Linear models are widely used in computational neuroimaging to identify biomarkers associated with brain pathologies. However, interpreting the learned weights remains challenging, as they do not always yield clinically meaningful insights. This difficulty arises in part from the inherent correlation between brain regions, which causes linear weights to reflect shared rather than region-specific contributions. In particular, some groups of regions, including homologous structures in the left and right hemispheres, are known to exhibit strong anatomical correlations. In this work, we leverage this prior neuroanatomical knowledge to introduce a whitening approach applied to groups of regions with known shared variance, designed to disentangle overlapping information across correlated brain measures. We additionally propose a regularized variant that allows controlled tuning of the degree of decorrelation. We evaluate this method using region-of-interest features in two psychiatric classification tasks, distinguishing individuals with bipolar disorder or schizophrenia from healthy controls. Importantly, unlike PCA or ICA which use whitening as a dimensionality reduction step, our approach decorrelates anatomically informed pairs of neuroanatomical regions while retaining the full input signal, making it specifically suited for feature interpretation rather than feature selection. Our findings demonstrate that whitening improves the interpretability of model weights while preserving predictive performance, providing a robust framework for linking linear model outputs to neurobiological mechanisms.

翻译：线性模型在计算神经影像学中广泛用于识别与脑病理相关的生物标志物。然而，对学习到的权重的解释仍具挑战性，因其并非总能产生有临床意义的见解。这一困难部分源于脑区之间的固有相关性，导致线性权重反映的是共享效应而非区域特异性贡献。特别地，包括左右半球同源结构在内的某些区域组已知具有强解剖相关性。本研究利用这一先验神经解剖学知识，引入一种针对已知共享方差区域组的白化方法，旨在解耦相关脑测量指标间的重叠信息。我们还提出了一种正则化变体，可对去相关程度进行可控调节。我们采用感兴趣区域特征在两个精神疾病分类任务中评估该方法：区分双相情感障碍或精神分裂症患者与健康对照。重要的是，与将白化作为降维步骤的PCA或ICA不同，我们的方法在保留完整输入信号的同时对符合解剖学先验的神经解剖区域对进行去相关，使其特别适用于特征解释而非特征选择。研究结果表明，白化在保持预测性能的同时提升了模型权重的可解释性，为将线性模型输出与神经生物学机制建立关联提供了稳健框架。