Objective: To investigate variation in the characteristics of regional cerebral blood flow (rCBF), brain activity, and intrinsic functional connectivity (FC) across the Alzheimer's disease spectrum (ADS).Methods: The study recruited 20 individuals in each of the following categories: Alzheimer's disease (AD), mild cognitive impairment (MCI), subjective cognitive decline (SCD), and healthy control (HC). All participants completed the 3.0T resting-state functional MRI (rs-fMRI) and arterial spin labeling scans in addition to neuropsychological tests. Additionally, the normalized CBF, regional homogeneity (ReHo), and amplitude of low-frequency fluctuation (ALFF) of individual subjects were compared in the ADS. Moreover, the changes in intrinsic FC were investigated across the ADS using the abnormal rCBF regions as seeds and behavioral correlations. Finally, a support-vector classifier model of machine learning was used to distinguish individuals with ADS from HC.Results: Compared to the HC subjects, patients with AD showed the poorest level of rCBF in the left precuneus (LPCUN) and right middle frontal gyrus (RMFG) among all participants. In addition, there was a significant decrease in the ALFF in the bilateral posterior cingulate cortex (PCC) and ReHo in the right PCC. Moreover, RMFG- and LPCUN-based FC analysis revealed that the altered FCs were primarily located in the posterior brain regions. Finally, a combination of altered rCBF, ALFF, and ReHo in posterior cingulate cortex/precuneus (PCC/PCUN) showed a better ability to differentiate ADS from HC, AD from SCD and MCI, but not MCI from SCD.Conclusions: The study demonstrated the significance of an altered rCBF and brain activity in the early stages of ADS. These findings, therefore, present a potential diagnostic neuroimaging-based biomarker in ADS. Additionally, the study provides a better understanding of the pathophysiology of AD.

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Suicide ideation (SI) is a most high-risk clinical sign for major depressive disorder (MDD). However, whether the rich-club network organization as a core structural network is associated with SI and how the related neural circuits are distributed in MDD patients remain unknown. Total 177 participants including 69 MDD patients with SI (MDDSI), 58 MDD without SI (MDDNSI) and 50 cognitively normal (CN) subjects were recruited and completed neuropsychological tests and diffusion-tensor imaging scan. The rich-club organization was identified and the global and regional topological properties of structural networks, together with the brain connectivity of specific neural circuit architectures, were analyzed. Further, the support vector machine (SVM) learning was applied in classifying MDDSI or MDDNSI from CN subjects. MDDSI and MDDNSI patients both exhibited disrupted rich-club organizations. However, MDDSI patients showed that the differential network was concentrated on the non-core low-level network and significantly destroyed betweeness centrality was primarily located in the regional non-hub regions relative to MDDNSI patients. The differential structural network connections involved the superior longitudinal fasciculus and the corpus callosum were incorporated in the cognitive control circuit and default mode network. Finally, the feeder serves as a potentially powerful indicator for distinguishing MDDSI patients from MDDNSI or CN subjects. The altered rich-club organization provides new clues to understand the underlying pathogenesis of MDD patients, and the feeder was useful as a diagnostic neuroimaging biomarker for differentiating MDD patients with or without SI.

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Childhood maltreatment (CM) is a major risk factor for developing the major depressive disorder (MDD), however, the neurobiological mechanism linking CM and MDD remains unclear. We recruited 34 healthy controls (HCs) and 44 MDD patients to complete the childhood maltreatment experience assessment with Childhood Trauma Questionnaire (CTQ) and resting-state fMRI scan. Multivariate linear regression analysis was employed to identify the main effects of CM and depressive symptoms total and subfactors scores on bilateral anterior and posterior insula functional connectivity (IFC) networks, respectively. Mediation analysis was performed to investigate whether IFC strength mediates the association between CM and depressive symptoms. MDD patients showed significantly decreased connectivity in the dorsal medial prefrontal cortex and increased connectivity in the medial frontal gyrus in the bipartite IFC networks, compared to HCs. The main effects of CM and depressive symptoms showed a large discrepancy on the anterior and posterior IFC networks, which primarily located in the frontal-limbic system. Further, conjunction analysis identified the overlapping regions linking CM and depressive symptoms were mainly implicated in self-regulation and cognitive processing circuits. More important, these IFC strengths could mediate the association between different types of CM, especially for childhood abuse and childhood neglect, and depressive symptoms in those overlapping regions. We demonstrated that early exposure to CM may increase the vulnerability to depression by influencing brain’s self-regulating and cognitive processing circuitry. These findings provide new insight into the understanding of pathological mechanism underlying CM-induced depressive symptoms.

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