Reconstructing the Hemodynamic Response Function via a Bimodal Transformer

Yoni Choukroun; Lior Golgher; Pablo Blinder; Lior Wolf

doi:https://doi.org/10.1007/978-3-031-43895-0_35

Reconstructing the Hemodynamic Response Function via a Bimodal Transformer

Yoni Choukroun, Lior Golgher, Pablo Blinder, Lior Wolf

Tel Aviv University, The Hebrew University of Jerusalem

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The relationship between blood flow and neuronal activity is widely recognized, with blood flow frequently serving as a surrogate for neuronal activity in fMRI studies. At the microscopic level, neuronal activity has been shown to influence blood flow in nearby blood vessels. This study introduces the first predictive model that addresses this issue directly at the explicit neuronal population level. Using in vivo recordings in awake mice, we employ a novel spatiotemporal bimodal transformer architecture to infer current blood flow based on both historical blood flow and ongoing spontaneous neuronal activity. Our findings indicate that incorporating neuronal activity significantly enhances the model’s ability to predict blood flow values. Through analysis of the model’s behavior, we propose hypotheses regarding the largely unexplored nature of the hemodynamic response to neuronal activity.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings
Editors	Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	371-381
Number of pages	11
ISBN (Print)	9783031438943
DOIs	https://doi.org/10.1007/978-3-031-43895-0_35
State	Published - 2023
Event	26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023 - Vancouver, Canada Duration: 8 Oct 2023 → 12 Oct 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14221 LNCS

Conference

Conference	26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023
Country/Territory	Canada
City	Vancouver
Period	8/10/23 → 12/10/23

Keywords

Bimodal transformers
Hemodynamic Response Function

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

https://doi.org/10.1007/978-3-031-43895-0_35

Cite this

Choukroun, Y., Golgher, L., Blinder, P., & Wolf, L. (2023). Reconstructing the Hemodynamic Response Function via a Bimodal Transformer. In H. Greenspan, A. Madabhushi, P. Mousavi, S. Salcudean, J. Duncan, T. Syeda-Mahmood, & R. Taylor (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings (pp. 371-381). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14221 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-43895-0_35

Choukroun, Yoni ; Golgher, Lior ; Blinder, Pablo et al. / Reconstructing the Hemodynamic Response Function via a Bimodal Transformer. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings. editor / Hayit Greenspan ; Anant Madabhushi ; Parvin Mousavi ; Septimiu Salcudean ; James Duncan ; Tanveer Syeda-Mahmood ; Russell Taylor. Springer Science and Business Media Deutschland GmbH, 2023. pp. 371-381 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The relationship between blood flow and neuronal activity is widely recognized, with blood flow frequently serving as a surrogate for neuronal activity in fMRI studies. At the microscopic level, neuronal activity has been shown to influence blood flow in nearby blood vessels. This study introduces the first predictive model that addresses this issue directly at the explicit neuronal population level. Using in vivo recordings in awake mice, we employ a novel spatiotemporal bimodal transformer architecture to infer current blood flow based on both historical blood flow and ongoing spontaneous neuronal activity. Our findings indicate that incorporating neuronal activity significantly enhances the model{\textquoteright}s ability to predict blood flow values. Through analysis of the model{\textquoteright}s behavior, we propose hypotheses regarding the largely unexplored nature of the hemodynamic response to neuronal activity.",

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Choukroun, Y, Golgher, L , Blinder, P & Wolf, L 2023, Reconstructing the Hemodynamic Response Function via a Bimodal Transformer. in H Greenspan, A Madabhushi, P Mousavi, S Salcudean, J Duncan, T Syeda-Mahmood & R Taylor (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14221 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 371-381, 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, Vancouver, Canada, 8/10/23. https://doi.org/10.1007/978-3-031-43895-0_35

Reconstructing the Hemodynamic Response Function via a Bimodal Transformer. / Choukroun, Yoni; Golgher, Lior ; Blinder, Pablo et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings. ed. / Hayit Greenspan; Anant Madabhushi; Parvin Mousavi; Septimiu Salcudean; James Duncan; Tanveer Syeda-Mahmood; Russell Taylor. Springer Science and Business Media Deutschland GmbH, 2023. p. 371-381 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14221 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Reconstructing the Hemodynamic Response Function via a Bimodal Transformer

AU - Choukroun, Yoni

AU - Golgher, Lior

AU - Blinder, Pablo

AU - Wolf, Lior

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

PY - 2023

Y1 - 2023

N2 - The relationship between blood flow and neuronal activity is widely recognized, with blood flow frequently serving as a surrogate for neuronal activity in fMRI studies. At the microscopic level, neuronal activity has been shown to influence blood flow in nearby blood vessels. This study introduces the first predictive model that addresses this issue directly at the explicit neuronal population level. Using in vivo recordings in awake mice, we employ a novel spatiotemporal bimodal transformer architecture to infer current blood flow based on both historical blood flow and ongoing spontaneous neuronal activity. Our findings indicate that incorporating neuronal activity significantly enhances the model’s ability to predict blood flow values. Through analysis of the model’s behavior, we propose hypotheses regarding the largely unexplored nature of the hemodynamic response to neuronal activity.

AB - The relationship between blood flow and neuronal activity is widely recognized, with blood flow frequently serving as a surrogate for neuronal activity in fMRI studies. At the microscopic level, neuronal activity has been shown to influence blood flow in nearby blood vessels. This study introduces the first predictive model that addresses this issue directly at the explicit neuronal population level. Using in vivo recordings in awake mice, we employ a novel spatiotemporal bimodal transformer architecture to infer current blood flow based on both historical blood flow and ongoing spontaneous neuronal activity. Our findings indicate that incorporating neuronal activity significantly enhances the model’s ability to predict blood flow values. Through analysis of the model’s behavior, we propose hypotheses regarding the largely unexplored nature of the hemodynamic response to neuronal activity.

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SN - 9783031438943

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings

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A2 - Salcudean, Septimiu

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A2 - Syeda-Mahmood, Tanveer

A2 - Taylor, Russell

PB - Springer Science and Business Media Deutschland GmbH

T2 - 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023

Y2 - 8 October 2023 through 12 October 2023

ER -

Choukroun Y, Golgher L , Blinder P , Wolf L. Reconstructing the Hemodynamic Response Function via a Bimodal Transformer. In Greenspan H, Madabhushi A, Mousavi P, Salcudean S, Duncan J, Syeda-Mahmood T, Taylor R, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 371-381. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: https://doi.org/10.1007/978-3-031-43895-0_35

Reconstructing the Hemodynamic Response Function via a Bimodal Transformer

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Keywords

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