利用双峰变压器重构血流动力学响应函数

Yoni Choukroun, Lior Golgher, P. Blinder, L. Wolf
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引用次数: 0

摘要

血流与神经元活动之间的关系已得到广泛认可,在功能磁共振成像研究中,血流经常被用作神经元活动的替代指标。在显微水平上,神经元活动已被证明影响附近血管的血流。本研究引入了第一个直接在显式神经元种群水平上解决这一问题的预测模型。利用清醒小鼠的体内记录,我们采用了一种新的时空双峰变压器结构,根据历史血流量和正在进行的自发神经元活动来推断当前的血流量。我们的研究结果表明,结合神经元活动显著提高了模型预测血流量值的能力。通过对模型行为的分析,我们提出了关于神经元活动的血流动力学反应在很大程度上未被探索的性质的假设。
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Reconstructing the Hemodynamic Response Function via a Bimodal Transformer
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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