Data Sheet 1_BrainInsights: a comprehensive framework for pre-processing, analysis, and interpretation of neuroimaging data using traditional statistics and machine learning.docx

Neuroimaging presents us with an in-depth understanding about brain structure and function, yet the data complexity poses significant analytical challenges. Current frameworks suffer from issues such as scalability, poor integration with traditional statistics and a need for a programing background, which hinder researchers from focusing on neuroscience questions. To address these limitations, we present BrainInsights, an integrated and automated GUI-based pipeline ecosystem designed to facilitate the analysis of multi-modal or multi-parametric neuroimaging data in a flexible way. The framework comprises three core tools: MARIA (MAgnetic Resonance Imaging data Analysis and inspection tool) for data inspection and hypotheses testing, ML Pipeline for automated feature selection and model construction, and ML DaViz for model evaluation and bio-signature generation. Deployed as a singularity container, the system ensures reproducibility and scalability across computing environments. We validated BrainInsights using diverse datasets, including multi-parametric MRI studies of Anorexia Nervosa, Crohn’s disease, and Rheumatoid Arthritis. Specifically, the framework distinguished young Anorexia Nervosa patients from controls with a balanced accuracy of 65%, while in the PreCePRA trial, it predicted Rheumatoid Arthritis treatment response with a balanced accuracy of up to 95.4% using functional pain markers. The results demonstrate the ability of the framework to achieve high separation of subgroups and treatment success and additionally bridge hypotheses-driven statistical analysis with data-driven machine learning analysis. By enabling interpretability tools like SHAP, BrainInsights empowers researchers to move beyond “black-box” modeling to uncover stable, biologically plausible bio-signatures. Ultimately, this framework aids in accelerating the translation of complex neuroimaging data into meaningful clinical insights.神经影像学能够帮助我们深入洞悉大脑的结构与功能，但其数据固有的复杂性却带来了严峻的分析挑战。当前主流的分析框架存在可扩展性不足、与传统统计学方法整合性差，且要求使用者具备编程背景等诸多问题，极大阻碍了研究者将精力聚焦于神经科学核心问题的探索。为解决上述局限，我们推出了脑洞察分析平台 (BrainInsights)，这是一款集成化、自动化的基于图形用户界面 (GUI, Graphical User Interface) 的流程生态系统，旨在以灵活易用的方式支持多模态或多参数神经影像学数据的分析工作。该框架包含三大核心工具：用于数据检视与假设检验的MARIA (磁共振成像数据分析与检视工具，Magnetic Resonance Imaging data Analysis and inspection tool)、用于自动化特征选择与模型构建的ML Pipeline，以及用于模型评估与生物特征签名生成的ML DaViz。该系统以Singularity容器 (Singularity container) 的形式部署，可确保在不同计算环境下的可复现性与可扩展性。我们通过多组多样化的数据集对脑洞察分析平台 (BrainInsights) 进行了验证，其中涵盖神经性厌食症、克罗恩病以及类风湿关节炎的多参数磁共振成像 (MRI, Magnetic Resonance Imaging) 研究。具体而言，该框架以65%的平衡准确率将年轻神经性厌食症患者与健康对照人群有效区分开来；而在PreCePRA临床试验中，其借助功能性疼痛标志物以最高达95.4%的平衡准确率精准预测了类风湿关节炎的治疗响应情况。研究结果证实，该框架能够实现亚组与治疗结局的高效区分，同时还可打通假设驱动的统计学分析与数据驱动的机器学习分析之间的壁垒。通过集成SHAP等可解释性工具，脑洞察分析平台 (BrainInsights) 可帮助研究者跳出“黑箱”建模范式，挖掘出稳定且具备生物学合理性的生物特征签名。最终，该框架有助于加速将复杂的神经影像学数据转化为具有实际临床价值的科学洞察。