Graph theory on brain cortical sources in parkinson’s disease: The analysis of ‘small world’ organization from EEG

Maria Cotelli, Fabrizio Vecchio, Chiara Pappalettera, Francesca Alù, Alessandro Orticoni, Elda Judica, Francesca Pistoia

Research output: Contribution to journalArticlepeer-review

Abstract

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 followup of PD progression, as well as for evaluating pharmacological and neurorehabilitation treatments.
Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalSensors
Volume21
DOIs
Publication statusPublished - 2021
Externally publishedYes

Keywords

  • EEG
  • ELORETA
  • Functional connectivity
  • Graph theory
  • Parkinson’s disease
  • Small world
  • Parkinson Disease
  • Brain
  • Electroencephalography
  • Humans
  • Pilot Projects
  • Neurodegenerative Diseases
  • Aged

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