用概率方法构建脑图谱，提升认知状态分类准确率。
Ensemble-based graph representation of fMRI data for cognitive brain state classification
- 基于边缘概率分类器的集成图表示，量化不同认知状态的连接强度。
- 在7个任务数据集上平均准确率达97.07%-99.74%，优于传统相关图。
- 支持区域和连接层面解释，适用于多类、回归及临床场景。
fMRI是一种非侵入性技术，可高分辨率揭示神经活动。解码认知脑状态的关键在于功能交互的表征方式。本文提出一种基于集成的图表示方法，其中每条边权重编码两个状态后验概率之差，由基于简单成对时间序列特征的边缘级概率分类器集成估计。我们在人类连接组计划的七个任务范式上评估该方法，每个范式内进行二分类。使用紧凑的节点摘要（邻接边权重均值）与逻辑回归，平均准确率为97.07%-99.74%。进一步将集成图与传统相关图对比，使用相同图神经网络分类器时，集成图始终表现更优（88.00-99.42% 对比 61.86-97.94%）。由于边权重具有概率性和状态导向的解释性，该表示支持连接与区域层面的可解释性，并可扩展至多类解码、回归、其他神经影像模态及临床分类。
原文摘要 · Abstract (English)
fMRI is a non-invasive technique for investigating brain activity, offering high-resolution insights into neural processes. Understanding and decoding cognitive brain states from fMRI depends on how functional interactions are represented. We propose an ensemble-based graph representation in which each edge weight encodes state evidence as the difference between posterior probabilities of two states, estimated by an ensemble of edge-wise probabilistic classifiers from simple pairwise time-series features. We evaluate the method on seven task-fMRI paradigms from the Human Connectome Project, performing binary classification within each paradigm. Using compact node summaries (mean incident edge weights) and logistic regression, we obtain average accuracies of 97.07-99.74 %. We further compare ensemble graphs with conventional correlation graphs using the same graph neural network classifier; ensemble graphs consistently yield higher accuracy (88.00-99.42 % vs 61.86-97.94 % across tasks). Because edge weights have a probabilistic, state-oriented interpretation, the representation supports connection- and region-level interpretability and can be extended to multiclass decoding, regression, other neuroimaging modalities, and clinical classification.
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