Tackling the challenges of group network inference from intracranial EEG data
IntroductionIntracranial EEG (iEEG) data is a powerful way to map brain function, characterized by high temporal and spatial resolution, allowing the study of interactions among neuronal populations that orchestrate cognitive processing. However, the statistical inference and analysis of brain netwo...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-12-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.1061867/full |
| _version_ | 1828125415010467840 |
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| author | Anna Pidnebesna Anna Pidnebesna Pavel Sanda Adam Kalina Jiri Hammer Petr Marusic Kamil Vlcek Jaroslav Hlinka Jaroslav Hlinka |
| author_facet | Anna Pidnebesna Anna Pidnebesna Pavel Sanda Adam Kalina Jiri Hammer Petr Marusic Kamil Vlcek Jaroslav Hlinka Jaroslav Hlinka |
| author_sort | Anna Pidnebesna |
| collection | DOAJ |
| description | IntroductionIntracranial EEG (iEEG) data is a powerful way to map brain function, characterized by high temporal and spatial resolution, allowing the study of interactions among neuronal populations that orchestrate cognitive processing. However, the statistical inference and analysis of brain networks using iEEG data faces many challenges related to its sparse brain coverage, and its inhomogeneity across patients.MethodsWe review these challenges and develop a methodological pipeline for estimation of network structure not obtainable from any single patient, illustrated on the inference of the interaction among visual streams using a dataset of 27 human iEEG recordings from a visual experiment employing visual scene stimuli. 100 ms sliding window and multiple band-pass filtered signals are used to provide temporal and spectral resolution. For the connectivity analysis we showcase two connectivity measures reflecting different types of interaction between regions of interest (ROI): Phase Locking Value as a symmetric measure of synchrony, and Directed Transfer Function—asymmetric measure describing causal interaction. For each two channels, initial uncorrected significance testing at p < 0.05 for every time-frequency point is carried out by comparison of the data-derived connectivity to a baseline surrogate-based null distribution, providing a binary time-frequency connectivity map. For each ROI pair, a connectivity density map is obtained by averaging across all pairs of channels spanning them, effectively agglomerating data across relevant channels and subjects. Finally, the difference of the mean map value after and before the stimulation is compared to the same statistic in surrogate data to assess link significance.ResultsThe analysis confirmed the function of the parieto-medial temporal pathway, mediating visuospatial information between dorsal and ventral visual streams during visual scene analysis. Moreover, we observed the anterior hippocampal connectivity with more posterior areas in the medial temporal lobe, and found the reciprocal information flow between early processing areas and medial place area.DiscussionTo summarize, we developed an approach for estimating network connectivity, dealing with the challenge of sparse individual coverage of intracranial EEG electrodes. Its application provided new insights into the interaction between the dorsal and ventral visual streams, one of the iconic dualities in human cognition. |
| first_indexed | 2024-04-11T15:20:13Z |
| format | Article |
| id | doaj.art-505c5f03c5534c6fa88d85313370f92d |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-11T15:20:13Z |
| publishDate | 2022-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-505c5f03c5534c6fa88d85313370f92d2022-12-22T04:16:23ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2022-12-011610.3389/fnins.2022.10618671061867Tackling the challenges of group network inference from intracranial EEG dataAnna Pidnebesna0Anna Pidnebesna1Pavel Sanda2Adam Kalina3Jiri Hammer4Petr Marusic5Kamil Vlcek6Jaroslav Hlinka7Jaroslav Hlinka8Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences, Prague, CzechiaNational Institute of Mental Health, Prague, CzechiaDepartment of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences, Prague, CzechiaDepartment of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, CzechiaDepartment of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, CzechiaDepartment of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, CzechiaDepartment of Neurophysiology of Memory, Institute of Physiology of the Czech Academy of Sciences, Prague, CzechiaDepartment of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences, Prague, CzechiaNational Institute of Mental Health, Prague, CzechiaIntroductionIntracranial EEG (iEEG) data is a powerful way to map brain function, characterized by high temporal and spatial resolution, allowing the study of interactions among neuronal populations that orchestrate cognitive processing. However, the statistical inference and analysis of brain networks using iEEG data faces many challenges related to its sparse brain coverage, and its inhomogeneity across patients.MethodsWe review these challenges and develop a methodological pipeline for estimation of network structure not obtainable from any single patient, illustrated on the inference of the interaction among visual streams using a dataset of 27 human iEEG recordings from a visual experiment employing visual scene stimuli. 100 ms sliding window and multiple band-pass filtered signals are used to provide temporal and spectral resolution. For the connectivity analysis we showcase two connectivity measures reflecting different types of interaction between regions of interest (ROI): Phase Locking Value as a symmetric measure of synchrony, and Directed Transfer Function—asymmetric measure describing causal interaction. For each two channels, initial uncorrected significance testing at p < 0.05 for every time-frequency point is carried out by comparison of the data-derived connectivity to a baseline surrogate-based null distribution, providing a binary time-frequency connectivity map. For each ROI pair, a connectivity density map is obtained by averaging across all pairs of channels spanning them, effectively agglomerating data across relevant channels and subjects. Finally, the difference of the mean map value after and before the stimulation is compared to the same statistic in surrogate data to assess link significance.ResultsThe analysis confirmed the function of the parieto-medial temporal pathway, mediating visuospatial information between dorsal and ventral visual streams during visual scene analysis. Moreover, we observed the anterior hippocampal connectivity with more posterior areas in the medial temporal lobe, and found the reciprocal information flow between early processing areas and medial place area.DiscussionTo summarize, we developed an approach for estimating network connectivity, dealing with the challenge of sparse individual coverage of intracranial EEG electrodes. Its application provided new insights into the interaction between the dorsal and ventral visual streams, one of the iconic dualities in human cognition.https://www.frontiersin.org/articles/10.3389/fnins.2022.1061867/fullconnectivity analysisPhase Locking ValueDirected Transfer Functionintracranial EEGinformation flowvisual pathways |
| spellingShingle | Anna Pidnebesna Anna Pidnebesna Pavel Sanda Adam Kalina Jiri Hammer Petr Marusic Kamil Vlcek Jaroslav Hlinka Jaroslav Hlinka Tackling the challenges of group network inference from intracranial EEG data Frontiers in Neuroscience connectivity analysis Phase Locking Value Directed Transfer Function intracranial EEG information flow visual pathways |
| title | Tackling the challenges of group network inference from intracranial EEG data |
| title_full | Tackling the challenges of group network inference from intracranial EEG data |
| title_fullStr | Tackling the challenges of group network inference from intracranial EEG data |
| title_full_unstemmed | Tackling the challenges of group network inference from intracranial EEG data |
| title_short | Tackling the challenges of group network inference from intracranial EEG data |
| title_sort | tackling the challenges of group network inference from intracranial eeg data |
| topic | connectivity analysis Phase Locking Value Directed Transfer Function intracranial EEG information flow visual pathways |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.1061867/full |
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