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.

Collateral circulation detected by arterial spin labeling predicts outcome in acute ischemic stroke.

BACKGROUND: Robust collateral circulation is strongly associated with good outcomes in acute ischemic stroke (AIS). AIMS: To determine whether collateral circulation detected by arterial spin labeling (ASL) magnetic resonance imaging could predict good clinical outcome in AIS patients with 90 days follow-up. MATERIALS AND METHODS: Total 58 AIS patients with anterior circulation stroke were recruited. Collateral circulation was defined as arterial transit artifact in ASL images. Modified Rankin Scale (mRS), the Barthel Index, and National Institutes of Health Stroke Scale (NIHSS) were employed to evaluate neurological function for the baseline and 90 days follow-up. The percent changes of these scores were also calculated, respectively. Finally, a support vector classifier model of machine learning and receiver operating characteristic curve were employed to estimate the power of ASL collaterals (ASLcs) predicting the clinical outcome. RESULTS: Patients with ASLcs represented higher rate of good outcome (83.30% vs. 31.25%, p < .001) and lower follow-up mRS scores (p < .001), when compared to patients without ASLcs. There were significant differences for percent changes of mRS scores and NIHSS scores between these two groups. Further, the presence of ASLcs could predict good clinical outcome (OR, 1.54; 95% CI, 1.10-2.16), even after controlling for baseline NIHSS scores. The SVC model incorporating baseline NIHSS scores and ASLcs had significant predictive effect (accuracy, 79.3%; AUC, 0.806) on clinical prognosis for AIS patients. DISCUSSION: We targeted on the non-invasive assessment of collateral circulation using ASL technique and found that patients with ASLcs were more likely to have a good clinical outcome after AIS. This finding is of guiding significance for treatment selection and prognostic prediction. CONCLUSIONS: Early ASLcs assessment provides a good powerful tool to predict clinical outcome for AIS patients with 90 days follow-up.

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.

APOE genotype moderates the relationship between LRP1 polymorphism and cognition across the Alzheimer's disease spectrum via disturbing default mode network.

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.

Endocytosis-pathway polygenic scores affects the hippocampal network connectivity and individualized identification across the high-risk of Alzheimer's disease.

The neural mechanisms underlying the polygenic effects of the endocytosis pathway on the brain function of Alzheimer's Disease (AD) remain unclear, especially in the prodromal stages of AD from early mild cognitive impairment (EMCI) to late mild cognitive impairment (LMCI). We used an imaging genetic approach to investigate the polygenic effects of the endocytosis pathway on the hippocampal network across the prodromal stages of AD. The subjects' data were selected from the Alzheimer's Disease Neuroimaging Initiative. Hippocampal volumes were examined in subjects of cognitive normal (CN), EMCI and LMCI groups. Multivariate linear regression analysis was employed to measure the effects of disease and endocytosis-based multilocus genetic risk scores (MGRS) on the hippocampal network which was constructed using the bilateral hippocampal regions. We identified hippocampal volumes in LMCI group were smaller than those in CN and EMCI groups. Endocytosis-based MGRS was widely influenced the neural structures within the hippocampal network, especially in the prefrontal-occipital regions and striatum. Compared to low endocytosis-based MGRS carriers, high MGRS carriers showed the opposite trajectory of hippocampal network functional connectivity (FC) across the prodromal stages of AD. Further, a model composed of selected hippocampal FCs and hippocampal volume yielded strong classification powers of EMCI and LMCI. These findings expand our understanding of the pathophysiology of polygenic effects underlying brain network in the prodromal stages of AD.

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.

Connectome-based model predicts episodic memory performance in individuals with subjective cognitive decline and amnestic mild cognitive impairment.

OBJECTIVE: To explore whether the whole brain resting-state functional connectivity (rs-FC) could predict episodic memory performance in individuals with subjective cognitive decline and amnestic mild cognitive impairment. METHOD: This study included 33 cognitive normal (CN), 26 subjective cognitive decline (SCD) and 27 amnestic mild cognitive impairment (aMCI) patients, and all the participants completed resting-state fMRI (rs-fMRI) scan and neuropsychological scale test data. Connectome-based predictive modeling (CPM) based on the rs-FC data was used to predict the auditory verbal learning test-delayed recall (AVLT-DR) scores, which measured episodic memory in individuals. Pearson correlation between each brain connection in the connectivity matrices and AVLT-DR scores was computed across the patients in predementia stages of Alzheimer's disease (AD). The Pearson correlation coefficient values separated into a positive network and a negative network. Predictive networks were then defined and employed by calculating positive and negative network strengths. CPM with leave-one-out cross-validation (LOOCV) was conducted to train linear models to respectively relate positive and negative network strengths to AVLT-DR scores in the training set. During the testing procedure, each left-out testing subject's strengths of positive and negative network was normalized using the parameters acquired during training procedure, and then the trained models were used to predict the testing participant's AVLT-DR score. RESULTS: The negative network predictive model tested LOOCV significantly predicted individual differences in episodic memory from rs-FC. Key nodes that brain regions contributed to the prediction model were mainly located in the prefrontal cortex, frontal cortex, parietal cortex and temporal lobe. CONCLUSION: Our findings demonstrated that rs-FC among multiple neural systems could predict episodic memory at the individual level.

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.

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.

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.

Very low birth weight infants in China: the predictive value of the motor repertoire at 3 to 5months for the motor performance at 12months.

BACKGROUND: Studies on motor performance and its early markers are rare in China, especially in very low birth weight (VLBW) infants. OBJECTIVE: Apart from the assessment of the inter-scorer agreement, we aimed to analyze to what extent the motor repertoire at 10 to 18weeks postterm was related to neonatal complications, and gross and fine motor performance at 12months after term. STUDY DESIGN: Exploratory prospective study. SUBJECTS: Seventy-four VLBW infants (58 males; mean gestational age=29weeks; mean birth weight=1252g). METHOD: Five-minute video recordings were performed at 10 to 18weeks after term; fidgety movements and the concurrent motor patterns (resulting in a motor optimality score) were assessed according to the Prechtl general movements assessment (GMA). The gross and fine motor performance was assessed by means of the Peabody Developmental Motor Scales, second edition, at 12months. RESULTS: Reliability was excellent. Pneumonia was associated with absent fidgety movements; the motor optimality score was lower in infants with pneumonia and/or bronchopulmonary dysplasia. Both absent fidgety movements and a lower motor optimality score were associated with a poor or very poor gross and fine motor performance at the 12-month-assessment. CONCLUSION: Both the assessment of fidgety movements and the evaluation of the concurrent motor repertoire contribute significantly to an identification of VLBW children with a poor gross and fine motor outcome at 12months. The results of this study document the need for an early identification of infants at high risk for a poor motor performance.

Are sporadic fidgety movements as clinically relevant as is their absence?

BACKGROUND: Infants with normal fidgety movements at 3 to 5 months after term are very likely to show neurologically normal development, while the absence of fidgety movements is an early marker for an adverse neurological outcome, mainly cerebral palsy (CP). The clinical significance of so-called sporadic fidgety movements (i.e., fidgety movements occur isolated in a few body parts and are of 1- to 3-second-duration) is not yet known. AIMS: Our objective was to determine whether infants who had developed CP and had sporadic fidgety movements have a better outcome than infants who did not have fidgety movements. STUDY DESIGN: Longitudinal study. Retrospective analysis of prospectively collected data. SUBJECTS: 61 infants who developed CP (46 male, 15 female; 29 infants born preterm; videoed for the assessment of movements and postures at 9 to 16 weeks post-term age). OUTCOME MEASURES: The Gross Motor Function Classification System (GMFCS) was applied at 3 to 5 years of age. RESULTS: There was no difference between children diagnosed with CP who had sporadic fidgety movements at 9 to 16 weeks post-term age (n = 9) and those who never developed fidgety movements (n = 50) with regard to their functional mobility and activity limitation at 3 to 5 years of age. One infant had normal FMs and developed unilateral CP, GMFCS Level I; the remaining infant had abnormal FMs and developed bilateral CP, GMFCS Level II. CONCLUSIONS: There is no evidence that the occurrence of occasional isolated fidgety bursts indicates a milder type of CP.