University at Buffalo, The State University of New York, United States.
University at Buffalo, The State University of New York, United States.
Neuroimage. 2020 Mar;208:116412. doi: 10.1016/j.neuroimage.2019.116412. Epub 2019 Nov 30.
Traditional general linear model-based brain mapping efforts using functional neuroimaging are complemented by more recent multivariate pattern analyses (MVPA) that apply machine learning techniques to identify the cognitive states associated with regional BOLD activation patterns, and by connectivity analyses that identify networks of interacting regions that support particular cognitive processes. We introduce a novel analysis representing the union of these approaches, and explore the insights gained when MVPA and functional connectivity analyses are allowed to mutually constrain each other within a single model. We explored multisensory semantic representations of concrete object concepts using a self-paced multisensory imagery task. Multilayer neural networks learned the real-world categories associated with macro-scale cortical BOLD activity patterns from the task, with some models additionally encoding regional functional connectivity. Models trained to encode functional connections demonstrated superior classification accuracy and more pronounced lesion-site appropriate category-specific impairments. We replicated these results in a data set from the openneuro.org open fMRI data repository. We conclude that mutually constrained network analyses encourage parsimonious models that may benefit from improved biological plausibility and facilitate discovery.
传统的基于广义线性模型的脑映射研究使用功能神经影像学，最近的多元模式分析（MVPA）则补充了这一方法，该方法应用机器学习技术来识别与局部 BOLD 激活模式相关的认知状态，并通过连接分析来识别支持特定认知过程的相互作用区域网络。我们引入了一种新的分析方法，该方法结合了这些方法，探索了当 MVPA 和功能连接分析在单个模型中相互约束时所获得的见解。我们使用自我调节的多感觉意象任务探索了具体对象概念的多感觉语义表示。多层神经网络从任务中学习与宏观皮层 BOLD 活动模式相关的真实世界类别，一些模型还对区域功能连接进行编码。对编码功能连接进行训练的模型表现出更高的分类准确性和更明显的病变部位适当的类别特异性损伤。我们在 openneuro.org 开放 fMRI 数据存储库中的数据集上复制了这些结果。我们的结论是，相互约束的网络分析鼓励使用更简单的模型，这些模型可能受益于提高生物学合理性，并有助于发现。
