High-gamma and beta bursts in the left supramarginal gyrus can differentiate verbal memory states and performance
- Autores
- Weiss, Shennan Aibel; Sawczuk, Nicolás; Rubinstein, Daniel Y.; Sperling, Michael R.; Wendel Mitoraj, Katrina; Österman, Päivi; Dumay Roscher, René; Mikell, Charles B.; Mofakham, Sima; Coulehan, Kelly; Djuric, Petar M.; Fernandez Slezak, Diego; Kamienkowski, Juan Esteban
- Año de publicación
- 2025
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Introduction: The left supramarginal gyrus (LSMG) contributes to attentional allocation for memory encoding and may also reflect memory state and performance. Given the roles of high-gamma and beta bursts in cognition and memory, this proof-of-concept study investigated whether these signals within the LSMG could classify memory state and performance.Methods: Using secondary data from 103 epilepsy patients undergoing presurgical iEEG evaluation, we analyzed 141 delayed verbal free recall experiments. Intracranial EEG (iEEG) data, recorded solely from LSMG electrode contacts, were processed to create two-dimensional (2D) tensors of convolved high-gamma (HG), and beta (15–40 Hz) burst activity. Convolutional neural networks (CNNs) were trained and cross-validated on these 2D tensors to classify memory state (encoding versus recall) and performance (remembered versus forgotten items) within subjects.Results: The latter CNN, used to label subsequently recalled words based on iEEG recorded during the encoding epoch, performed at or below chance in 79 of the 141 experiments. In all but 3 of these 79 experiments, the iEEG was contaminated or low amplitude. In the other 62 experiments this CNN labeled recalled words with an area under the receiver operating curve (AUROC) score of greater than 0.52. A generalized linear model explained the variance of the AUROC score for labelling recalled words correctly in these 62 experiments (n = 62, d.f. = 20, F = 1.7, p = 1 × 10−4). The most significant term in the model was a positive interaction between (1) mean HG burst signal to noise ratio; (2) mean beta burst signal to noise ratio; (3) the number of electrode contacts in the LSMG; and (4) recall probability (t = 3.04, p = 0.006). We identified 14 experiments that labeled subsequently recalled words during encoding with an AUROC score greater than 0.6. To address over-training, we also trained and then tested the CNN on distinct datasets in four subjects. In most of these experiments CNN performed better than chance. We also found that a CNN utilizing 2D tensors of HG and beta bursts could distinguish encoding from scrambled recall epochs.Discussion: This work indicates LSMG is a memory hotspot and that HG and beta bursts may serve as temporal memory information packets or signify attention related to memory.
Fil: Weiss, Shennan Aibel. Stony Brook University ; State University Of New York;
Fil: Sawczuk, Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina
Fil: Rubinstein, Daniel Y.. Thomas Jefferson University; Estados Unidos
Fil: Sperling, Michael R.. Thomas Jefferson University; Estados Unidos
Fil: Wendel Mitoraj, Katrina. No especifíca;
Fil: Österman, Päivi. No especifíca;
Fil: Dumay Roscher, René. No especifíca;
Fil: Mikell, Charles B.. Stony Brook University ; State University Of New York;
Fil: Mofakham, Sima. Stony Brook University ; State University Of New York;
Fil: Coulehan, Kelly. Stony Brook University ; State University Of New York;
Fil: Djuric, Petar M.. Stony Brook University ; State University Of New York;
Fil: Fernandez Slezak, Diego. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina
Fil: Kamienkowski, Juan Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina - Materia
-
verbal memory
left supramaginal gyrus
posterior parietal cortex
encoding - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
.jpg)
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/290938
Ver los metadatos del registro completo
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High-gamma and beta bursts in the left supramarginal gyrus can differentiate verbal memory states and performanceWeiss, Shennan AibelSawczuk, NicolásRubinstein, Daniel Y.Sperling, Michael R.Wendel Mitoraj, KatrinaÖsterman, PäiviDumay Roscher, RenéMikell, Charles B.Mofakham, SimaCoulehan, KellyDjuric, Petar M.Fernandez Slezak, DiegoKamienkowski, Juan Estebanverbal memoryleft supramaginal gyrusposterior parietal cortexencodinghttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Introduction: The left supramarginal gyrus (LSMG) contributes to attentional allocation for memory encoding and may also reflect memory state and performance. Given the roles of high-gamma and beta bursts in cognition and memory, this proof-of-concept study investigated whether these signals within the LSMG could classify memory state and performance.Methods: Using secondary data from 103 epilepsy patients undergoing presurgical iEEG evaluation, we analyzed 141 delayed verbal free recall experiments. Intracranial EEG (iEEG) data, recorded solely from LSMG electrode contacts, were processed to create two-dimensional (2D) tensors of convolved high-gamma (HG), and beta (15–40 Hz) burst activity. Convolutional neural networks (CNNs) were trained and cross-validated on these 2D tensors to classify memory state (encoding versus recall) and performance (remembered versus forgotten items) within subjects.Results: The latter CNN, used to label subsequently recalled words based on iEEG recorded during the encoding epoch, performed at or below chance in 79 of the 141 experiments. In all but 3 of these 79 experiments, the iEEG was contaminated or low amplitude. In the other 62 experiments this CNN labeled recalled words with an area under the receiver operating curve (AUROC) score of greater than 0.52. A generalized linear model explained the variance of the AUROC score for labelling recalled words correctly in these 62 experiments (n = 62, d.f. = 20, F = 1.7, p = 1 × 10−4). The most significant term in the model was a positive interaction between (1) mean HG burst signal to noise ratio; (2) mean beta burst signal to noise ratio; (3) the number of electrode contacts in the LSMG; and (4) recall probability (t = 3.04, p = 0.006). We identified 14 experiments that labeled subsequently recalled words during encoding with an AUROC score greater than 0.6. To address over-training, we also trained and then tested the CNN on distinct datasets in four subjects. In most of these experiments CNN performed better than chance. We also found that a CNN utilizing 2D tensors of HG and beta bursts could distinguish encoding from scrambled recall epochs.Discussion: This work indicates LSMG is a memory hotspot and that HG and beta bursts may serve as temporal memory information packets or signify attention related to memory.Fil: Weiss, Shennan Aibel. Stony Brook University ; State University Of New York;Fil: Sawczuk, Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Rubinstein, Daniel Y.. Thomas Jefferson University; Estados UnidosFil: Sperling, Michael R.. Thomas Jefferson University; Estados UnidosFil: Wendel Mitoraj, Katrina. No especifíca;Fil: Österman, Päivi. No especifíca;Fil: Dumay Roscher, René. No especifíca;Fil: Mikell, Charles B.. Stony Brook University ; State University Of New York;Fil: Mofakham, Sima. Stony Brook University ; State University Of New York;Fil: Coulehan, Kelly. Stony Brook University ; State University Of New York;Fil: Djuric, Petar M.. Stony Brook University ; State University Of New York;Fil: Fernandez Slezak, Diego. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Kamienkowski, Juan Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFrontiers Media2025-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/290938Weiss, Shennan Aibel; Sawczuk, Nicolás; Rubinstein, Daniel Y.; Sperling, Michael R.; Wendel Mitoraj, Katrina; et al.; High-gamma and beta bursts in the left supramarginal gyrus can differentiate verbal memory states and performance; Frontiers Media; Frontiers in Neurology; 16; 7-2025; 1-181664-2295CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/articles/10.3389/fneur.2025.1627528/fullinfo:eu-repo/semantics/altIdentifier/doi/10.3389/fneur.2025.1627528info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-08-25T15:50:41Zoai:ri.conicet.gov.ar:11336/290938instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982026-08-25 15:50:42.113CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
| dc.title.none.fl_str_mv |
High-gamma and beta bursts in the left supramarginal gyrus can differentiate verbal memory states and performance |
| title |
High-gamma and beta bursts in the left supramarginal gyrus can differentiate verbal memory states and performance |
| spellingShingle |
High-gamma and beta bursts in the left supramarginal gyrus can differentiate verbal memory states and performance Weiss, Shennan Aibel verbal memory left supramaginal gyrus posterior parietal cortex encoding |
| title_short |
High-gamma and beta bursts in the left supramarginal gyrus can differentiate verbal memory states and performance |
| title_full |
High-gamma and beta bursts in the left supramarginal gyrus can differentiate verbal memory states and performance |
| title_fullStr |
High-gamma and beta bursts in the left supramarginal gyrus can differentiate verbal memory states and performance |
| title_full_unstemmed |
High-gamma and beta bursts in the left supramarginal gyrus can differentiate verbal memory states and performance |
| title_sort |
High-gamma and beta bursts in the left supramarginal gyrus can differentiate verbal memory states and performance |
| dc.creator.none.fl_str_mv |
Weiss, Shennan Aibel Sawczuk, Nicolás Rubinstein, Daniel Y. Sperling, Michael R. Wendel Mitoraj, Katrina Österman, Päivi Dumay Roscher, René Mikell, Charles B. Mofakham, Sima Coulehan, Kelly Djuric, Petar M. Fernandez Slezak, Diego Kamienkowski, Juan Esteban |
| author |
Weiss, Shennan Aibel |
| author_facet |
Weiss, Shennan Aibel Sawczuk, Nicolás Rubinstein, Daniel Y. Sperling, Michael R. Wendel Mitoraj, Katrina Österman, Päivi Dumay Roscher, René Mikell, Charles B. Mofakham, Sima Coulehan, Kelly Djuric, Petar M. Fernandez Slezak, Diego Kamienkowski, Juan Esteban |
| author_role |
author |
| author2 |
Sawczuk, Nicolás Rubinstein, Daniel Y. Sperling, Michael R. Wendel Mitoraj, Katrina Österman, Päivi Dumay Roscher, René Mikell, Charles B. Mofakham, Sima Coulehan, Kelly Djuric, Petar M. Fernandez Slezak, Diego Kamienkowski, Juan Esteban |
| author2_role |
author author author author author author author author author author author author |
| dc.subject.none.fl_str_mv |
verbal memory left supramaginal gyrus posterior parietal cortex encoding |
| topic |
verbal memory left supramaginal gyrus posterior parietal cortex encoding |
| purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 |
| dc.description.none.fl_txt_mv |
Introduction: The left supramarginal gyrus (LSMG) contributes to attentional allocation for memory encoding and may also reflect memory state and performance. Given the roles of high-gamma and beta bursts in cognition and memory, this proof-of-concept study investigated whether these signals within the LSMG could classify memory state and performance.Methods: Using secondary data from 103 epilepsy patients undergoing presurgical iEEG evaluation, we analyzed 141 delayed verbal free recall experiments. Intracranial EEG (iEEG) data, recorded solely from LSMG electrode contacts, were processed to create two-dimensional (2D) tensors of convolved high-gamma (HG), and beta (15–40 Hz) burst activity. Convolutional neural networks (CNNs) were trained and cross-validated on these 2D tensors to classify memory state (encoding versus recall) and performance (remembered versus forgotten items) within subjects.Results: The latter CNN, used to label subsequently recalled words based on iEEG recorded during the encoding epoch, performed at or below chance in 79 of the 141 experiments. In all but 3 of these 79 experiments, the iEEG was contaminated or low amplitude. In the other 62 experiments this CNN labeled recalled words with an area under the receiver operating curve (AUROC) score of greater than 0.52. A generalized linear model explained the variance of the AUROC score for labelling recalled words correctly in these 62 experiments (n = 62, d.f. = 20, F = 1.7, p = 1 × 10−4). The most significant term in the model was a positive interaction between (1) mean HG burst signal to noise ratio; (2) mean beta burst signal to noise ratio; (3) the number of electrode contacts in the LSMG; and (4) recall probability (t = 3.04, p = 0.006). We identified 14 experiments that labeled subsequently recalled words during encoding with an AUROC score greater than 0.6. To address over-training, we also trained and then tested the CNN on distinct datasets in four subjects. In most of these experiments CNN performed better than chance. We also found that a CNN utilizing 2D tensors of HG and beta bursts could distinguish encoding from scrambled recall epochs.Discussion: This work indicates LSMG is a memory hotspot and that HG and beta bursts may serve as temporal memory information packets or signify attention related to memory. Fil: Weiss, Shennan Aibel. Stony Brook University ; State University Of New York; Fil: Sawczuk, Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina Fil: Rubinstein, Daniel Y.. Thomas Jefferson University; Estados Unidos Fil: Sperling, Michael R.. Thomas Jefferson University; Estados Unidos Fil: Wendel Mitoraj, Katrina. No especifíca; Fil: Österman, Päivi. No especifíca; Fil: Dumay Roscher, René. No especifíca; Fil: Mikell, Charles B.. Stony Brook University ; State University Of New York; Fil: Mofakham, Sima. Stony Brook University ; State University Of New York; Fil: Coulehan, Kelly. Stony Brook University ; State University Of New York; Fil: Djuric, Petar M.. Stony Brook University ; State University Of New York; Fil: Fernandez Slezak, Diego. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina Fil: Kamienkowski, Juan Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina |
| description |
Introduction: The left supramarginal gyrus (LSMG) contributes to attentional allocation for memory encoding and may also reflect memory state and performance. Given the roles of high-gamma and beta bursts in cognition and memory, this proof-of-concept study investigated whether these signals within the LSMG could classify memory state and performance.Methods: Using secondary data from 103 epilepsy patients undergoing presurgical iEEG evaluation, we analyzed 141 delayed verbal free recall experiments. Intracranial EEG (iEEG) data, recorded solely from LSMG electrode contacts, were processed to create two-dimensional (2D) tensors of convolved high-gamma (HG), and beta (15–40 Hz) burst activity. Convolutional neural networks (CNNs) were trained and cross-validated on these 2D tensors to classify memory state (encoding versus recall) and performance (remembered versus forgotten items) within subjects.Results: The latter CNN, used to label subsequently recalled words based on iEEG recorded during the encoding epoch, performed at or below chance in 79 of the 141 experiments. In all but 3 of these 79 experiments, the iEEG was contaminated or low amplitude. In the other 62 experiments this CNN labeled recalled words with an area under the receiver operating curve (AUROC) score of greater than 0.52. A generalized linear model explained the variance of the AUROC score for labelling recalled words correctly in these 62 experiments (n = 62, d.f. = 20, F = 1.7, p = 1 × 10−4). The most significant term in the model was a positive interaction between (1) mean HG burst signal to noise ratio; (2) mean beta burst signal to noise ratio; (3) the number of electrode contacts in the LSMG; and (4) recall probability (t = 3.04, p = 0.006). We identified 14 experiments that labeled subsequently recalled words during encoding with an AUROC score greater than 0.6. To address over-training, we also trained and then tested the CNN on distinct datasets in four subjects. In most of these experiments CNN performed better than chance. We also found that a CNN utilizing 2D tensors of HG and beta bursts could distinguish encoding from scrambled recall epochs.Discussion: This work indicates LSMG is a memory hotspot and that HG and beta bursts may serve as temporal memory information packets or signify attention related to memory. |
| publishDate |
2025 |
| dc.date.none.fl_str_mv |
2025-07 |
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info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
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article |
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publishedVersion |
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http://hdl.handle.net/11336/290938 Weiss, Shennan Aibel; Sawczuk, Nicolás; Rubinstein, Daniel Y.; Sperling, Michael R.; Wendel Mitoraj, Katrina; et al.; High-gamma and beta bursts in the left supramarginal gyrus can differentiate verbal memory states and performance; Frontiers Media; Frontiers in Neurology; 16; 7-2025; 1-18 1664-2295 CONICET Digital CONICET |
| url |
http://hdl.handle.net/11336/290938 |
| identifier_str_mv |
Weiss, Shennan Aibel; Sawczuk, Nicolás; Rubinstein, Daniel Y.; Sperling, Michael R.; Wendel Mitoraj, Katrina; et al.; High-gamma and beta bursts in the left supramarginal gyrus can differentiate verbal memory states and performance; Frontiers Media; Frontiers in Neurology; 16; 7-2025; 1-18 1664-2295 CONICET Digital CONICET |
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eng |
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eng |
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Frontiers Media |
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Frontiers Media |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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