IEEE Trans Neural Syst Rehabil Eng. 2024;32:3310-3319. doi: 10.1109/TNSRE.2024.3434353. Epub 2024 Sep 16.
In precision medicine and clinical pain management, the creation of quantitative, objective indicators to assess somatosensory sensitivity was essential. This study proposed a fusion approach for decoding human somatosensory sensitivity, which combined multimodal (quantitative sensory test and neurophysiology) features to classify the dataset on individual somatosensory sensitivity and reveal distinct types of brain activation patterns. Sixty healthy participants took part in the experiment on somatosensory sensitivity that implemented cold, heat, mechanical punctate, and pressure stimuli, and the resting-state electroencephalography (EEG) was collected using BrainVision. The quantitative sensory testing (QST) scores of the participants were clustered using the unsupervised k-means algorithm into four subgroups: generally hypersensitive (HS), generally non-sensitive (NS), predominantly thermally sensitive (TS), and predominantly mechanically sensitive (MS). Furthermore, two types of power spectral density (PSD), band-based PSD (BB-PSD) and frequency-based PSD (FB-PSD), and two types of inter-electrode connectivity (IEC), band-based connectivity (BBC) and frequency-based connectivity (FBC), derived from resting-state EEG were subjected to feature selection with a proposed prior-compared minimum-redundancy maximum-relevance (PCMRMR) protocol. Their effectiveness was then tested by the supervised classification tasks using support vector machine (SVM), k-nearest neighbor (kNN), random forest (RF), and Gaussian classifier (GC). Brain networks of four somatosensory types were revealed by decoding fused multimodal data, namely type-averaged connectivity. The data from sixty healthy individuals were divided into training (n =59) and validation (n =1) datasets according to leave-one-subject-out (LOSO) criteria. The FBC was identified, which can serve as better brain signatures than BB-PSD, FB-PSD, and BBC to classify subjects as HS, NS, TS, or MS groups. Using the SVM, kNN, RF, and GC models, the best accuracy of 87% was obtained when classifying participants into HS, NS, TS, or MS groups. Moreover, the brain networks were decoded from HS, NS, TS, and MS groups by decoding the type-averaged connectivity fused from somatosensory phenotypes and selected FBC. It indicated that quantified multi-parameter somatosensory sensitivity could be achieved with acceptable accuracy, leading to considerable possibilities for using objective pain perception evaluation in clinical practice.
在精准医学和临床疼痛管理中，创建用于评估躯体感觉敏感性的定量、客观指标至关重要。本研究提出了一种融合方法，用于解码人类躯体感觉敏感性，该方法结合了多模态（定量感觉测试和神经生理学）特征，对个体躯体感觉敏感性数据集进行分类，并揭示了不同类型的大脑激活模式。60 名健康参与者参与了躯体感觉敏感性实验，该实验实施了冷、热、机械刺痛和压力刺激，并使用 BrainVision 采集了静息态脑电图（EEG）。使用无监督 k-均值算法对参与者的定量感觉测试（QST）评分进行聚类，将其分为四个亚组：普遍敏感（HS）、普遍不敏感（NS）、主要热敏（TS）和主要机械敏感（MS）。此外，从静息态 EEG 中提取了两种功率谱密度（PSD），即基于频带的 PSD（BB-PSD）和基于频率的 PSD（FB-PSD），以及两种电极间连通性（IEC），即基于频带的连通性（BBC）和基于频率的连通性（FBC），并使用提出的基于先验比较的最小冗余最大相关性（PCMRMR）协议进行特征选择。然后使用支持向量机（SVM）、k-最近邻（kNN）、随机森林（RF）和高斯分类器（GC）的监督分类任务来测试它们的有效性。通过解码融合的多模态数据，即类型平均连通性，揭示了四种躯体感觉类型的大脑网络。根据留一法（LOSO）准则，将 60 名健康个体的数据分为训练（n=59）和验证（n=1）数据集。确定了 FBC，可以作为比 BB-PSD、FB-PSD 和 BBC 更好的大脑特征，用于将受试者分类为 HS、NS、TS 或 MS 组。使用 SVM、kNN、RF 和 GC 模型，当将参与者分类为 HS、NS、TS 或 MS 组时，获得了 87%的最佳准确性。此外，通过解码融合了躯体感觉表型和选择的 FBC 的类型平均连通性，从 HS、NS、TS 和 MS 组解码大脑网络。这表明，通过量化多参数躯体感觉敏感性，可以获得可接受的准确性，为在临床实践中使用客观疼痛感知评估提供了很大的可能性。
