Opioid use disorder (OUD) is associated with persistent catecholaminergic dysfunction, but its impact on large-scale cortical organization remains unclear. We used pharmacological resting-state fMRI and functional gradient mapping to test whether OUD alters catecholaminergic modulation of cortical hierarchy. Fifty-three individuals with OUD and 40 healthy controls underwent resting-state fMRI after placebo and methylphenidate. Under placebo, principal and secondary cortical gradients showed canonical organization with no group differences. In controls, methylphenidate compressed the dynamic range of the principal gradient while preserving its spatial topology, indicating state-dependent modulation of the unimodal-transmodal hierarchy. This response was attenuated in OUD and further reduced among participants receiving methadone or buprenorphine. Methylphenidate improved visual attention in controls but not OUD, and gradient modulation predicted attentional benefit only in controls. Gradient features also predicted years of opioid use. These findings identify blunted catecholaminergic modulation of cortical hierarchy as a clinically relevant systems-level feature of OUD.

Functional constipation (FC) is a common gastrointestinal condition often accompanied by anxiety and depression status (FCAD). Gastrointestinal symptoms in FCAD patients are not fully resolved with medication, and the responses to treatment can differ significantly from those of patients without anxiety and depression (FCNAD). Given the distinct effects of FC and mental status on brain function and structure, we hypothesize that these brain differences could serve as imaging features to differentiate FCAD and FCNAD. Patients with FC (N = 187) underwent structural magnetic resonance imaging, diffusion tensor imaging scans, and completed self-reported assessments of depression and anxiety. The current study first identified FCNAD and FCAD patients with high- and low-confidence labels based on self-reported ratings. Brain structural imaging features were subsequently extracted to train and refine the classification model using a stagewise training approach. The classification model achieved an average accuracy of 89.23% during the cross-validation, and the predicted probability of FCAD was significantly correlated with mental ratings and gastrointestinal symptoms. The top 30 imaging features contributing to classification were primarily located in brain regions involved in emotional processing (temporal pole, amygdala, orbitofrontal cortex), somatosensory (insula), and motor control (corticospinal tract, inferior cerebellar peduncle). These findings highlight potential brain imaging features distinguishing FCAD and FCNAD, which may provide the incremental value over standard behavioral assessments for future personalized diagnosis and treatment strategies.

Evidence is emerging that chronic exposure to opioids can impact brain structure, with preclinical studies demonstrating alterations in subcortical regions, including the hippocampus; however, corresponding evidence in humans remains limited. The present study investigates whether individuals with opioid use disorder (OUD) exhibit smaller hippocampal volumes relative to controls and explores heterogeneity within the OUD group as well as associations between hippocampal volume and cognitive performance. Participants with OUD (n = 94) and controls (n = 40) completed a structural magnetic resonance imaging scan, and a subset of 18 OUD and 10 controls who completed a standardized neurocognitive battery. Hippocampal volumes were extracted using the Automatic Segmentation of Hippocampal Subfields (ASHS). Primary analyses examined group differences in bilateral whole, anterior, and posterior hippocampus. Exploratory analysis assessed heterogeneity within OUD (e.g., methadone-treated vs buprenorphine-treated vs no treatment) and associations between hippocampal volumes and cognitive performance. Individuals with OUD exhibited significantly smaller left and right hippocampal volumes relative to controls. When examining anterior vs posterior subregions, significant effects were strongest in the posterior right hippocampus. Exploratory analyses further suggested significant heterogeneity within the OUD group, with methadone-treated individuals exhibiting smaller posterior volumes relative to controls and buprenorphine-treated participants. In the neurocognitive subset, hippocampal volume was associated with episodic memory performance. These findings suggest that OUD is associated with smaller hippocampal volumes and that clinically meaningful heterogeneity exists within OUD populations, warranting further investigation into the mechanisms underlying these differences.

Deep learning models have shown promising performance in analyzing functional magnetic resonance imaging (fMRI) data for brain disorder identification. However, most approaches focus on single or dual measures with limited cross-view interaction, failing to fully capture information across time series (TS), static functional connectivity (sFC), and dynamic functional connectivity (dFC). Moreover, interpretability relies primarily on qualitative visualization rather than quantitative assessment. Thus, we propose MVF-XT, an interpretable multi-view fusion Transformer that integrates all three fMRI measures through explicit cross-view interaction. Unlike conventional patch embedding designed for images, we introduce connectional patch embedding (CPE) that preserves the ROI-aligned semantic structure of functional connectivity matrices. Based on CPE, a static-dynamic cross-attention (SDCA) module is designed to enable bidirectional information exchange between sFC and dFC, allowing them to mutually enrich each other. The TS view is processed through a temporal Transformer to capture global temporal dynamics. These three view-specific representations are then concatenated and fed to a classification head for final prediction. To understand how the model makes these predictions and to validate the neurobiological plausibility of learned patterns, we introduce two quantitative interpretability methods: Difference Alignment (DiA) correlates model decisions with group-level connectivity differences, while Physiological Relevance (PR) assesses associations with behavioral measures. MVF-XT achieved the highest classification accuracies of 77.20%, 88.12% and 60.54% on an in-house dataset (Obesity) and two publicly available datasets. For interpretability analysis on Obesity, DiA and PR quantitatively demonstrate that MVF-XT better captures neurobiologically relevant patterns than existing methods, achieving correlations of r=0.29 with group differences and r=0.10 with body mass-index.

Preterm infants have a higher risk for developing neurocognitive deficits and behavioral abnormalities across the lifespan, but the individual long-term neurobiological heterogeneity presents a challenge for developmental risk assessment and outcome prediction. Thus, we took advantage of the large sample size of the Adolescent Brain Cognitive Development dataset to investigate neuroanatomical subtypes that were previously unobservable in typical case-control designs, and explored potential neurodevelopmental mechanisms by analyzing the brain maturation of preterm subtypes. Our findings revealed two markedly distinct and reproducible brain structural and behavioral features that differentiated subtypes of preterm adolescents: 1) the “at-risk” subtype had an “average preterm brain pattern” of widespread lower cortical volume/area and subcortical volume and higher cortical thickness, and showed cognitive deficiencies and psychopathological risk; 2) the “resilient” subtype demonstrated more complex brain structural differences including both higher and lower cortical and subcortical volumes in different regions simultaneously, as well as widespread higher cortical area and lower cortical thickness. In addition, the “resilient: subtype showed accelerated brain maturation that may contribute to their normal cognitive function though they still had psychopathological risk. These neurodevelopmental alterations had significant associations with gestational age, birth weight, puberty development levels, psychopathological risk and cognitive deficits. The findings provide mechanistic insights into neurobiological heterogeneities in long-term neurodevelopmental trajectories in preterm adolescents, aiming to guide risk stratification and support healthy development in preterm adolescents.

Previous studies have linked opioid use to altered metabolic profiles, but findings have been inconsistent and mechanisms remain unclear. One potential mechanism involves increased adiposity, leading to chronic low-grade inflammation that elevates metabolic risk. Here, we examined metabolic profiles in individuals with opioid use disorder (OUD) and matched non-OUD controls, focusing on the sequential mediating roles of BMI and inflammation. Data from individuals with OUD (n = 281) and non-OUD (n = 246) were drawn from a natural history screening protocol from the National Institute on Alcohol Abuse and Alcoholism intramural program. Groups were matched on age, sex, race, ethnicity, socioeconomic status, and education via propensity score matching. Metabolic measures included body mass index (BMI), hemoglobin A1c (HbA1c), and lipid profiles, with lipid imbalance indexed by the atherogenic index of plasma (AIP). Inflammatory markers included C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR). Individuals with OUD had significantly higher BMI (F1,481 = 12.9, p < 0.001), higher HbA1c (F1,481 = 10.5, p = 0.001), lower high-density lipoprotein cholesterol (HDL-C; F1,481 = 46.2, p < 0.001), higher low-density lipoprotein cholesterol (LDL-C; F1, 481 = 11.9, p < 0.001), and higher AIP (F1,481 = 20.7, p < 0.001) compared to non-OUD. Inflammatory markers were also elevated in individuals with OUD, including CRP (F1,481 = 9.4, p = 0.002) and ESR (F1,481 = 7.4, p = 0.007),and statistically mediated group differences in AIP and HbA1c, respectively. Our results are consistent with prior evidence of metabolic dysfunctions in individuals with OUD and suggest inflammation as a contributing mechanism. Targeting metabolic health and inflammation may offer new avenues for improving long-term health outcomes in OUD.

OBJECTIVE:The aim of this study was to investigate the relationship between obesity (OB) progression and brain structural changes. METHODS:T1-weighted magnetic resonance images were acquired from 258 participants with overweight (OW) or OB and 74 participants with normal weight. Participants with OW or OB were divided into four groups according to BMI grades. Two-sample t tests compared disparities between the four subgroups and the participants with normal weight. We used causal structural covariance networks to examine the progressive impact of OB on brain structure. RESULTS:With increasing BMI values, reductions in gray matter volume originated in the left caudate nucleus, medial orbitofrontal cortex, and left insula and expanded to the right hippocampus and left lateral orbitofrontal cortex and then to the right parahippocampal gyrus, left precuneus, and left dorsolateral prefrontal cortex (p < 0.05, false discovery rate corrected). The left caudate nucleus and medial orbitofrontal cortex are the primary hubs of the directional network, exhibiting positive causality to the right hippocampus and left dorsolateral prefrontal cortex. Moreover, the right hippocampus is identified as an important transition hub. CONCLUSIONS:These findings suggest that changes in gray matter volume in individuals with OB may originate from reward/motivation processing regions, subsequently progressing to inhibitory control/learning memory regions, providing a new reference direction for clinical intervention and treatment of OB.

Certain medications carry a risk by providing therapeutic benefits at the expense of misuse potential. Ketamine is increasingly being used for the treatment of depression, and studies indicate that it may also have utility for substance use disorder (SUD) treatment. However, it has recreational appeal and known misuse potential. Driven by the ongoing expansion of its use in clinical settings, concerns for misuse of ketamine are escalating. In this review, we summarize neurochemical, molecular, and brain circuit mechanisms associated with ketamine reinforcement and misuse potential and discuss their relevance toward its potential for SUD treatment. We focus on ketamine's direct actions on opioid and glutamatergic systems, highlighting recent discoveries on its interactions with mu opioid receptors (MORs) and NMDA receptors (NMDARs) in addiction-relevant brain circuits. We propose that ketamine's reinforcing properties and misuse potential stem from its bifunctional engagement with these receptors, with its (S)-ketamine enantiomer, compared with (R)-ketamine, being ketamine's primary risk driver. We contextualize this bifunctional NMDAR/MOR mechanism within ketamine's known efficacy for treatment of depression and other mental health conditions, including its potential for SUD treatment. We conclude that the brain mechanisms contributing to ketamine reinforcement, its recreational appeal, and its misuse potential are intertwined with its antidepressant properties and potential for SUD treatment.

BACKGROUND:Preterm infants with very low birth weight are at high risk for long-term neurocognitive deficits. However, whether these neurocognitive deficits are improved or worsened in adolescence remains unclear. METHODS:We took advantage of the large sample from the Adolescent Brain Cognitive Development dataset to investigate alterations in brain structure, behavior, including cognitive function and mental health symptoms, and in puberty among preterm children with very low/normal birth weight (Pre_VLBW/Pre_NBW) and full-term children with normal birth weight (Con_NBW) from baseline to 2-year follow-up. RESULTS:Pre_VLBW children relative to the other two groups had higher cortical thickness, lower cortical area and cortical/subcortical volumes in large portions of frontal cortex, temporal and occipital gyrus, insula, thalamus, and cerebellum; and attenuated fiber tract volumes in the fornix and foreceps major at baseline. Pre_VLBW children for their baseline measures also had lower cognitive function, higher pubertal levels and psychopathological risk. Furthermore, there were significant interaction effects on increased adrenarche score and cortical and subcortical volumes in medial orbitofrontal cortex (mOFC) and thalamus from baseline to 2-year follow-up. Pre_VLBW individuals showed higher adrenarche scores and lower volumes in the mOFC and thalamus than the other two groups at 2-year follow-up, but not at baseline. These brain structural changes showed associations with pubertal development levels, psychopathological risk and cognitive deficits. CONCLUSION:These findings support a view that preterm children with VLBW showed distinctive developmental alterations during adolescence, which potentially lead to long-lasting deviations in various brain regions and might be associated with behavioral problems and neurocognitive deficits.

Purpose: We aimed to identify significant contributing factors to the risk of maladaptive behaviors, such as alcohol use disorder or obesity, in children. To achieve this, we utilized the extensive adolescent brain cognitive development data set, which encompasses a wide range of environmental, social, and nutritional factors. Methods: We divided our sample into equal sets (test, validation; n = 3,415 each). On exploratory factor analysis, six factor domains were identified as most significant (fat/sugar intake, screen time, and prenatal alcohol exposure, parental aggressiveness, hyperactivity, family violence, parental education, and family income) and used to stratify the children into low- (n = 975), medium- (n = 967), high(n = 977) risk groups. Regression models were used to analyze the relationship between identified risk groups, and differences in reward sensitivity, and behavioral problems at 2-year follow-up. Results: The functional magnetic resonance imaging analyses showed reduced activation in several brain regions during reward or loss anticipation in high/medium-risk (vs. low-risk) children on a monetary incentive delay task. High-risk children exhibited heightened middle frontal cortex activity when receiving large rewards. They also displayed increased impulsive and motivated reward-seeking behaviors, along with behavioral problems. These findings replicated in our validation set, and a negative correlation between middle frontal cortexthickness and impulsivity behavior was observed in high-risk children. Discussion: Our findings show altered reward function and increased impulsiveness in high-risk adolescents. This study has implications for early risk identification and the development of prevention strategies for maladaptive behaviors in children, particularly those at high risk. Published by Elsevier Inc. on behalf of Society for Adolescent Health and Medicine. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

BACKGROUND:Sleep deprivation (SD) negatively affects brain function. Most brain imaging studies have investigated the effects of SD on static brain function. SD effects on functional brain dynamics and their relationship with molecular changes remain relatively unexplored. METHODS:We used functional magnetic resonance imaging to examine resting-brain state dynamics after one night of SD compared with rested wakefulness (N = 41) and assessed the association of brain state dynamics with striatal brain dopamine D2 receptor availability measured by positron emission tomography [11C]raclopride using network control theory. RESULTS:SD reduced dwell time and persistence probabilities, with the strongest effects in two brain states, one characterized by high default mode network and low dorsal attention network activity and the other by high frontoparietal network and low somatomotor network activity. Using network control theory, we showed that after SD, there was an overall increase in the control energy required for brain state transitions, with effects varying for different brain state transitions. Control energy requirement was negatively associated with transition probabilities under SD and restful wakefulness and accounted for SD-induced changes in transition probabilities. Alteration in the energy landscape was associated with SD-induced changes in striatal D2 receptor distribution. CONCLUSIONS:These findings demonstrate altered occurrence of internally and externally oriented brain states following acute SD and suggest an association with energy requirements for brain state transitions modulated by striatal D2 receptors.

Background: Opioid use disorder (OUD) is a chronic relapsing condition with a high mortality rate. While medications such as methadone are valuable first-line therapies, retention is poor, with the highest dropout rates early in a treatment attempt. Poor outcomes are due in part to the very high rates of co-morbid depression in people with OUD, as depression can drive opioid use. Therefore, administering a rapid-acting antidepressant such as ketamine early in a treatment attempt may be an effective strategy to improve outcomes.Objectives: Here, we describe a case series of three patients (two males, one female) diagnosed with OUD initiating methadone treatment and endorsing symptoms of depression, who met criteria for a single-arm open-label feasibility trial (NCT05051449) at an opioid treatment program in Baltimore, Maryland.Methods: Participants underwent a 2-week ketamine regimen (0.5 mg/kg infusion over 40 min, three times per week for 2 weeks).Results: Ketamine was safe and generally well-tolerated. At 10-day follow-up post-ketamine infusions, participant acceptability ratings were mostly favorable. All three patients remained in treatment through the 3-month timepoint with strong treatment adherence. With treatment, self-reported depression symptoms decreased from severe to mild/moderate in two patients, and from moderate to remission in the third.Conclusions: Randomized controlled trials are warranted to test whether ketamine may be a feasible and safe adjunctive treatment for OUD in patients initiating methadone treatment.

ImportanceCannabis use has increased globally, but its effects on brain function are not fully known, highlighting the need to better determine recent and long-term brain activation outcomes of cannabis use.ObjectiveTo examine the association of lifetime history of heavy cannabis use and recent cannabis use with brain activation across a range of brain functions in a large sample of young adults in the US.Design, Setting, and ParticipantsThis cross-sectional study used data (2017 release) from the Human Connectome Project (collected between August 2012 and 2015). Young adults (aged 22-36 years) with magnetic resonance imaging (MRI), urine toxicology, and cannabis use data were included in the analysis. Data were analyzed from January 31 to July 30, 2024.ExposuresHistory of heavy cannabis use was assessed using the Semi-Structured Assessment for the Genetics of Alcoholism, with variables for lifetime history and diagnosis of cannabis dependence. Individuals were grouped as heavy lifetime cannabis users if they had greater than 1000 uses, as moderate users if they had 10 to 999 uses, and as nonusers if they had fewer than 10 uses. Participants provided urine samples on the day of scanning to assess recent use. Diagnosis of cannabis dependence (per Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria) was also included.Main Outcomes and MeasuresBrain activation was assessed during each of the 7 tasks administered during the functional MRI session (working memory, reward, emotion, language, motor, relational assessment, and theory of mind). Mean activation from regions associated with the primary contrast for each task was used. The primary analysis was a linear mixed-effects regression model (one model per task) examining the association of lifetime cannabis and recent cannabis use on the mean brain activation value.ResultsThe sample comprised 1003 adults (mean [SD] age, 28.7 [3.7] years; 470 men [46.9%] and 533 women [53.1%]). A total of 63 participants were Asian (6.3%), 137 were Black (13.7%), and 762 were White (76.0%). For lifetime history criteria, 88 participants (8.8%) were classified as heavy cannabis users, 179 (17.8%) as moderate users, and 736 (73.4%) as nonusers. Heavy lifetime use (Cohen d = −0.28 [95% CI, −0.50 to −0.06]; false discovery rate corrected P = .02) was associated with lower activation on the working memory task. Regions associated with a history of heavy use included the anterior insula, medial prefrontal cortex, and dorsolateral prefrontal cortex. Recent cannabis use was associated with poorer performance and lower brain activation in the working memory and motor tasks, but the associations between recent use and brain activation did not survive false discovery rate correction. No other tasks were associated with lifetime history of heavy use, recent use, or dependence diagnosis.Conclusions and RelevanceIn this study of young adults, lifetime history of heavy cannabis use was associated with lower brain activation during a working memory task. These findings identify negative outcomes associated with heavy lifetime cannabis use and working memory in healthy young adults that may be long lasting.

Stimulant drugs that boost dopamine, like methylphenidate (MP), enhance attention and are effective treatments for attention-deficit hyperactivity disorder (ADHD). Yet there is large individual variation in attentional capacity and response to MP. It is unclear whether this variation is driven by individual differences in relative density of dopamine receptor subtypes, magnitude of dopamine increases induced by MP, or both. Here, we extensively characterized the brain dopamine system with positron emission tomography (PET) imaging (including striatal dopamine D1 and D2/3 receptor availability and MP-induced dopamine increases) and measured attention task-evoked fMRI brain activity in two separate sessions (placebo and 60 mg oral MP; single-blind, counterbalanced) in 37 healthy adults. A network of lateral frontoparietal and visual cortices was sensitive to increasing attentional (and working memory) load, whose activity positively correlated with performance across individuals (partial r = 0.474, P = 0.008; controlling for age). MP-induced change in activity within this network correlated with MP-induced change in performance (partial r = 0.686, P < 0.001). The ratio of D1-to-D2/3 receptors in dorsomedial caudate positively correlated with baseline attentional network activity and negatively correlated with MP-induced changes in activity (all pFWE < 0.02). MP-induced changes in attentional load network activity mediated the association between D1-to-D2/3 ratio and MP-induced improvements in performance (mediation estimate = 23.20 [95%CI: -153.67 -81.79], P = 0.004). MP attention-boosting effects were not linked to the magnitude of striatal dopamine increases, but rather showed dependence on an individual's baseline receptor density. Individuals with lower D1-to-D2/3 ratios tended to have lower frontoparietal activity during sustained attention and experienced greater improvement in brain function and task performance with MP.

Importance:Sleep and circadian disruptions are highly prevalent in opioid use disorder (OUD) and are a barrier to successful treatment and recovery; yet few objective data are available, especially for individuals in OUD treatment with opioid agonist therapy. If disruptions remain present despite OUD treatment, this information would yield potential new targets for adjunctive therapy. Objective:To systematically investigate different aspects of rest-activity rhythms (RAR), including sleep, physical activity, circadian rhythmicity, and brain functional correlates in individuals with OUD. Design, Setting, and Participants:This cross-sectional study conducted from October 12, 2017, through January 11, 2024, recruited participants with OUD from treatment programs or the community in the District of Columbia, Maryland, and Virginia area. Participants included individuals with OUD treated with methadone or buprenorphine, individuals with OUD who remained abstinent without medications, and healthy controls (HCs). Healthy participants were recruited from advertisements. Statistical analyses were conducted between March 1 and May 31, 2024. Main Outcomes and Measures:In total, 21 RAR features were derived from 1-week actigraphy data, and principal components were used to extract independent RAR components. Modulators and brain and clinical correlates of RAR were also examined. Results:This study included 73 participants (46 [63%] male; mean [SD] age, 43.5 [11.3] years). Among 42 patients with OUD (16 [38%] female; mean [SD] age, 42.7 [11.4] years), 33 receiving medications for opioid use disorder (MOUD) exhibited greater sleep-wake irregularity than 9 patients without MOUD (mean difference, 0.85 [95% CI, 0.00-1.69]) or 31 age- and sex-matched HCs (11 [36%] female; mean [SD] age, 44.5 [11.3] years; mean difference, 0.75 [95% CI, 0.19-1.31). Among participants receiving MOUD, greater sleep irregularity was associated with longer heroin use history (r26 = 0.45; P = .02) and lower daytime light exposure (r33 = -0.57; P < .001). Compared with HCs, participants with OUD exhibited lower fractional occupancy (percentage of occurrence) in a default mode network-dominated brain state, with individuals experiencing more pronounced sleep-wake irregularities displaying exacerbated impairments (r23 = -0.55; P = .007). Conclusions and Relevance:Findings of this cross-sectional study showed that sleep irregularity in participants with OUD receiving opioid agonist medications correlated with years of opioid misuse and shorter daylight exposures and was associated with impaired brain state dynamics. These findings suggest that interventions increasing light exposure may improve sleep-wake irregularity and brain functional network dynamics in individuals with OUD receiving opioid agonist medications.

Dopaminergic signaling shapes large-scale brain network architecture, constraining neural communication along a principal gradient that spans unimodal sensorimotor to transmodal association cortices. While more differentiated gradients are typically linked to enhanced cognition, it remains unclear whether dopamine-enhancing psychostimulants, such as methylphenidate (MP), amplify or compress this functional hierarchy to support attention. Across two double-blind, placebo-controlled studies in healthy adults (n = 38 and n = 20), we combined 60 mg oral MP with PET and fMRI to assess striatal dopamine function and cortical organization. MP consistently compressed the principal gradient, reducing segregation between sensory and association areas. The degree of compression predicted individual variation in striatal D1 and D2 receptor availability. MP-induced gradient compression in inferior parietal cortex tracked attention improvements. Critically, we validated key findings in a large, independent cohort from the Adolescent Brain Cognitive Development (ABCD) study (n = 4,958). These results highlight a dopamine-sensitive mechanism linking cortical functional reorganization with cognitive performance.