Division of Neuroscience & Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.
Centre for Medical Image Computing, Department of Medical Physics & Biomedical Engineering and Department of Neuroinflammation, Queen Square Institute of Neurology, University College London, London, UK; Bioxydyn Limited, Manchester, UK.
Neuroimage Clin. 2022;34:102995. doi: 10.1016/j.nicl.2022.102995. Epub 2022 Mar 24.
Understanding the brain changes underlying cognitive dysfunction is a key priority in multiple sclerosis (MS) to improve monitoring and treatment of this debilitating symptom. Functional connectivity network changes are associated with cognitive dysfunction, but it is less well understood how changes in normal appearing white matter relate to cognitive symptoms. If white matter tracts have network structure it would be expected that tracts within a network share susceptibility to MS pathology. In the present study, we used a tractometry approach to explore patterns of variance in white matter metrics across white matter (WM) tracts, and assessed how such patterns relate to neuropsychological test performance across cognitive domains. A sample of 102 relapsing-remitting MS patients and 27 healthy controls underwent MRI and neuropsychological testing. Tractography was performed on diffusion MRI data to extract 40 WM tracts and microstructural measures were extracted from each tract. Principal component analysis (PCA) was used to decompose metrics from all tracts to assess the presence of any co-variance structure among the tracts. Similarly, PCA was applied to cognitive test scores to identify the main cognitive domains. Finally, we assessed the ability of tract co-variance patterns to predict test performance across cognitive domains. We found that a single co-variance pattern which captured microstructure across all tracts explained the most variance (65% variance explained) and that there was little evidence for separate, smaller network patterns of pathology. Variance in this pattern was explained by effects related to lesions, but one main co-variance pattern persisted after this effect was regressed out. This main WM tract co-variance pattern contributed to explaining a modest degree of variance in one of our four cognitive domains in MS. These findings highlight the need to investigate the relationship between the normal appearing white matter and cognitive impairment further and on a more granular level, to improve the understanding of the network structure of the brain in MS.
了解认知功能障碍背后的大脑变化是多发性硬化症（MS）的一个关键重点，这有助于改善对这种使人衰弱的症状的监测和治疗。功能连接网络的变化与认知功能障碍有关，但人们对正常表现的白质变化与认知症状的关系了解较少。如果白质束具有网络结构，则可以预期网络内的束具有对 MS 病理学的易感性。在本研究中，我们使用束测量方法来探索白质束的白质度量的方差模式，并评估这些模式如何与认知领域的神经心理学测试表现相关。 102 名复发缓解型 MS 患者和 27 名健康对照者接受了 MRI 和神经心理学测试。在弥散 MRI 数据上进行了追踪，以提取 40 个 WM 束，并从每个束中提取微观结构测量值。主成分分析（PCA）用于分解所有束的指标，以评估束之间是否存在任何协方差结构。同样，PCA 应用于认知测试分数，以识别主要认知领域。最后，我们评估了束协方差模式预测认知领域测试表现的能力。我们发现，一个单一的协方差模式可以捕获所有束的微观结构，可以解释最多的方差（65％的方差解释），并且几乎没有证据表明存在更小的、单独的网络病理学模式。该模式的方差与与病变相关的效应有关，但在回归此效应后，仍然存在一个主要的协方差模式。这种主要的 WM 束协方差模式有助于解释 MS 中四个认知领域之一的适度方差。这些发现强调了需要进一步在更细粒度的水平上研究正常表现的白质与认知障碍之间的关系，以提高对 MS 大脑网络结构的理解。
