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||American 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|
|Number of pages||11|
|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
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023|
|Period||8/10/23 → 12/10/23|
Bibliographical notePublisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
- Bimodal transformers
- Hemodynamic Response Function