Tag: Mental health

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Centering People, Centering Stories: Folklore as an Unlikely Ally in the OCD Misdiagnosis Crisis

By: Rebecca Bernstein, MA Folklore

The International OCD Foundation’s (IOCDF) recent landmark white paper reveals more than 80% of OCD cases in America remain undiagnosed (International OCD Foundation, 2025). Considering the size of this clinical challenge, it might seem odd to suggest that a small, humanities-based field like folklore— yes, folklore — has any role to play in the solution. As someone who studies OCD personal narratives (stories people tell about their lived experiences), my research suggests otherwise. In a situation that invokes the feeling of all-hands-on-deck, the tools and perspectives of this field may offer more benefit than we might initially give it credit for.

Folklore is the study of informal, creative communication. Dr. Lynn McNeill describes it as anything people “say, do, make, or believe” (McNeill, 2013). Folklore includes everything you’d think of (quilts, traditional music, fairy tales) and a lot of things you wouldn’t (occupational culture, gossip, internet memes.) We find examples of folklore everywhere. It’s in our holidays and our hobbies, our food and our fads, our jokes and our grieving. Folklorists study the infinite ways people express themselves in daily life. This, in turn, helps us better understand the cultural realities in which they live. And because what we “say, do, make, or believe” describes most of human behavior, the folkloric lens can be an indispensable one with which to investigate the world.

The benefit of studying how people express themselves is obvious when we recognize that in mental health, conversation and narrative are the primary tools we use to give and receive care. OCD isn’t just a diagnosis. It’s also a fundamentally creative experience. (Creative things don’t necessarily have to be beautiful, pleasing, or even wanted. They just have to be new and meaningful.) (Dictionary.com, 2023). Although ego-dystonic, extraordinary beliefs and elaborate rituals are hallmark features of OCD. When sufferers discuss their experiences, they are naturally inclined to do so through their own personal and cultural lenses. Therefore, descriptions of OCD vary infinitely. If the issue is our failure to recognize OCD when it presents itself, an approach designed to make sense of something as messy as human expression may offer insights that quantitative research methods still struggle to obtain.

How Folklorists Research

Just like in biomedical research, the research methods folklorists use matter. Our goal is to better understand people and their communities. That means we strategically build relationships, listen deeply, and intentionally embrace the complexity of those we talk to.

When I started researching OCD narratives, I wanted to know: What were the internal realities like for people who lived with this illness? What made their stories distinct? And how might those stories be connected? One of the biggest challenges I faced in my fieldwork was the potential for my participants to self-censor. As someone who also lives with OCD, I knew all too well the role shame and fear could play in the choice to fully share one’s reality with others. Using both field-tested approaches and my own lived knowledge, I conducted interviews with people with OCD, approaching them in a way I hoped would ease interviewees into difficult conversations:

  • I provided anonymity. I held all interviews on Zoom, where participants were free to keep their cameras off. I also assigned each one an alphanumeric signifier (A1, B2, etc.) in my writing.
  • I emphasized the importance of story. Although I asked specific questions, I also allowed participants to go off topic and engage in two-way conversation. The story was the point.
  • I used the “kitchen table” interview method. Based on the work of Carl Lindahl, this method tries to recreate the intimacy of two individuals talking around a kitchen table. It discourages framing the interviewer as an objective party, recognizes storytellers as experts in their own experiences, and suggests that interviewers only ask questions they themselves would be willing to answer (Lindahl, 2012).
  • I disclosed. My choice to openly discuss my own OCD diagnosis with interviewees allowed conversations to proceed with a certain warmth and vulnerability.
  • I emphasized participants’ humanity. I treated each participant as a full individual rather than just a source of information. This meant I worked on a model of enthusiastic consent. It also meant I asked them for feedback on my writing to ensure I portrayed their experiences accurately.
  • I compensated participants well. Each received a $100 gift card.

The Results

The universal theme I discovered during these interviews was a profound concern with social isolation. Every single participant mentioned this issue. Interviewees shared how OCD made it difficult for them to maintain relationships and how challenging it was to hide their illness from others. They also recalled their joy and gratitude when discussing moments in which they felt understood.

Their narratives also contained four other common themes:

1.) Logic and patterns of personal concern. Participants often discussed their particular obsessions and compulsions, and the influence those specific thoughts and behaviors had on their daily lives.

2.) Issues of negotiation. People talked about navigating certain types of conflicts as a result of their illness. These conflicts generally fell into two categories: self-negotiation and existential negotiation. In the first, people struggled with the desire to take their thoughts and urges seriously despite knowing they didn’t make sense. In the second, they wrestled with their relationships to the divine.

3.) Positive approaches to the illness. Many interviewees made a point to mention the silver linings they saw in being sick. They noted how OCD made them safer, more empathetic, or provided them with particular skills. Others discussed productive choices they’d made despite living with such a debilitating condition.

4.) Interactions with medical systems. Participants talked about their experiences as patients. For some, dealing with doctors, therapists, and other health professionals helped them understand their experience or relieved their suffering. For others, these encounters were confusing, unhelpful, or even traumatizing.

It’s important to note these themes represent a truly broad range of content. Not every story included every theme, and within those themes, the specific details I heard varied as much as the individuals themselves.

Implications

Say you were to hear four stories: one about someone’s preferred cleaning routine, one about someone’s waning belief in God, one about a good decision made in a difficult circumstance, and one about a doctor’s visit. It’s unlikely you’d consider these stories connected. And yet the data shows they are. The fact that stories with dramatically different content can reflect the same illness highlights the way OCD can remain elusive and camouflaged.

The problem with recognizing these stories as OCD stories isn’t just the variation in content. It’s also in how others hear them. In folklore, we don’t just study cultural expressions. We also study how they move from person to person. “Tellable narratives” travel easily. Both speakers and listeners understand what a certain type of story should sound like and the meaning it’s supposed to convey. If I tell you a tale about a persecuted young woman who escapes a bad home life and marries a prince, you can probably guess you’ve heard Cinderella. If we’re both excited that she went from rags to riches, we share an understanding that her journey is a positive one. In contrast, an “untellable narrative” hits some kind of barrier. If you’ve never heard Cinderella before or think the stepmother is actually the hero, my meaning in telling you the story gets lost. Untellable narratives can be misinterpreted.

This misalignment between the stories people tell and the ones listeners expect to hear happens all the time. We’ve all said things misunderstood by others. Sometimes this process is harmless; other times it results in difficult consequences. Dr. Kristiana Willsey writes about veterans who censor themselves in front of civilian audiences. Because civilians usually only expect to hear tales of “war heroes” or “PTSD survivors,” veterans often choose not to tell the full and complicated stories of their service experiences (Willsey, 2015). Dr. Amy Shuman and Carol Bohmer discuss the case of rejected asylum seekers. If asylum applicants don’t tell their stories of oppression and escape in a way that fits immigration officials’ expectations of what a traumatic asylum story should look like, their applications get denied (Shuman & Bohmer, 2016). If we consider just how different any two OCD stories can be and add the public assumption that OCD is an illness of specific doings (hand washing, checking locks) rather than tellings, it highlights just how difficult it is for most of these narratives to get heard, and heard correctly.

Patient/practitioner interactions can be particularly vulnerable to this type of miscommunication. The problem with considering OCD as just a medical issue is that most people don’t think of their lives as medical events. Practitioners enter the room ready to make sense of problems in clinical terms. Patients enter with stories. They share their concerns in a way that cannot be easily separated from their personal frames of reference or cultural understandings of life. Practitioners are often taught to mistrust the details that emerge from these narratives, to kindly but efficiently work around them in order to do their jobs. But for patients, these details are how they make meaning. If misdiagnoses also occur during these interactions, it’s worth taking a closer look at what’s being lost in translation.

Folklore ultimately offers the promise of new solutions to old problems. It allows us to reconsider how we listen to patients, collect data, and address communication issues— all clear benefits in the fight for better diagnostic care. It is also equipped to help us make sense out of the lived reality of OCD— perhaps uniquely so. I see folklore as an exciting potential ally to traditional research and clinical spaces. My hope is that this partnership can help us work more effectively toward our common goals: a better understanding of OCD, and quicker ease for its sufferers.

Works Cited

Dictionary.com. (2023). Creativity. In Random House Unabridged Dictionary. Random House, Inc. https://www.dictionary.com/browse/creativity.

International OCD Foundation. (2025). America’s OCD care crisis: National findings on the failure of effective OCD treatment to research patients. International OCD Foundation. https://iocdf.org/wp-content/uploads/2025/12/Full-Report-Americas-OCD-Care-Crisis-12-9-2025.pdf.

Lindahl, C. (2012). Legends of Hurricane Katrina: The right to be wrong, survivor-to- survivor storytelling, and healing. The Journal of American Folklore, 125 (496), 139–176. https://doi.org/10.5406/jamerfolk.125.496.0139.

McNeill, L. (2013). Folklore rules: A fun, quick, and useful introduction to the field of academic folklore studies. Utah State University Press. https://muse.jhu.edu/book/27822.

Shuman, A. & Bohmer, C. (2016). The stigmatized vernacular: Political asylum and the politics of visibility/recognition. In D. Goldstein & A. Shuman (Eds.), The stigmatized vernacular: Where reflexivity meets untellability. Indiana University Press.

Willsey, K. (2015). Falling out of performance: Pragmatic breakdown in veterans’ storytelling. In T.J. Blank & A. Kitta (Eds.), Diagnosing folklore: Perspectives on disability, health and trauma. University Press of Mississippi.

The post Centering People, Centering Stories: Folklore as an Unlikely Ally in the OCD Misdiagnosis Crisis appeared first on International OCD Foundation.

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BrainBaseline Assessment of Cognition and Everyday Functioning (“BRACE”-ing for the Future): Establishing iPad-Based Norms for Cognitive Function in the Multicenter AIDS Cohort Study and Women’s Interagency HIV Study Combined Cohort Study

Background: Digital cognitive assessments are increasingly used in large-scale studies to assess brain health, offering scalable, standardized, and self-directed testing solutions. Cognitive function remains a concern for people with HIV despite antiretroviral therapy. The BRACE (BrainBaseline Assessment of Cognition and Everyday Functioning) is a validated tablet-based screener for cognition in people with HIV. Preliminary pilot norms were established in a small sample (n=144), but full regression-based normative data have not yet been developed. Consequently, HIV serostatus differences based on standardized BRACE scores and cognitive correlates have not been systematically examined. Objective: This study aims to develop regression-based normative data for BRACE performance in people without HIV who were demographically and behaviorally comparable to people with HIV within biological sex; to examine differences in cognitive performance by HIV status and biological sex; and to evaluate sociodemographic, behavioral, and clinical correlates of BRACE performance. Methods: A total of 2937 participants (1063 people without HIV [499 women] and 1874 people with HIV [1053 women]) in the Multicenter AIDS Cohort Study/Women’s Interagency HIV Study Combined Cohort Study completed BRACE once between November 2020 and March 2025. BRACE includes the Trail Making Test (A and B), Stroop-Color, and visual spatial learning. Regression-based norms were derived from people without HIV using multiple demographic models (eg, age-only, age + education, and age + education + sex). The age + education model was selected for primary analyses because it provided the best balance of interpretability, parsimony, and generalizability while avoiding race-based corrections. HIV serostatus and sex differences were examined using ANOVA and tests, with effect sizes calculated using Cohen’s . Results: Cognitive performance was largely comparable between people with HIV and people without HIV across all BRACE outcome measures. Statistically significant differences were very small in magnitude (all effect sizes<0.11) and primarily observed among men on Stroop-Color. Across groups, older age and fewer years of education were associated with poorer raw BRACE performance, although these associations attenuated after demographic adjustment using T-scores. Most clinical and behavioral factors (eg, hypertension, smoking, and noncannabis substance use) were related to poorer raw scores but not standardized performance. However, diabetes and cannabis use remained independently associated with T-scores across multiple measures—diabetes with poorer scores and cannabis use with higher scores, an association that should be interpreted cautiously. HIV-specific clinical factors, such as nadir CD4 count and antiretroviral therapy duration, were linked primarily to raw scores. Conclusions: This study establishes the first regression-based normative data for BRACE, derived from a large, demographically diverse people without HIV, and demonstrates its applicability for evaluating cognitive function in people with HIV. Findings indicate minimal cognitive differences between people with HIV and people without HIV and highlight the influence of common sociodemographic and metabolic factors. These results support BRACE as a scalable, reliable, and self-administered digital tool for assessing cognitive health in diverse populations and underscore its potential for longitudinal monitoring and precision phenotyping in both research and clinical contexts.
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When AI Colludes: Clinical Reliability of Training and Preference Data as a Trustworthy-AI Criterion

Research on artificial intelligence (AI) and mental health has focused largely on harms at deployment, including chatbot safety, sycophancy, and AI-associated delusions. Less attention has been paid to a prior question: whether the human-generated text and preference judgments that shape large language models are themselves clinically reliable, particularly when self-report may be distorted. This Viewpoint aims to develop the clinical psychiatric construct of collusion—the uncritical acceptance of an unreliable account—as an analytic lens for AI training and deployment, and to argue that the clinical reliability of training and preference data should be treated as an explicit trustworthy-AI criterion in mental-health–relevant systems. A conceptual synthesis of psychiatry, clinical psychology, and AI safety literature was undertaken. The analysis distinguishes three pipeline layers: pretraining corpora, preference data and posttraining methods, and deployment-time interaction. It maps the clinical construct of collusion against adjacent technical concepts, including sycophancy, reward overoptimization, grounding, refusal training, red-teaming, and live monitoring. The synthesis suggests that collusion-like dynamics are least applicable at the pretraining layer and most applicable at the preference-data and deployment layers, where unassessed user or labeler input can be reinforced without corroboration. Existing mitigations, including data curation, Constitutional AI, reward-model evaluation, grounded generation, refusal training, red-teaming, and postdeployment monitoring, address parts of this problem. However, these approaches are not yet organized around a clinically informed account of when self-report is unreliable. The central novelty is therefore not a generic claim about bias, but the proposal that clinical self-report reliability should be assessed as a distinct data-quality and governance dimension. Trustworthy-AI frameworks for mental-health–relevant applications should incorporate clinical expertise in self-report reliability into preference-data design, red-teaming, and postmarket surveillance. Adding the clinical reliability of training and preference data as an explicit criterion could complement existing technical safeguards while leaving empirical evaluation of clinician involvement as an open research agenda.
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Efficacy of a World Health Organization–Guided Self-Help Intervention for Reducing Psychological Distress in Afghan Refugees: Randomized Controlled Trial

Background: Common mental health disorders are highly prevalent among refugees. There is an urgent need to address the mental health burden in this population. Objective: This study tested the efficacy of an individually supported self-help stress-management intervention developed by the World Health Organization—Doing What Matters in Times of Stress (DWM)—in reducing psychological distress and improving functioning among refugees in Indonesia, a major transit country in the Asia-Pacific region. Methods: A single-blind randomized controlled trial with 303 Farsi-speaking refugees was conducted between June 2024 and June 2025. Participants with moderate to high psychological distress (Kessler Psychological Distress Scale [K10] score≥20) were randomly allocated to the facilitator-guided individual DWM condition (n=202) or a repeated assessment control condition (n=101). The primary outcome was psychological distress (K10 score) at the posttreatment assessment. Secondary outcomes were posttraumatic stress disorder symptoms, functional impairment, social functioning, and personally identified problems. Results: Intent-to-treat analysis indicated that participants in the DWM condition showed greater reductions in K10 scores than those in the repeated assessment control condition (posttreatment: =−.563, SE=0.124; <.001; Cohen =0.56; 1-month follow-up: =−.447, SE=0.140; =.002; Cohen =0.45). Similarly, those participants in the DWM condition reported greater improvements in posttraumatic stress disorder symptoms, well-being, social functioning, functional impairment, and personally identified psychological problems. No serious adverse events were reported. Conclusions: The findings provide the first evidence for the effectiveness of DWM in reducing psychological distress and improving overall functioning among urban refugees living in a transit setting. Individually supported self-help interventions such as DWM mayoffer an effective, feasible, and scalable approach to improving mental health for refugees. Trial Registration: ANZCTR ACTRN12624000609550; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=387637&isReview=true
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Drug Target for Fragile X Syndrome Identified Through Preclinical Study

UCLA Health researchers have identified a potential drug target for treating fragile X syndrome (FXS), the most common genetic cause of intellectual disability and autism that affects roughly one in 2,000 boys.

Fragile X syndrome is caused by a mutation in a single gene, FMR1, that results in the loss of a protein critical for normal brain development and function. Headed by Carlos Portera-Cailliau, MD, PhD, professor of neurology at UCLA and member of the UCLA Brain Research Institute, the researchers, the team’s work in genetically engineered mice lacking the Fmr1 gene identified the synaptic protein EPAC2 as a potential therapeutic target for fragile X syndrome. Their study showed that blocking EPAC2 in the fragile X mouse model restored abnormal patterns of brain activity and improved several FXS-associated behavioral symptoms.

Pertera-Cailliau is senior and corresponding author of the researchers published paper in Neuron, titled “Translatome profiling reveals opposing alterations in inhibitory and excitatory neurons of fragile X mice and identifies EPAC2 as a therapeutic target.”

Fragile X syndrome is a prototypical neurodevelopmental disorder (NDD) characterized by intellectual disability, social anxiety, atypical sensory processing characterized heightened sensitivity to sensory input such as sound and touch, and a higher risk of seizures. Many also meet the criteria for an autism spectrum disorder diagnosis. “Symptoms of fragile X syndrome (FXS), the leading monogenic cause of intellectual disability and autism, are thought to arise from an excitation/inhibition (E/I) imbalance,” the authors stated.

FXS is caused by mutations in the FMR1 gene, resulting in near complete loss of the fragile X messenger ribonucleoprotein (FMRP), an RNA-binding protein in neurons that plays different roles in cell compartments including the nucleus, axons and dendrites, including regulating mRNA translation at synapses, they explained. As it is caused by a change in a single gene, fragile X syndrome has long been considered a promising candidate for targeted therapies yet clinical trials to date have not produced an effective treatment. “Since the discovery of the genetic basis of FXS in 1991, several clinical trials have been undertaken—without success—and no specific treatments for FXS are currently available,” the investigators continued. “Thus, there is an urgent need to rethink therapeutic strategies for FXS.”

For their newly reported study the researchers used genetically engineered knockout (KO) mice that lack Fmr1 to simulate fragile X syndrome. Using genetic sequencing, they found that levels of the gene EPAC2 were increased in the brain of fragile X mice. This was of potential interest as a target for therapy because the gene’s protein, EPAC2, is localized to synapses and is known to be important for learning and memory.

The researchers then demonstrated that blocking EPAC2 in the fragile X mouse model, either genetically, or using an EPAC2 inhibitor compound, restored cortical circuit function and improved multiple behavioral symptoms associated with fragile X syndrome, including heightened sensitivity to touch, difficulties with social interaction and their susceptibility for seizures. “Perhaps the most exciting result is that treatment with an EPAC2 antagonist can rescue several behavioral phenotypes in Fmr1 KO mice,” the authors stated.

“EPAC2 emerged as an attractive target because it was consistently altered across multiple types of brain cells in our analysis,” said the study’s first author Anand Suresh, PhD, a post-doctoral fellow in the laboratory of Portera-Cailliau. “When we blocked it, either genetically or with a drug compound, we saw meaningful improvements in both brain circuit function and behavior.”

EPAC2 is expressed almost exclusively in the brain, which means drugs targeting it are less likely to cause unwanted effects elsewhere in the body. Suresh said this is an important consideration as researchers continue preclinical studies. “This bodes well for future preclinical trials and safety studies in humans, as compounds that target EPAC2 should not have off-target effects,” the authors stated in their report.

For their study the UCLA investigators used an RNA sequencing technique to examine gene activity separately in two major classes of brain cells: those that excite and those that inhibit neural activity. Fragile X syndrome is thought to arise from an imbalance between these two systems. The analysis revealed striking differences in how the genetic mutation underlying Fragile X syndrome affects each cell type but also identified a small set of genes, including the one that encodes EPAC2, that were dysregulated in both.

The researchers also found that EPAC2 levels appear to rise gradually as the brain matures, suggesting it may be a particularly relevant target for older children and adults with Fragile X syndrome, rather than only in early development. They concluded, “Our results should encourage the development of novel EPAC2 inhibitors for the treatment of FXS. More generally, our study exemplifies how transcriptomic approaches in animal models of neuropsychiatric conditions can be used to prioritize potential novel therapeutic targets.”

The post Drug Target for Fragile X Syndrome Identified Through Preclinical Study appeared first on GEN – Genetic Engineering and Biotechnology News.

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AI Model Predicts Alzheimer’s Progression from a Single MRI Scan

Researchers at the University of California, San Francisco (UCSF) have developed an artificial intelligence model capable of predicting cognitive impairment and Alzheimer’s disease progression using only a single baseline MRI scan and basic demographic information. The approach, published in Nature Aging, could help make early Alzheimer’s assessment faster, more accessible, and less dependent on costly specialized testing.

Alzheimer’s diagnosis remains complex and resource-intensive

Alzheimer’s disease accounts for approximately 60% to 70% of dementia cases worldwide. Although structural brain changes and cognitive decline are hallmarks of the disease, accurately forecasting who will develop progressive impairment remains difficult.

Current diagnostic workflows often rely on multiple complementary techniques, including PET imaging, cerebrospinal fluid or blood biomarkers, genetic testing, and comprehensive neuropsychological assessments. While effective, these approaches can be expensive, time-consuming, and inaccessible in many healthcare settings.

MRI scans are among the most widely available clinical imaging tools for neurological assessment, but MRI data alone has historically struggled to capture the complexity and heterogeneity of Alzheimer’s disease progression when used in conventional AI frameworks.

To address this challenge, the UCSF team designed a multitask deep learning framework that combines domain-specific imaging knowledge with advanced machine learning methods to predict cognitive outcomes directly from structural MRI scans.

AI framework predicts cognition without invasive testing

Unlike many earlier Alzheimer’s prediction models, the new system does not require longitudinal imaging data, baseline cognitive testing, PET scans, or molecular biomarker analysis.

The researchers instead focused on extracting clinically meaningful information from a single baseline MRI scan. The framework was trained to perform several related tasks simultaneously, including tissue segmentation, Alzheimer’s diagnosis prediction, and estimation of both present and future cognitive performance.

A key innovation of the study was the development of a specialized image model that segments brain tissue into gray matter, white matter, and cerebrospinal fluid before generating cognitive predictions. According to the authors, this task-specific segmentation step allowed the model to learn biologically relevant spatial brain features more effectively than standard transfer-learning approaches.

Senior study author Ashish Raj, PhD, professor of radiology and biomedical imaging at UCSF, said the goal was to create a system that could be realistically implemented in routine clinical environments.

“Unlike previous approaches, our model does not require baseline cognitive assessment, specialized image pipelines, expensive PET scans, genetic analysis, or fluid proteomics, making it a fast, accurate, and easily implementable tool for most clinical settings,” Raj said in a statement.

Large imaging datasets improved robustness and generalizability

To train and validate the framework, the researchers used imaging and clinical data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), including MRI scans, demographic information, diagnoses, and cognitive assessments.

The team also incorporated MRI data from the Human Connectome Project Young Adult cohort, which contains scans from healthy younger adults with minimal age-related brain atrophy. According to the authors, exposing the model to healthy brain anatomy improved its ability to distinguish pathological neurodegeneration from normal aging.

An external validation cohort from the Dallas Lifespan Brain Study was additionally used to test the generalizability of the framework across independent datasets.

The researchers reported that the multitask framework outperformed existing AI methods, including standard transfer-learning approaches, in predicting clinically relevant outcomes. The model generated accurate predictions for Alzheimer’s diagnosis, tissue segmentation, current cognitive function, and future cognitive decline using only baseline MRI data.

The study also reported improvements in computational efficiency and processing speed compared with more complex MRI morphometry pipelines commonly used in neuroimaging research.

First author Daren Ma, MSc, a machine learning specialist in the Raj Lab at UCSF, said the framework could help clinicians identify at-risk patients earlier and streamline referrals for advanced neurological evaluation.

“We reported meaningful gains in speed and performance over other pipelines, which could prove valuable in developing a quick clinical prediction of cognitive impairment prior to referring the patient to a more advanced imaging lab and/or a full neuroradiology report,” Ma said.

Potential implications beyond Alzheimer’s disease

The researchers believe the framework could eventually be adapted for other neurodegenerative disorders characterized by structural brain changes and progressive cognitive decline.

Potential future applications include Parkinson’s disease, amyotrophic lateral sclerosis (ALS), and Huntington’s disease. The ability to estimate cognitive impairment using minimal baseline data may also prove useful in community healthcare settings where access to specialist neuropsychological testing is limited.

In addition, the model may have implications for clinical trial design. Identifying likely disease progressors early could help reduce trial size requirements and improve patient selection for studies evaluating disease-modifying therapies.

“The ability to correctly predict progressors from non-progressors using only baseline data can dramatically reduce sample sizes and cost,” Raj said.

The authors emphasized, however, that further validation will be necessary before the model can be broadly implemented in routine clinical practice. Future iterations of the framework may incorporate additional clinical measurements where available, including longitudinal MRI imaging, PET scans, genetics, and blood or cerebrospinal fluid biomarkers.

The study highlights the growing role of AI-driven imaging analysis in neurology and suggests that clinically accessible tools such as MRI may eventually support earlier and more scalable prediction of Alzheimer’s disease progression.

The post AI Model Predicts Alzheimer’s Progression from a Single MRI Scan appeared first on Inside Precision Medicine.

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Psychological inflexibility and resilience in anxiety: insights from machine-learning and robust mediation-based models

IntroductionPsychological inflexibility (PI) has been associated with anxiety symptoms, while resilience serves as a protective factor; however, their roles and interrelationship remain poorly understood. We investigated the role of PI on anxiety-related symptoms while assessing the mediating role of resilience and testing the moderating effect of sex and psychiatric history.MethodsFrom April to July 2021, an online protocol employing self-reported measures assessed PI (Acceptance and Action Questionnaire), resilience dimensions (Resilience Scale for Adults), and anxiety-related symptoms (Generalized Anxiety Disorder (GAD) Scale; Depression, Anxiety, and Stress Scales). A model generation approach, using machine-learning and robust mediation-based models, was applied to investigate the relationships between these constructs.ResultsIn a sample of 313 adults (72.20% females; 39.29 ± 11.81 years), Random Forest analysis indicated PI and the resilience dimensions perception of self (R-PS) and planned future (R-PF) as the strongest predictors of anxiety-related symptoms. PI showed a positive direct association with GAD, anxiety, and stress (respectively β = 0.28, β = 0.07, β = 0.20, p ≤ 0.001). Significant indirect associations emerged: PI–Stress regarding R-PS (β = 0.08, p = 0.004), PI–Anxiety regarding R-PF (β = 0.03; p = 0.03), PI–GAD (β = 0.08, p = 0.001) and PI–Stress (β = 0.11, p < 0.001) regarding R-PS and R-PF together.DiscussionThese findings highlight the importance of PI and resilience as interconnected processes underlying mental health outcomes. Additionally, they suggest that psychological intervention programs targeting PI, along with resilience, could foster healthier strategies for coping with anxiety-related symptoms.
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Heatwave-related variations in psychiatric consultations and admissions: a time-series analysis

BackgroundHeatwaves are becoming increasingly frequent and intense across Europe, posing significant risks to physical and mental health. Emerging evidence suggests that prolonged exposure to high temperatures may exacerbate psychiatric symptoms and increase the demand for acute mental health services.ObjectivesThis study examined the relationship between extreme heat events and psychiatric service utilization in Bolzano, Italy, by analyzing emergency psychiatric consultations and acute psychiatric admissions across three non-consecutive years.MethodsA retrospective observational analysis was conducted using daily psychiatric consultations in the Emergency Department (ED) and daily admissions to acute psychiatric wards from 2013, 2018, and 2023. Meteorological data were obtained from the provincial environmental agency. Time-series analyses employed ARIMA models, incorporating daily minimum and maximum temperatures, tropical nights, and a cumulative heatwave index (n_hot_htwv). Model selection was based on BIC, and the effect of exogenous temperature variables was evaluated through changes in AIC. Residual diagnostics guided the inclusion of weekly seasonal dummy variables.ResultsNon-seasonal ARIMA models with day-of-week dummies provided the best fit for both consultations and admissions. Adding the cumulative heatwave variable (n_hot_htwv) consistently improved model fit across all years, whereas minimum and maximum temperatures alone did not. Heatwave duration emerged as a more sensitive predictor of psychiatric service utilization than isolated temperature peaks. No evidence of yearly seasonality was found, and residual diagnostics supported the robustness of models including weekly dummy variables.ConclusionHeatwaves are associated with increased psychiatric consultations and hospital admissions in Bolzano, with cumulative heat exposure representing a critical determinant. These effects cannot be explained solely by seasonal patterns, suggesting an independent climatic influence. Given the projected rise in heatwave intensity and duration, mental health services should incorporate climate-responsive planning and early-warning strategies.