Background: Functional magnetic resonance imaging (fMRI) provides non-invasive measures of neuronal activity using an endogenous Blood Oxygenation-Level Dependent (BOLD) contrast. This article introduces a nonlinear dimensionality reduction (Locally Linear Embedding) to extract informative measures of the underlying neuronal activity from BOLD time-series. The method is validated using the Leave-One-Out-Cross-Validation (LOOCV) accuracy of classifying psychiatric diagnoses using resting-state and task-related fMRI. Methods: Locally Linear Embedding of BOLD time-series (into each voxel’s respective tensor) was used to optimise feature selection. This uses Gauß’ Principle of Least Constraint to conserve quantities over both space and time. This conservation was assessed using LOOCV to greedily select time points in an incremental fashion on training data that was categorised in terms of psychiatric diagnoses. Findings: The embedded fMRI gave highly diagnostic performances (> 80%) on eleven publicly-available datasets containing healthy controls and patients with either Schizophrenia, Attention-Deficit Hyperactivity Disorder (ADHD), or Autism Spectrum Disorder (ASD). Furthermore, unlike the original fMRI data before or after using Principal Component Analysis (PCA) for artefact reduction, the embedded fMRI furnished significantly better than chance classification (defined as the majority class proportion) on ten of eleven datasets. Interpretation: Locally Linear Embedding appears to be a useful feature extraction procedure that retains important information about patterns of brain activity distinguishing among psychiatric cohorts.

中文翻译：

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精神病学分类中的局部线性嵌入和功能磁共振成像特征选择

背景：功能磁共振成像 (fMRI) 使用内源性血氧水平依赖性 (BOLD) 对比提供神经元活动的非侵入性测量。本文介绍了一种非线性降维（局部线性嵌入）来从 BOLD 时间序列中提取底层神经元活动的信息测量。该方法使用静息状态和任务相关功能磁共振成像对精神病学诊断进行分类的留一交叉验证 (LOOCV) 准确性进行验证。方法：使用 BOLD 时间序列的局部线性嵌入（到每个体素各自的张量中）来优化特征选择。这使用高斯最小约束原理来在空间和时间上保持数量守恒。使用 LOOCV 评估这种保守性，以增量方式贪婪地选择根据精神病学诊断分类的训练数据的时间点。研究结果：嵌入式 fMRI 对 11 个公开数据集（包含健康对照者和精神分裂症、注意力缺陷多动障碍 (ADHD) 或自闭症谱系障碍 (ASD) 患者）提供了很高的诊断性能 (> 80%)。此外，与使用主成分分析 (PCA) 减少伪影之前或之后的原始 fMRI 数据不同，嵌入式 fMRI 在 11 个数据集中的 10 个上明显优于机会分类（定义为多数类比例）。解释：局部线性嵌入似乎是一种有用的特征提取过程，它保留了有关区分精神病群体的大脑活动模式的重要信息。