IntroductionThere is a need to better understand the neurophysiological changes associated with early brain dysfunction in Type 2 diabetes mellitus (T2DM) before vascular or structural lesions. Our aim was to use a novel unbiased data-driven approach to detect and characterize hemodynamic response function (HRF) alterations in T2DM patients, focusing on their potential as biomarkers.MethodsWe meshed task-based event-related (visual speed discrimination) functional magnetic resonance imaging with DL to show, from an unbiased perspective, that T2DM patients’ blood-oxygen-level dependent response is altered. Relevance analysis determined which brain regions were more important for discrimination. We combined explainability with deconvolution generalized linear model to provide a more accurate picture of the nature of the neural changes.ResultsThe proposed approach to discriminate T2DM patients achieved up to 95% accuracy. Higher performance was achieved at higher stimulus (speed) contrast, showing a direct relationship with stimulus properties, and in the hemispherically dominant left visual hemifield, demonstrating biological interpretability. Differences are explained by physiological asymmetries in cortical spatial processing (right hemisphere dominance) and larger neural signal-to-noise ratios related to stimulus contrast. Relevance analysis revealed the most important regions for discrimination, such as extrastriate visual cortex, parietal cortex, and insula. These are disease/task related, providing additional evidence for pathophysiological significance. Our data-driven design allowed us to compute the unbiased HRF without assumptions.ConclusionWe can accurately differentiate T2DM patients using a data-driven classification of the HRF. HRF differences hold promise as biomarkers and could contribute to a deeper understanding of neurophysiological changes associated with T2DM.

中文翻译：

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血流动力学反应函数作为 2 型糖尿病生物标志物：数据驱动的方法

简介需要更好地了解与 2 型糖尿病 (T2DM) 血管或结构病变之前的早期脑功能障碍相关的神经生理学变化。我们的目标是使用一种新颖的无偏数据驱动方法来检测和表征 T2DM 患者的血流动力学反应函数 (HRF) 变化，重点关注其作为生物标志物的潜力。方法我们将基于任务的事件相关（视觉速度辨别）功能磁共振结合起来DL 成像从公正的角度显示 T2DM 患者的血氧水平依赖性反应发生了改变。相关性分析确定哪些大脑区域对于辨别更重要。我们将可解释性与反卷积广义线性模型相结合，以提供神经变化本质的更准确图像。结果所提出的区分 T2DM 患者的方法达到了高达 95% 的准确度。在较高的刺激（速度）对比度下实现了较高的性能，显示出与刺激特性的直接关系，并且在半球占优势的左视觉半场中实现了生物可解释性。差异可以通过皮层空间处理的生理不对称性（右半球优势）和与刺激对比度相关的较大神经信噪比来解释。相关性分析揭示了最重要的辨别区域，例如纹外视觉皮层、顶叶皮层和岛叶。这些与疾病/任务相关，为病理生理学意义提供了额外的证据。我们的数据驱动设计使我们能够在没有假设的情况下计算无偏 HRF。结论我们可以使用 HRF 的数据驱动分类来准确地区分 T2DM 患者。HRF 差异有望作为生物标志物，并有助于更深入地了解与 T2DM 相关的神经生理变化。