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 Gau\ss' 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.
翻译:功能磁共振成像(fMRI) 提供非侵入性神经活动测量方法, 使用内生血液显色度- 依赖水平( BOLD) 对比。 本条引入了非线性维度降低( 本地线性嵌入), 以从 BOLD 时间序列中提取内在神经活动的信息量度。 该方法使用 leave- One- Out- Cross- Veridation (LOOCV) 的精确度来验证 使用 休息状态 和任务相关FMRI 的神经活动。 方法 : BOLD 时间序列( 每类 voxel 的直径直线 ) 本地线性嵌入( 对每类直径直径直线的直线性嵌入) 用于优化特征选择 。 这使用 Gaus 最不直径直线性原则来保存空间和时间序列内 。 使用 LOOOCV 来进行贪婪地选择时间点选择在原始诊断中进行分解的培训数据 。 结果: 嵌入的 FMRIL 解, 对11 公开性 Oder- real- delifliflistal Amadedeal 进行 进行快速分析之前, 对 进行 进行 ASmadeal 进行 进行 madeal max max max: max max 进行 。