Pain is highly subjective, so it is always desirable to develop objective pain assessment methods. Brain imaging techniques, such as functional magnetic resonance imaging (fMRI), have the potential to provide a physiological and quantitative pain assessment tool. However, the ultra-high-dimensional fMRI data and the nonlinear relationship between fMRI and pain greatly degrade the efficiency of fMRI-based pain decoding models. In this article, a novel pain decoding model is proposed based on the whale optimization algorithm (WOA), reinforcement learning (RL), and self-organizing fuzzy logic (SOFL), namely RLWOA-SOFL. The new non-linear WOA method incorporates RL and repository experiences (RE), which is based on a back-propagation neural network (BPNN) to map a set of agents states to appropriate actions, to extract and select features that are highly predictive of pain. More specifically, the proposed RLWOA is self-learning and self-optimizing so it can deal with the high-dimensional and complex fMRI data. On the other hand, to establish a fMRI-based pain decoding model, a novel SOFL method is proposed as a new type of deep fuzzy rule that can learn continuously from new data and identify prototypes to construct fuzzy rules. The proposed RLWOA-SOFL model is applied to real-world pain-evoked fMRI data, and the results show that the new model can decode pain intensity more accurately and can identify pain-related fMRI patterns more reliably. Therefore, the proposed RLWOA-SOFL model has great potential to evaluate the intensity of pain perception in clinical uses.

中文翻译：

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RLWOA-SOFL：一种基于新学习模型的强化群智能和自组织深度模糊规则，用于 fMRI 疼痛解码


疼痛是高度主观的，因此总是需要开发客观的疼痛评估方法。脑成像技术，例如功能性磁共振成像（fMRI），有可能提供生理和定量疼痛评估工具。然而，超高维的fMRI数据以及fMRI与疼痛之间的非线性关系极大地降低了基于fMRI的疼痛解码模型的效率。本文提出了一种基于鲸鱼优化算法（WOA）、强化学习（RL）和自组织模糊逻辑（SOFL）的新型疼痛解码模型，即RLWOA-SOFL。新的非线性 WOA 方法结合了 RL 和存储库经验 (RE)，该方法基于反向传播神经网络 (BPNN)，将一组代理状态映射到适当的操作，以提取和选择高度预测的特征疼痛。更具体地说，所提出的 RLWOA 是自学习和自优化的，因此它可以处理高维和复杂的功能磁共振成像数据。另一方面，为了建立基于功能磁共振成像的疼痛解码模型，提出了一种新颖的SOFL方法作为一种新型的深度模糊规则，可以从新数据中不断学习并识别原型来构建模糊规则。所提出的 RLWOA-SOFL 模型应用于现实世界中疼痛诱发的 fMRI 数据，结果表明新模型可以更准确地解码疼痛强度，并且可以更可靠地识别与疼痛相关的 fMRI 模式。因此，所提出的 RLWOA-SOFL 模型在临床应用中评估疼痛感知强度具有巨大的潜力。