Huckins Grace, Poldrack Russell A
Neurosciences Interdepartmental Program, Stanford University, Stanford, CA, USA.
Department of Psychology, Stanford University, Stanford, CA, USA.
Netw Neurosci. 2024 Dec 10;8(4):1613-1633. doi: 10.1162/netn_a_00412. eCollection 2024.
The growing availability of large-scale neuroimaging datasets and user-friendly machine learning tools has led to a recent surge in studies that use fMRI data to predict psychological or behavioral variables. Many such studies classify fMRI data on the basis of static features, but fewer try to leverage brain dynamics for classification. Here, we pilot a generative, dynamical approach for classifying resting-state fMRI (rsfMRI) data. By fitting separate hidden Markov models to the classes in our training data and assigning class labels to test data based on their likelihood under those models, we are able to take advantage of dynamical patterns in the data without confronting the statistical limitations of some other dynamical approaches. Moreover, we demonstrate that hidden Markov models are able to successfully perform within-subject classification on the MyConnectome dataset solely on the basis of transition probabilities among their hidden states. On the other hand, individual Human Connectome Project subjects cannot be identified on the basis of hidden state transition probabilities alone-although a vector autoregressive model does achieve high performance. These results demonstrate a dynamical classification approach for rsfMRI data that shows promising performance, particularly for within-subject classification, and has the potential to afford greater interpretability than other approaches.
大规模神经影像数据集和用户友好型机器学习工具的日益普及，导致最近使用功能磁共振成像（fMRI）数据预测心理或行为变量的研究激增。许多此类研究基于静态特征对fMRI数据进行分类，但尝试利用脑动力学进行分类的研究较少。在此，我们试用一种生成式动态方法对静息态功能磁共振成像（rsfMRI）数据进行分类。通过将单独的隐马尔可夫模型拟合到训练数据中的类别，并根据测试数据在这些模型下的似然性为其分配类别标签，我们能够利用数据中的动态模式，而无需面对其他一些动态方法的统计局限性。此外，我们证明隐马尔可夫模型能够仅基于其隐藏状态之间的转移概率在MyConnectome数据集上成功进行个体内分类。另一方面，仅根据隐藏状态转移概率无法识别个体人类连接组计划受试者——尽管向量自回归模型确实具有高性能。这些结果证明了一种用于rsfMRI数据的动态分类方法，该方法显示出有前景的性能，特别是对于个体内分类，并且有可能比其他方法提供更大的可解释性。
