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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.

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.

Aims This study aims to investigate the mechanisms by which apolipoprotein E (APOE) genotype modulates the relationship between low‐density lipoprotein receptor‐related protein 1 (LRP1) rs1799986 variant on the default mode network (DMN) and cognition in Alzheimer's disease (AD) spectrum populations. Methods Cross‐sectional 168 subjects of AD spectrum were obtained from Alzheimer's Disease Neuroimaging Initiative database with resting‐state fMRI scans and neuropsychological scores data. Multivariable linear regression analysis was adopted to investigate the main effects and interaction of LRP1 and disease on the DMN. Moderation and interactive analyses were performed to assess the relationships among APOE, LRP1, and cognition. A support vector machine model was used to classify AD spectrum with altered connectivity as an objective diagnostic biomarker. Results The main effects and interaction of LRP1 and disease were mainly focused on the core hubs of frontal‐parietal network. Several brain regions with altered connectivity were correlated with cognitive scores in LRP1‐T carriers, but not in non‐carriers. APOE regulated the effect of LRP1 on cognitive performance. The functional connectivity of numerous brain regions within LRP1‐T carriers yielded strong power for classifying AD spectrum. Conclusion These findings suggested LRP1 could affect DMN and provided a stage‐dependent neuroimaging biomarker for classifying AD spectrum populations.

The relationships among cerebral blood flow (CBF), functional connectivity (FC) and suicidal ideation (SI) in major depressive disorder (MDD) patients have remained elusive. In this study, we characterized the changes in CBF and FC among 175 individuals including 47 MDD without SI (MDDNSI), 59 MDD with SI (MDDSI), and 69 healthy control (HC) who underwent arterial spin labeling and resting-state functional MRI scans. Then the voxel-wise CBF, seed-based FC and partial correlation analyses were measured. Mediation analysis was carried out to reveal the effects of FC on the association between CBF and behavioral performances in both subgroups. Results showed that CBF was higher in MDDSI patients in the bilateral precuneus compared to HC and MDDNSI participants. MDDSI patients exhibited enhanced FC in the prefrontal-limbic system and decreased FC in the sensorimotor cortex (SMC) relative to MDDNSI patients. CBF and FC were significantly correlated with clinical variables. More importantly, exploratory mediation analyses identified that abnormal FC can mediate the association between regional CBF and behavioral performances. These results highlight the potential role of precuneus gyrus, prefrontal-limbic system as well as SMC in the process of suicide and provide new insights into the neural mechanism underlying suicide in MDD patients.

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Background Although lipid metabolite dysfunction contributes substantially to clinical signs and pathophysiology of Alzheimer’s disease (AD), how dyslipidemia promoting neuropathological processes and brain functional impairment subsequently facilitates the progression of AD remains unclear. Methods We combined large-scale brain resting-state networks (RSNs) approaches with canonical correlation analysis to explore the accumulating effects of lipid gene- and protein-centric levels on cerebrospinal fluid (CSF) biomarkers, dynamic trajectory of large-scale RSNs, and cognitive performance across entire AD spectrum. Support vector machine model was used to distinguish AD spectrum and pathway analysis was used to test the influences among these variables. Results We found that the effects of accumulation of lipid-pathway genetic variants and lipoproteins were significantly correlated with CSF biomarkers levels and cognitive performance across the AD spectrum. Dynamic trajectory of large-scale RSNs represented a rebounding mode, which is characterized by a weakened network cohesive connector role and enhanced network incohesive provincial role following disease progression. Importantly, the fluctuating large-scale RSNs connectivity was significantly correlated with the summative effects of lipid-pathway genetic variants and lipoproteins, CSF biomarkers, and cognitive performance. Moreover, SVM model revealed that the lipid-associated twenty-two brain network connections represented higher capacity to classify AD spectrum. Pathway analysis further identified dyslipidemia directly influenced brain network reorganization or indirectly affected the CSF biomarkers and subsequently caused cognitive decline. Conclusions Dyslipidemia exacerbated cognitive decline and increased the risk of AD via mediating large-scale brain networks integrity and promoting neuropathological processes. These findings reveal a role for lipid metabolism in AD pathogenesis and suggest lipid management as a potential therapeutic target for AD.