Abstract
Patients with late-life depression (LLD) have a higher incident of developing dementia, especially individuals with memory deficits. However, little is known about the white matter characteristics of LLD with memory deficits (LLD-MD) in the human connectome, especially for the rich-club coefficient, which is an indicator that describes the organization pattern of hub in the network. To address this question, diffusion tensor imaging of 69 participants [15 LLD-MD patients; 24 patients with LLD with intact memory (LLD-IM); and 30 healthy controls (HC)] was applied to construct a brain network for each individual. A full-scale battery of neuropsychological tests were used for grouping, and evaluating executive function, processing speed and memory. Rich-club analysis and global network properties were utilized to describe the topological features in each group. Network-based statistics (NBS) were calculated to identify the impaired subnetwork in the LLD-MD group relative to that in the LLD-IM group. We found that compared with HC participants, patients with LLD (LLD-MD and LLD-IM) had relatively impaired rich-club organizations and rich-club connectivity. In addition, LLD-MD group exhibited lower feeder and local connective average strength than LLD-IM group. Furthermore, global network properties, such as the shortest path length, connective strength, efficiency and fault tolerant efficiency, were significantly decreased in the LLD-MD group relative to those in the LLD-IM and HC groups. According to NBS analysis, a subnetwork, including right cognitive control network (CCN) and corticostriatal circuits, were disrupted in LLD-MD patients. In conclusion, the disease effects of LLD were prevalent in rich-club organization. Feeder and local connections, especially in the subnetwork including right CCN and corticostriatal circuits, were further impaired in those with memory deficits. Global network properties were disrupted in LLD-MD patients relative to those in LLD-IM patients.
Highlights
Late-life depression (LLD) is one of the most common psychological diseases in old age, with a prevalence that ranges from 3.5 to 7.5% (Weyerer et al, 2013)
Our findings in the white matter network change in LLD patients with or without memory deficits demonstrate that (1) impaired rich-club organization, rich-club connective average strength and assortativity was found in 39 LLD patients compared to 30 healthy controls (HC) subjects; (2) compared to LLD with intact memory (LLD-IM) patients, LLD with memory deficits (LLD-MD) patients had disruptive feeder and local connections, especially in cognitive control network (CCN) and corticostriatal circuits; and (3) the alterations of global network properties were accompanied by slight cognitive function change, such as processing speed and memory in the current study
Our findings were compatible with the previous study in the white matter network topological features of LLD patients (Bai et al, 2012; Li et al, 2017) to some extent, and extend our understanding in the pathological progression of LLD by describing the rich-club properties of LLD-MD patients
Summary
Late-life depression (LLD) is one of the most common psychological diseases in old age, with a prevalence that ranges from 3.5 to 7.5% (Weyerer et al, 2013) It is characterized by a heavy economic burden (Zivin et al, 2013) and is frequently accompanied by cognitive deficits (Butters et al, 2004) even after remission (Bhalla et al, 2006; Baba et al, 2010). The connectome is an approach to help us understand the organization of the complex brain connective network through brain network construction and application of graph theory (Sporns et al, 2005) This technology has been applied to discover the underlying brain structural changes under neuropsychological diseases (Daianu et al, 2015; Gong and He, 2015; Schmidt et al, 2017). There is still largely unknown about the topological organization, especially the role of hub, in LLD
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