Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) - Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA.
Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) - Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA.
Neuroimage Clin. 2022;35:103056. doi: 10.1016/j.nicl.2022.103056. Epub 2022 May 23.
Multiple authors have noted overlapping symptoms and alterations across clinical, anatomical, and functional brain features in schizophrenia (SZ), schizoaffective disorder (SZA), and bipolar disorder (BPI). However, regarding brain features, few studies have approached this line of inquiry using analytical techniques optimally designed to extract the shared features across anatomical and functional information in a simultaneous manner. Univariate studies of anatomical or functional alterations across these disorders can be limited and run the risk of omitting small but potentially crucial overlapping or joint neuroanatomical (e.g., structural images) and functional features (e.g., fMRI-based features) which may serve as informative clinical indicators of across multiple diagnostic categories. To address this limitation, we paired an unsupervised multimodal canonical correlation analysis (mCCA) together with joint independent component analysis (jICA) to identify linked spatial gray matter (GM), resting-state functional network connectivity (FNC), and white matter fractional anisotropy (FA) features across these diagnostic categories. We then calculated associations between the identified linked features and trans-diagnostic behavioral measures (MATRICs Consensus Cognitive Battery, MCCB). Component number 4 of the 13 identified displayed a statistically significant relationship with overall MCCB scores across GM, resting-state FNC, and FA. These linked modalities of component 4 consisted primarily of positive correlations within subcortical structures including the caudate and putamen in the GM maps with overall MCCB, sparse negative correlations within subcortical and cortical connection tracts (e.g., corticospinal tract, superior longitudinal fasciculus) in the FA maps with overall MCCB, and negative relationships with MCCB values and loading parameters with FNC matrices displaying increased FNC in subcortical-cortical regions with auditory, somatomotor, and visual regions.
多位作者注意到精神分裂症 (SZ)、分裂情感障碍 (SZA) 和双相情感障碍 (BPI) 在临床、解剖和功能脑特征方面存在重叠症状和改变。然而，就脑特征而言，很少有研究使用最佳分析技术来研究这一问题，这些技术可以同时提取解剖和功能信息中的共同特征。这些疾病的解剖或功能改变的单变量研究可能存在局限性，并存在遗漏小但潜在关键的重叠或联合神经解剖（例如结构图像）和功能特征（例如 fMRI 特征）的风险，这些特征可能是多个诊断类别中具有信息性的临床指标。为了解决这个局限性，我们将无监督多模态典型相关分析 (mCCA) 与联合独立成分分析 (jICA) 结合在一起，以识别这些诊断类别之间的相关空间灰质 (GM)、静息状态功能网络连接 (FNC) 和白质各向异性分数 (FA) 特征。然后，我们计算了识别出的相关特征与跨诊断行为测量值（MATRICs 共识认知电池，MCCB）之间的关联。在 13 个识别出的组件中，第 4 个组件与 GM、静息状态 FNC 和 FA 中的整体 MCCB 评分之间存在统计学显著关系。第 4 个组件的这些相关模态主要包括 GM 图谱中皮质下结构（包括尾状核和壳核）内的正相关，FA 图谱中皮质下和皮质连接束（例如皮质脊髓束、上纵束）内稀疏的负相关，与整体 MCCB 的 FNC 矩阵的负相关与 MCCB 值和加载参数与 FNC 矩阵显示皮质下-皮质区域的 FNC 增加，与听觉、躯体感觉和视觉区域相关。
