Magnetic resonance imaging (MRI) biomarkers require complex processing routines that are time-consuming and labor-intensive for clinical users. The Single Subject Brain Analysis Toolbox (SeSBAT) is a fully automated MATLAB toolbox with a graphical user interface (GUI) that offers standardized and optimized protocols for the pre-processing and analysis of anatomical MRI data at the single-subject level. In this study, the two-fold strategy provided by SeSBAT is illustrated through its application on a cohort of 42 patients with Huntington’s disease (HD), in pre-manifest and early manifest stages, as a suitable model of neurodegenerative processes. On the one hand, hypothesis-driven analysis can be used to extract biomarkers of neurodegeneration in specific brain regions of interest (ROI-based analysis). On the other hand, an exploratory voxel-based morphometry (VBM) approach can detect volume changes due to neurodegeneration throughout the whole brain (whole-brain analysis). That illustration reveals the potential of SeSBAT in providing potential prognostic biomarkers in neurodegenerative processes in clinics, which could be critical to overcoming the limitations of current qualitative evaluation strategies, and thus improve the diagnosis and monitoring of neurodegenerative disorders. Furthermore, the importance of the availability of tools for characterization at the single-subject level has been emphasized, as there is high interindividual variability in the pattern of neurodegeneration. Thus, tools like SeSBAT could pave the way towards more effective and personalized medicine.

中文翻译：

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SeSBAT：单一主题大脑分析工具箱。应用于亨廷顿病的初步研究

磁共振成像 (MRI) 生物标志物需要复杂的处理程序，这对临床用户来说既费时又费力。Single Subject Brain Analysis Toolbox (SeSBAT) 是一个全自动 MATLAB 工具箱，带有图形用户界面 (GUI)，它提供标准化和优化的协议，用于在单个受试者级别对解剖 MRI 数据进行预处理和分析。在这项研究中，SeSBAT 提供的双重策略通过它在 42 名亨廷顿舞蹈病 (HD) 患者的队列中的应用得到说明，这些患者处于先兆和早期表现阶段，作为神经退行性过程的合适模型。一方面，假设驱动分析可用于提取特定大脑感兴趣区域中神经变性的生物标志物（基于 ROI 的分析）。另一方面，探索性的基于体素的形态测量 (VBM) 方法可以检测由于整个大脑的神经变性引起的体积变化（全脑分析）。该图揭示了 SeSBAT 在提供临床神经退行性疾病潜在预后生物标志物方面的潜力，这对于克服当前定性评估策略的局限性至关重要，从而改善神经退行性疾病的诊断和监测。此外，由于神经退行性疾病的模式存在高度的个体差异，因此强调了在单一受试者水平上提供表征工具的重要性。因此，像 SeSBAT 这样的工具可以为更有效和个性化的医疗铺平道路。