Disrupted topological organization of functional brain networks in Alzheimer’s disease patients with depressive symptoms

(2022) 22:810 Guo et al. BMC Psychiatry https://doi.org/10.1186/s12888-022-04450-9 Open Access RESEARCH Disrupted topological organization of functional brain networks in Alzheimer’s disease patients with depressive symptoms Zhongwei Guo1, Kun Liu2, Jiapeng Li1, Haokai Zhu3, Bo Chen1* and Xiaozheng Liu2* Abstract Background: Depression is a common symptom of Alzheimer’s disease (AD), but the underlying neural mechanism is unknown. The aim of this study was to explore the topological properties of AD patients with depressive symptoms (D-AD) using graph theoretical analysis. Methods: We obtained 3-Tesla rsfMRI data from 24 D-AD patients, 20 non-depressed AD patients (nD-AD), and 20 normal controls (NC). Resting state networks were identified using graph theory analysis. ANOVA with a two-sample t-test post hoc analysis in GRETNA was used to assess the topological measurements. Results: Our results demonstrate that the three groups show characteristic properties of a small-world network. NCs showed significantly larger global and local efficiency than D-AD and nD-AD patients. Compared with nD-AD patients, D-AD patients showed decreased nodal centrality in the pallidum, putamen, and right superior temporal gyrus. They also showed increased nodal centrality in the right superior parietal gyrus, the medial orbital portion of the right superior frontal gyrus, and the orbital portion of the right superior frontal gyrus. Compared with nD-AD patients, NC showed decreased nodal betweenness in the right superior temporal gyrus, and increased nodal betweenness in medial orbital part of the right superior frontal gyrus. Conclusions: These results indicate that D-AD is associated with alterations of topological structure. Our study provides new insights into the brain mechanisms underlying D-AD. Keywords: Alzheimer’s disease, Depression, Functional brain network, Graph theory analysis Introduction Depression is one of the major psychobehavioral symptoms in Alzheimer’s disease (AD). It increases the difficulty of interventions and may lead to death [1]. Understanding the pathogenesis of depression associated with AD will be helpful in discovering effective therapies and early interventions. *Correspondence: ; 1 Tongde Hospital of Zhejiang Province, Zhejiang Provincial Health Commission, Hangzhou 310012, China 2 The Second Affiliated Hospital and Yuying Children’s Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China Full list of author information is available at the end of the article A few studies of functional magnetic resonance imaging (fMRI) have shown that changes of brain function in multiple brain regions are involved in the pathogenesis of depression in AD. These studies adopted several analysis methods, including amplitude of low frequency fluctuations (ALFF) [2, 3], functional connectivity (FC) [4, 5], and degree centrality (DC) [6]. Mu et al. [2] reported lower ALFF in the bilateral superior frontal gyrus, left middle frontal gyrus, and the left inferior frontal gyrus, in depressed AD patients (D-AD) compared with non-depressed AD patients (nD-AD). Our previous studies also showed that D-AD patients had increased FC between amygdala and orbitofrontal cortex, © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativeco mmons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Guo et al. BMC Psychiatry (2022) 22:810 and decreased FC among amygdala, medial prefrontal cortex, and inferior frontal gyrus [4]. Furthermore, we reported lower DC in the right middle frontal, precentral, and postcentral gyrus [6]. The above studies show that depression in AD is associated with dysfunctional neural activity in multiple brain regions. Several studies have also shown that neuronal connections undergo functional changes in D-AD patients. Using diffusion tensor imaging, Yatawara et al. [7] reported reduced tract integrity of right hemisphere subcortical and the corpus callosum geniculate in depressed patients with mild AD. When compared with nD-AD patients, D-AD patients showed significantly increased mean diffusivity and radial diffusivity in the bilateral cingulum bundle (CB) and right uncinate fasciculus (UF). These results suggest that myelin injury in the bilateral CB and right UF might contribute to the pathophysiology of depressive symptoms in AD [8]. The aforementioned studies strongly suggest that the regulation of depression in AD patients involves several brain circuits, including the emotional circuit [9], the default mode network [10], and the sensorimotor network [11]. However, the methodological approaches adopted by previous studies did not assess the complexity of regional interactions at the level of the entire brain network. To overcome this limitation and gain a more comprehensive understanding of the neural mechanisms associated with depression in AD, we explore the topological organization of intrinsic brain networks on a large scale that encompasses the entire structure. Graph theory has become popular for describing the characteristics of brain neural networks. In this approach, networks are represented graphically via global network parameters and regional nodal parameters [12]. Using specific graph measures, it is possible to characterize functional specialization and integration of the brain as a network. Small-worldness is a metric that reflects the optimal balance between network separation and consolidation. Global efficiency is a scalar measure of information flow, defined as the inverse of all shortest path lengths in a given network. Local efficiency and global efficiency are calculated similarly, but the former is computed at the level of individual nodes rather than the entire network. For a given node, nodal degree is the number of neighbors connected to it, which reflects the importance of the node within the network. Betweenness centrality indicates the ability to connect between different nodes connected to a given node [12]. Using graph theory, some studies have concluded tha (...truncated)