Brain Cell-based Genetic Subtyping and Drug Repositioning for Alzheimer Disease.

Alzheimer's Disease (AD) is characterized by its complex and heterogeneous etiology and gradual progression, leading to high drug failure rates in late-stage clinical trials. In order to better stratify individuals at risk for AD and discern potential therapeutic targets we employed a novel procedure utilizing cell-based co-regulated gene networks and polygenic risk scores (cbPRSs). After defining genetic subtypes using extremes of cbPRS distributions, we evaluated correlations of the genetic subtypes with previously defined AD subtypes defined on the basis of domain-specific cognitive functioning and neuroimaging biomarkers. Employing a PageRank algorithm, we identified priority gene targets for the genetic subtypes. Pathway analysis of priority genes demonstrated associations with neurodegeneration and suggested candidate drugs currently utilized in diabetes, hypertension, and epilepsy for repositioning in AD. Experimental validation utilizing human induced pluripotent stem cell (hiPSC)-derived astrocytes demonstrated the modifying effects of estradiol, levetiracetam, and pioglitazone on expression of APOE and complement C4 genes, suggesting potential repositioning for AD.

Correction: Budesonide promotes airway epithelial barrier integrity following double-stranded RNA challenge.

[This corrects the article DOI: 10.1371/journal.pone.0260706.].

Longitudinal patient-reported outcomes on genotype-guided irinotecan dosing: feasibility and clinical relevance.

INTRODUCTION: Standard investigator-based adverse events (AE) assessment is via CTCAE for clinical trials. However, including the patient perspective through PRO (patient-reported outcomes) enhances clinicians' understanding of patient toxicity and fosters early detection of AEs. We assessed longitudinal integration of PRO-CTCAE within clinical workflow in a phase II trial. MATERIALS AND METHODS: As a sub-study in a phase II trial of genotype-directed irinotecan dosing evaluating efficacy in patients with metastatic colorectal cancer receiving FOLFIRI and bevacizumab, patients reported on 13 AEs generating a PRO-CTCAE form. The primary objective was to estimate forms completed by patients and clinicians at least 80% of time. Secondary objectives were estimating concordance and time to first score of specific symptoms between patient and clinician pairs. RESULTS: Feasibility of longitudinal PRO-CTCAE integration was met as 96% of patients and clinician-patient pairs completed at least 80% of PRO-CTCAE forms available to them with 79% achieving 100% completion. Concordance between patient and clinician reporting a severe symptom was 73% with 24 disconcordant pairs, 21 involved patients who reported a severe symptom that the clinician did not. Although protocol-mandated dose reductions were guided by CTCAE not PRO-CTCAE responses, the median time to dose reduction of 2.53 months, and the time-to-event curve closely approximated time to patient-reported toxicity. CONCLUSION: Longitudinal integration of PRO-CTCAE paired CTCAE proved feasible. Compared to clinicians, patients reported severe symptoms more frequently and earlier. Patient-reported toxicity more closely aligned with dose decreases indicating incorporation into routine clinical practice may enhance early detection of toxicity improving patient safety and quality of life.

A Randomized, Double-Blind, Placebo-Controlled Pilot Trial of the Acute Antisuicidal and Antidepressant Effects of Intranasal (R,S)-Ketamine in Severe Unipolar and Bipolar Depression With and Without Comorbid Alcohol Use Disorder.

Objective: Although individuals with a family history of alcohol use disorder (AUD) have a superior antidepressant response to ketamine, outcomes in patients with current AUD remain unclear. This study sought to investigate whether intranasal (IN) racemic (R,S)-ketamine had antisuicidal and antidepressant effects in unipolar and bipolar depression and whether comorbid AUD conferred superior antisuicidal outcomes for patients.Methods: This was a double-blind, randomized, placebo-controlled trial (May 2018 to January 2022) of single administration, fixed-dose (50 mg) IN (R,S)-ketamine (or saline comparator) in unmedicated inpatients meeting Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision, criteria for a current major depressive episode (bipolar or unipolar), with current suicidal ideation (SI) and past attempt. Patients with and without comorbid AUD were enrolled. Change in Scale for Suicide Ideation score was the primary outcome measure, and change in Montgomery-Åsberg Depression Rating Scale score was the secondary outcome measure.Results: No significant group × time effect was noted for SI (F = 1.1, P = .36). A statistical trend toward superior improvement in suicidality was observed in participants with comorbid AUD. The group × time interaction was significant for improvements in depression (F = 3.06, P = .03) and largely unaffected by comorbid AUD or primary mood disorder type. Within the ketamine group, a significant correlation was observed between improvement in depressive symptoms and SI for patients without comorbid AUD (r =0.927, P = .023) that was absent in patients with AUD (r = 0.39, P = .44).Conclusion: IN ketamine induced rapid antidepressant effects compared to placebo but did not significantly alter SI scores. The treatment was well tolerated. Continued investigation with IN ketamine as a practical alternative to current formulations is warranted.Trial Registration: ClinicalTrials.gov identifier: NCT03539887.

Microstructural Mapping of Neural Pathways in Alzheimer's Disease using Macrostructure-Informed Normative Tractometry.

Introduction: Diffusion MRI is sensitive to the microstructural properties of brain tissues, and shows great promise in detecting the effects of degenerative diseases. However, many approaches analyze single measures averaged over regions of interest, without considering the underlying fiber geometry. Methods: Here, we propose a novel Macrostructure-Informed Normative Tractometry (MINT) framework, to investigate how white matter microstructure and macrostructure are jointly altered in mild cognitive impairment (MCI) and dementia. We compare MINT-derived metrics with univariate metrics from diffusion tensor imaging (DTI), to examine how fiber geometry may impact interpretation of microstructure. Results: In two multi-site cohorts from North America and India, we find consistent patterns of microstructural and macrostructural anomalies implicated in MCI and dementia; we also rank diffusion metrics' sensitivity to dementia. Discussion: We show that MINT, by jointly modeling tract shape and microstructure, has potential to disentangle and better interpret the effects of degenerative disease on the brain's neural pathways.

Distinct changes to hippocampal and medial entorhinal circuits emerge across the progression of cognitive deficits in epilepsy.

Temporal lobe epilepsy (TLE) causes pervasive and progressive memory impairments, yet the specific circuit changes that drive these deficits remain unclear. To investigate how hippocampal-entorhinal dysfunction contributes to progressive memory deficits in epilepsy, we performed simultaneous in vivo electrophysiology in hippocampus (HPC) and medial entorhinal cortex (MEC) of control and epileptic mice 3 or 8 weeks after pilocarpine-induced status epilepticus (Pilo-SE). We found that HPC synchronization deficits (including reduced theta power, coherence, and altered interneuron spike timing) emerged within 3 weeks of Pilo-SE, aligning with early-onset, relatively subtle memory deficits. In contrast, abnormal synchronization within MEC and between HPC-MEC emerged later, by 8 weeks after Pilo-SE, when spatial memory impairment was more severe. Furthermore, a distinct subpopulation of MEC layer 3 excitatory neurons (active at theta troughs) was specifically impaired in epileptic mice. Together, these findings suggest that hippocampal-entorhinal circuit dysfunction accumulates and shifts as cognitive impairment progresses in TLE.

A new perspective on the spatial, environmental, and metacommunity controls of local biodiversity.

Influential ecological research in the 1980s, elucidating that local biodiversity (LB) is a function of local ecological factors and the size of the regional species pool (γ-diversity), has prompted numerous investigations on the local and regional origins of LB. These investigations, however, have been mostly limited to single scales and target groups and centered exclusively on γ-diversity. Here we developed a unified framework including scale, environmental factors (heterogeneity and ambient levels), and metacommunity properties (intraspecific spatial aggregation, regional evenness, and γ-diversity) as hierarchical predictors of LB. We tested this framework with variance partitioning and structural equation modeling using subcontinental data on stream diatoms, insects, and fish as well as local physicochemistry, climate, and land use. Pure aggregation + regional evenness outperformed pure γ-diversity in explaining LB across groups. The covariance of the environment with aggregation + regional evenness rather than with γ-diversity generally explained a much greater proportion of the variance in diatom and insect LB, especially at smaller scales. Thus, disregarding aggregation and regional evenness, as commonly done, may lead to gross underestimation of the pure metacommunity effects and the indirect environmental effects on LB. We examined the shape of the local-regional species richness relationship, which has been widely used to infer local vs. regional effects on LB. We showed that this shape has an ecological basis, but its interpretation is not straightforward. Therefore, we advocate that the variance partitioning analysis under the proposed framework is adopted instead. In diatoms, metacommunity properties had the greatest total effects on LB, while in insects and fish, it was the environment, suggesting that larger organisms are more strongly controlled by the environment. Broader use of our framework may lead to novel biogeographical insights into the drivers of LB and improved projections of its trends along current and future environmental gradients.

Counterfactual MRI Generation with Denoising Diffusion Models for Interpretable Alzheimer's Disease Effect Detection.

Generative AI models have recently achieved mainstream attention with the advent of powerful approaches such as stable diffusion, DALL-E and MidJourney. The underlying breakthrough generative mechanism of denoising diffusion modeling can generate high quality synthetic images and can learn the underlying distribution of complex, high-dimensional data. Recent research has begun to extend these models to medical and specifically neuroimaging data. Typical neuroimaging tasks such as diagnostic classification and predictive modeling often rely on deep learning approaches based on convolutional neural networks (CNNs) and vision transformers (ViTs), with additional steps to help in interpreting the results. In our paper, we train conditional latent diffusion models (LDM) and denoising diffusion probabilistic models (DDPM) to provide insight into Alzheimer's disease (AD) effects on the brain's anatomy at the individual level. We first created diffusion models that could generate synthetic MRIs, by training them on real 3D T1-weighted MRI scans, and conditioning the generative process on the clinical diagnosis as a context variable. We conducted experiments to overcome limitations in training dataset size, compute time and memory resources, testing different model sizes, effects of pretraining, training duration, and latent diffusion models. We tested the sampling quality of the disease-conditioned diffusion using metrics to assess realism and diversity of the generated synthetic MRIs. We also evaluated the ability of diffusion models to conditionally sample MRI brains using a 3D CNN-based disease classifier relative to real MRIs. In our experiments, the diffusion models generated synthetic data that helped to train an AD classifier (using only 500 real training scans) -and boosted its performance by over 3% when tested on real MRI scans. Further, we used implicit classifier-free guidance to alter the conditioning of an encoded individual scan to its counterfactual (representing a healthy subject of the same age and sex) while preserving subject-specific image details. From this counterfactual image (where the same person appears healthy), a personalized disease map was generated to identify possible disease effects on the brain. Our approach efficiently generates realistic and diverse synthetic data, and may create interpretable AI-based maps for neuroscience research and clinical diagnostic applications.

Deep Normative Tractometry for Identifying Joint White Matter Macro- and Micro-structural Abnormalities in Alzheimer's Disease.

This study introduces the Deep Normative Tractometry (DNT) framework, that encodes the joint distribution of both macrostructural and microstructural profiles of the brain white matter tracts through a variational autoencoder (VAE). By training on data from healthy controls, DNT learns the normative distribution of tract data, and can delineate along-tract micro-and macro-structural abnormalities. Leveraging a large sample size via generative pre-training, we assess DNT's generalizability using transfer learning on data from an independent cohort acquired in India. Our findings demonstrate DNT's capacity to detect widespread diffusivity abnormalities along tracts in mild cognitive impairment and Alzheimer's disease, aligning closely with results from the Bundle Analytics (BUAN) tractometry pipeline. By incorporating tract geometry information, DNT may be able to distinguish disease-related abnormalities in anisotropy from tract macrostructure, and shows promise in enhancing fine-scale mapping and detection of white matter alterations in neurodegenerative conditions.

A Multimethod Exploration of the COVID-Era Transition to Virtual Hearings in Child Welfare Court Cases.

To promote open and safe access to court hearings for families and professionals during the COVID-19 pandemic, juvenile and family courts across the United States shifted towards virtual hearings in child welfare cases. Drawing on findings from a four state court observation study conducted pre and post emergence of the pandemic, as well as from a national survey of court professionals and a survey of parents, we reflect on the experiences of courts and court participants with virtual hearings in child welfare cases and specific impacts on hearing quality. Findings indicate that despite stakeholder perceptions, there is little difference in the quality of hearings with respect to judicial engagement and discussion. Differences were found however, in the attendance of fathers and relatives at hearings, with less fathers and relatives attending virtual hearings. Parents had mixed experiences with the virtual hearing process. Professionals noted challenges, as well as strategies for overcoming challenges, to engage parents and ensure their access to virtual hearings.

Genotype-specific spinal cord damage in spinocerebellar ataxias: an ENIGMA-Ataxia study.

BACKGROUND: Spinal cord damage is a feature of many spinocerebellar ataxias (SCAs), but well-powered in vivo studies are lacking and links with disease severity and progression remain unclear. Here we characterise cervical spinal cord morphometric abnormalities in SCA1, SCA2, SCA3 and SCA6 using a large multisite MRI dataset. METHODS: Upper spinal cord (vertebrae C1-C4) cross-sectional area (CSA) and eccentricity (flattening) were assessed using MRI data from nine sites within the ENIGMA-Ataxia consortium, including 364 people with ataxic SCA, 56 individuals with preataxic SCA and 394 nonataxic controls. Correlations and subgroup analyses within the SCA cohorts were undertaken based on disease duration and ataxia severity. RESULTS: Individuals in the ataxic stage of SCA1, SCA2 and SCA3, relative to non-ataxic controls, had significantly reduced CSA and increased eccentricity at all examined levels. CSA showed large effect sizes (d>2.0) and correlated with ataxia severity (r<-0.43) and disease duration (r<-0.21). Eccentricity correlated only with ataxia severity in SCA2 (r=0.28). No significant spinal cord differences were evident in SCA6. In preataxic individuals, CSA was significantly reduced in SCA2 (d=1.6) and SCA3 (d=1.7), and the SCA2 group also showed increased eccentricity (d=1.1) relative to nonataxic controls. Subgroup analyses confirmed that CSA and eccentricity are abnormal in early disease stages in SCA1, SCA2 and SCA3. CSA declined with disease progression in all, whereas eccentricity progressed only in SCA2. CONCLUSIONS: Spinal cord abnormalities are an early and progressive feature of SCA1, SCA2 and SCA3, but not SCA6, which can be captured using quantitative MRI.

Brain Age Analysis and Dementia Classification using Convolutional Neural Networks trained on Diffusion MRI: Tests in Indian and North American Cohorts.

Deep learning models based on convolutional neural networks (CNNs) have been used to classify Alzheimer's disease or infer dementia severity from T1-weighted brain MRI scans. Here, we examine the value of adding diffusion-weighted MRI (dMRI) as an input to these models. Much research in this area focuses on specific datasets such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), which assesses people of North American, largely European ancestry, so we examine how models trained on ADNI, generalize to a new population dataset from India (the NIMHANS cohort). We first benchmark our models by predicting 'brain age' - the task of predicting a person's chronological age from their MRI scan and proceed to AD classification. We also evaluate the benefit of using a 3D CycleGAN approach to harmonize the imaging datasets before training the CNN models. Our experiments show that classification performance improves after harmonization in most cases, as well as better performance for dMRI as input.

Acute and chronic alcohol modulation of extended amygdala calcium dynamics.

The central amygdala (CeA) and bed nucleus of the stria terminalis (BNST) are reciprocally connected nodes of the extended amygdala thought to play an important role in alcohol consumption. Studies of immediate-early genes indicate that BNST and CeA are acutely activated following alcohol drinking and may signal alcohol reward in nondependent drinkers, while stress signaling in the extended amygdala following chronic alcohol exposure drives increased drinking via negative reinforcement. However, the temporal dynamics of neuronal activation in these regions during drinking behavior are poorly understood. In this study, we used fiber photometry and the genetically encoded calcium sensor GCaMP6s to assess acute changes in neuronal activity during alcohol consumption in BNST and CeA before and after a chronic drinking paradigm. Activity was examined in the pan-neuronal population and separately in dynorphinergic neurons. BNST and CeA showed increased pan-neuronal activity during acute consumption of alcohol and other fluid tastants of positive and negative valence, as well as highly palatable chow. Responses were greatest during initial consummatory bouts and decreased in amplitude with repeated consumption of the same tastant, suggesting modulation by stimulus novelty. Dynorphin neurons showed similar consumption-associated calcium increases in both regions. Following three weeks of continuous alcohol access (CA), calcium increases in dynorphin neurons during drinking were maintained, but pan-neuronal activity and BNST-CeA coherence were altered in a sex-specific manner. These results indicate that BNST and CeA, and dynorphin neurons specifically, are engaged during drinking behavior, and activity dynamics are influenced by stimulus novelty and chronic alcohol.

Parasite-induced IFN-γ regulates host defense via CD115 and mTOR-dependent mechanism of tissue-resident macrophage death.

Host resistance to a common protozoan parasite Toxoplasma gondii relies on a coordinated immune response involving multiple cell types, including macrophages. Embryonically seeded tissue-resident macrophages (TRMs) play a critical role in maintaining tissue homeostasis, but their role in parasite clearance is poorly understood. In this study, we uncovered a crucial aspect of host defense against T. gondii mediated by TRMs. Through the use of neutralizing antibodies and conditional IFN-γ receptor-deficient mice, we demonstrated that IFN-γ directly mediated the elimination of TRMs. Mechanistically, IFN-γ stimulation in vivo rendered macrophages unresponsive to macrophage colony-stimulating factor (M-CSF) and inactivated mTOR signaling by causing the shedding of CD115 (CSFR1), the receptor for M-CSF. Further experiments revealed the essential role of macrophage IFN-γ responsiveness in host resistance to T. gondii. The elimination of peritoneal TRMs emerged as an additional host defense mechanism aimed at limiting the parasite's reservoir. The identified mechanism, involving IFN-γ-induced suppression of CD115-dependent mTOR signaling in macrophages, provides insights into the adaptation of macrophage subsets during infection and highlights a crucial aspect of host defense against intracellular pathogens.

Smaller total and subregional cerebellar volumes in posttraumatic stress disorder: a mega-analysis by the ENIGMA-PGC PTSD workgroup.

Although the cerebellum contributes to higher-order cognitive and emotional functions relevant to posttraumatic stress disorder (PTSD), prior research on cerebellar volume in PTSD is scant, particularly when considering subregions that differentially map on to motor, cognitive, and affective functions. In a sample of 4215 adults (PTSD n = 1642; Control n = 2573) across 40 sites from the ENIGMA-PGC PTSD working group, we employed a new state-of-the-art deep-learning based approach for automatic cerebellar parcellation to obtain volumetric estimates for the total cerebellum and 28 subregions. Linear mixed effects models controlling for age, gender, intracranial volume, and site were used to compare cerebellum volumes in PTSD compared to healthy controls (88% trauma-exposed). PTSD was associated with significant grey and white matter reductions of the cerebellum. Compared to controls, people with PTSD demonstrated smaller total cerebellum volume, as well as reduced volume in subregions primarily within the posterior lobe (lobule VIIB, crus II), vermis (VI, VIII), flocculonodular lobe (lobule X), and corpus medullare (all p-FDR < 0.05). Effects of PTSD on volume were consistent, and generally more robust, when examining symptom severity rather than diagnostic status. These findings implicate regionally specific cerebellar volumetric differences in the pathophysiology of PTSD. The cerebellum appears to play an important role in higher-order cognitive and emotional processes, far beyond its historical association with vestibulomotor function. Further examination of the cerebellum in trauma-related psychopathology will help to clarify how cerebellar structure and function may disrupt cognitive and affective processes at the center of translational models for PTSD.

Mapping gray and white matter volume abnormalities in early-onset psychosis: an ENIGMA multicenter voxel-based morphometry study.

INTRODUCTION: Regional gray matter (GM) alterations have been reported in early-onset psychosis (EOP, onset before age 18), but previous studies have yielded conflicting results, likely due to small sample sizes and the different brain regions examined. In this study, we conducted a whole brain voxel-based morphometry (VBM) analysis in a large sample of individuals with EOP, using the newly developed ENIGMA-VBM tool. METHODS: 15 independent cohorts from the ENIGMA-EOP working group participated in the study. The overall sample comprised T1-weighted MRI data from 482 individuals with EOP and 469 healthy controls. Each site performed the VBM analysis locally using the standardized ENIGMA-VBM tool. Statistical parametric T-maps were generated from each cohort and meta-analyzed to reveal voxel-wise differences between EOP and healthy controls as well as the individual-based association between GM volume and age of onset, chlorpromazine (CPZ) equivalent dose, and other clinical variables. RESULTS: Compared with healthy controls, individuals with EOP showed widespread lower GM volume encompassing most of the cortex, with the most marked effect in the left median cingulate (Hedges' g = 0.55, p = 0.001 corrected), as well as small clusters of lower white matter (WM), whereas no regional GM or WM volumes were higher in EOP. Lower GM volume in the cerebellum, thalamus and left inferior parietal gyrus was associated with older age of onset. Deficits in GM in the left inferior frontal gyrus, right insula, right precentral gyrus and right superior frontal gyrus were also associated with higher CPZ equivalent doses. CONCLUSION: EOP is associated with widespread reductions in cortical GM volume, while WM is affected to a smaller extent. GM volume alterations are associated with age of onset and CPZ equivalent dose but these effects are small compared to case-control differences. Mapping anatomical abnormalities in EOP may lead to a better understanding of the role of psychosis in brain development during childhood and adolescence.

Genome sequence of Microbacterium foliorum phage CandC.

We report the discovery and genome sequence of CandC, a lytic bacteriophage with siphovirus morphology. CandC was isolated from a soil sample from Plattsburgh, NY, USA (Fall 2021). It has a genome size of 62,344 bp with 106 predicted protein-encoding genes, 30 of which are assigned putative functions.

Childhood trauma moderates schizotypy-related brain morphology: analyses of 1182 healthy individuals from the ENIGMA schizotypy working group.

BACKGROUND: Schizotypy represents an index of psychosis-proneness in the general population, often associated with childhood trauma exposure. Both schizotypy and childhood trauma are linked to structural brain alterations, and it is possible that trauma exposure moderates the extent of brain morphological differences associated with schizotypy. METHODS: We addressed this question using data from a total of 1182 healthy adults (age range: 18-65 years old, 647 females/535 males), pooled from nine sites worldwide, contributing to the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Schizotypy working group. All participants completed both the Schizotypal Personality Questionnaire Brief version (SPQ-B), and the Childhood Trauma Questionnaire (CTQ), and underwent a 3D T1-weighted brain MRI scan from which regional indices of subcortical gray matter volume and cortical thickness were determined. RESULTS: A series of multiple linear regressions revealed that differences in cortical thickness in four regions-of-interest were significantly associated with interactions between schizotypy and trauma; subsequent moderation analyses indicated that increasing levels of schizotypy were associated with thicker left caudal anterior cingulate gyrus, right middle temporal gyrus and insula, and thinner left caudal middle frontal gyrus, in people exposed to higher (but not low or average) levels of childhood trauma. This was found in the context of morphological changes directly associated with increasing levels of schizotypy or increasing levels of childhood trauma exposure. CONCLUSIONS: These results suggest that alterations in brain regions critical for higher cognitive and integrative processes that are associated with schizotypy may be enhanced in individuals exposed to high levels of trauma.

Examining the association between posttraumatic stress disorder and disruptions in cortical networks identified using data-driven methods.

Posttraumatic stress disorder (PTSD) is associated with lower cortical thickness (CT) in prefrontal, cingulate, and insular cortices in diverse trauma-affected samples. However, some studies have failed to detect differences between PTSD patients and healthy controls or reported that PTSD is associated with greater CT. Using data-driven dimensionality reduction, we sought to conduct a well-powered study to identify vulnerable networks without regard to neuroanatomic boundaries. Moreover, this approach enabled us to avoid the excessive burden of multiple comparison correction that plagues vertex-wise methods. We derived structural covariance networks (SCNs) by applying non-negative matrix factorization (NMF) to CT data from 961 PTSD patients and 1124 trauma-exposed controls without PTSD. We used regression analyses to investigate associations between CT within SCNs and PTSD diagnosis (with and without accounting for the potential confounding effect of trauma type) and symptom severity in the full sample. We performed additional regression analyses in subsets of the data to examine associations between SCNs and comorbid depression, childhood trauma severity, and alcohol abuse. NMF identified 20 unbiased SCNs, which aligned closely with functionally defined brain networks. PTSD diagnosis was most strongly associated with diminished CT in SCNs that encompassed the bilateral superior frontal cortex, motor cortex, insular cortex, orbitofrontal cortex, medial occipital cortex, anterior cingulate cortex, and posterior cingulate cortex. CT in these networks was significantly negatively correlated with PTSD symptom severity. Collectively, these findings suggest that PTSD diagnosis is associated with widespread reductions in CT, particularly within prefrontal regulatory regions and broader emotion and sensory processing cortical regions.

A Comparison of Mental Health Symptoms among Adults Who Vape Nicotine, Cannabis, or Both.

OBJECTIVES: Dual use of combustible cannabis and nicotine is related to worse mental health symptoms (MHS); however, little is known about MHS among those who vape cannabis and nicotine. The current study aimed to determine if dual use of cannabis and nicotine vapes is associated with worse MHS compared to single use and to identify correlates of MHS for dual users. METHODS: We used Amazon Mechanical Turk to survey adults (N = 492) who used nicotine or cannabis vapes in the past 30 days on stress, anxiety, depression, vape use behaviors and sociodemographic information. We conducted hierarchical linear regressions to compare MHS between dual vs. single substance vape use and to identify correlates of MHS, including sociodemographic variables and vape use characteristics. RESULTS: The final sample was 37.6% female, 87.6% White, and 11% Hispanic/Latinx with a mean age of 34.15 years. After controlling for sociodemographic characteristics and combustible product use, dual users had significantly higher mean MHS severity than single users. For dual users, younger age and being married were associated with higher symptoms of depression and stress. Holding a medical cannabis card was associated with higher anxiety symptoms. CONCLUSION: The findings suggest that dual use of cannabis and nicotine vapes is associated with worse MHS severity compared to single substance use.