Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition whose pathophysiology is thought to arise from complex interactions among genetic variation, molecular and cellular perturbations, and neural circuit dysfunction. Although the distinction between syndromic and non-syndromic ASD was introduced as a pragmatic clinical classification, these clinically defined subgroups may exhibit overlapping yet distinct patterns of genetic liability and molecular dysregulation while also exhibiting mechanistic convergence. This narrative review examines the molecular mechanisms underlying synaptic development, structure, and function, with particular emphasis on the roles of chromatin remodeling and epigenetic regulation in neurodevelopment. It further considers how molecular and cellular alterations may contribute to impaired functional integration at the neural circuit level. By comparing the shared and unique genes and pathways associated with syndromic and non-syndromic ASD, the review identifies potential points of mechanistic convergence across multiple levels of biological organization. To integrate these findings, the review proposes a three-layer hierarchical framework that links gene function (Layer 1), core biological pathways (Layer 2), and clinical phenotypes (Layer 3). By mapping ASD-associated genes onto a functional continuum ranging from local synaptic effectors to global regulators of chromatin and gene expression, this framework illustrates how subtype-enriched genetic factors may give rise to both shared and unique molecular, cellular, and circuit-level alterations. The proposed framework may help identify common and subtype-enriched biological mechanisms, inform biomarker discovery, and provide a conceptual basis for mechanism-informed ASD research and biologically grounded clinical stratification.
Non-motor correlates of anxiety in de novo Parkinson’s disease: a cross-sectional study with exploratory subgroup analyses
ObjectiveAnxiety is a prevalent early non-motor symptom of Parkinson’s disease (PD), but its clinical correlates remain poorly defined in de novo, drug-naïve patients. This retrospective cross-sectional study enrolled 284 medication-naïve de novo PD patients and 147 age- and sex-matched healthy controls to identify non-motor correlates of anxiety after adjustment for demographic and motor covariates, and explore potential subgroup heterogeneity.MethodsAnxiety was assessed with the Hamilton Anxiety Rating Scale; multivariate logistic regression and prespecified stratified analyses by age, sex, disease duration, Hoehn-Yahr stage and motor severity were performed.ResultsClinically significant anxiety was detected in 44.0% of PD patients versus 8.1% of controls (p < 0.001), with psychic anxiety as the dominant subtype. After multivariate adjustment, higher depressive symptom scores were positively associated with anxiety (OR = 1.386), whereas better sleep quality (OR = 0.965) and intact olfactory identification (OR = 0.815) were negatively associated with anxiety; motor severity showed no significant association after multivariable adjustment. Exploratory subgroup analyses revealed heterogeneous patterns of sleep and olfactory correlates across clinical strata. The three-factor predictive model yielded an overall classification accuracy of 84.81%.ConclusionThese findings indicate that non-motor abnormalities, rather than motor impairment, are associated with anxiety in early PD after adjustment for demographic and motor covariates. The distinct correlative patterns across subgroups provide preliminary clinical clues for the heterogeneous neurodegenerative processes underlying PD-related anxiety, and support routine screening of depressive, sleep and olfactory symptoms to facilitate anxiety risk stratification at initial diagnosis.
Metabolic dysfunction associates with neuroimaging measures of brain structure and connectivity
IntroductionMetabolic syndrome (MetS) and insulin resistance are established risk factors for Alzheimer’s disease (AD) and have been linked to alterations in brain structure and function, including within the Default Mode Network (DMN).MethodsHere, we examined associations between metabolic dysfunction, brain structure, and DMN functional connectivity using data from the Health and Aging Brain Study: Health Disparities (HABS-HD) cohort. Participants were classified as being cognitively unimpaired (CU; n = 1,045; mean age = 66.1 years; 65% female), or having mild cognitive impairment (MCI; n = 186; mean age = 66.5 years; 52% female) or dementia (n = 76; mean age = 69.0 years; 47% female).ResultsAcross diagnostic groups, cortical thickness in AD-vulnerable regions varied by both cognitive status and HbA1c grouping, with the strongest effects observed in the CU cohort. HbA1c grouping was also associated with white matter microstructure, including stepwise reduc tions in fractional anisotropy of the anterior corona radiata across glycemic cat egories (normoglycemia > prediabetes > diabetes) in CU individuals. Functional connectivity of the posterior DMN (pDMN) showed main effects of both cognitive status and HbA1c grouping. Using a continuous index of cardiometabolic risk (MetS-Z), pDMN connectivity decreased linearly with increasing cardiometabolic burden in the CU cohort, whereas other DMN subnetworks exhibited positive associations with MetS-Z.DiscussionThese findings suggest network-specific associations between cardiometabolic risk and DMN organization that are detectable in indi viduals with MCI, a clinical state preceding dementia, as well as in some degree within cognitively unimpaired individuals. Longitudinal analyses are needed to determine whether metabolic dysfunction predicts divergent trajectories of DMN connectivity over time.
Editorial: Panoramic view of cognitive impairment: interdisciplinary cognitive research, volume II
Correction: Clinical relevance of loneliness in the treatment of depression: study protocol for a naturalistic prospective longitudinal study
Factors associated with school absenteeism in adolescents with mental illness: a multivariate analysis
Backgroundschool absenteeism is a common functional impairment among adolescents with mental disorders, yet its associated factors remain limited. This study aimed to identify key demographic, clinical, and social factors associated with school absenteeism in this population.MethodsA retrospective cohort study was conducted among 217 adolescents (12–18 years) with mental illness, categorized into school-attending (n=163) and school-suspended (n=54) groups. Baseline data on demographics, clinical characteristics, social support, and family factors were collected. Univariate analyses were used to compare group differences, followed by multivariate logistic regression and restricted cubic spline (RCS) analysis to identify independent predictors of school absenteeism.ResultsIn univariate analysis, school-suspended adolescents had significantly lower social support scores (36.2 ± 10.9 vs. 45.5 ± 12.2, p<0.001), higher rates of parental divorce (46.3% vs. 22.1%, p<0.001), and a higher proportion of pre-hospitalization school absenteeism (51.9% vs. 31.3%, p=0.006). Multivariate logistic regression revealed that parental divorce (OR = 3.04, 95%CI=1.59–5.83, p=0.001), pre-hospitalization school absenteeism (OR = 2.37, 95%CI=1.26–4.43, p=0.007), and longer disease course (OR = 1.02, 95%CI=1.00–1.04, p=0.022) were independent risk factors for school absenteeism. Conversely, higher social support was a strong protective factor: compared to the low social support group (score 17–34), the medium (35–51) and high (52–85) support groups had markedly lower odds of school absenteeism (OR = 0.24, 95%CI=0.08–0.74, p=0.013; OR = 0.05, 95%CI=0.01–0.24, p<0.001, respectively). RCS analysis confirmed a linear negative association (P for non-linearity=0.189), with higher social support scores correlating with progressively lower suspension risk.ConclusionsParental divorce, pre-hospitalization school absenteeism, and longer disease course are independent risk factors for school absenteeism in adolescents with mental illness, while higher social support acts as a robust protective factor. These findings highlight the need for targeted interventions addressing family stability, social support enhancement, and early intervention for pre-existing school impairment to reduce school absenteeism and promote functional recovery in this vulnerable population.Clinical trial registrationhttps://clinicaltrials.gov/, identifier ChiCTR2100046396.
Maternal emotional distress, caregiving burden, and service access among mothers of children with autism spectrum disorder: a qualitative study
IntroductionMothers raising children with autism spectrum disorder (ASD) may experience substantial emotional distress and caregiving burden, whilst family support andaccess to services can shape how these demands are managed. Yet in-depthqualitative evidence on these public mental health dimensions remains limited inSaudi Arabia. This study explored mothers’ experiences of raising a child diagnosedwith ASD.MethodsUsing a qualitative descriptive design informed by a contextualistposition, the study recruited ten mothers through a specialist autism centre inMadinah and the surrounding community. Trained female researchers, all registered nurses, conducted semi-structured interviews in Arabic, each lastingapproximately 60 to 90 minutes. Recordings were transcribed verbatim inArabic; a professional translator produced English transcripts, which a secondbilingual researcher checked before reflexive thematic analysis.Results and discussionSeven interrelated themes captured the mothers’ accounts: diagnosis as an emotional rupture; disruption of daily life by behavioural challenges; learning to understand the child across communication barriers; the cumulative toll of continuous care; adaptation through acceptance, patience, and faith; family support as a buffer or added burden; and compensating for uneven service experiences. Mothersdescribed substantial emotional and caregiving demands, but their experiences varied according to family support and access to services. One account of comprehensive state support provided an important counter-case to the morecommon experience of costly or fragmented provision. The findings position maternal emotional distress and caregiving burden as public mental health concerns shaped by family, social, and service contexts. They support a public mental health response that links maternal mental health promotion with accessible, coordinated, and family-centred care.
Emotional maltreatment and problematic short-video use among adolescents: an indirect association through narcissism and moderation of the direct association by physical activity
IntroductionEmotional maltreatment has been associated with problematic short-video use during adolescence, but the nature of this association remains unclear. This cross-sectional study examined the association between emotional maltreatment and problematic short-video use, estimated the indirect statistical association through narcissism, and tested whether physical activity moderated the direct association between emotional maltreatment and problematic short-video use.MethodsData were obtained in November 2025 from six schools across three Chinese provinces. The analytic sample included 1,622 adolescents, comprising 772 males and 850 females. Participants completed measures of emotional maltreatment, narcissism, problematic short-video use, and physical activity.ResultsEmotional maltreatment was positively associated with problematic short-video use, and an indirect statistical association was observed through narcissism. Physical activity moderated the direct association between emotional maltreatment and problematic short-video use, which was weaker at higher levels of physical activity.DiscussionConsidering a personality characteristic and a health-related behavior within the same model provides a broader understanding of the circumstances under which emotional maltreatment is associated with problematic short-video use. These findings may inform family- and school-based support concerning adolescents’ emotional needs, healthy short-video use, and regular physical activity.
STAT+: The head of Kennedy’s new autism project served in Iraq War, worked with Sesame Street
If you want to understand the federal government’s new initiative to improve autism care and diagnosis, it’s helpful to understand its leader, Russell Shilling.
Sitting in his Fairfax, Va., office, Shilling is flanked by a colorful tableau: a coterie of medals from his time in the armed forces and a sky blue puppet from his six years working with Sesame Street. He shares his felt doppelganger’s glasses and close-cropped hair, though not its hue.
An experimental psychologist, Shilling has led many lives. During his time in the Navy and at the Defense Advanced Research Projects Agency, he developed virtual reality therapy to help Iraq War veterans with PTSD and constructed early versions of chatbots to destigmatize mental health issues, bringing them into clinics.
Identifying Suicide Risk Based on the Words People Use
Researchers from the Child Mind Institute and MIT have developed a tool that can identify warning signs in text conversations, helping experts better understand who may need urgent support.
When someone is experiencing a mental health crisis, getting the right help quickly can be lifesaving. But it can be very difficult to determine who is in immediate danger.
New research suggests that the language people use during a crisis may offer important clues.
Daniel Low, PhD, a research scientist who leads the AI, Risk, and Contemplative Science (ARC) Lab at the Child Mind Institute, helped develop a tool that analyzes text conversations for language connected to known suicide risk factors. Dr. Low conducted the work while he was a graduate student at Harvard and MIT, in collaboration with Satrajit Ghosh, PhD, a senior research scientist and director of the Open Data in Neuroscience Initiative in the McGovern Institute at MIT.
The hope is that tools like this could one day help clinicians and crisis counselors spot warning signs more quickly. This way, they’ll have a better understanding of who may need immediate support.
Learning from real crisis conversations
Much of what researchers know about suicide risk comes from surveys asking people to reflect on their thoughts and experiences after a crisis has passed. But memory is not always complete, and a person’s experience may feel different once the immediate crisis is over.
To study what happens in the moment, the researchers partnered with Crisis Text Line, a nonprofit organization that provides confidential mental health support by text message. Following specialized training, the researchers were given protected access to a de-identified dataset of about 16,000 conversations. The data provided a rare opportunity to study what people were saying while actively seeking help during a crisis.
The conversations were grouped into three severity levels based on Crisis Text Line’s risk assessments: non-suicidal, suicidal ideation without imminent risk, and imminent risk. The imminent-risk group included conversations involving a plan for suicide within a 48-hour timeframe, immediate danger, or emergency service intervention.
To analyze the conversations, the team first built a library of words and phrases linked to established suicide risk factors. They used artificial intelligence to generate an initial list, then worked with expert clinicians to review and validate the results. The final lexicon contains thousands of terms spanning 49 suicide risk factors.
Researchers then trained a computer model to identify those patterns in the crisis conversations and estimate risk levels. And because the model is built on the lexicon, it can show which language contributed to a particular assessment. This allows counselors and clinicians to review the evidence behind a result rather than relying solely on a score from a computer — an important feature when decisions involve a person’s safety.
“This is such a complex space that having a human in the loop is, I think, going to be critical for a long, long time,” says Dr. Ghosh.
What the researchers found
Language related to lethal means, substance use, and active thoughts of suicide or self-harm appeared more often in imminent risk conversations. References to anxiety, post-traumatic stress disorder (PTSD), and emotional pain were also associated with risk, but were not among the highest-risk group. And while depression is a well-known risk factor for suicidal ideation, the model found expressions of depressed mood and fatigue were less closely associated with imminent risk than some of the more direct warning signs.
It’s important to note that this does not mean depression or any single warning sign is unimportant. It also does not mean that a word or phrase can predict what one person will do. Instead, the findings show which patterns of language were most strongly associated with the highest-risk conversations in this dataset.
What this could mean for mental health care
This tool is still in the research stage and requires further testing before it could be used in clinical settings.
Still, the work offers a new way to study how distress appears in language. By examining conversations that occur during a crisis rather than after the fact, researchers can gain a clearer picture of the thoughts, feelings, and experiences associated with different levels of risk.
The potential applications extend beyond crisis conversations. The suicide-risk lexicon is already being used to explore how language from sources such as social media and electronic health records might help researchers better understand and estimate suicide risk in other settings. The team has also made the lexicon and software publicly available so other researchers can build on the work and explore additional applications across a range of mental health conditions.
Dr. Low’s work is part of a broader effort at the Child Mind Institute. Through projects such as Mirror Journal, researchers at the Child Mind Institute are exploring how thoughtfully designed digital tools can help young people express what they are experiencing, process their emotions, and connect with support when they need it.
The findings were published in the Journal of Psychopathology and Clinical Science. Read the full paper.
The Suicide Risk Lexicon and tutorials to create lexicons are available here.
If you or someone you know is in immediate danger, call 911.
In the United States, call or text 988 to reach the 988 Suicide & Crisis Lifeline. The Crisis Text Line can be reached by texting HOME to 74174.
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