Waveform-based classification of dentate spikes
Abstract Synchronous excitatory discharges from the entorhinal cortex (EC) to the dentate gyrus (DG) generate fast and prominent patterns in the hilar local field potential (LFP), called dentate spikes (DSs). As sharp-wave ripples in CA1, DSs are more likely to occur in quiet behavioral states, when...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-53075-3 |
| _version_ | 1797274676550959104 |
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| author | Rodrigo M. M. Santiago Vítor Lopes-dos-Santos Emily A. Aery Jones Yadong Huang David Dupret Adriano B. L. Tort |
| author_facet | Rodrigo M. M. Santiago Vítor Lopes-dos-Santos Emily A. Aery Jones Yadong Huang David Dupret Adriano B. L. Tort |
| author_sort | Rodrigo M. M. Santiago |
| collection | DOAJ |
| description | Abstract Synchronous excitatory discharges from the entorhinal cortex (EC) to the dentate gyrus (DG) generate fast and prominent patterns in the hilar local field potential (LFP), called dentate spikes (DSs). As sharp-wave ripples in CA1, DSs are more likely to occur in quiet behavioral states, when memory consolidation is thought to take place. However, their functions in mnemonic processes are yet to be elucidated. The classification of DSs into types 1 or 2 is determined by their origin in the lateral or medial EC, as revealed by current source density (CSD) analysis, which requires recordings from linear probes with multiple electrodes spanning the DG layers. To allow the investigation of the functional role of each DS type in recordings obtained from single electrodes and tetrodes, which are abundant in the field, we developed an unsupervised method using Gaussian mixture models to classify such events based on their waveforms. Our classification approach achieved high accuracies (> 80%) when validated in 8 mice with DG laminar profiles. The average CSDs, waveforms, rates, and widths of the DS types obtained through our method closely resembled those derived from the CSD-based classification. As an example of application, we used the technique to analyze single-electrode LFPs from apolipoprotein (apo) E3 and apoE4 knock-in mice. We observed that the latter group, which is a model for Alzheimer’s disease, exhibited wider DSs of both types from a young age, with a larger effect size for DS type 2, likely reflecting early pathophysiological alterations in the EC-DG network, such as hyperactivity. In addition to the applicability of the method in expanding the study of DS types, our results show that their waveforms carry information about their origins, suggesting different underlying network dynamics and roles in memory processing. |
| first_indexed | 2024-03-07T15:01:41Z |
| format | Article |
| id | doaj.art-b7a25586916b42b1b5272dde42e584f3 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:01:41Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-b7a25586916b42b1b5272dde42e584f32024-03-05T19:07:47ZengNature PortfolioScientific Reports2045-23222024-02-0114112110.1038/s41598-024-53075-3Waveform-based classification of dentate spikesRodrigo M. M. Santiago0Vítor Lopes-dos-Santos1Emily A. Aery Jones2Yadong Huang3David Dupret4Adriano B. L. Tort5Computational Neurophysiology Lab, Brain Institute, Federal University of Rio Grande do NorteMedical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of OxfordDepartment of Neurobiology, Stanford University School of MedicineGladstone Institute of Neurological Disease, Gladstone InstitutesMedical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of OxfordComputational Neurophysiology Lab, Brain Institute, Federal University of Rio Grande do NorteAbstract Synchronous excitatory discharges from the entorhinal cortex (EC) to the dentate gyrus (DG) generate fast and prominent patterns in the hilar local field potential (LFP), called dentate spikes (DSs). As sharp-wave ripples in CA1, DSs are more likely to occur in quiet behavioral states, when memory consolidation is thought to take place. However, their functions in mnemonic processes are yet to be elucidated. The classification of DSs into types 1 or 2 is determined by their origin in the lateral or medial EC, as revealed by current source density (CSD) analysis, which requires recordings from linear probes with multiple electrodes spanning the DG layers. To allow the investigation of the functional role of each DS type in recordings obtained from single electrodes and tetrodes, which are abundant in the field, we developed an unsupervised method using Gaussian mixture models to classify such events based on their waveforms. Our classification approach achieved high accuracies (> 80%) when validated in 8 mice with DG laminar profiles. The average CSDs, waveforms, rates, and widths of the DS types obtained through our method closely resembled those derived from the CSD-based classification. As an example of application, we used the technique to analyze single-electrode LFPs from apolipoprotein (apo) E3 and apoE4 knock-in mice. We observed that the latter group, which is a model for Alzheimer’s disease, exhibited wider DSs of both types from a young age, with a larger effect size for DS type 2, likely reflecting early pathophysiological alterations in the EC-DG network, such as hyperactivity. In addition to the applicability of the method in expanding the study of DS types, our results show that their waveforms carry information about their origins, suggesting different underlying network dynamics and roles in memory processing.https://doi.org/10.1038/s41598-024-53075-3 |
| spellingShingle | Rodrigo M. M. Santiago Vítor Lopes-dos-Santos Emily A. Aery Jones Yadong Huang David Dupret Adriano B. L. Tort Waveform-based classification of dentate spikes Scientific Reports |
| title | Waveform-based classification of dentate spikes |
| title_full | Waveform-based classification of dentate spikes |
| title_fullStr | Waveform-based classification of dentate spikes |
| title_full_unstemmed | Waveform-based classification of dentate spikes |
| title_short | Waveform-based classification of dentate spikes |
| title_sort | waveform based classification of dentate spikes |
| url | https://doi.org/10.1038/s41598-024-53075-3 |
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