NPP-digital psychiatry and neuroscience最新文献

{"title":"Time-on-task-related decrements in performance in the rodent continuous performance test are not caused by physical disengagement from the task.","authors":"Ye Li, Thomas van Kralingen, Megan Masi, Brandon Villanueva Sanchez, Beyonca Mitchell, Joshua Johnson, Jorge Miranda-Barrientos, Jason Rehg, Keri Martinowich, Gregory V Carr","doi":"10.1038/s44277-025-00025-0","DOIUrl":"10.1038/s44277-025-00025-0","url":null,"abstract":"<p><p>Attention deficits, a hallmark of many neuropsychiatric disorders, significantly impair quality of life and functional outcome for patients. Continuous Performance Tests (CPTs) are widely used to assess attentional function in clinical settings and have been adapted for mice as the rodent Continuous Performance Test (rCPT). In this study, we combined traditional analyses of rCPT performance with markerless pose estimation using DeepLabCut and visual field analysis (VFA) to objectively measure the orientation of mice toward stimuli during rCPT sessions. Additionally, we extended the session length to assess performance decrements over time. Our findings show that extending rCPT sessions from 45-90 min results in a significant decline in performance in male mice, which aligns with performance decrements observed in clinical research. Importantly, physical engagement with the task remained relatively stable throughout the session, even as performance deteriorated. This suggests that the performance decline specifically reflects a time-on-task (TOT)-dependent vigilance decrement rather than physical disengagement. We also investigated the effects of amphetamine, an FDA-approved treatment for attention-deficit/hyperactivity disorder (ADHD), on rCPT performance. Amphetamine significantly reduced false alarms without affecting orientation or physical engagement with task stimuli in both male and female mice. This improved rCPT performance in males, but not in females because hit rate also declined following amphetamine administration in females. Collectively, these findings validate a behavioral tracking platform for objectively measuring physical engagement in the rCPT and a task modification that accentuates TOT-dependent performance decrements, enhancing the translational value of the rCPT for studies related to human neuropsychiatric disorders.</p>","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"3 1","pages":"4"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11825365/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143434772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smartphone language features may help identify adverse post-traumatic neuropsychiatric sequelae and their trajectories.","authors":"Lisa Vizer, Jennifer Pierce, Yinyao Ji, Meredith A Bucher, Mochuan Liu, Lyle Ungar, Salvatore Giorgi, Zhaopeng Xing, Stacey L House, Francesca L Beaudoin, Jennifer S Stevens, Thomas C Neylan, Gari D Clifford, Tanja Jovanovic, Sarah D Linnstaedt, Donglin Zeng, Laura T Germine, Kenneth A Bollen, Scott L Rauch, John P Haran, Alan B Storrow, Christopher Lewandowski, Paul I Musey, Phyllis L Hendry, Sophia Sheikh, Christopher W Jones, Brittany E Punches, Lauren A Hudak, Jose L Pascual, Mark J Seamon, Erica Harris, Claire Pearson, David A Peak, Roland C Merchant, Robert M Domeier, Brian J O'Neil, Paulina Sergot, Leon D Sanchez, Steven E Bruce, Steven E Harte, Ronald C Kessler, Karestan C Koenen, Samuel A McLean, Xinming An","doi":"10.1038/s44277-025-00028-x","DOIUrl":"10.1038/s44277-025-00028-x","url":null,"abstract":"<p><p>Language features may reflect underlying cognitive and emotional processes following a traumatic event that portend clinical outcomes. The authors sought to determine whether language features from usual smartphone use were markers associated with concurrent posttraumatic symptoms and worsening or improving posttraumatic symptoms over time following a traumatic exposure. This investigation was a secondary analysis of the Advancing Understanding of RecOvery afteR traumA study, a longitudinal study of traumatic outcomes among survivors recruited from 33 emergency departments across the United States. Adverse posttraumatic sequelae were assessed over the six months following the initial traumatic exposure. Language features were extracted from usual smartphone use in a specialized app. Bivariate linear mixed models were used to identify and validate language features that are markers associated with posttraumatic symptoms. Participants were 1744 trauma survivors, with a mean age of 39 [SD = 13] years old, and 56% were female. Fourteen language features were associated with severity level of posttraumatic symptoms at specific timepoints (cross-sectional markers) and five features were associated with change in severity level of posttraumatic symptoms (longitudinal markers). References to the body and health or illness were predictive of worsening pain, somatic, and thinking/concentration/fatigue symptom severity over time. An increase in references to others was associated with improvement in somatic symptom severity over time and increases in expressions of causation or cognitive processes were associated with improvement in pain symptom severity over time. Language features derived from usual smartphone use can convey important information about health, functioning, and recovery following a traumatic event. Clinicians might utilize such information to determine who may experience a high symptom burden or risk of worsening posttraumatic symptoms.</p>","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"3 1","pages":"8"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12092297/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144129920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Protocol for evaluation of iTEST, a novel blended intervention to enhance introspective accuracy in psychotic disorders.","authors":"Sarah A Berretta, Nicole Abaya, Emma Parrish, Lauren E McBride, Raeanne C Moore, Robert Ackerman, Philip D Harvey, Amy E Pinkham, Colin A Depp","doi":"10.1038/s44277-024-00024-7","DOIUrl":"10.1038/s44277-024-00024-7","url":null,"abstract":"<p><p>Poor introspective accuracy (IA), defined as inaccurate judgments of one's abilities and performance, is a strong and independent predictor of functional impairment in people with psychotic disorders. However, there are currently no treatments that directly target IA in this population as a primary outcome. We describe a protocol for a clinical trial to test a newly developed blended digital intervention, Improving Thinking through Everyday SelfAssessment Training (iTEST), aimed at improving IA in people with psychotic disorders to improve functional outcomes. iTEST involves daily training consisting of feedback on IA in mobile cognitive tests, coupled with individual coaching that applies improved IA to participant-identified recovery goals. Following the NIMH experimental therapeutics paradigm, the first step in the evaluation of iTEST is an open trial in 60 individuals with psychotic disorders to assess 1) feasibility and acceptability, and 2) whether the intervention leads to clinically significant improvement in an objective target: IA on trained tasks along with transfer to an untrained task-based measure of IA. After programming of the mobile intervention and the creation of treatment manuals, enrollment for an open trial started in November 2023 and will be completed by April 2025. If effective, iTEST could be integrated with cognitive training and other rehabilitative interventions to boost the impact on functional outcomes. Trial registration: ClinicalTrials.gov NCT05899348.</p>","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"3 1","pages":"5"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11825358/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143434766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tapping into truth: an exploratory cross-sectional analysis of psychomotor symptoms and typing behaviour in an adolescent observational cohort.","authors":"Taylor A Braund, Debopriyo Bal, Helen Christensen, Philip J Batterham, Bojana Vilus, Kate Maston, Mark E Larsen, Aliza Werner-Seidler, Kit Huckvale, Alexis E Whitton, Gabriel Tillman, Bridianne O'Dea","doi":"10.1038/s44277-025-00033-0","DOIUrl":"10.1038/s44277-025-00033-0","url":null,"abstract":"<p><p>Typing behaviour derived from smartphone keystroke metadata is an emerging digital phenotype that may assist in diagnosing and monitoring depressive symptoms. While psychomotor agitation and slowing have been hypothesised as depressive symptoms that may influence typing behaviour, no studies have directly tested this assumption. Here, we tested whether specific depressive symptoms were associated with various keystroke features of typing behaviour in adolescents. Adolescents from an Australian cohort study (<i>n</i> = 895) completed a typing task on their smartphones. Common features of keystroke timing (i.e., median, dwell, interval, latency, down-down time, and up-up time) and frequency (i.e., total keystrokes, backspaces, spaces, backspace ratio, and spaces ratio) were extracted. Depressive symptoms were assessed using the Patient Health Questionnaire-Adolescent version (PHQ-A). Multiple linear regression models were used to test associations between symptom items and keystroke features. Non-linear effects and moderating effects of sex were also explored. Psychomotor symptoms (i.e., PHQ-A item 8) were not associated with keystroke timing or frequency. However, higher appetite symptoms (i.e., PHQ-A item 5) were associated with faster down-down time and a greater number of total key presses. Symptoms of anhedonia (i.e., PHA item 1) showed non-linear associations with keystroke features. The results do not support a relationship between psychomotor symptoms and typing behaviour in adolescents. However, appetite-related symptoms were associated with faster and more frequent typing. Further research into the relationship between typing behaviour and mental health in young people is warranted. Clinical Trial Registry: ACTRN12619000855123.</p>","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"3 1","pages":"14"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12173933/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144334775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Traumatic stress alters neural reactivity to visual stimulation.","authors":"Nathaniel G Harnett, Grace E Rowland, E Kate Webb, Tianyi Li, Soumyaa Joshi, Kerry J Ressler, Isabelle M Rosso","doi":"10.1038/s44277-025-00030-3","DOIUrl":"10.1038/s44277-025-00030-3","url":null,"abstract":"<p><p>Traumatic stress is a precursor to the development of posttraumatic stress disorder (PTSD). Emergent research suggests visual processing regions may be relevant to PTSD development; however, no previous research to date has investigated the potential effects of trauma exposure on neural reactivity to non-affective visual stimulation. In the present study, 24 recently trauma-exposed (RTE) and 16 without recent exposure to trauma (NRTE) individuals completed functional magnetic resonance imaging during alternating blocks of flickering checkerboard presentations and attention/rest with an attentional check. RTE participants were recruited within ~2-4 weeks of trauma, and PTSD symptoms were assessed both at the time of the magnetic resonance imaging scan and 6 months following trauma exposure. RTE participants showed greater deactivation within the visual cortex compared to NRTE participants. Further, NRTE participants showed greater neural reactivity within the dorsomedial prefrontal cortex during stimulation compared to attention/rest, while no difference was observed in RTE participants. Connectivity analyses also revealed that visual cortex to paracentral gyrus connectivity was greater during stimulation compared to attention/rest, but only for the NRTE participants. Finally, neural reactivity to visual stimulation was negatively associated with PTSD symptoms within the RTE group. Our findings suggest that trauma exposure is associated with acute alterations in the neural function that underlies basic visual processing. Furthermore, trauma-induced variability in visual circuit function may be related to the development and expression of PTSD symptoms.</p>","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"3 1","pages":"9"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12095038/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neuropsychopharmacology in the era of artificial intelligence and biomolecule prediction software.","authors":"Rubén A García-Reyes, Laura N Massó Quiñones, Hajin Ruy, Daniel C Castro","doi":"10.1038/s44277-025-00038-9","DOIUrl":"10.1038/s44277-025-00038-9","url":null,"abstract":"<p><p>The development and adoption of artificial intelligence (AI) provides moonshot opportunities to redefine how we generate treatments for neuropsychiatric disease. Despite the rapid advancement of AI across biomedical spheres, its implementation in drug discovery, proteomics, and neurobiology has been met with new and unexpected limitations. Historically, neuropharmacology research has used observational and invasive experimental approaches to identify novel therapeutics. Unfortunately, this classic approach suffers from laborious chemical synthesis and in vivo testing which ultimately leads to translational bottlenecks. With the implementation of AI, we are now able to expedite this early testing by modeling how a drug or protein complex may interact with a receptor of interest. By applying powerful, precision-based protein structure prediction tools, we can better tailor therapeutics and minimize undesired outcomes. Though promising, important caveats like predicting chirality of molecules, conformational changes upon binding, and determining downstream signaling elements remain critical roadblocks that functionally limit the efficacy of prediction software. This Perspective article will briefly discuss how AI-powered protein prediction software will impact drug development to transform neuropsychopharmacology research and therapeutics, while also providing insights into the limitations of these digital tools.</p>","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"3 1","pages":"16"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12208904/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144556425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Taking a look at your speech: identifying diagnostic status and negative symptoms of psychosis using convolutional neural networks.","authors":"Gleb Melshin, Anthony DiMaggio, Nadia Zeramdini, Michael MacKinley, Lena Palaniyappan, Alban Voppel","doi":"10.1038/s44277-025-00040-1","DOIUrl":"10.1038/s44277-025-00040-1","url":null,"abstract":"<p><p>Speech-based indices are promising objective biomarkers for identifying schizophrenia and monitoring symptom burden. Static acoustic features show potential but often overlook time-varying acoustic cues that clinicians naturally evaluate-such as negative symptoms-during clinical interviews. A similar dynamic, unfiltered approach can be applied using speech spectrograms, preserving acoustic-temporal nuances. Here, we investigate if this method has the potential to assist in the determination of diagnostic and symptom severity status. Speech recordings from 319 participants (227 with schizophrenia spectrum disorders, 92 healthy controls) were segmented into 10 s fragments of uninterrupted audio (<i>n</i> = 110,246) and transformed into log-Mel spectrograms to preserve both acoustic and temporal features. Participants were partitioned into training (70%), validation (15%), and test (15%) datasets without overlap. Modified ResNet-18 convolutional neural networks (CNNs) performed three classification tasks; (1) schizophrenia-spectrum vs healthy controls, within 179 clinically-rated patients, (2) individuals with more severe vs less severe negative symptom burden, and (3) clinically obvious vs subtle blunted affect. Grad-CAM was used to visualize salient regions of the spectrograms that contributed to classification. CNNs distinguished schizophrenia-spectrum participants from healthy controls with 87.8% accuracy (AUC = 0.86). The classifier trained on negative symptom burden performed with somewhat less accuracy (80.5%; AUC = 0.73) but the model detecting blunted affect above a predefined clinical threshold achieved 87.8% accuracy (AUC = 0.79). Importantly, acoustic information contributing to diagnostic classification was distinct from those identifying blunted affect. Grad-CAM visualization indicated that the CNN targeted regions consistent with human speech signals at the utterance level, highlighting clinically relevant vocal patterns. Our results suggest that spectrogram-based CNN analyses of short conversational segments can robustly detect both schizophrenia-spectrum disorders and ascertain burden of negative symptoms. This interpretable framework underscores how time-frequency feature maps of natural speech may facilitate more nuanced tracking and detection of negative symptoms in schizophrenia.</p>","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"3 1","pages":"19"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12237691/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144610895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impaired arbitration between reward-related decision-making strategies in Alcohol Users compared to Alcohol Non-Users: a computational modeling study.","authors":"Srinivasan A Ramakrishnan, Riaz B Shaik, Tamizharasan Kanagamani, Gopi Neppala, Jeffrey Chen, Vincenzo G Fiore, Christopher J Hammond, Shankar Srinivasan, Iliyan Ivanov, V Srinivasa Chakravarthy, Wouter Kool, Muhammad A Parvaz","doi":"10.1038/s44277-024-00023-8","DOIUrl":"https://doi.org/10.1038/s44277-024-00023-8","url":null,"abstract":"<p><p>Reinforcement learning studies propose that decision-making is guided by a tradeoff between computationally cheaper model-free (habitual) control and costly model-based (goal-directed) control. Greater model-based control is typically used under highly rewarding conditions to minimize risk and maximize gain. Although prior studies have shown impairments in sensitivity to reward value in individuals with frequent alcohol use, it is unclear how these individuals arbitrate between model-free and model-based control based on the magnitude of reward incentives. In this study, 81 individuals (47 frequent Alcohol Users and 34 Alcohol Non-Users) performed a modified 2-step learning task where stakes were sometimes high, and other times they were low. Maximum <i>a posteriori</i> fitting of a dual-system reinforcement-learning model was used to assess the degree of model-based control, and a utility model was used to assess risk sensitivity for the low- and high-stakes trials separately. As expected, Alcohol Non-Users showed significantly higher model-based control in higher compared to lower reward conditions, whereas no such difference between the two conditions was observed for the Alcohol Users. Additionally, both groups were significantly less risk-averse in higher compared to lower reward conditions. However, Alcohol Users were significantly less risk-averse compared to Alcohol Non-Users in the higher reward condition. Lastly, greater model-based control was associated with a less risk-sensitive approach in Alcohol Users. Taken together, these results suggest that frequent Alcohol Users may have impaired metacontrol, making them less flexible to varying monetary rewards and more prone to risky decision-making, especially when the stakes are high.</p>","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"3 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11698690/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142934406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the relationship between mean performance and within-person variability on smartphone-based cognitive testing in adults across the lifespan.","authors":"Laura M Campbell, Andrea M Weinstein, Ashley Henneghan, Emily W Paolillo, Robert Ackerman, Jessica Bomyea, Colin A Depp, Philip D Harvey, Amy E Pinkham, Elizabeth W Twamley, Raeanne C Moore","doi":"10.1038/s44277-025-00036-x","DOIUrl":"10.1038/s44277-025-00036-x","url":null,"abstract":"<p><p>Cognitive performance is classically measured through measures of central tendency. However, intraindividual cognitive variability (IIV) also holds important information about cognitive functioning. Smartphone-based ecological momentary cognitive testing (EMCT) can capture IIV across days. This study examined predictors of IIV, including demographics, affect, and mean performance, in EMCTs completed on the NeuroUX platform among two US-based samples: adults and older adults with high rates of mild cognitive impairment. The adult sample (<i>n</i> = 375) completed eight EMCTs assessing memory, processing speed, executive functioning, and working memory; each test was self-administered five times over ten days. The older adult sample (<i>n</i> = 94) completed three EMCTs assessing memory, processing speed/executive function, and working memory at three difficulty levels; each test at each difficulty level was self-administered five times over thirty days. Mean performance demonstrated the strongest association with IIV across groups. In the adult group, better mean performance was associated with less variability on tests of memory, executive functioning, and two out of three tests of processing speed. On tests of working memory, better mean performance was associated with greater variability, possibly due to the difficulty of these measures at higher performance levels. In the older adult sample, better mean performance was associated with two of three versions of the memory and working memory EMCTs and all difficulty levels of the processing speed/executive function task. Better average performance was associated with greater consistency across most measures. Broader and more diverse data collection methods like EMCT can provide valuable insights into cognitive functioning beyond traditional mean-based measures.</p>","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"3 1","pages":"15"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12176645/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144478495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mobile EEG examination of putative biomarkers of mental health in individuals that use cannabis.","authors":"Conor H Murray, Kaihan Danesh, Ziva D Cooper","doi":"10.1038/s44277-025-00039-8","DOIUrl":"10.1038/s44277-025-00039-8","url":null,"abstract":"<p><p>Healthy individuals that use cannabis are at greater risk of developing mental health conditions than those that do not use cannabis. Here, using mobile electroencephalography (EEG) in controlled laboratory settings, we examined two putative biomarkers of mental health across two studies of people who use cannabis (<i>N</i> = 100, 50% male; <i>N</i> = 40, 60% male). We examined associations to cannabis use and mood and assessed the influence of sex and age on the outcomes. Specifically, in the first study, we examined prefrontal broadband power, previously found to be related to healthy neurocognitive development, in relation cannabis use. We also examined left prefrontal alpha power, previously found to be related to anxiety and depression, in relation to Beck Depression Inventory (BDI) and Beck Anxiety Inventory (BAI) scores. In the second study, we examined only left prefrontal alpha power during the cold pressor test (CPT), which elicits a stress response. We found that in the first study, young males (ages 21-23) showed the greatest association between prefrontal broadband power and cannabis use (R = 0.50; <i>p</i> = 0.007), while females showed associations between left prefrontal alpha power and BAI scores (R = 0.61, <i>p</i> = 0.013). In the second study, the CPT increased anxiousness (<i>p</i> < 0.001) but did not affect left prefrontal alpha power. Together, our findings help to characterize these putative biomarkers in individuals that use cannabis, while informing the utility of mobile EEG devices for tracking markers of mental health and wellness outside of laboratory settings.</p>","PeriodicalId":520008,"journal":{"name":"NPP-digital psychiatry and neuroscience","volume":"3 1","pages":"18"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12226337/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144577552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}