The Individualized Neural Tuning Model: Precise and generalizable cartography of functional architecture in individual brains
The Individualized Neural Tuning Model: Precise and generalizable cartography of functional architecture in individual brains
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DOI：
10.1162/imag_a_00032
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发表时间：
2023-02
期刊：
影响因子：
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通讯作者：
Ma Feilong;Samuel A. Nastase;G. Jiahui;Y. Halchenko;M. Gobbini;J. Haxby
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文献类型：
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作者：
Ma Feilong;Samuel A. Nastase;G. Jiahui;Y. Halchenko;M. Gobbini;J. Haxby
量化大脑功能结构如何因人而异是人类神经科学的一个关键挑战。目前脑功能组织的个体化模型是基于脑区域和网络的，限制了它们在研究细粒度顶点水平差异方面的应用。在这项工作中，我们提出了个性化神经调节（INT）模型，一个细粒度的个性化模型的大脑功能组织。INT模型被设计为具有顶点级粒度，以捕获代表性和地形差异，并对刺激-一般神经调节进行建模。通过一系列的分析，我们证明，（a）我们的INT模型提供了一个可靠的个性化测量细粒度的大脑功能组织，（B）它准确地预测个性化的大脑反应模式，以新的刺激，（c）它只需要10-20分钟的数据，良好的性能。我们的INT模型的高可靠性，特异性，精确性和普遍性为基于自然神经成像范式构建基于大脑的生物标志物提供了新的机会。
Quantifying how brain functional architecture differs from person to person is a key challenge in human neuroscience. Current individualized models of brain functional organization are based on brain regions and networks, limiting their use in studying fine-grained vertex-level differences. In this work, we present the Individualized Neural Tuning (INT) model, a fine-grained individualized model of brain functional organization. The INT model is designed to have vertex-level granularity, to capture both representational and topographic differences, and to model stimulus-general neural tuning. Through a series of analyses, we demonstrate that (a) our INT model provides a reliable individualized measure of fine-grained brain functional organization, (b) it accurately predicts individualized brain response patterns to new stimuli, and (c) it requires only 10–20 minutes of data for good performance. The high reliability, specificity, precision, and generalizability of our INT model affords new opportunities for building brain-based biomarkers based on naturalistic neuroimaging paradigms.