Code for Topological scale framework analysis of rs-fMRI networks of inhaled substance abuse

This dataset contains the full source code (and results) used in the study “Topological Scale Framework Analysis of rs-fMRI Networks of Inhaled Substance Abuse”. The aim of this research was to test the hypothesis that adolescents diagnosed with Inhaled Substance Abuse Disorder (ISAD) exhibit reduced structural complexity and robustness in their brain functional networks compared to healthy controls. To this end, we employed a topological scale framework (scale-space model) to extract multiscale algebraic descriptors from resting-state fMRI (rs-fMRI) correlation matrices. The dataset includes three Python files: the functions module – implements the scale-space model, hypergraph construction, and descriptor computation; an analysis script – loads subject-specific correlation matrices, applies the scale-space framework, and computes descriptors across scales; a comparison script – performs statistical analysis of descriptors (MANOVA, permutation tests, and OLS regression) to evaluate group differences. The correlation matrices used in the study are not included here due to data-sharing restrictions. However, they are publicly available from the dataset repository described in Mijangos et al. (2024), which must be downloaded separately. The present code is fully compatible with that dataset and allows complete reproduction of the reported results once the matrices are obtained. Our analyses demonstrated that ISAD subjects show increased network fragmentation, reduced incidence matrix rank, and overall lower mean vertex degree across scales. Furthermore, statistical testing confirmed diagnosis as the only significant explanatory factor, with 31 out of 48 descriptors strongly associated with ISAD. These findings support the hypothesis of reduced topological robustness in ISAD-affected brain networks and highlight the sensitivity of the scale-space model to scale-dependent degradation. Researchers can reuse this dataset to apply the scale-space model to other rs-fMRI or neuroimaging datasets; extend the topological analysis framework to different networked data, such as social, biological, or computational systems; and reproduce or benchmark alternative topological descriptors against those used in this study. The scripts are documented with comments and a README file describing dependencies and usage instructions.本数据集包含研究《吸入性物质滥用患者静息态功能磁共振网络的拓扑尺度框架分析》所用的完整源代码与实验结果。本研究旨在验证下述假说：相较于健康对照群体，被诊断为吸入性物质滥用障碍（Inhaled Substance Abuse Disorder, ISAD）的青少年，其大脑功能网络的结构复杂度与鲁棒性均显著降低。为此，本研究采用拓扑尺度框架（scale-space model），从静息态功能磁共振成像（resting-state fMRI, rs-fMRI）的相关矩阵中提取多尺度代数描述符。 本数据集包含三个Python脚本文件：其一为函数模块，用于实现尺度空间模型、超图构建与描述符计算；其二为分析脚本，用于加载个体专属相关矩阵、应用尺度空间框架并跨尺度计算描述符；其三为比较脚本，用于对描述符开展统计分析（含多变量方差分析（MANOVA）、置换检验（permutation tests）与普通最小二乘回归（OLS regression））以评估组间差异。 受数据共享限制，本数据集未包含研究中使用的相关矩阵。此类矩阵可从Mijangos等人2024年发表的相关数据集仓库中公开获取，需单独下载。本代码与该数据集完全兼容，获取相关矩阵后即可完整复现论文报告的实验结果。 本研究分析表明，ISAD受试者的网络碎片化程度更高、关联矩阵秩更低，且跨尺度下的平均顶点度整体更低。此外，统计检验证实，诊断类别是唯一具有显著解释力的因素，48个描述符中有31个与ISAD存在强关联。上述发现验证了ISAD患者大脑网络拓扑鲁棒性降低的假说，并凸显了尺度空间模型对尺度依赖性退化的敏感性。 研究人员可复用本数据集开展如下工作：将尺度空间模型应用于其他rs-fMRI或神经影像数据集；将拓扑分析框架拓展至各类网络化数据，如社会系统、生物系统或计算系统；针对本研究使用的拓扑描述符，复现或基准测试替代方案。 所有脚本均附带详细注释，且包含README文件以说明依赖项与使用方法。