Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:4449-4452. doi: 10.1109/EMBC48229.2022.9871548.
Dynamic functional network connectivity (dFNC) data extracted from resting state functional magnetic resonance imaging (rs-fMRI) recordings has played a significant role in characterizing the role that brain network interactions play in a variety of brain disorders and cognitive functions. dFNC analyses frequently use clustering methods to identify states of network activity. However, it is possible that these states are dominated by a few highly influential networks or nodes, which could obscure condition-related insights that might be gained from networks or nodes less influential to the clustering. In this study, we propose a novel feature learning-based approach that could contribute to the identification of condition-related activity in formerly less influential networks or nodes. We demonstrate the viability of our approach within the context of schizophrenia (SZ), applying our approach to a dataset consisting of 151 participants with SZ and 160 controls (HCs). We find that the removal of some connectivity pairs significantly affects the underlying states and magnifies the differences between participants with SZ and HCs in each state. Given our findings, we hope that our approach will contribute to the characterization and improved diagnosis of a variety of neurological conditions and functions. Clinical Relevance- Our approach could contribute to the characterization and diagnosis of many neurological conditions.
从静息态功能磁共振成像(rs-fMRI)记录中提取的动态功能网络连接(dFNC)数据，在刻画大脑网络相互作用在各种大脑疾病和认知功能中的作用方面发挥了重要作用。dFNC 分析经常使用聚类方法来识别网络活动状态。然而，这些状态可能由少数几个具有高度影响力的网络或节点主导，这可能会掩盖从对聚类影响较小的网络或节点中获得的与条件相关的见解。在这项研究中，我们提出了一种基于新特征学习的方法，该方法有助于识别以前影响较小的网络或节点中的与条件相关的活动。我们在精神分裂症(SZ)的背景下证明了我们方法的可行性，将我们的方法应用于一个由 151 名精神分裂症患者和 160 名对照者(HCs)组成的数据集。我们发现，去除一些连接对会显著影响潜在状态，并放大每个状态中精神分裂症患者和 HCs 之间的差异。鉴于我们的发现，我们希望我们的方法将有助于对各种神经疾病和功能的特征描述和改善诊断。临床相关性-我们的方法可以为许多神经疾病的特征描述和诊断做出贡献。
