The aim of this study was to investigate age-related changes in the topological organization of structural brain networks by applying a longitudinal design over 6 years. Structural brain networks were derived from measurements of regional gray matter volume and were constructed in age-specific groups from baseline and follow-up scans. The structural brain networks showed economical small-world properties, providing high global and local efficiency for parallel information processing at low connection costs. In the analysis of the global network properties, the local and global efficiency of the baseline scan were significantly lower compared to the follow-up scan. Moreover, the annual rate of change in local and global efficiency showed a positive and negative quadratic correlation with the baseline age, respectively; both curvilinear correlations peaked at approximately the age of 50. In the analysis of the regional nodal properties, significant negative correlations between the annual rate of change in nodal strength and the baseline age were found in the brain regions primarily involved in the visual and motor/control systems, whereas significant positive quadratic correlations were found in the brain regions predominately associated with the default-mode, attention, and memory systems. The results of the longitudinal study are consistent with the findings of our previous cross-sectional study: the structural brain networks develop into a fast distribution from young to middle age (approximately 50 years old) and eventually became a fast localization in the old age. Our findings elucidate the network topology of structural brain networks and its longitudinal changes, thus enhancing the understanding of the underlying physiology of normal aging in the human brain.

No abstract

Comorbidity with anxiety disorder is a relatively common occurrence in major depressive disorder. However, the unique and shared neuroanatomical characteristics of depression and anxiety disorders have not been fully identified. The aim of this study was to identify gray matter abnormalities and their clinical correlates in depressive patients with and without anxiety disorders.We applied voxel-based morphometry and region-of-interest analyses of gray matter volume (GMV) in normal controls (NC group, n = 28), depressive patients without anxiety disorder (DP group, n = 18), and depressive patients with anxiety disorder (DPA group, n = 20). The correlations between regional GMV and clinical data were analyzed.The DP group showed decreased GMV in the left insula (INS) and left triangular part of the inferior frontal gyrus when compared to the NC group. The DPA group showed greater GMV in the midbrain, medial prefrontal cortex, and primary motor/somatosensory cortex when compared to the NC group. Moreover, the DPA group showed greater GMV than the DP group in the frontal, INS, and temporal lobes. Most gray matter anomalies were significantly correlated with depression severity or anxiety symptoms. These correlations were categorized into 4 trend models, of which 3 trend models (ie, Models I, II, and IV) revealed the direction of the correlation between regional GMV and depression severity to be the opposite of that between regional GMV and anxiety symptoms. Importantly, the left INS showed a trend Model I, which might be critically important for distinguishing depressive patients with and without anxiety disorder.Our findings of gray matter abnormalities, their correlations with clinical data, and the trend models showing opposite direction may reflect disorder-specific symptom characteristics and help explain the neurobiological differences between depression and anxiety disorder.

No abstract

Abstract. Comorbidity with anxiety disorders is a relatively common occurrence in major depressive disorder. However, there are no objective, neurological markers which can be used to identify depressive disorder with and without anxiety disorders. The aim of this study was to examine the diagnostic value of structural MRI to distinguish depressive patients with and without ss using support vector machine. In this paper, we applied voxel-based morphometry of gray matter volume (GMV), then choose discriminative features to classify different group using linear support vector machine (SVM) classifier. The experimental results showed that specific structural brain regions may be a potential biomarkers for disease diagnosis.