Drug addiction or substance use disorder (SUD), has been conceptualized as a three-stage (i.e. binge/intoxication, withdrawal/negative affect, and preoccupation/anticipation/craving) recurring cycle that involves complex changes in neuroplasticity, reward, motivation, desire, stress, memory, and cognitive control, and other related brain regions and brain circuits. Neuroimaging approaches, including magnetic resonance imaging, have been key to mapping neurobiological changes correlated to complex brain regions of SUD. In this review, we highlight the neurobiological mechanisms of these three stages of addiction. The abnormal activity of the ventral tegmental, nucleus accumbens, and caudate nucleus in the binge/intoxication stage involve the reward circuit of the midbrain limbic system. The changes in the orbitofrontal cortex, dorsolateral prefrontal cortex, amygdala, and hypothalamus emotional system in the withdrawal/negative affect stage involve increases in negative emotional states, dysphoric-like effects, and stress-like responses. The dysregulation of the insula and prefrontal lobes is associated with craving in the anticipation stage. Then, we review the present treatments of SUD based on these neuroimaging findings. Finally, we conclude that SUD is a chronically relapsing disorder with complex neurobiological mechanisms and multimodal stages, of which the craving stage with high relapse rate may be the key element in treatment efficacy of SUD. Precise interventions targeting different stages of SUD and characteristics of individuals might serve as a potential therapeutic strategy for SUD.

Information stored in working memory can guide perception selection, and this process is modulated by cognitive control. Although previous studies have demonstrated that neurostimulation over the left dorsolateral prefrontal cortex (lDLPFC) contributes to restore cognitive control among individuals with substance use disorder (SUD), there remains an open question about the potential stimulation effects on memory-driven attention. To address this issue, the present study adopted a combined working memory/attention paradigm while employing high-definition transcranial direct current stimulation (HD-tDCS) to stimulate the lDLPFC. Observers were asked to maintain visual or audiovisual information in memory while executing a search task, while the validity of the memory contents for the subsequent search task could be either invalid or neutral. The results showed a faint memory-driven attentional suppression effect in sham stimulation only under the audiovisual condition. Moreover, anodal HD-tDCS facilitated attentional suppression effect in both the strength and temporal dynamics under the visual-only condition, whereas the effect was impaired or unchanged under the audiovisual condition. Surprisingly, cathodal HD-tDCS selectively improved temporal dynamics of the attentional suppression effect under the audiovisual condition. The present study revealed the differential enhancement of HD-tDCS on cognitive control over visual and audiovisual memory-driven attention among individuals with SUD.

UNASSIGNED: This study investigated the impact of different modes of physical exercise on the emotional and cognitive levels of patients with Substance Use Disorder (SUD). By exploring the most effective intervention types, cycle, frequency, and duration, we aimed to provide evidence-based recommendations for the adjunctive treatment of SUD.
UNASSIGNED: We conducted a systematic search in five databases, including PubMed, Web of Science, The Cochrane Library, ScienceDirect, and EBSCO, from database inception up to May 2023, and identified 4,255 randomized controlled trials addressing the influence of physical exercise on the emotional and cognitive levels of SUD patients. Data extraction and analysis were performed using Review Manager 5.4 software, focusing on 11 studies that met the inclusion criteria and included 895 participants. Subsequently, a meta-analysis was conducted using Stata 16.0 software, presenting the results in the form of standardized mean differences (SMD) and 95% confidence intervals (CI).
UNASSIGNED: Our findings indicate that physical exercise significantly alleviates anxiety and depression in SUD patients while improving their cognitive function. Specifically, physical exercise was found to reduce anxiety (SMD = -0.726 [-1.349, -0.103], p < 0.05) and depression (SMD = -0.666 [-1.077, -0.255], p < 0.05) and enhance cognitive levels (SMD = -0.523 [-0.887, -0.159], p < 0.05) among patients. Subgroup analysis further revealed that SUD patients benefitted most from physical exercise when engaging in aerobic exercises lasting over 12 weeks, with a frequency exceeding 40 sessions and each session lasting more than 60 min.
UNASSIGNED: In conclusion, our study affirms that physical exercise mitigates anxiety and depression while enhancing cognitive function in SUD patients, making it an effective measure for adjunctive clinical treatment.

BACKGROUND: Addiction is a chronic and relapsing brain disorder. Despite numerous neuroimaging and neurophysiological studies on individuals with substance use disorder (SUD) or behavioral addiction (BEA), currently a clear neural activity signature for the addicted brain is lacking.
METHODS: We first performed systemic coordinate-based meta-analysis and partial least-squares regression to identify shared or distinct brain regions across multiple addictive disorders, with abnormal resting-state activity in SUD and BEA based on 46 studies (55 contrasts), including regional homogeneity (ReHo) and low-frequency fluctuation amplitude (ALFF) or fractional ALFF. We then combined Neurosynth, postmortem gene expression, and receptor/transporter distribution data to uncover the potential molecular mechanisms underlying these neural activity signatures.
RESULTS: The overall comparison between addiction cohorts and healthy subjects indicated significantly increased ReHo and ALFF in the right striatum (putamen) and bilateral supplementary motor area, as well as decreased ReHo and ALFF in the bilateral anterior cingulate cortex and ventral medial prefrontal cortex, in the addiction group. On the other hand, neural activity in cingulate cortex, ventral medial prefrontal cortex, and orbitofrontal cortex differed between SUD and BEA subjects. Using molecular analyses, the altered resting activity recapitulated the spatial distribution of dopaminergic, GABAergic, and acetylcholine system in SUD, while this also includes the serotonergic system in BEA.
CONCLUSIONS: These results indicate both common and distinctive neural substrates underlying SUD and BEA, which validates and supports targeted neuromodulation against addiction.
BACKGROUND: This work was supported by the National Natural Science Foundation of China and Intramural Research Program of the National Institute on Drug Abuse, National Institutes of Health.

Behavioral addiction (BA) and substance use disorder (SUD) share similarities and differences in clinical symptoms, cognitive functions, and behavioral attributes. However, little is known about whether and how functional networks in the human brain manifest commonalities and differences between BA and SUD. Voxel-wise meta-analyses of resting-state functional connectivity (rs-FC) were conducted in BA and SUD separately, followed by quantitative conjunction analyses to identify the common and distinct alterations across both the BA and SUD groups. A total of 92 datasets with 2444 addicted patients and 2712 healthy controls (HCs) were eligible for the meta-analysis. Our findings demonstrated that BA and SUD exhibited common alterations in rs-FC between frontoparietal network (FPN) and other high-level neurocognitive networks (i.e. default mode network (DMN), affective network (AN), and salience network (SN)) as well as hyperconnectivity between SN seeds and the Rolandic operculum in SSN. In addition, compared with BA, SUD exhibited several distinct within- and between-network rs-FC alterations mainly involved in the DMN and FPN. Further, altered within- and between-network rs-FC showed significant association with clinical characteristics such as the severity of addiction in BA and duration of substance usage in SUD. The common rs-FC alterations in BA and SUD exhibited the relationship with consistent aberrant behaviors in both addiction groups, such as impaired inhibition control and salience attribution. By contrast, the distinct rs-FC alterations might suggest specific substance effects on the brain neural transmitter systems in SUD.

Aldehyde dehydrogenase-2 (ALDH2) is a crucial enzyme participating in intracellular aldehyde metabolism and is acknowledged as a potential therapeutic target for the treatment of alcohol use disorder and other addictive behaviors. Using previously reported ALDH2 inhibitors of Daidzin, CVT-10216, and CHEMBL114083 as reference molecules, here we perform a ligand-based virtual screening of world-approved drugs via 2D/3D similarity search methods, followed by the assessments of molecular docking, toxicity prediction, molecular simulation, and the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) analysis. The 2D molecular fingerprinting of ECFP4 and FCFP4 and 3D molecule-shape-based USRCAT methods show good performances in selecting compounds with a strong binding behavior with ALDH2. Three compounds of Zeaxanthin (q = 0), Troglitazone (q = 0), and Sequinavir (q = +1 e) are singled out as potential inhibitors; Zeaxanthin can only be hit via USRCAT. These drugs displayed a stronger binding strength compared to the reported potent inhibitor CVT-10216. Sarizotan (q = +1 e) and Netarsudil (q = 0/+1 e) displayed a strong binding strength with ALDH2 as well, whereas they displayed a shallow penetration into the substrate-binding tunnel of ALDH2 and could not fully occupy it. This likely left a space for substrate binding, and thus they were not ideal inhibitors. The MM-PBSA results indicate that the selected negatively charged compounds from the similarity search and Vina scoring are thermodynamically unfavorable, mainly due to electrostatic repulsion with the receptor (q = -6 e for ALDH2). The electrostatic attraction with positively charged compounds, however, yielded very strong binding results with ALDH2. These findings reveal a deficiency in the modeling of electrostatic interactions (in particular, between charged moieties) in the virtual screening via the 2D/3D similarity search and molecular docking with the Vina scoring system.

Over the past few years, there has been increasing evidence highlighting the strong connection between gut microbiota and overall well-being of the host. This has led to a renewed emphasis on studying and addressing substance use disorder from the perspective of brain-gut axis. Previous studies have suggested that alcohol, food, and cigarette addictions are strongly linked to gut microbiota and faecal microbiota transplantation or the use of probiotics achieved significant efficacy. Unfortunately, little is known about the relationship between drug abuse and gut microbiota. This paper aims to reveal the potential correlation between gut microbiota and drug abuse and to develop an accurate identification model for drug-related faeces samples by machine learning. Faecal samples were collected from 476 participants from three regions in China (Shanghai, Yunnan, and Shandong). Their gut microbiota information was obtained using 16S rRNA gene sequencing, and a substance use disorder identification model was developed by machine learning. Analysis revealed a lower diversity and a more homogeneous gut microbiota community structure among participants with substance use disorder. Bacteroides, Prevotella_9, Faecalibacterium, and Blautia were identified as important biomarkers associated with substance use disorder. The function prediction analysis revealed that the citrate and reductive citrate cycles were significantly upregulated in the substance use disorder group, while the shikimate pathway was downregulated. In addition, the machine learning model could distinguish faecal samples between substance users and nonsubstance users with an AUC = 0.9, indicating its potential use in predicting and screening individuals with substance use disorder within the community in the future.

Smartphone-based apps are increasingly used to prevent relapse among those with substance use disorders (SUDs). These systems collect a wealth of data from participants, including the content of messages exchanged in peer-to-peer support forums. How individuals self-disclose and exchange social support in these forums may provide insight into their recovery course, but a manual review of a large corpus of text by human coders is inefficient.
The study sought to evaluate the feasibility of applying supervised machine learning (ML) to perform large-scale content analysis of an online peer-to-peer discussion forum. Machine-coded data were also used to understand how communication styles relate to writers\' substance use and well-being outcomes.
Data were collected from a smartphone app that connects patients with SUDs to online peer support via a discussion forum. Overall, 268 adult patients with SUD diagnoses were recruited from 3 federally qualified health centers in the United States beginning in 2014. Two waves of survey data were collected to measure demographic characteristics and study outcomes: at baseline (before accessing the app) and after 6 months of using the app. Messages were downloaded from the peer-to-peer forum and subjected to manual content analysis. These data were used to train supervised ML algorithms using features extracted from the Linguistic Inquiry and Word Count (LIWC) system to automatically identify the types of expression relevant to peer-to-peer support. Regression analyses examined how each expression type was associated with recovery outcomes.
Our manual content analysis identified 7 expression types relevant to the recovery process (emotional support, informational support, negative affect, change talk, insightful disclosure, gratitude, and universality disclosure). Over 6 months of app use, 86.2% (231/268) of participants posted on the app\'s support forum. Of these participants, 93.5% (216/231) posted at least 1 message in the content categories of interest, generating 10,503 messages. Supervised ML algorithms were trained on the hand-coded data, achieving F1-scores ranging from 0.57 to 0.85. Regression analyses revealed that a greater proportion of the messages giving emotional support to peers was related to reduced substance use. For self-disclosure, a greater proportion of the messages expressing universality was related to improved quality of life, whereas a greater proportion of the negative affect expressions was negatively related to quality of life and mood.
This study highlights a method of natural language processing with potential to provide real-time insights into peer-to-peer communication dynamics. First, we found that our ML approach allowed for large-scale content coding while retaining moderate-to-high levels of accuracy. Second, individuals\' expression styles were associated with recovery outcomes. The expression types of emotional support, universality disclosure, and negative affect were significantly related to recovery outcomes, and attending to these dynamics may be important for appropriate intervention.

BACKGROUND: Epidemiologic evidence on whether iron accumulation in brain modified the association between artificial light at night (ALAN) and incident mental disorders is lacking. The authors aims to investigate modification of brain iron deposition on the associations of ALAN with multiple mental disorders in the middle-aged and older adults.
METHODS: This prospective study used data from the UK Biobank. ALAN was drawn from satellite datasets. Susceptibility-weighted magnetic resonance imaging was used to ascertain iron content of each brain region. T2* signal loss was used as indices of iron deposition. The main outcomes are impacts of ALAN exposure on onset of wide spectrum of physician-diagnosed mental disorders, which was estimated by time-varying Cox proportional hazard model. The authors further conducted stratified analyses by levels of iron brain deposition to examine the potential modifying effects.
RESULTS: Among 298,283 participants followed for a median of 10.91 years, higher ALAN exposure was associated with increased risk of mental disorders. An IQR (11.37 nW/cm2/sr) increase in annual levels of ALAN was associated with an HR of 1.050 (95 % CI: 1.034,1.066) for any mental disorder, 1.076 (95 % CI: 1.053,1.099) for substance use disorder, and 1.036 (95 % CI: 1.004,1.069) for depression disorder in fully adjusted models. The exposure-response curves showed steeper trends at lower ALAN levels and a plateau at higher exposures. The associations were stronger in participants with high iron deposition in left hippocampus, left accumbens and left pallidum.
CONCLUSIONS: ALAN was associated with multiple mental disorders in the middle-aged and older adults, and the findings indicated stricter standards of ALAN is needed and targeted preventive measures are warranted, especially with high brain iron deposition.

Background: Explicitly expressing the hidden opportunity cost in intertemporal choice significantly reduces healthy participants\' delay discounting - a phenomenon named the \"hidden-zero effect,\" which is undetermined in individuals with substance use disorder (SUD).Objectives: This study aimed to determine whether the hidden-zero effect occurs among individuals with opioid use disorder (OUD) and the degree to which this effect differs between the OUD group and healthy controls.Methods: In two different experiments, Exp#1) 29 male individuals with OUD (13.5 ± 6.0 months abstinence) and 29 male controls performed an intertemporal choice task (ICT); Exp#2) 28 male individuals with OUD (17.5 ± 5.6 months abstinence) and 27 male controls performed a delay discounting task (DDT). The OUD group was recruited from a mandatory treatment, and controls from WeChat. There were two choice conditions in both two tasks: the hidden-zero (H0) condition (standard), and the explicit-zero (E0) condition (explicitly expressing opportunity cost).Results: Compared with the H0 condition, all participants\' delay discounting was significantly decreased in the E0 condition (ps < .05, ηp2 = 0.254, 0.110). There was no significant difference in the changed degree between these two groups in either experiment (ps > .05). The delay discounting of the OUD group was significantly higher than that of controls only in Experiment 2 (p < .001, ηp2 = 0.376).Conclusion: This study extended the population in which the hidden-zero effect occurs to individuals with OUD. With respect to delay discounting, the hidden-zero effect benefit did not differ in OUD and control participants.