功能磁共振成像是神经科学研究不可或缺的工具,但是这种技术受到生理和测量噪声的多个来源的限制。这些噪声源对于稳定模型拟合需要高信噪比的分析技术尤其成问题,例如体素建模。与回声时间相关的ICA去噪(ME-ICA)相结合的多回声数据采集代表了一种有希望的策略,可以减轻生理和硬件相关的噪声源以及运动相关的伪影。然而,迄今为止,大多数采用ME-ICA的研究都是静息态功能磁共振成像研究,因此,我们对ME-ICA对复杂任务或基于模型的fMRI范例的影响了解有限。这里,我们通过比较在视觉群体感受野(pRF)映射(N=13参与者)实验中获得的数据的数据质量和模型拟合性能,在应用以下三种预处理程序之一后解决了这一知识差距:ME-ICA,无需ICA去噪的最优组合多回波数据,和典型的单回声处理。不出所料,与单回波fMRI相比,多回波fMRI改善了时间信噪比,与ME-ICA扩增相比,单独的最佳组合有所改善。然而,出乎意料的是,时间信噪比的提升并没有直接转化为改进的模型拟合性能:与单回波采集相比,模型拟合只有在ICA去噪后才有所改善。具体来说,与单回波采集相比,ME-ICA导致我们的pRF模型在整个视觉系统中解释的方差得到改善,包括SNR通常较低的颞叶和顶叶的前部区域,而没有ICA的最优组合没有。与单回波相比,ME-ICA还提高了参数估计的可靠性,并且在没有ICA去噪的情况下最佳地组合了多回波数据。总的来说,这些结果表明,ME-ICA对基于任务的fMRI数据去噪进行建模分析是有效的,并且保持了原始数据的完整性.因此,ME-ICA可能对复杂的fMRI实验有益,包括体素建模和自然主义范式。
fMRI is an indispensable tool for neuroscience investigation, but this technique is limited by multiple sources of physiological and measurement noise. These noise sources are particularly problematic for analysis techniques that require high signal-to-noise ratio for stable model fitting, such as voxel-wise modeling. Multi-echo data acquisition in combination with echo-time dependent ICA denoising (ME-ICA) represents one promising strategy to mitigate physiological and hardware-related noise sources as well as motion-related artifacts. However, most studies employing ME-ICA to date are resting-state fMRI studies, and therefore we have a limited understanding of the impact of ME-ICA on complex task or model-based fMRI paradigms. Here, we addressed this knowledge gap by comparing data quality and model fitting performance of data acquired during a visual population receptive field (pRF) mapping (N = 13 participants) experiment after applying one of three preprocessing procedures: ME-ICA, optimally combined multi-echo data without ICA-denoising, and typical single echo processing. As expected, multi-echo fMRI improved temporal signal-to-noise compared to single echo fMRI, with ME-ICA amplifying the improvement compared to optimal combination alone. However, unexpectedly, this boost in temporal signal-to-noise did not directly translate to improved model fitting performance: compared to single echo acquisition, model fitting was only improved after ICA-denoising. Specifically, compared to single echo acquisition, ME-ICA resulted in improved variance explained by our pRF model throughout the visual system, including anterior regions of the temporal and parietal lobes where SNR is typically low, while optimal combination without ICA did not. ME-ICA also improved reliability of parameter estimates compared to single echo and optimally combined multi-echo data without ICA-denoising. Collectively, these results suggest that ME-ICA is effective for denoising task-based fMRI data for modeling analyzes and maintains the integrity of the original data. Therefore, ME-ICA may be beneficial for complex fMRI experiments, including voxel-wise modeling and naturalistic paradigms.