A comparison of uni- and multi-variate methods for identifying brain networks activated by cognitive tasks using intracranial EEG
Cognitive tasks are commonly used to identify brain networks involved in the underlying cognitive process. However, inferring the brain networks from intracranial EEG data presents several challenges related to the sparse spatial sampling of the brain and the high variability of the EEG trace due to...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.946240/full |
| _version_ | 1798001256405401600 |
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| author | Cristian Donos Bogdan Blidarescu Constantin Pistol Irina Oane Irina Oane Ioana Mindruta Andrei Barborica |
| author_facet | Cristian Donos Bogdan Blidarescu Constantin Pistol Irina Oane Irina Oane Ioana Mindruta Andrei Barborica |
| author_sort | Cristian Donos |
| collection | DOAJ |
| description | Cognitive tasks are commonly used to identify brain networks involved in the underlying cognitive process. However, inferring the brain networks from intracranial EEG data presents several challenges related to the sparse spatial sampling of the brain and the high variability of the EEG trace due to concurrent brain processes. In this manuscript, we use a well-known facial emotion recognition task to compare three different ways of analyzing the contrasts between task conditions: permutation cluster tests, machine learning (ML) classifiers, and a searchlight implementation of multivariate pattern analysis (MVPA) for intracranial sparse data recorded from 13 patients undergoing presurgical evaluation for drug-resistant epilepsy. Using all three methods, we aim at highlighting the brain structures with significant contrast between conditions. In the absence of ground truth, we use the scientific literature to validate our results. The comparison of the three methods’ results shows moderate agreement, measured by the Jaccard coefficient, between the permutation cluster tests and the machine learning [0.33 and 0.52 for the left (LH) and right (RH) hemispheres], and 0.44 and 0.37 for the LH and RH between the permutation cluster tests and MVPA. The agreement between ML and MVPA is higher: 0.65 for the LH and 0.62 for the RH. To put these results in context, we performed a brief review of the literature and we discuss how each brain structure’s involvement in the facial emotion recognition task. |
| first_indexed | 2024-04-11T11:33:23Z |
| format | Article |
| id | doaj.art-3fc4a8420f834d528785a5a8a575897d |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-11T11:33:23Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-3fc4a8420f834d528785a5a8a575897d2022-12-22T04:26:03ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2022-09-011610.3389/fnins.2022.946240946240A comparison of uni- and multi-variate methods for identifying brain networks activated by cognitive tasks using intracranial EEGCristian Donos0Bogdan Blidarescu1Constantin Pistol2Irina Oane3Irina Oane4Ioana Mindruta5Andrei Barborica6Department of Physics, University of Bucharest, Bucharest, RomaniaDepartment of Physics, University of Bucharest, Bucharest, RomaniaDepartment of Physics, University of Bucharest, Bucharest, RomaniaDepartment of Physics, University of Bucharest, Bucharest, RomaniaEpilepsy Monitoring Unit, Department of Neurology, Emergency University Hospital Bucharest, Bucharest, RomaniaDepartment of Physics, University of Bucharest, Bucharest, RomaniaDepartment of Physics, University of Bucharest, Bucharest, RomaniaCognitive tasks are commonly used to identify brain networks involved in the underlying cognitive process. However, inferring the brain networks from intracranial EEG data presents several challenges related to the sparse spatial sampling of the brain and the high variability of the EEG trace due to concurrent brain processes. In this manuscript, we use a well-known facial emotion recognition task to compare three different ways of analyzing the contrasts between task conditions: permutation cluster tests, machine learning (ML) classifiers, and a searchlight implementation of multivariate pattern analysis (MVPA) for intracranial sparse data recorded from 13 patients undergoing presurgical evaluation for drug-resistant epilepsy. Using all three methods, we aim at highlighting the brain structures with significant contrast between conditions. In the absence of ground truth, we use the scientific literature to validate our results. The comparison of the three methods’ results shows moderate agreement, measured by the Jaccard coefficient, between the permutation cluster tests and the machine learning [0.33 and 0.52 for the left (LH) and right (RH) hemispheres], and 0.44 and 0.37 for the LH and RH between the permutation cluster tests and MVPA. The agreement between ML and MVPA is higher: 0.65 for the LH and 0.62 for the RH. To put these results in context, we performed a brief review of the literature and we discuss how each brain structure’s involvement in the facial emotion recognition task.https://www.frontiersin.org/articles/10.3389/fnins.2022.946240/fullintracranial EEG (iEEG)brain networksearchlight analysismultivariate pattern analysis (MVPA)facial emotion recognition (FER)machine learning |
| spellingShingle | Cristian Donos Bogdan Blidarescu Constantin Pistol Irina Oane Irina Oane Ioana Mindruta Andrei Barborica A comparison of uni- and multi-variate methods for identifying brain networks activated by cognitive tasks using intracranial EEG Frontiers in Neuroscience intracranial EEG (iEEG) brain network searchlight analysis multivariate pattern analysis (MVPA) facial emotion recognition (FER) machine learning |
| title | A comparison of uni- and multi-variate methods for identifying brain networks activated by cognitive tasks using intracranial EEG |
| title_full | A comparison of uni- and multi-variate methods for identifying brain networks activated by cognitive tasks using intracranial EEG |
| title_fullStr | A comparison of uni- and multi-variate methods for identifying brain networks activated by cognitive tasks using intracranial EEG |
| title_full_unstemmed | A comparison of uni- and multi-variate methods for identifying brain networks activated by cognitive tasks using intracranial EEG |
| title_short | A comparison of uni- and multi-variate methods for identifying brain networks activated by cognitive tasks using intracranial EEG |
| title_sort | comparison of uni and multi variate methods for identifying brain networks activated by cognitive tasks using intracranial eeg |
| topic | intracranial EEG (iEEG) brain network searchlight analysis multivariate pattern analysis (MVPA) facial emotion recognition (FER) machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.946240/full |
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