Lei Wang

Background: Metabolic syndrome (MetS) serves as a precursor to Alzheimer's disease, a condition characterized by initial memory impairment and driven by dysregulated brain fluid dynamics. While time-restricted eating (TRE) improves memory in MetS, the mechanistic role of brain fluid dynamics in this process remains unclear. Objective: To investigate whether a 1-month early TRE (eTRE) intervention enhances memory performance by improving brain fluid dynamics in individuals with MetS. Methods: Twenty-eight men with MetS and 30 matched healthy male controls underwent MRI scans. Brain fluid dynamics was assessed using the diffusion tensor image analysis along the perivascular space (DTI-ALPS) index and global blood-oxygen-level-dependent-cerebrospinal fluid (gBOLD–CSF) coupling. In the MetS group, metabolic risk factors, cognitive and psychological status, and memory performance were evaluated pre- and post-eTRE. Relationships between imaging indices and memory scores were examined by correlation analysis. Results: At baseline, men with MetS exhibited a lower DTI-ALPS index and higher gBOLD–CSF coupling than healthy controls ( p < 0.05). After 1-month eTRE, the DTI-ALPS index increased ( p < 0.001), while gBOLD–CSF coupling decreased ( p = 0.027), approaching healthy control levels. Metabolic parameters and long-term memory performance also improved significantly (all p < 0.05). Changes in DTI-ALPS were positively correlated with delayed recall ( r = 0.445, p < 0.05), whereas changes in gBOLD-CSF coupling were negatively correlated ( r = -0.550, p < 0.05). Conclusions: A 1-month eTRE intervention improved long-term memory and brain fluid dynamics in males with MetS, providing a promising non-pharmacological strategy to counteract metabolic-driven neurodegenerative diseases. Trial registration: National Medical Research Registration and Filing Information System ( https://www.medicalresearch.org.cn/ ), MR-61-24-042065.

The triple‐network model has been widely applied in neuropsychiatric disorders including autism spectrum disorder (ASD). However, the mechanism of causal regulations within the triple‐network and their relations with symptoms of ASD remains unclear. 81 male ASD and 80 well matched typically developing control (TDC) were included in this study, recruited from Autism Brain Image Data Exchange‐I datasets. Spatial reference‐based independent component analysis was used to identify the anterior and posterior part of default‐mode network (aDMN and pDMN), salience network (SN), and bilateral executive‐control network (ECN) from resting‐state functional magnetic resonance imaging data. Spectral dynamic causal model and parametric empirical Bayes with Bayesian model reduction/average were adopted to explore the effective connectivity (EC) within triple‐network and the relationship between EC and autism diagnostic observation schedule (ADOS) scores. After adjusting for age and site effect, ASD and TDC groups both showed inhibition patterns. Compared with TDC, ASD group showed weaker self‐inhibition in aDMN and pDMN, stronger inhibition in pDMN→aDMN, weaker inhibition in aDMN→LECN, pDMN→SN, LECN→SN, and LECN→RECN. Furthermore, negative relationships between ADOS scores and pDMN self‐inhibition strength, as well as with the EC of pDMN→aDMN were observed in ASD group. The present study reveals imbalanced effective connections within triple‐networks in ASD children. More attentions should be focused at the pDMN, which modulates the core symptoms of ASD and may serve as an important region for ASD diagnosis and the target region for ASD treatments.

This study investigates the impact of methadone maintenance treatment (MMT) on the brain glymphatic system (GS) in opioid addiction in China. A total of 51 male MMT patients, 48 demographically matched healthy controls (HCs), and 20 heroin dependents (HDs) were recruited for this study. The GS functioning was assessed using diffusion-tensor-imaging analysis along perivascular spaces (DTI-ALPS index) and the bilateral ALPS divergency (DivALPS). Group differences were analyzed utilizing ANOVA and two-sample t-tests. The relationship between DivALPS and relapse rate was explored using regression analysis. The DTI-ALPS index was significantly higher for the left-side brain than the right side in all three groups. There was a significant difference for the right side (p = 0.0098) between the groups. The MMT and HD groups showed significantly higher DTI-ALPS than the HC group (p = 0.018 and 0.016, respectively). The DivALPS varied significantly among the three groups (p = 0.04), with the HD group showing the lowest and the HC group the highest values. Significant negative relationships were found between relapse count, DivALPS (p < 0.0001, Exp(B) = 0.6047), and age (p < 0.0001, Exp(B) = 0.9142). The findings suggest that MMT may contribute to promoting brain GS recovery in heroin addicts, and modulation of the GS may serve as a potential biomarker for relapse risk, providing insights into novel therapeutic strategies.

Background Dopamine receptor D2 (DRD2) TaqIA polymorphism has an influence on addiction treatment response and prognosis by mediating brain dopaminergic system efficacy. Insula is crucial for conscious urges to take drugs and maintain drug use. However, it remains unclear about the contribution of DRD2 TaqIA polymorphism to the regulation of insular on addiction behavioral and its relation with the therapeutic effect of methadone maintenance treatment (MMT). Methods 57 male former heroin dependents receiving stable MMT and 49 matched male healthy controls (HC) were enrolled. Salivary genotyping for DRD2 TaqA1 and A2 alleles, brain resting-state functional MRI scan and a 24-month follow-up for collecting illegal-drug-use information was conducted and followed by clustering of functional connectivity (FC) patterns of HC insula, insula subregion parcellation of MMT patients, comparing the whole brain FC maps between the A1 carriers and non-carriers and analyzing the correlation between the genotype-related FC of insula sub-regions with the retention time in MMT patients by Cox regression. Results Two insula subregions were identified: the anterior insula (AI) and the posterior insula (PI) subregion. The A1 carriers had a reduced FC between the left AI and the right dorsolateral prefrontal cortex (dlPFC) relative to no carriers. And this reduced FC was a poor prognostic factor for the retention time in MMT patients. Conclusion DRD2 TaqIA polymorphism affects the retention time in heroin-dependent individuals under MMT by mediating the functional connectivity strength between left AI and right dlPFC, and the two brain regions are promising therapeutic targets for individualized treatment.

Purpose: Brain glymphatic dysfunction is involved in the pathologic process of acute ischemic stroke (IS). The relationship between brain glymphatic activity and dysfunction in subacute IS has not been fully elucidated. Diffusion tensor image analysis along the perivascular space (DTI-ALPS) index was used in this study to explore whether glymphatic activity was related to motor dysfunction in subacute IS patients.Methods: Twenty-six subacute IS patients with a single lesion in the left subcortical region and 32 healthy controls (HCs) were recruited in this study. The DTI-ALPS index and DTI metrics (fractional anisotropy, FA, and mean diffusivity, MD) were compared within and between groups. Spearman's and Pearson's partial correlation analyses were performed to analyze the relationships of the DTI-ALPS index with Fugl-Meyer assessment (FMA) scores and with corticospinal tract (CST) integrity in the IS group, respectively.Results: Six IS patients and two HCs were excluded. The left DTI-ALPS index of the IS group was significantly lower than that of the HC group (t = −3.02, p = 0.004). In the IS group, a positive correlation between the left DTI-ALPS index and the simple Fugl-Meyer motor function score (ρ = 0.52, p = 0.019) and a significant negative correlation between the left DTI-ALPS index and the FA (R = −0.55, p = 0.023) and MD (R = −0.48, p = 0.032) values of the right CST were found.Conclusions: Glymphatic dysfunction is involved in subacute IS. DTI-ALPS could be a potential magnetic resonance (MR) biomarker of motor dysfunction in subacute IS patients. These findings contribute to a better understanding of the pathophysiological mechanisms of IS and provide a new target for alternative treatments for IS.

BACKGROUND AND PURPOSE: Exploring the directionality of neural information in the brain is important for understanding brain mechanisms and neurodisease development. Granger causality analysis and the metabolic connectivity map can be used to investigate directional transmission of information between brain regions, but their differences in depicting functional effective connectivity are not clear. MATERIALS AND METHODS: Using the Monash rs-PET/MR imaging data set, we conducted Granger causality and metabolic connectivity map analyses of the dopamine reward circuit in the brain. The dopamine reward circuit is a well-known system consisting primarily of the bilateral orbital frontal cortex, caudate, nucleus accumbens, thalamus, and substantia nigra. We validated these circuit pathways using Granger causality and the metabolic connectivity map for identifying effective connectivities against a priori knowledge by testing the significance of directed pathways (P < .05, false discovery rate–corrected). RESULTS: We found 3 types of effective connectivities in the dopamine reward circuit: long-range, neighborhood, and symmetric. Granger causality analysis revealed long-range connections in the orbital frontal cortex–caudate and orbital frontal cortex–nucleus accumbens regions. Metabolic connectivity map analysis revealed neighborhood connections in the nucleus accumbens–caudate, substantia nigra–thalamus, and thalamus-caudate regions. Metabolic connectivity map analysis also found symmetric connections in each of the bilateral nucleus accumbens, caudate, thalamus, and orbital frontal cortex–caudate regions. Different patterns in directional networks of the dopamine reward circuit were revealed by Granger causality and metabolic connectivity map analyses. CONCLUSIONS: Granger causality analysis primarily identified bidirectional cortico-nucleus connections, while the metabolic connectivity map primarily identified direct connections among neighborhood and symmetric regions. The results of this study indicated that investigations of effective connectivities should use an appropriate analysis method depending on the purpose of the study.

Background: Based on hubs of neural circuits associated with addiction and their degree centrality (DC), this study aimed to construct the addiction-related brain networks for patients diagnosed with heroin dependence undertaking stable methadone maintenance treatment (MMT) and further prospectively identify the ones at high risk for relapse with cluster analysis.Methods: Sixty-two male MMT patients and 30 matched healthy controls (HC) underwent brain resting-state functional MRI data acquisition. The patients received 26-month follow-up for the monthly illegal-drug-use information. Ten addiction-related hubs were chosen to construct a user-defined network for the patients. Then the networks were discriminated with K-means-clustering-algorithm into different groups and followed by comparative analysis to the groups and HC. Regression analysis was used to investigate the brain regions significantly contributed to relapse.Results: Sixty MMT patients were classified into two groups according to their brain-network patterns calculated by the best clustering-number-K. The two groups had no difference in the demographic, psychological indicators and clinical information except relapse rate and total heroin consumption. The group with high-relapse had a wider range of DC changes in the cortical−striatal−thalamic circuit relative to HC and a reduced DC in the mesocorticolimbic circuit relative to the low-relapse group. DC activity in NAc, vACC, hippocampus and amygdala were closely related with relapse.Conclusion: MMT patients can be identified and classified into two subgroups with significantly different relapse rates by defining distinct brain-network patterns even if we are blind to their relapse outcomes in advance. This may provide a new strategy to optimize MMT.