Reconstructing the Hemodynamic Response Function via a Bimodal Transformer

Yoni Choukroun; Lior Golgher; Pablo Blinder; Lior Wolf

doi: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

^*Corresponding author for this work

Edmond & Lily Safra Center for Brain Sciences

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, 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
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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

Bibliographical note

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

Keywords

Bimodal transformers
Hemodynamic Response Function

Access to Document

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, 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 ; 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, 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; 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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Choukroun Y, Golgher L, Blinder P, Wolf L. Reconstructing the Hemodynamic Response Function via a Bimodal Transformer. In Greenspan H, 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: 10.1007/978-3-031-43895-0_35

Reconstructing the Hemodynamic Response Function via a Bimodal Transformer

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