Linear and Non-Linear Methods to Discriminate Cortical Parcels Based on Neurodynamics: Insights from sEEG Recordings
Understanding human cortical neurodynamics is increasingly important, as highlighted by the European Innovation Council, which prioritises tools for measuring and stimulating brain activity. Unravelling how cytoarchitecture, morphology, and connectivity shape neurodynamics is essential for developin...
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MDPI AG
2025-04-01
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| author | Karolina Armonaite Livio Conti Luigi Laura Michele Primavera Franca Tecchio |
| author_facet | Karolina Armonaite Livio Conti Luigi Laura Michele Primavera Franca Tecchio |
| author_sort | Karolina Armonaite |
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| description | Understanding human cortical neurodynamics is increasingly important, as highlighted by the European Innovation Council, which prioritises tools for measuring and stimulating brain activity. Unravelling how cytoarchitecture, morphology, and connectivity shape neurodynamics is essential for developing technologies that target specific brain regions. Given the dynamic and non-stationary nature of neural interactions, there is an urgent need for non-linear signal analysis methods, in addition to the linear ones, to track local neurodynamics and differentiate cortical parcels. Here, we explore linear and non-linear methods using data from a public stereotactic intracranial EEG (sEEG) dataset, focusing on the superior temporal gyrus (STG), postcentral gyrus (postCG), and precentral gyrus (preCG) in 55 subjects during resting-state wakefulness. For this study, we used a linear Power Spectral Density (PSD) estimate and three non-linear measures: the Higuchi fractal dimension (HFD), a one-dimensional convolutional neural network (1D-CNN), and a one-shot learning model. The PSD was able to distinguish the regions in α, β, and γ frequency bands. The HFD showed a tendency of a higher value in the preCG than in the postCG, and both were higher in the STG. The 1D-CNN showed promise in identifying cortical parcels, with an 85% accuracy for the training set, although performance in the test phase indicates that further refinement is needed to integrate dynamic neural electrical activity patterns into neural networks for suitable classification. |
| format | Article |
| id | doaj-art-dbbb1343ec764e6298bc3318c9cfa761 |
| institution | DOAJ |
| issn | 2504-3110 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Fractal and Fractional |
| spelling | doaj-art-dbbb1343ec764e6298bc3318c9cfa7612025-08-20T03:14:39ZengMDPI AGFractal and Fractional2504-31102025-04-019527810.3390/fractalfract9050278Linear and Non-Linear Methods to Discriminate Cortical Parcels Based on Neurodynamics: Insights from sEEG RecordingsKarolina Armonaite0Livio Conti1Luigi Laura2Michele Primavera3Franca Tecchio4Faculty of Engineering, Uninettuno University, 00186 Rome, ItalyFaculty of Engineering, Uninettuno University, 00186 Rome, ItalyFaculty of Engineering, Uninettuno University, 00186 Rome, ItalyFaculty of Engineering, Uninettuno University, 00186 Rome, ItalyLaboratory of Electrophysiology for Translational Neuroscience, Institute of Cognitive Sciences and Technologies, Consiglio Nazionale delle Ricerche, 00185 Rome, ItalyUnderstanding human cortical neurodynamics is increasingly important, as highlighted by the European Innovation Council, which prioritises tools for measuring and stimulating brain activity. Unravelling how cytoarchitecture, morphology, and connectivity shape neurodynamics is essential for developing technologies that target specific brain regions. Given the dynamic and non-stationary nature of neural interactions, there is an urgent need for non-linear signal analysis methods, in addition to the linear ones, to track local neurodynamics and differentiate cortical parcels. Here, we explore linear and non-linear methods using data from a public stereotactic intracranial EEG (sEEG) dataset, focusing on the superior temporal gyrus (STG), postcentral gyrus (postCG), and precentral gyrus (preCG) in 55 subjects during resting-state wakefulness. For this study, we used a linear Power Spectral Density (PSD) estimate and three non-linear measures: the Higuchi fractal dimension (HFD), a one-dimensional convolutional neural network (1D-CNN), and a one-shot learning model. The PSD was able to distinguish the regions in α, β, and γ frequency bands. The HFD showed a tendency of a higher value in the preCG than in the postCG, and both were higher in the STG. The 1D-CNN showed promise in identifying cortical parcels, with an 85% accuracy for the training set, although performance in the test phase indicates that further refinement is needed to integrate dynamic neural electrical activity patterns into neural networks for suitable classification.https://www.mdpi.com/2504-3110/9/5/278neurodynamicscortical parcellingsEEGdeep learning |
| spellingShingle | Karolina Armonaite Livio Conti Luigi Laura Michele Primavera Franca Tecchio Linear and Non-Linear Methods to Discriminate Cortical Parcels Based on Neurodynamics: Insights from sEEG Recordings Fractal and Fractional neurodynamics cortical parcelling sEEG deep learning |
| title | Linear and Non-Linear Methods to Discriminate Cortical Parcels Based on Neurodynamics: Insights from sEEG Recordings |
| title_full | Linear and Non-Linear Methods to Discriminate Cortical Parcels Based on Neurodynamics: Insights from sEEG Recordings |
| title_fullStr | Linear and Non-Linear Methods to Discriminate Cortical Parcels Based on Neurodynamics: Insights from sEEG Recordings |
| title_full_unstemmed | Linear and Non-Linear Methods to Discriminate Cortical Parcels Based on Neurodynamics: Insights from sEEG Recordings |
| title_short | Linear and Non-Linear Methods to Discriminate Cortical Parcels Based on Neurodynamics: Insights from sEEG Recordings |
| title_sort | linear and non linear methods to discriminate cortical parcels based on neurodynamics insights from seeg recordings |
| topic | neurodynamics cortical parcelling sEEG deep learning |
| url | https://www.mdpi.com/2504-3110/9/5/278 |
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