HRF估计提高了fMRI编码和解码模型的灵敏度

Fabian Pedregosa, Michael Eickenberg, B. Thirion, Alexandre Gramfort
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引用次数: 9

摘要

由于血氧水平依赖性(BOLD)信号的固有延迟,从功能磁共振图像(fMRI)数据集中提取激活模式在快速事件设计中仍然具有挑战性。一般线性模型(GLM)允许从设计矩阵和固定的血流动力学响应函数(HRF)估计激活。然而,已知HRF在受试者和大脑区域之间有很大差异。在本文中,我们提出了一个通过任务效应的低秩表示来联合估计血流动力学反应函数(HRF)和激活模式的模型。该模型基于GLM背后的线性假设,可以使用标准的基于梯度的求解器进行计算。我们使用我们的模型计算的激活模式作为编码和解码研究的输入数据,并报告在这两种设置下的性能改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
HRF Estimation Improves Sensitivity of fMRI Encoding and Decoding Models
Extracting activation patterns from functional Magnetic Resonance Images (fMRI) datasets remains challenging in rapid-event designs due to the inherent delay of blood oxygen level-dependent (BOLD) signal. The general linear model (GLM) allows to estimate the activation from a design matrix and a fixed hemodynamic response function (HRF). However, the HRF is known to vary substantially between subjects and brain regions. In this paper, we propose a model for jointly estimating the hemodynamic response function (HRF) and the activation patterns via a low-rank representation of task effects. This model is based on the linearity assumption behind the GLM and can be computed using standard gradient-based solvers. We use the activation patterns computed by our model as input data for encoding and decoding studies and report performance improvement in both settings.
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