Time-Resolved EEG Decoding of Semantic Processing Reveals Altered Neural Dynamics in Depression and Suicidality
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| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917044253884416 |
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| author | Jeong, Woojae Kommineni, Aditya Avramidis, Kleanthis McDaniel, Colin Berry, Donald Hughes, Myzelle McGee, Thomas Kaiser, Elsi Byrd, Dani Habibi, Assal Cahn, B. Rael Blank, Idan A. Lerman, Kristina Pantazis, Dimitrios Kadiri, Sudarsana R. Medani, Takfarinas Narayanan, Shrikanth Leahy, Richard M. |
| author_facet | Jeong, Woojae Kommineni, Aditya Avramidis, Kleanthis McDaniel, Colin Berry, Donald Hughes, Myzelle McGee, Thomas Kaiser, Elsi Byrd, Dani Habibi, Assal Cahn, B. Rael Blank, Idan A. Lerman, Kristina Pantazis, Dimitrios Kadiri, Sudarsana R. Medani, Takfarinas Narayanan, Shrikanth Leahy, Richard M. |
| contents | Depression and suicidality affect cognitive and emotional processes, yet objective, task-evoked neural readouts of mental health remain limited. We investigated the spatiotemporal dynamics of affective semantic processing using multivariate decoding of time-resolved, 64-channel electroencephalography (EEG). Participants (N=137) performed a sentence-evaluation task with emotionally salient, self-referential statements. We identified robust neural signatures of semantic processing, with peak decoding accuracy between 300-600 ms -- a window associated with rapid, stimulus-driven semantic evaluation and conflict monitoring. Relative to healthy controls, individuals with depression and suicidal ideation showed earlier onset, longer duration, and greater amplitude decoding responses, along with broader cross-temporal generalization and enhanced contributions from frontocentral and parietotemporal components. These findings suggest altered sensitivity and impaired disengagement from emotionally salient content in the clinical groups, advancing our understanding of the neurocognitive basis of mental health and establishing a compact and interpretable EEG-based index of semantic-evaluation dynamics with potential diagnostic relevance. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_22313 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Time-Resolved EEG Decoding of Semantic Processing Reveals Altered Neural Dynamics in Depression and Suicidality Jeong, Woojae Kommineni, Aditya Avramidis, Kleanthis McDaniel, Colin Berry, Donald Hughes, Myzelle McGee, Thomas Kaiser, Elsi Byrd, Dani Habibi, Assal Cahn, B. Rael Blank, Idan A. Lerman, Kristina Pantazis, Dimitrios Kadiri, Sudarsana R. Medani, Takfarinas Narayanan, Shrikanth Leahy, Richard M. Neurons and Cognition Machine Learning Signal Processing Depression and suicidality affect cognitive and emotional processes, yet objective, task-evoked neural readouts of mental health remain limited. We investigated the spatiotemporal dynamics of affective semantic processing using multivariate decoding of time-resolved, 64-channel electroencephalography (EEG). Participants (N=137) performed a sentence-evaluation task with emotionally salient, self-referential statements. We identified robust neural signatures of semantic processing, with peak decoding accuracy between 300-600 ms -- a window associated with rapid, stimulus-driven semantic evaluation and conflict monitoring. Relative to healthy controls, individuals with depression and suicidal ideation showed earlier onset, longer duration, and greater amplitude decoding responses, along with broader cross-temporal generalization and enhanced contributions from frontocentral and parietotemporal components. These findings suggest altered sensitivity and impaired disengagement from emotionally salient content in the clinical groups, advancing our understanding of the neurocognitive basis of mental health and establishing a compact and interpretable EEG-based index of semantic-evaluation dynamics with potential diagnostic relevance. |
| title | Time-Resolved EEG Decoding of Semantic Processing Reveals Altered Neural Dynamics in Depression and Suicidality |
| topic | Neurons and Cognition Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2507.22313 |