Unveiling Parkinson's Disease Features from a Primate Model with Deep Neural Networks

Caetano M. Ranieri, Renan C. Moioli, Roseli A. F. Romero, Mariana F. P. de Araujo, Maxwell Barbosa De Santana, Jhielson Montino Pimentel, Patricia A. Vargas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)
46 Downloads (Pure)


Parkinson’s Disease (PD) is a neurodegenerative disorder with increasing prevalence in the world population and is Characterised by motor and cognitive symptoms. Although cortical EEG readings from PD-affected humans have being commonly used to feed different machine learning frameworks, the directly affected areas are concentrated in a group of subcortical nuclei and related areas, the so-called motor loop. As those areas may only be directly accessed through invasive procedures, such as Local Field Potential (LFP) measurements, most data collection must rely on animal models. To the best of our knowledge, no neural networks-based analysis centred on LFP data from the motor loop was reported so far. In this work, we trained and evaluated a set of deep neural networks on a dataset recorded from marmoset monkeys, with LFP readings from healthy and parkinsonian subjects. We analysed each trained neural network with respect to its inputs and representations from intermediate layers. CNN and ConvLSTM classifiers were applied, reaching accuracies up to 99.80%, as well as a CNN-based autoencoder, which has also shown to learn PD-related representations. The results and analysis provided further insights and foster research on the correlates of Parkinson’s Disease.
Original languageEnglish
Title of host publication2020 International Joint Conference on Neural Networks (IJCNN)
ISBN (Electronic)9781728169262
Publication statusPublished - 28 Sept 2020
EventIEEE World Congress on Computational Intelligence 2020 - Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020

Publication series

NameInternational Joint Conference on Neural Networks
ISSN (Electronic)2161-4407


ConferenceIEEE World Congress on Computational Intelligence 2020
Abbreviated titleIEEE WCCI 2020
Country/TerritoryUnited Kingdom
Internet address


  • LFP analysis
  • Parkinson's disease
  • attribution methods
  • computational neuroscience
  • deep learning

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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