Adolescent Idiopathic Scoliosis (AIS) is a common spinal deformity arising during adolescence, a critical period for brain development. Although most AIS analyses predominantly focus on spinal or behavioral aspects, it remains unclear what brain functional alteration is associated with AIS. This gap in knowledge impedes a holistic understanding of the condition’s pathophysiology. In this study, functional near-infrared spectroscopy (fNIRS) was used to explore the resting-state brain network in AIS. The study recruited 25 AIS patients and 25 age-matched healthy controls and measured their brain activities during resting-state lying using fNIRS. Brain functional connectivity and network topology were evaluated for the two groups and their correlations with demographic and pathological variables were examined. The functional connectivity in the patients, particularly single-curve patients, decreased and was sparser in the parietal and prefrontal regions in comparison to the healthy controls. The regional nodal metrics in the patients were significantly altered, with smaller nodal degrees and efficiency observed in certain nodes. Among the affected regions, the dorsolateral prefrontal cortex emerged repeatedly as a hub showing altered connectivity in patients, particularly in relation to parietal and sensorimotor regions. In addition, different correlations between brain network metrics and demographic as well as pathological parameters were identified within patients. For instance, a larger primary Cobb angle was associated with poorer small-world properties, suggesting greater structural deformity may be linked to less efficient functional network organization. Furthermore, a support vector machine-based classification model using functional connectivity achieved an average accuracy of 81.0

OBJECTIVE:This study uses resting-state functional magnetic resonance imaging (fMRI) to understand and compare the effects of hypoxic conditions at high and ultra-high altitudes. METHODS:Regional homogeneity (ReHo) and degree centrality (DC) values were calculated and compared between 47 low-altitude (LA, <500 m), 39 high-altitude (HA, 1520 m), and 34 ultra-high-altitude (UHA, 3650 m) healthy adults. Correlations with heart rate and blood oxygen saturation (SpO₂) were analyzed. RESULTS:Compared to the LA group, the UHA/HA group had significantly lower ReHo values in the bilateral basal ganglia, prefrontal lobes (left/right), left paracentral lobule, and these were positively correlated with SpO₂. Conversely, ReHo values were significantly higher in the bilateral posterior occipital and left superior parietal lobes, and were negatively correlated with SpO₂. DC values were significantly lower in the left orbitofrontal cortex, bilateral pallidum and left inferior frontal gyrus, and were positively correlated with SpO₂. Synchronous decreases in ReHo and DC were found in the left prefrontal cortex, bilateral pallidum and putamen. CONCLUSION:In high-altitude environments, functional activity is decreased in the basal ganglia, prefrontal cortex, and hippocampus, is accompanied by a compensatory increase in the occipital and superior parietal lobes. Concurrent reductions in DC and ReHo within the left prefrontal cortex, bilateral pallidum and putamen might serve as biomarkers for high-altitude hypoxic functional alterations and aid early detection and intervention of hypoxia-induced brain damage.

Achieving consistent surface quality in direct laser interference patterning (DLIP) demands real‐time insight into ultrafast laser–material interactions, particularly when structuring complex alloys such as Ti‐6Al‐4V. This work presents a hybrid image‐to‐signal machine learning framework that links offline topography characterization with real‐time sensor data to enable predictive surface quality assessment. Periodic microstructures are fabricated using a picosecond pulsed laser equipped with a two‐beam interference head and an off‐axis photodiode for in situ optical monitoring. Ground‐truth labels are generated from white light interferometry (WLI) images processed in the frequency domain via a 2D fast Fourier transform to extract radial power spectral density profiles. Spectral entropy serves as a quantitative indicator of texture order and enables unsupervised KMeans clustering into acceptable and nonacceptable quality classes. These entropy‐based labels are assigned to the corresponding time‐resolved photodiode signals and laser parameters recorded during fabrication. A supervised 1D convolutional neural network (1D‐CNN) is then trained to predict surface quality using only the sensor data and process inputs. The model achieves a classification accuracy of 90%, demonstrating reliable detection of structural deviations without post‐process metrology. This entropy‐informed, sensor‐driven framework highlights the potential of machine learning for real‐time quality assurance in laser‐based manufacturing systems.

The impact of high-altitude environments on the three core large-scale brain networks (default mode network (DMN), executive control network (ECN), and salience network (SN)) remains poorly understood. This study included 64 ultra-high-altitude residents (U-HA, 3600 m), 51 high-altitude residents (HA, 1500 m), and 49 low-altitude residents (LA, 400 m). Large-scale networks (DMN, SN, and bilateral ECN) were identified by group independent component analysis. The within- and between-network functional connectivity (FC) was quantified. Groups were compared using one-way analysis of variance. Partial correlations assessed the associations between network FC and oxygen saturation and HA residence duration after controlling confounders. The HA and U-HA groups exhibited higher medial prefrontal cortex (mPFC)-left posterior parietal cortex (PPC)/right dorsolateral prefrontal cortex (dlPFC), posterior cingulate cortex (PCC)-right anterior insula (AI), left and right dlPFC, and left dlPFC-dorsal anterior cingulate cortex (dACC) FC than the LA group (FDR-corrected, LSD post hoc). The blood oxygen saturation negatively correlated with the above FC, surviving correction for age, sex, education, and motion. Our findings may indicate that HA hypoxia is associated with reduced functional segregation between the DMN and ECN, as well as enhanced coupling of the bilateral ECN and SN-ECN.

Protracted abstinence (PA) is the commonly implemented treatment of heroin-dependent individuals (HDIs) in China. However, the effect of abstinence duration on the brain function of HDIs during PA using resting-state functional magnetic resonance imaging (fMRI) remains unclear. Fourteen HDIs who had finished PA for about 6 months (PA6), 16 HDIs who had completed PA for about 11 months (PA11) and 15 demographically matched healthy controls (HC) underwent this fMRI study. We analysed the difference in amplitude of low-frequency fluctuation (ALFF) values among the three groups. Then we analysed the difference in functional connectivity (FC) based on the differential regions of ALFF. Additionally, we examined the relationship between FC of differential brain regions and abstinence duration. The differences in ALFF among the three groups were found to be significant in the bilateral putamen and left inferior parietal lobule (single voxel p < 0.001, cluster level p < 0.05 and GRF-corrected). Compared with the PA6 group, the PA11 group showed lower ALFF values of the differential regions with a tendency toward the HC group. Meanwhile, the PA11 group showed lower FC between the left putamen and left insula, between the right putamen and left insula and between the left inferior parietal lobule and bilateral inferior frontal gyrus (IFG), but higher FC between the left putamen and left inferior temporal gyrus. The above FC of HDIs negatively correlated with the abstinence duration, except for the left putamen-inferior temporal gyrus FC. The prolonged abstinence duration may be useful to restore the impaired brain function of HDIs to some extent, although more data are needed to validate this in future studies.

BackgroundMethamphetamine stands as one of the most widely abused drugs globally. Methamphetamine Use Disorder not only impairs the physical and mental wellbeing of addicts but also elevates their risk of suicide. Despite the high suicide rate among individuals with methamphetamine use disorder, research on their clinical characteristics and suicide risk factors remains scarce. Therefore, it is imperative to investigate the risk factors associated with suicidal ideation in individuals with methamphetamine use disorder.MethodsEmploying Respondent Driven Sampling (RDS), a total of 11,825 individuals with methamphetamine use disorder were selected from April to May 2023 in Guangdong, China. The individuals with methamphetamine use disorder were assessed using the Beck Scale for Suicide Ideation (BSSI), and the detection rate of suicidal ideation among these patients was 23.92%. The Bayesian Mindsponge Framework (BMF) analysis was utilized to examine the risk factors for suicidal ideation among these individuals.ResultsThe result revealed that trait depression and cognitive impairment are positively correlated with suicidal ideation in people, whereas social support has a moderating effect on the relationship between trait depression and cognitive impairment with suicidal ideation.ConclusionSuicidal ideation in individuals with methamphetamine use disorder is influenced by a multitude of factors, including family, society, and stress. Consequently, comprehensive intervention measures are essential to address this issue.

In this study, we introduce Event AutoAugment (EAA), a novel adaptive data augmentation (DA) technique tailored for event detection in Natural Language Processing (NLP). EAA optimizes word-level operations using a reinforcement learning framework, where the augmentation strategies for each are determined through a discrete search over a predefined set of actions, including deletions, synonyms replacement, and more. The efficacy of these strategies hinges on a carefully formulated reward function that assesses the impact of augmentations on the event detection model's performance. This reward function provides feedback on the suitability of applied augmentations, guiding the augmentation model to discover a jointly optimal policy that adaptively enhances data diversity and model robustness. Our framework evaluated on two established benchmark datasets, ACE05 and MAVEN, which feature a wide variety of types. To simulate low-resource conditions-a common challenge in real-world NLP applications-we employ stratified subsets of these datasets, using 1% to 30% of the original data size while preserving the distribution of event types. The adaptability and efficacy of EAA are evidenced by substantial improvements in accuracy across these varying data sizes. For instance, on the ACE05 dataset, EAA achieves a 78% increase in the F1 score in the smallest data subset compared to the baseline without augmentation. Similarly, MAVEN dataset, it shows a 120% increase, highlighting EAA's capability to significantly enhance data diversity and robustness, outperforming both learnable and rule-based augmentation methods across different scales data scarcity.

This study presents a pioneering advancement in the application of Direct Laser Interference Patterning (DLIP) by integrating it with a high-power, multimode nanosecond fiber laser based on an innovative technology called Extended Laser Interference Patterning System (ELIPSYS (R), SurFunction GmbH). This configuration demonstrated the ability to create well-defined interference patterns with a periodicity of 24 mu m over a deep focus range of approximately 0.6 mm, making it suitable for large-area surface microprocessing. The influence of laser parameters, particularly pulse energy (E-p) and pulse-to-pulse overlap (O-p), on the depth and quality of the laser-produced structures is investigated. By taking into consideration both structure depth and its variation across the patterned area, an optimal process window for superior structure quality is identified. Considering a relative deviation lower than 15 % in the structure depth, periodic structures with a 24.0 mu m spatial period and depths from similar to 1.0 mu m to 18.7 mu m could be fabricated with pulse overlaps from similar to 60 % to 95 % and pulse energies from 20 mJ to 50 mJ.

The preparation of multifunctional microwave absorbing materials with the characteristics of stronger absorption, wider bandwidth, lighter weight, higher strength and thinner thickness is an urgent research direction. Due to the difficulty of controllable distribution of absorbers, excellent microwave absorption performance is usually accompanied by large absorber amount and thickness, which can result in microwave absorption composite materials lack flexibility, mechanical properties, and adaptability to the environment. In this study, Ti3C2Tx/ Fe3O4/glass fiber paper composite was prepared by vacuum-assisted filtration. The controllable pore structure constructed by glass fiber enables uniform distribution of MXene/Fe3O4 and effectively spreading of MXene monolayer film, enhancing conductivity loss and interface loss. By controlling the diameter of glass fiber, the pore size and the distribution of absorber can be regulated, thereby achieving a minimum reflection loss of up to -57.2 dB at an ultra-low filling ratio of 1.66 wt%. With the addition amount of 1.90 wt%, the effective absorption bandwidth can cover the whole X-band. The ultra-low specific reflection loss exceeds most of fabric materials and MXene-based absorbers. Meanwhile, the composite material exhibited excellent mechanical properties with a tensile strength of 3.054 kN/m and a burst resistance of 290 kPa. In addition, the adhesion between the filler and the glass fiber was increased by adding acrylic resin-based aqueous adhesive, and the surface grafting functional group (-F) made the composite material superhydrophobic. This work provides a new strategy to synthesize multifunctional absorbing material with low filling ratio and strong environmental practicality.

Substance use disorders (SUDs), including Alcohol Use Disorder, are pressing global public health problems. Executive functions (EFs) are prominently featured in mechanistic models of addiction. However, significant gaps remain in our understanding of EFs in SUDs, including the dimensional relationships of EFs to underlying neural circuits, molecular biomarkers, disorder heterogeneity, and functional ability. Transforming health outcomes for people with SUDs requires an integration of clinical, biomedical, preclinical, and health services research. Through such interdisciplinary research, we can develop policies and interventions that align with biopsychosocial models of addiction, addressing the complex cognitive concerns of people with SUDs in a more holistic and effective way. Here, we introduce the design and procedures underlying Cognitive Dysfunction in the Addictions (CDiA), an integrative research program, which aims to fill these knowledge gaps and facilitate research discoveries to enhance treatments for people living with SUDs. The CDiA Program comprises seven interdisciplinary projects that aim to evaluate the central thesis that EF has a crucial role in functional outcomes in SUDs. The projects draw on a diverse sample of adults aged 18-60 (target N=400) seeking treatment for SUD, who are followed over one year to identify specific EF domains most associated with improved functioning. Projects 1-3 investigate SUD symptoms, brain circuits, and blood biomarkers and their associations with key EF domains (inhibition, working memory, and set-shifting) and functional outcomes (disability, quality of life). Projects 4 and 5 evaluate interventions for SUDs and their impacts on EF: a clinical trial of repetitive transcranial magnetic stimulation and a preclinical study of potential new pharmacological treatments in rodents. Project 6 links EF to healthcare utilization and is supplemented with a qualitative investigation of EF-related barriers to treatment engagement. Project 7 uses whole-person modeling to integrate the multi-modal data generated across projects, applying clustering and deep learning methods to identify patient subtypes and drive future cross-disciplinary initiatives. The CDiA Program will bring scientific domains together to uncover novel ways in which EFs are linked to SUD severity and functional recovery, and facilitate future discoveries to improve health outcomes in individuals living with SUDs.

Background Methadone maintenance treatment (MMT) and protracted abstinence (PA) effectively reduce the craving for heroin among individuals with heroin use disorder (HUD). However, the difference in their effects on brain function, especially the coupling among the large-scale brain networks (default mode [DMN], salience [SN], and executive control [ECN] networks), remains unclear. This study analyzed the effects of the MMT and PA on these networks and the predictive value of the bilateral resource allocation index (RAI) for craving for heroin.Methods Twenty-five individuals undergoing the MMT, 22 undergoing the PA, and 51 healthy controls underwent resting-state functional magnetic resonance imaging (rs-fMRI). Independent component analysis identified the ECN, DMN, and SN. The SN-ECN and SN-DMN connectivity and the bilateral RAI were evaluated. The relationships between network coupling and clinical and psychological characteristics were analyzed. The multiple linear regression model identified significant variables for predicting craving scores.Results The MMT group showed significantly stronger SN-left ECN (lECN) coupling and left RAI than the PA group. In the MMT group, SN-lECN connectivity and bilateral RAI were positively correlated with the total methadone dose. In both treatment groups, SN-right ECN (rECN) connectivity and right RAI were negatively correlated with craving. The models revealed that the bilateral RAI and the MMT and PA were associated with the craving.Conclusions The MMT enhances SN-lECN coupling and the left RAI more than the PA, possibly due to higher control modulation. The RAI could help predict heroin craving in individuals with HUD undergoing either treatment program.

Objective: Parkinson's disease (PD) exhibits high heterogeneity in terms of clinical features and prognosis. It can be classified into tremor-dominant (TD) and akinetic rigid-dominant (ARD) subtypes. However, changes in dynamic brain connectivity characteristics in patients with ARD PD remain unclear. Therefore, this study aimed to explore brain networks’ dynamic functional connectivity characteristics in patients with TD and ARD PD and their correlation with clinical symptoms. Methods: This study included 37 patients with TD PD, 33 with ARD PD, and 30 healthy controls (HCs). Resting-state functional magnetic resonance imaging (fMRI) data were obtained from all participants. Independent component analysis (ICA) and graph theory analyses were used to calculate the three groups’ dynamic functional network connectivity characteristics. We also examined the correlations between these characteristics and the clinical assessment indices of motor function. Results: Eight functional networks were identified, and four connectivity states were clustered. Compared with the ARD group, the TD group exhibited significantly increased connectivity between the Executive Control Network (ECN) and Visual Network (VIS) in the sparse high-frequency state. In the two tightly coupled states, the TD and ARD groups showed increased connectivity within the default mode network (DMN), decreased connectivity within the sensorimotor network (SMN), and decreased connectivity between the attention network (ATN) and VIS than the HCs. However, the TD group showed decreased connectivity between the VIS and SMN or auditory networks, and the ARD group exhibited reduced connectivity between the VIS and ECN. The Unified Parkinson's Disease Rating Scale Part III scores of both groups of patients with PD were negatively correlated with the number of state transitions and positively correlated with the score window and average dwelling time of the sparse high-frequency state. The graph theory analysis revealed a significantly reduced global network efficiency in patients with PD compared to HCs. Regarding nodal local efficiency, the ARD group exhibited a lower variance in SMN than the TD group and a lower variance in DMN than the HCs. Conclusion: Significant differences were noted in brain connectivity properties between the different PD motor subtypes, primarily in the SMN, DMN, and VIS. The activity of the brain connectivity states in patients with PD is significantly correlated with the severity of motor deficits.

Direct Laser Interference Patterning (DLIP) is an emerging manufacturing technology for creating functional surfaces. To integrate DLIP into industrial processes, reliable and rapid methods for determining the quality of the produced microstructures are essential. Surface roughness evaluation methods are typically designed to quantify variations in surface morphology relative to an ideal uniform surface. As a result, these methods are inadequate for assessing the quality of periodic structures generated by DLIP, requiring the implementation of additional algorithms to mitigate their influence. Recently, a method that uses Gini analysis on surface topographical parameters was established for this purpose. It first divides the surface into a series of decomposed parts according to the period and then uses the Gini coefficient to statistically describe the roughness parameters obtained from each part to evaluate the homogeneity. However, the resulting value is strongly dependent on the type and number of parameters selected. This work introduces a novel approach that employs Gini analysis of two-dimensional Fast Fourier Transform (2D FFT) on topography images, providing both qualitative and quantitative assessments of texture homogeneity. The comparison of those two methods indicates that the FFT Gini method correlates well with homogeneity measurements directly taken from the surface topography and demonstrates the advantage of evaluating the structuring process at high proceeding speed.

In this study, we present EventRL, a reinforcement learning approach developed to enhance event extraction for large language models (LLMs). EventRL utilizes outcome supervision with specific reward functions to tackle prevalent challenges in LLMs, such as instruction following and hallucination, manifested as the mismatch of event structure and the generation of undefined event types. We evaluate EventRL against existing methods like Few-Shot Prompting (FSP) (based on GPT4) and Supervised Fine-Tuning (SFT) across various LLMs, including GPT-4, LLaMa, and CodeLLaMa models. Our findings show that EventRL significantly outperforms these conventional approaches by improving the performance in identifying and structuring events, particularly in handling novel event types. The study emphasizes the critical role of reward function selection and demonstrates the benefits of incorporating code data for better event extraction. While increasing model size leads to higher accuracy, maintaining the ability to generalize is essential to avoid overfitting.

Individuals with opioid use disorder (OUD) have been reported to show abnormal brain metabolism and impaired coupling among brain networks such as the default mode network (DMN), salience network (SN), and executive control network (ECN). However, the characteristics of brain glucose metabolism and its related functions in the brain networks in individuals with OUD remain unknown. Thirty-six individuals with OUD and thirty matched healthy controls (HCs) were recruited in this integrated positron emission tomography/magnetic resonance imaging (PET/MRI) study. Differences in glucose metabolism were analyzed by using 18F-fluorodeoxyglucose (18F-FDG), and the corresponding coupling characteristics of the individuals with OUD were also analyzed. The individuals with OUD showed widespread bilateral hypometabolism in the middle temporal gyrus (MTG), superior temporal gyrus, angular gyrus, supramarginal gyrus, inferior parietal lobe, Rolandic operculum, and left insula, but obvious hypermetabolism in the brainstem and left cerebellum. Meanwhile, in individuals with OUD, the hypometabolism of right MTG which is included in the DMN was accompanied by decreased coupling with the left superior frontal gyrus and right superior parietal gyrus which are included in the ECN. Furthermore, individuals with OUD showed a positive correlation between the duration of heroin use and glucose metabolism of the left MTG. The individuals with OUD were characterized by widespread bilateral hypometabolism in the temporal and parietal regions but obvious hypermetabolism in the brainstem and left cerebellum. The results suggest that the hypometabolism in the temporal and parietal regions might be related to DMN dysfunction and the hypermetabolism in the brainstem and left cerebellum may be compensate for other brain regions showing hypometabolism. In particular, hypometabolism in the self-referential-related DMN regions in OUD might attenuate their relationships with the inhibitory-control-related ECN regions. These findings highlight the importance of evaluating the metabolic and functional profiles of the right MTG in future studies on the treatment of OUD.

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.

Studies have found that the absence of glial cell line-derived neurotrophic factor may be the primary risk factor for Parkinson's disease. However, there have not been any studies conducted on the potential relationship between glial cell line-derived neurotrophic factor and cognitive performance in Parkinson's disease. We first performed a retrospective case-control study at the Affiliated Hospital of Xuzhou Medical University between September 2018 and January 2020 and found that a decreased serum level of glial cell line-derived neurotrophic factor was a risk factor for cognitive disorders in patients with Parkinson's disease. We then established a mouse model of Parkinson's disease induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine and analyzed the potential relationships among glial cell line-derived neurotrophic factor in the prefrontal cortex, dopamine transmission, and cognitive function. Our results showed that decreased glial cell line-derived neurotrophic factor in the prefrontal cortex weakened dopamine release and transmission by upregulating the presynaptic membrane expression of the dopamine transporter, which led to the loss and primitivization of dendritic spines of pyramidal neurons and cognitive impairment. In addition, magnetic resonance imaging data showed that the long-term lack of glial cell line-derived neurotrophic factor reduced the connectivity between the prefrontal cortex and other brain regions, and exogenous glial cell line-derived neurotrophic factor significantly improved this connectivity. These findings suggested that decreased glial cell line-derived neurotrophic factor in the prefrontal cortex leads to neuroplastic degeneration at the level of synaptic connections and circuits, which results in cognitive impairment in patients with Parkinson's disease.

Background: Increasing evidence suggests that heroin addiction may be related to the dysfunction among the triple brain network (default mode network [DMN], salience network [SN] and executive control network [ECN]). However, the characteristics of glucose metabolism and metabolic connectivity among core regions of the triple brain network remain unknown. Therefore, we hypothesized that individuals with heroin dependence would show abnormal glucose metabolism and accompanied abnormal metabolic connectivity within the triple brain network. Methods: Individuals with heroin dependence and healthy controls matched for age and sex underwent integrated positron emission tomography/magnetic resonance imaging (PET/MRI). Differences in glucose metabolism and metabolic connectivity among the DMN, SN and ECN were analyzed based on 18F-fluorodeoxyglucose PET and resting-state fMRI data. Results: We included 36 individuals with heroin dependence and 30 matched healthy controls in our study. The heroin dependence group showed a significant reduction of glucose metabolism in the bilateral anterior insula (AI) and inferior parietal lobule (IPL), and a significantly decreased metabolic connectivity between the right AI and the left dorsolateral prefrontal cortex (DLPFC). The daily dose of methadone was negatively correlated with glucose metabolism of the right AI and right IPL. Limitations: The results revealed the glucose metabolism alterations and metabolic connectivity only within the triple brain network in individuals with heroin dependence; additional brain networks should be investigated in future studies. Although methadone is an opioid with a similar neurophysiological mechanism as heroin, the specific chronic effects of methadone on cerebral metabolism and metabolic connectivity should also be investigated in future studies. Conclusion: Our findings suggest that long-term opioid use might, to some extent, be associated with reduced synergistic ability between the SN and ECN, which may be associated with the dysfunction of cognitive control. In particular, the right AI, which showed hypometabolism and related reduction in SN–ECN metabolic connectivity, should receive increasing attention in future studies.

BackgroundMethadone maintenance treatment (MMT) is a common treatment for heroin use disorder (HUD). Although individuals with HUD have been reported to show impaired coupling among the salience network (SN), executive control network (ECN), and default mode network (DMN), the effects of MMT on the coupling among three large-scale networks in individuals with HUD remains unclear. MethodsThirty-seven individuals with HUD undergoing MMT and 57 healthy controls were recruited. The longitudinal one-year follow-up study aimed to evaluate the effects of methadone on anxiety, depression, withdrawal symptoms and craving and number of relapse, and brain function (SN, DMN and bilateral ECN) in relation to heroin dependence. The changes in psychological characteristics and the coupling among large-scale networks after 1 year of MMT were analyzed. The associations between the changes in coupling among large-scale networks and psychological characteristics and the methadone dose were also examined. ResultsAfter 1 year of MMT, individuals with HUD showed a reduction in the withdrawal symptom score. The number of relapses was negatively correlated with the methadone dose over 1 year. The functional connectivity between the medial prefrontal cortex (mPFC) and the left middle temporal gyrus (MTG; both key nodes of the DMN) was increased, and the connectivities between the mPFC and the anterior insular and middle frontal gyrus (key nodes of the SN) were also increased. The mPFC-left MTG connectivity was negatively correlated with the withdrawal symptom score. ConclusionLong-term MMT enhanced the connectivity within the DMN which might be related to reduced withdrawal symptoms, and that between the DMN and SN which might be related to increase in salience values of heroin cues in individuals with HUD. Long-term MMT may be a double-edged sword in treatment for HUD.