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Hierarchical Structural Mapping for Globally Optimized Estimation of Functional Networks

机译:功能网络的全局优化估计的层次结构映射

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In this study, we propose a framework to map functional MRI (fMRI) activation signals using DTI-tractography. This framework, which we term functional by structural hierarchical (FSH) mapping, models the regional origin of fMRI brain activation to construct "N-step reachable structural maps". Linear combinations of these N-step reachable maps are then used to predict the observed fMRI signals. Additionally, we constructed a utilization matrix, which numerically estimates whether the inclusion of a specific structural connection better predicts fMRI, using simulated annealing. We applied this framework to a visual fMRI task in a sample of body dysmorphic disorder (BDD) subjects and comparable healthy controls. Group differences were inferred by comparing the observed utilization differences against 10,000 permutations under the null hypothesis. Results revealed that BDD subjects under-utilized several key local connections in the visual system, which may help explain previously reported fMRI findings and further elucidate the underlying pathophysiology of BDD.
机译:在这项研究中,我们提出了一个使用DTI图像描记功能性MRI(fMRI)激活信号的框架。我们称其为通过结构分层(FSH)映射起作用的框架,该模型对fMRI脑部激活的区域起源进行建模,以构建“ N步可达结构图”。然后,将这些N步可及图的线性组合用于预测观察到的fMRI信号。此外,我们构建了一个利用率矩阵,该矩阵使用模拟退火从数值上估计是否包含特定的结构连接能更好地预测fMRI。我们将此框架应用于身体畸形障碍(BDD)受试者和可比较的健康对照者的样本中的视觉fMRI任务。通过将观察到的利用率差异与原假设下的10,000个排列进行比较,可以推断出组差异。结果显示,BDD受试者未充分利用视觉系统中的几个关键局部连接,这可能有助于解释先前报道的功能磁共振成像发现,并进一步阐明BDD的潜在病理生理学。

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