Feilong Ma, Nastase Samuel A, Jiahui Guo, Halchenko Yaroslav O, Gobbini M Ida, Haxby James V
Center for Cognitive Neuroscience, Dartmouth College, Hanover, NH, United States.
Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States.
Imaging Neurosci (Camb). 2023;1. doi: 10.1162/imag_a_00032. Epub 2023 Nov 22.
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) for many benchmarks, 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.
量化大脑功能结构在个体之间的差异是人类神经科学中的一项关键挑战。当前大脑功能组织的个体化模型基于脑区和网络，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，我们提出了个体化神经调谐（INT）模型，这是一种大脑功能组织的细粒度个体化模型。INT模型旨在具有顶点水平的粒度，以捕捉表征和拓扑差异，并对刺激通用的神经调谐进行建模。通过一系列分析，我们证明：（a）我们的INT模型提供了一种可靠的细粒度大脑功能组织个体化测量方法；（b）它能准确预测个体对新刺激的大脑反应模式；（c）对于许多基准测试，它只需10 - 20分钟的数据就能实现良好性能。我们INT模型的高可靠性、特异性、精确性和通用性为基于自然主义神经成像范式构建基于大脑的生物标志物提供了新机会。
