Dynamic functional network connectivity and its association with lipid metabolism in Alzheimer's disease.

AIMS: The study aims to examine the changing trajectory characteristics of dynamic functional network connectivity (dFNC) and its correlation with lipid metabolism-related factors across the Alzheimer's disease (AD) spectrum populations. METHODS: Data from 242 AD spectrum subjects, including biological, neuroimaging, and general cognition, were obtained from the Alzheimer's Disease Neuroimaging Initiative for this cross-sectional study. The study utilized a sliding-window approach to assess whole-brain dFNC, investigating group differences and associations with biological and cognitive factors. Abnormal dFNC was used in the classification of AD spectrum populations by support vector machine. Mediation analysis was performed to explore the relationships between lipid-related indicators, dFNC, cerebrospinal fluid (CSF) biomarkers, and cognitive performance. RESULTS: Significant group difference concerning were observed in relation to APOE-ε4 status, CSF biomarkers, and cognitive scores. Two reoccurring connectivity states were identified: state-1 characterized by frequent but weak connections, and state-II characterized by less frequent but strong connections. Pre-AD subjects exhibited a preference for spending more time in state-I, whereas AD patients tended remain in state-II for longer periods. Group difference in dFNC was primarily found between AD and non-AD participants within each state. The dFNC of state-I yielded strong power to distinguish AD from other groups compared with state-II. APOE-ε4+, high polygenic score, and high serum lipid group were strongly associated with network disruption between association cortex system and sensory cortex system that characterized elevation of cognitive function, which may suggest a compensatory mechanism of dFNC in state-I, whereas differential connections of state-II mediated the relationships between APOE-ε4 genotype and CSF biomarkers, and cognitive indicators. CONCLUSION: The dysfunction of dFNC temporal-spatial patterns and increased cognition in individuals with APOE-ε4, high polygenic score, and higher serum lipid levels shed light on the lipid-related mechanisms of dynamic network reorganization in AD.

Transcriptomic decoding of regional cortical vulnerability to major depressive disorder.

Previous studies in small samples have identified inconsistent cortical abnormalities in major depressive disorder (MDD). Despite genetic influences on MDD and the brain, it is unclear how genetic risk for MDD is translated into spatially patterned cortical vulnerability. Here, we initially examined voxel-wise differences in cortical function and structure using the largest multi-modal MRI data from 1660 MDD patients and 1341 controls. Combined with the Allen Human Brain Atlas, we then adopted transcription-neuroimaging spatial correlation and the newly developed ensemble-based gene category enrichment analysis to identify gene categories with expression related to cortical changes in MDD. Results showed that patients had relatively circumscribed impairments in local functional properties and broadly distributed disruptions in global functional connectivity, consistently characterized by hyper-function in associative areas and hypo-function in primary regions. Moreover, the local functional alterations were correlated with genes enriched for biological functions related to MDD in general (e.g., endoplasmic reticulum stress, mitogen-activated protein kinase, histone acetylation, and DNA methylation); and the global functional connectivity changes were associated with not only MDD-general, but also brain-relevant genes (e.g., neuron, synapse, axon, glial cell, and neurotransmitters). Our findings may provide important insights into the transcriptomic signatures of regional cortical vulnerability to MDD.

Occipital connectivity networks mediate the neural effects of childhood maltreatment on depressive symptoms in major depressive disorder.

BACKGROUND: Childhood maltreatment (CM) is a well-established risk factor for major depressive disorder (MDD). The neural mechanisms linking childhood maltreatment experiences to changes in brain functional networks and the onset of depression are not fully understood. METHODS: In this study, we enrolled 66 patients with MDD and 31 healthy controls who underwent resting-state fMRI scans and neuropsychological assessments. We employed multivariate linear regression to examine the neural associations of CM and depression, specifically focusing on the bilateral occipital functional connectivity (OFC) networks relevant to MDD. Subsequently, a two-step mediation analysis was conducted to assess whether the OFC network mediated the relationship between CM experiences and the severity of depression. RESULTS: Our study showed that patients with MDD exhibited reduced OFC strength, particularly in the occipito-temporal, parietal, and premotor regions. These reductions were negatively correlated with CM scores and the severity of depression. Notably, the overlapping regions in the bilateral OFC networks, affected by both CM experiences and depressive severity, were primarily observed in the bilateral cuneus, left angular and calcarine, as well as the right middle frontal cortex and superior parietal cortex. Furthermore, the altered strengths of the OFC networks were identified as positive mediators of the impact of CM history on depression symptoms in patients with MDD. CONCLUSION: We have demonstrated that early exposure to CM may increase vulnerability to depression by influencing the brain's network. These findings provide new insights into understanding the pathological mechanism underlying depressive symptoms induced by CM.

MicroRNA-124 influenced depressive symptoms via large-scale brain connectivity in major depressive disorder patients.

This study aimed to investigate the neurobiological mechanisms by which microRNA 124 (miR-124) is involved in major depressive disorder (MDD). We enrolled 53 untreated MDD patients and 38 healthy control (HC) subjects who completed behavior assessments and resting-state functional MRI (rs-fMRI) scans. MiR-124 expression levels were detected in the peripheral blood of all participants. We determined that miR-124 levels could influence depressive symptoms via disrupted large-scale intrinsic intra- and internetwork connectivity, including the default mode network (DMN)-DMN, dorsal attention network (DAN)-salience network (SN), and DAN-cingulo-opercular network (CON). This study deepens our understanding of how miR-124 dysregulation contributes to depression.

Resting-state cerebral blood flow and functional connectivity abnormalities in depressed patients with childhood maltreatment: Potential biomarkers of vulnerability?

AIM: Childhood maltreatment (CM) is an important risk factor for major depressive disorder (MDD). This study aimed to explore the specific effect of CM on cerebral blood flow (CBF) and brain functional connectivity (FC) in MDD patients. METHODS: A total of 150 subjects were collected including 55 MDD patients with CM, 34 MDD patients without CM, 19 healthy controls (HC) with CM, and 42 HC without CM. All subjects completed MRI scans and neuropsychological tests. Two-way analysis of covariance was used to detect the main and interactive effects of disease and CM on CBF and FC across subjects. Then, partial correlation analyses were conducted to explore the behavioral significance of altered CBF and FC in MDD patients. Finally, a support vector classifier model was applied to differentiate MDD patients. RESULTS: MDD patients represented increased CBF in bilateral temporal lobe and decreased CBF in right visual cortex. Importantly, significant depression-by-CM interactive effects on CBF were primarily located in the frontoparietal regions, including orbitofrontal cortex (OFC), lateral prefrontal cortex (PFC), and parietal cortex. Moreover, significant FC abnormalities were seen in OFC-PFC and frontoparietal-visual cortex. Notably, the abnormal CBF and FC were significantly associated with behavioral performance. Finally, a combination of altered CBF and FC behaved with a satisfactory classification ability to differentiate MDD patients. CONCLUSIONS: These results highlight the importance of frontoparietal and visual cortices for MDD with CM experience, proposing a potential neuroimaging biomarker for MDD identification.

Age- and gender-related dispersion of brain networks across the lifespan.

The effects of age and gender on large-scale resting-state networks (RSNs) reflecting within- and between-network connectivity in the healthy brain remain unclear. This study investigated how age and gender influence the brain network roles and topological properties underlying the ageing process. Ten RSNs were constructed based on 998 participants from the REST-meta-MDD cohort. Multivariate linear regression analysis was used to examine the independent and interactive influences of age and gender on large-scale RSNs and their topological properties. A support vector regression model integrating whole-brain network features was used to predict brain age across the lifespan and cognitive decline in an Alzheimer's disease spectrum (ADS) sample. Differential effects of age and gender on brain network roles were demonstrated across the lifespan. Specifically, cingulo-opercular, auditory, and visual (VIS) networks showed more incohesive features reflected by decreased intra-network connectivity with ageing. Further, females displayed distinctive brain network trajectory patterns in middle-early age, showing enhanced network connectivity within the fronto-parietal network (FPN) and salience network (SAN) and weakened network connectivity between the FPN-somatomotor, FPN-VIS, and SAN-VIS networks. Age - but not gender - induced widespread decrease in topological properties of brain networks. Importantly, these differential network features predicted brain age and cognitive impairment in the ADS sample. By showing that age and gender exert specific dispersion of dynamic network roles and trajectories across the lifespan, this study has expanded our understanding of age- and gender-related brain changes with ageing. Moreover, the findings may be useful for detecting early-stage dementia.

Connectomics-based resting-state functional network alterations predict suicidality in major depressive disorder.

Suicidal behavior is a major concern for patients who suffer from major depressive disorder (MDD). However, dynamic alterations and dysfunction of resting-state networks (RSNs) in MDD patients with suicidality have remained unclear. Thus, we investigated whether subjects with different severity of suicidal ideation and suicidal behavior may have different disturbances in brain RSNs and whether these changes could be used as the diagnostic biomarkers to discriminate MDD with or without suicidal ideation and suicidal behavior. Then a multicenter, cross-sectional study of 528 MDD patients with or without suicidality and 998 healthy controls was performed. We defined the probability of dying by the suicide of the suicidality components as a 'suicidality gradient'. We constructed ten RSNs, including default mode (DMN), subcortical (SUB), ventral attention (VAN), and visual network (VIS). The network connections of RSNs were analyzed among MDD patients with different suicidality gradients and healthy controls using ANCOVA, chi-squared tests, and network-based statistical analysis. And support vector machine (SVM) model was designed to distinguish patients with mild-to-severe suicidal ideation, and suicidal behavior. We found the following abnormalities with increasing suicidality gradient in MDD patients: within-network connectivity values initially increased and then decreased, and one-versus-other network values decreased first and then increased. Besides, within- and between-network connectivity values of the various suicidality gradients are mainly negatively correlated with HAMD anxiety and positively correlated with weight. We found that VIS and DMN-VIS values were affected by age (p < 0.05), cingulo-opercular network, and SUB-VAN values were statistically influenced by sex (p < 0.05). Furthermore, the SVM model could distinguish MDD patients with different suicidality gradients (AUC range, 0.73-0.99). In conclusion, we have identified that disrupted brain connections were present in MDD patients with different suicidality gradient. These findings provided useful information about the pathophysiological mechanisms of MDD patients with suicidality.

Dyslipidemia induced large-scale network connectivity abnormality facilitates cognitive decline in the Alzheimer's disease.

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.

Synergistic effect of VEGF and SDF-1α in endothelial progenitor cells and vascular smooth muscle cells.

Vascular endothelial growth factor (VEGF) is a potent agonist of angiogenesis that induces proliferation and differentiation of endothelial progenitor cells (EPCs) after vascular injury. Previous studies have suggested that stromal cell-derived factor 1-alpha (SDF-1α) and VEGF have a synergistic effect on vascular stenosis. The aim of the present study was to investigate whether VEGF and SDF-1α act synergistically in EPCs and vascular smooth muscle cells (VSMCs). In this study, EPCs were isolated from rat bone marrow and their morphology and function were studied. Subsequently, VEGF was delivered into EPCs using an adenoviral vector. Tube formation, migration, proliferation, and apoptosis of VEGF-overexpressing EPCs was analyzed. Then, EPCs were co-cultured with VSMCs in the presence or absence of SDF-1α, the migration, proliferation, apoptosis, and differentiation capacity of EPCs and VSMCs were analyzed respectively. The isolated EPCs showed typical morphological features, phagocytic capacity, and expressed surface proteins. While stable expression of VEGF remarkably enhanced tube formation, migration, and proliferation capacity of EPCs, apoptosis was decreased. Moreover, the proliferation, migration, and differentiation capacity of EPCs in the co-cultured model was enhanced in the presence of SDF-1α, and apoptosis was decreased. However, these effects were reversed in VSMCs. Therefore, our results showed that VEGF and SDF-1α synergistically increased the migration, differentiation, and proliferation capabilities of EPCs, but not VSMCs. This study suggests a promising strategy to prevent vascular stenosis.

Altered resting-state cerebral blood flow and functional connectivity mediate suicidal ideation in major depressive disorder.

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.

Insula network connectivity mediates the association between childhood maltreatment and depressive symptoms in major depressive disorder patients.

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.

Polygenic Effects of the Lipid Metabolic Pathway Accelerated Pathological Changes and Disrupted Default Mode Network Trajectory Across the Alzheimer's Disease Spectrum.

Objective: Dyslipidemia is a controversial risk for Alzheimer's disease (AD) with unknown mechanisms. This study aimed to investigate polygenic effects of the lipid metabolic pathway on cerebrospinal fluid (CSF) core biomarkers, cognition, and default mode network (DMN).Methods: Cross-sectional data on serum lipids, CSF core biomarkers, and functional MRI findings for 113 participants (25 cognitively normal, 20 with subjective cognitive decline, 24 early amnestic, 23 with late mild cognitive impairment, and 21 with AD) from the Alzheimer's Disease Neuroimaging Initiative were included. Different cognitive stages were categorized based on neuropsychological assessments. Multivariable linear regression analyses were conducted to investigate the polygenic and interactive effects on the DMN. The correlations of lipid-related polygenes and serum lipids with cognitive performance were also studied via regression analyses.Results: The polygenic scores were significantly correlated with CSF levels of core biomarkers (P < .05) but not with cognition. Several serum lipids were associated with total tau. CSF core biomarkers and 6 serum lipids both could impact cognition in a nonlinear manner. Polygenic effects exhibited diverse trajectories on the DMN subsystems across the AD spectrum. Extensive genetic and interactive effects were mainly concentrated in the cortical frontal-parietal network and subcortical regions. Brain regions of lipid metabolites linking to DMN involved sensorimotor network and occipital lobe.Conclusions: Polygenic effects of the lipid metabolic pathway could accelerate pathological changes and disrupted DMN subsystem trajectory across the AD spectrum. These results deepen the understanding of the mechanism of lipid metabolism affecting the neural system and provide several lipid indicators that enable the impairments of lipid metabolism on the brain to be monitored.

Sleep disturbance-related neuroimaging features as potential biomarkers for the diagnosis of major depressive disorder: A multicenter study based on machine learning.

BACKGROUND: Objective biomarkers are crucial for overcoming the clinical dilemma in major depressive disorder (MDD), and the individualized diagnosis is essential to facilitate the precise medicine for MDD. METHODS: Sleep disturbance-related magnetic resonance imaging (MRI) features was identified in the internal dataset (92 MDD patients) using the relevance vector regression algorithm, which was further verified in 460 MDD patients of an independent, multicenter dataset. Subsequently, using these MRI features, the eXtreme Gradient Boosting classification model was constructed in the current multicenter dataset (460 MDD patients and 470 normal controls). Meanwhile, the association between classification outputs and the severity of depressive symptoms was also investigated. RESULTS: In MDD patients, the combination of gray matter density and fractional amplitude of low-frequency fluctuation can accurately predict individual sleep disturbance score that was calculated by the sum of item 4 score, item 5 score, and item 6 score of the 17-Item Hamilton Rating Scale for Depression (HAMD-17) (R2 = 0.158 in the internal dataset; R2 = 0.110 in multicenter dataset). Furthermore, the classification model based on these MRI features distinguished MDD patients from normal controls with 86.3% accuracy (area under the curve = 0.937). Importantly, the classification outputs significantly correlated with HAMD-17 scores in MDD patients. LIMITATION: Lacking some specialized tools to assess the personal sleep quality, e.g. Pittsburgh Sleep Quality Index. CONCLUSION: Neuroimaging features can reflect accurately individual sleep disturbance manifestation and serve as potential diagnostic biomarkers of MDD.

Alterations of core structural network connectome associated with suicidal ideation in major depressive disorder patients.

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.

Altered Regional Cerebral Blood Flow and Brain Function Across the Alzheimer's Disease Spectrum: A Potential Biomarker.

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.

Disrupted rich-club network organization and individualized identification of patients with major depressive disorder.

BACKGROUND: Altered structural and functional brain networks have been extensively studied in major depressive disorder (MDD) patients. However, whether the differential connectivity patterns in the rich-club organization, assessed from structural brain network analyses, and the associated connections of these regions are particularly susceptible to depression remain unclear. METHODS: We acquired resting-state functional magnetic resonance imaging (R-fMRI) and diffusion tensor imaging (DTI) from 31 unmedicated MDD patients and 32 cognitively normal (CN) subjects and completed a series of neuropsychological tests. Rich-club organization, network properties, and coupling between structural and functional connectivity (SC-FC) were explored. Furthermore, whether these indices could potentially deliver effective clinical predictive value for MDD patients were examined. RESULTS: The MDD patients showed disrupted structural rich-club organization and modularity, as well as a distinct correlation pattern between global efficiency and rich-club organization. Importantly, reduced SC-FC coupling, reflecting a decreased agreement in the integrity of the networks, was significantly associated with the strength of structural rich-club connections in the MDD patients. Furthermore, the disrupted structural rich-club organization, which was primarily located in the default mode network (DMN) and executive control network (ECN), emerged as a valuable indicator to distinguish between MDD and CN. CONCLUSIONS: Findings of this study identified that the disrupted rich-club structural organization significantly influenced brain structural network modularity and integrity and could serve as a promising biological marker for the identification of MDD patients.

Identification of microRNA-9 linking the effects of childhood maltreatment on depression using amygdala connectivity.

Childhood maltreatment (CM) is regarded as an important risk factor for major depressive disorder (MDD). However, the neural links corresponding to the process of early CM experience producing brain alterations and then leading to depression later remain unclear. To explore the neural basis of the effects of CM on MDD and the potential role of microRNA-9 (miR-9) in these processes, we recruited 40 unmedicated MDD patients and 34 healthy controls (HCs) to complete resting-state fMRI scans and peripheral blood miR-9 tests. The neural substrates of CM, miR-9, and depression, as well as their interactive effects on intrinsic amygdala functional connectivity (AFC) networks were investigated in MDD patients. Two-step mediation analysis was separately employed to explore whether AFC strength mediates the association among CM severity, miR-9 levels, and depression. A support vector classifier (SVC) model of machine learning was used to distinguish MDD patients from HCs. MDD patients showed higher miR-9 levels that were negatively correlated with CM scores and depressive severity. Overlapping effects of CM, miR-9, and depressive severity on bilateral AFC networks in MDD patients were primarily located in the prefrontal-striatum pathway and limbic system. The connection of amygdala to prefrontal-limbic circuits could mediate the effects of CM severity on the miR-9 levels, as well as the impacts of miR-9 levels on the severity of depression in MDD patients. Furthermore, the SVC model, which integrated miR-9 levels, CM severity, and AFC strength in prefrontal-limbic regions, had good power in differentiating MDD patients from HCs (accuracy 85.1%). MiR-9 may play a crucial role in the process of CM experience-produced brain changes targeting prefrontal-limbic regions and that subsequently leads to depression. The present neuroimaging-epigenetic results provide new insight into our understanding of MDD pathophysiology.

Dynamic Connectivity Alteration Facilitates Cognitive Decline in Alzheimer's Disease Spectrum.

Introduction: It is unknown the alterations in the dynamic networks of the brain and the underlying molecular pathological mechanism of Alzheimer's disease (AD) spectrum. Here, we aim to explore the association between alterations in the dynamic brain networks' trajectory and cognitive decline in the AD spectrum. Methods: One hundred sixty subjects were recruited from the ADNI database, including 49 early mild cognitive impairment, 28 late mild cognitive impairment, 24 AD patients, and 59 cognitively normal. All participants completed the resting-state functional magnetic resonance imaging scan and neuropsychological tests. We integrated a new method combining large-scale network analysis and canonical correlation analysis to explore the dynamic spatiotemporal patterns within- and between resting-state networks (RSNs) and their significance in the AD spectrum. Results: All RSNs represented an increase in connectivity within networks by enhancing inner cohesive ability, while 7 out of 10 RSNs were characterized by a decrease in connectivity between networks, which indicated a weakened connector among networks from the early stage to dementia. This dichotomous mode presenting large-scale dynamic network abnormality was significantly correlated with the levels of molecular biomarkers of AD, and cognitive performance, as well as with the accumulating effects of 10 identified AD-related genetic risk factors. Discussion: These findings deepen our understanding of the associated mechanism underlying large-scale network disruption, linking known molecular biomarkers and phenotypic variations in the AD spectrum.

Cortical atrophy mediates the accumulating effects of vascular risk factors on cognitive decline in the Alzheimer's disease spectrum.

There are increasing concerns regarding the association of vascular risk factors (VRFs) and cognitive decline in the Alzheimer's disease (AD) spectrum. Currently, we investigated whether the accumulating effects of VRFs influenced gray matter volumes and subsequently led to cognitive decline in the AD spectrum. Mediation analysis was used to explore the association among VRFs, cortical atrophy, and cognition in the AD spectrum. 123 AD spectrum were recruited and VRF scores were constructed. Multivariate linear regression analysis revealed that higher VRF scores were correlated with lower Mini-Mental State Examination scores and higher Alzheimer's Disease Assessment Scale-Cognitive Subscale scores, indicating higher VRF scores lead to severer cognitive decline in the AD spectrum. In addition, subjects with higher VRF scores suffered severe cortical atrophy, especially in medial prefrontal cortex and medial temporal lobe. More importantly, common circuits of VRFs- and cognitive decline associated with gray matter atrophy were identified. Further, using mediation analysis, we demonstrated that cortical atrophy regions significantly mediated the relationship between VRF scores and cognitive decline in the AD spectrum. These findings highlight the importance of accumulating risk in the vascular contribution to AD spectrum, and targeting VRFs may provide new strategies for the therapeutic and prevention of AD.