Cognition is increasingly framed in terms of information processing, yet most fMRI analyses focus on activation or functional connectivity rather than quantifying how information is stored and transferred. To remedy this problem, we propose a framework for estimating measures of information processing: active information storage (AIS), transfer entropy (TE), and net synergy from task-based fMRI. AIS measures information maintained within a region, TE captures directed information flow, and net synergy contrasts higher-order synergistic to redundant interactions. Crucially, to enable this framework we utilised a recently developed approach for calculating information-theoretic measures: the cross mutual information. This approach combines resting-state and task data to address the challenges of limited sample size, non-stationarity and context in task-based fMRI. We applied this framework to the working memory (N-back) task from the Human Connectome Project (470 participants). Results show that AIS increases in fronto-parietal regions with working memory load, TE reveals enhanced directed information flows across control pathways, and net synergy indicates a global shift to redundancy. This work establishes a novel methodology for quantifying information processing in task-based fMRI.

翻译：认知过程日益被置于信息处理的框架下理解，然而大多数功能磁共振成像分析仍聚焦于激活或功能连接，而非量化信息的存储与传递。为解决这一问题，我们提出一个用于估计信息处理度量的框架：基于任务态功能磁共振成像的主动信息存储、传递熵与净协同效应。主动信息存储度量区域内维持的信息，传递熵捕捉定向信息流，净协同效应则对比高阶协同与冗余交互。关键在于，为实现此框架，我们采用了一种新近发展的信息论度量计算方法：交叉互信息。该方法结合静息态与任务态数据，以应对任务态功能磁共振成像中样本量有限、非平稳性及上下文依赖等挑战。我们将此框架应用于人类连接组计划的工作记忆任务数据。结果显示：随着工作记忆负荷增加，额顶叶区域的主动信息存储随之提升；传递熵揭示了控制通路中定向信息流的增强；净协同效应则表明全局向冗余状态的转变。本研究建立了一种量化任务态功能磁共振成像中信息处理过程的新方法。