Naikeng Mai1, Yujie Wu2, Xiaomei Zhong3, Ben Chen1, Min Zhang1, Qi Peng1, Yuping Ning1,4,5. 1. Department of Neurology, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangdong, China. 2. School of Psychology, South China Normal University, Guangdong, China. 3. Department of Geriatrics, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangdong, China. 4. The First School of Clinical Medicine, Southern Medical University, Guangdong, China. 5. Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangdong, China.
Abstract
Background: Modular organization reflects the activity patterns of our brain. Different disease states may lead to different activity patterns and clinical features. Early onset depression (EOD) and late onset depression (LOD) share the same clinical symptoms, but have different treatment strategies and prognosis. Thus, explored the modular organization of EOD and LOD might help us understand their pathogenesis. Method: The study included 82 patients with late life depression (EOD 40, LOD 42) and 90 healthy controls. We evaluated the memory, executive function and processing speed and performed resting-stage functional MRI for all participants. We constructed a functional network based on Granger causality analysis and carried out modularity, normalized mutual information (NMI), Phi coefficient, within module degree z-score, and participation coefficient analyses for all the participants. Result: The Granger function network analysis suggested that the functional modularity was different among the three groups (P auc = 0.0300), and NMI analysis confirmed that the partition of EOD was different from that of LOD (P auc = 0.0190). Rh.10d.ROI (polar frontal cortex) and Rh.IPS1.ROI (dorsal stream visual cortex) were shown to be the potential specific nodes in the modular assignment according to the Phi coefficient (P = 0.0002, P fdr = 0.0744 & P = 0.0004, P fdr = 0.0744). Conclusion: This study reveal that the functional modularity and partition were different between EOD and LOD in Granger function network. These findings support the hypothesis that different pathological changes might exist in EOD and LOD.
Background: Modular organization reflects the activity patterns of our brain. Different disease states may lead to different activity patterns and clinical features. Early onset depression (EOD) and late onset depression (LOD) share the same clinical symptoms, but have different treatment strategies and prognosis. Thus, explored the modular organization of EOD and LOD might help us understand their pathogenesis. Method: The study included 82 patients with late life depression (EOD 40, LOD 42) and 90 healthy controls. We evaluated the memory, executive function and processing speed and performed resting-stage functional MRI for all participants. We constructed a functional network based on Granger causality analysis and carried out modularity, normalized mutual information (NMI), Phi coefficient, within module degree z-score, and participation coefficient analyses for all the participants. Result: The Granger function network analysis suggested that the functional modularity was different among the three groups (P auc = 0.0300), and NMI analysis confirmed that the partition of EOD was different from that of LOD (P auc = 0.0190). Rh.10d.ROI (polar frontal cortex) and Rh.IPS1.ROI (dorsal stream visual cortex) were shown to be the potential specific nodes in the modular assignment according to the Phi coefficient (P = 0.0002, P fdr = 0.0744 & P = 0.0004, P fdr = 0.0744). Conclusion: This study reveal that the functional modularity and partition were different between EOD and LOD in Granger function network. These findings support the hypothesis that different pathological changes might exist in EOD and LOD.
Authors: Wenjun Li; B Douglas Ward; Xiaolin Liu; Gang Chen; Jennifer L Jones; Piero G Antuono; Shi-Jiang Li; Joseph S Goveas Journal: J Neurol Neurosurg Psychiatry Date: 2014-11-28 Impact factor: 10.154