 13 age-matched Nold subjects, applying to cortical reconstructions the graph theory analyses, a mathematical representation of brain architecture. Results showed that PD patients presented a more ordered structure at slow-frequency EEG rhythms (lower value of SW) than Nold subjects, particularly in the theta band, whereas in the high-frequency alpha, PD patients presented more random organization (higher SW) than Nold subjects. The current results suggest that PD could globally modulate the cortical connectivity of the brain, modifying the functional network organization and resulting in motor and non-motor signs. Future studies could validate whether such an approach, based on a low-cost and non-invasive technique, could be useful for early diagnosis, for the follow-up of PD progression, as well as for evaluating pharmacological and neurorehabilitation treatments.
format	article
author	Fabrizio Vecchio Chiara Pappalettera Francesca Miraglia Francesca Alù Alessandro Orticoni Elda Judica Maria Cotelli Francesca Pistoia Paolo Maria Rossini
author_facet	Fabrizio Vecchio Chiara Pappalettera Francesca Miraglia Francesca Alù Alessandro Orticoni Elda Judica Maria Cotelli Francesca Pistoia Paolo Maria Rossini
author_sort	Fabrizio Vecchio
title	Graph Theory on Brain Cortical Sources in Parkinson’s Disease: The Analysis of ‘Small World’ Organization from EEG
title_short	Graph Theory on Brain Cortical Sources in Parkinson’s Disease: The Analysis of ‘Small World’ Organization from EEG
title_full	Graph Theory on Brain Cortical Sources in Parkinson’s Disease: The Analysis of ‘Small World’ Organization from EEG
title_fullStr	Graph Theory on Brain Cortical Sources in Parkinson’s Disease: The Analysis of ‘Small World’ Organization from EEG
title_full_unstemmed	Graph Theory on Brain Cortical Sources in Parkinson’s Disease: The Analysis of ‘Small World’ Organization from EEG
title_sort	graph theory on brain cortical sources in parkinson’s disease: the analysis of ‘small world’ organization from eeg
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/503ddbd0495441a2a79b2ff3bcc61622
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Graph Theory on Brain Cortical Sources in Parkinson’s Disease: The Analysis of ‘Small World’ Organization from EEG

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