Tag: Substance use

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Rational causal induction from events in time.

Psychological Review, Vol 133(3), Apr 2026, 584-618; doi:10.1037/rev0000570

A longstanding focus in the causal learning literature has been on inferring causal relations from contingencies, where these abstract away from time by collating independent instances or by aggregating over regularly demarcated trials. In contrast, individual causal learners encounter events in their daily lives that occur in a continuous temporal flow with no such demarcation. Consequently, the process of learning causal relationships in naturalistic environments is comparatively less understood. In this article, we lay out a rational framework that foregrounds the role of time in causal learning. We work within the Bayesian rational analysis tradition, starting by considering how causal relations induce dependence between events in continuous time and how this can be modeled by stochastic processes from the Poisson–Gamma distribution family. We derive the qualitative signatures of causal influence and the general computations needed to infer structure from temporal patterns. We show that this rational account can parsimoniously explain the human preference for causal models that invoke shorter, more reliable, and more predictable causal influences. Furthermore, we show this provides a unifying explanation for human judgments across a wide variety of tasks in the reanalysis of seven experimental data sets. We anticipate the framework will help researchers better understand the many manifestations of continuous-time causal learning across human cognition and the tasks that probe it, from explicit causal structure induction settings to implicit associative or reinforcement learning settings. (PsycInfo Database Record (c) 2026 APA, all rights reserved)

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The temporal stability of core symptoms of social media addiction and their comorbidity with anxiety and depression in adolescents: a longitudinal network analysis

IntroductionSocial media addiction (SMA) is often comorbid with anxiety and depression. This study examined the temporal stability of core SMA symptoms and the bridging symptoms with anxiety and depression.MethodsA total of 1,240 adolescents (179 males, 1,061 females; mean age = 15.46 ± 0.63 years, age range: 14 – 18) completed the Bergen Social Media Addiction Scale (BSMAS), the Patient Health Questionnaire–9 (PHQ–9), and the Generalized Anxiety Disorder–7 (GAD–7) on two separate occasions in 2023 (T1) and 2024 (T2). The four symptom networks, including the BSMAS networks, two comorbidity networks (the BSMAS–GAD and the BSMAS–PHQ), and the integrated BSMAS–GAD–PHQ network, were estimated using Gaussian graphical models. Core symptom centrality was assessed using Expected Influence (EI), whereas bridge symptoms were identified using Bridge Expected Influence (BEI).Results1) Although SMA, anxiety, and depression levels of respondents rose significantly over the year, all four networks showed strong temporal stability, with the edge weights (r = .892 –.973, p < .001), the EI (r = .806 – .961, p ≤ .002), and the BEI (r = .699 – .804, p ≤ .008) highly correlated between T1 and T2; network comparison tests showed no significant changes in overall structures of all four networks, with most edges showing stable weights. 2) Within the BSMAS network, BSMAS2 (tolerance) and BSMAS6 (conflict) exhibited the highest EI at both time points. 3) In the comorbidity networks, BSMAS3 (mood modification), BSMAS5 (withdrawal), and BSMAS6 (conflict) consistently served as bridge symptoms on the SMA side at both T1 and T2. 4) Across both time points, PHQ1 (anhedonia) and PHQ7 (concentration problems) exhibited the highest BEI on the depression side, whereas GAD1 (nervousness) and GAD5 (restlessness) did so on the anxiety side. 5) These bridge symptoms were also confirmed in the integrated network.DiscussionThese findings illuminate the temporal persistence and development of symptom relationships, offering a more dynamic understanding of SMA–depression–anxiety comorbidity in adolescents.
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Internet addiction among nursing students: application of latent profile analysis and network analysis

BackgroundInternet addiction is widely reported and heterogeneous among nursing students. However, variable-centered approaches may not fully capture profile differences and core symptom patterns, potentially limiting precise interventions. Therefore, identifying distinct profiles and key symptoms is important for informing effective prevention.ObjectiveThis study aims to identify distinct internet addiction profiles among nursing students, explore the characteristics and core symptoms of these profiles, and investigate the factors associated with their variation.MethodsA cross-sectional survey was conducted among undergraduate nursing students from September to November 2025. Latent profile analysis (LPA) and network analysis were performed to characterize the patterns of problematic internet use across identified profiles.ResultLatent Profile Analysis revealed four distinct problematic internet use profiles: No-Problematic Internet Use Profile (17.895%), Low-Problematic Internet Use Profile (41.957%), Moderate-Problematic Internet Use Profile (26.676%), and High-Problematic Internet Use Profile (13.472%). Multinomial logistic regression identified gender, monthly household income, and physical activity as significant factors associated with profile membership. Network analysis highlighted central symptoms specific to each profile: Health-related problems (RP-IH) and compulsive internet use and withdrawal symptoms (Sym-C & Sym-W) exhibited the highest centrality within the Moderate- and High-Problematic Internet Use Profiles.ConclusionInternet addiction among undergraduate nursing students is a heterogeneous phenomenon that can be categorized into four distinct profiles. Our findings clarify key associated factors and identify central symptoms specific to each profile, potentially providing an empirical basis for nursing educators to develop targeted psychological interventions.
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Effect of low-intensity focused ultrasound on hippocampus of alcohol addicted mice: a preliminary study

Alcohol addiction is a chronic relapsing brain disorder characterized by significant neurobiological changes, particularly within the hippocampus, which mediates emotional regulation and reward-seeking behavior. Previous studies have shown that alcohol-induced neuronal injury contributes to withdrawal-associated anxiety and persistent alcohol preference. This study investigated the therapeutic effects of low-intensity focused ultrasound (LIFU) on the hippocampus in a mouse model of alcohol addiction. Twenty-six male C57BL/6 mice were allocated to an alcohol-exposed group (n = 20) and a control group (n = 6). Following a 28-day modeling period, the alcohol group was randomly subdivided into a therapy group and a sham group. The therapy group received LIFU treatment, while the sham group underwent an identical procedure with the ultrasound transducer powered off. After seven days of treatment, the therapy group exhibited less severe anxiety symptoms upon alcohol withdrawal and a reduced preference for alcohol compared to the sham group. The brain-derived neurotrophic factor (BDNF) concentration was significantly lower in the therapy group than in the sham group, but did not differ significantly from the control group. Hippocampal HE staining revealed more pronounced degeneration and apoptosis of granule cells in the dentate gyrus (DG) region in the sham group relative to the therapy group. These preliminary findings suggest that LIFU may modulate alcohol addiction by mitigating hippocampal neuronal injury.
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The patterns of relapse and abstinence: using machine learning to identify a multidimensional signature of long-term outcome after inpatient alcohol withdrawal treatment

AimsA machine learning approach to identify a multidimensional signature associated with relapse and long-term outcome in alcohol dependence treatment.DesignIn this observational naturalistic study, inpatients with alcohol dependence received qualified detoxification plus CBT (Cognitive Behavioral Therapy) and were followed up 6-months after discharge to assess abstinence and drinking behavior. Cross-validated multivariate sparse partial least squares analysis (SPLS) was used to investigate the relationship between clinical features and four long-term outcome variables.SettingGermany.Participants152 patients (on average 47.8 years old, 72% male) with alcohol dependence, who received inpatient qualified detoxification plus CBT.Measurements35 clinical features were used to cover all three phases of inpatient treatment (pre-, within-, post-treatment). Among these, sociodemographic characteristics, ICD-10 psychiatric diagnoses, previous detoxification treatments, and somatic measurements as well as inpatient treatment setting such as withdrawal medication, liver ultrasound, further information about the patients´ stay, and post-inpatient care were assessed. The four outcome dimensions included: continuous abstinence, abstinence at follow up, daily alcohol consumption, and days of abstinence after discharge.FindingsSix months after withdrawal treatment 46% of the patients achieved continuous abstinence. Socioeconomic, clinical and somatic features across the treatment timeline were analyzed and summarized into a multivariate signature associated with long-term treatment outcome. Thereby, the SPLS algorithm identified regular completion of withdrawal treatment, higher education, and employment status to be most strongly associated with a positive outcome. Alcohol-related hepatic and hematopoietic damage, number of previous withdrawal treatments and living in a shelter were most profoundly associated with a negative outcome.ConclusionConceiving treatment outcome as a multidimensional signature and moving beyond simple binary classifications of relapse versus abstinence may improve the understanding of relapse pathways and support more individualized treatment strategies.
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How sports betting apps hook users

For most of the last 80 years, sports betting was limited to Las Vegas. But after a 2018 Supreme Court decision loosened regulations on professional sports wagers, it became possible to place bets on games 24/7 — with nothing more than a smartphone and a bank account. 

In 2013, just five years prior to the landmark SCOTUS case, gambling was classified in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) in a new category called “Substance-Related and Addictive Disorders.” This grouped gambling with alcohol use disorder and other addictions. Gambling is also known to have the highest suicide rate of any addiction.

Read the rest…

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Social cognitive deficits and altered multi-brain dynamics during problem-solving in heroin abstainers: An fNIRS hyperscanning study

Despite extensive research on the neurobiology of addiction, little is known about how repeated drug use and withdrawal are related to social functioning impairments in humans, a highly social species. This obscures the broader societal impact of drug addiction and limits treatment efficacy. This study examined social cognitive impairment and its multi-brain neural underpinnings during socially interactive problem-solving in heroin use disorder (HUD), and further explored their co-occurrence with protracted withdrawal symptoms.

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IOCDF Calls for Reinstatement of SAMHSA Grants, Renewed Commitment to Mental Health Support

The International OCD Foundation is alarmed by the apparent sudden and widespread termination of grants supporting vital mental health and addiction services previously funded through the U.S. Substance Abuse and Mental Health Services Administration (SAMHSA).

These programs provide life-saving services for individuals experiencing acute mental health crises and help prevent symptoms from escalating to emergency or inpatient levels of care.

As detailed in our recent white paper, America’s OCD Care Crisis, 95% of Americans with obsessive compulsive disorder (OCD) are not receiving the most effective treatment. When OCD goes untreated or is treated with approaches that are not evidence-based, individuals face increased distress, functional impairment, isolation, and elevated risk of crisis. Access to trained clinicians and community-based mental health services is essential for helping people remain safe during periods of heightened distress and navigate next steps for treatment.

At a time when so many people with OCD and related disorders already struggle to access appropriate care, reducing support for frontline mental health professionals further weakens an already fragile system.

The IOCDF urges the reinstatement of these grants and continued federal commitment to accessible, evidence-based mental health and addiction services for all who need them.

Contact your congressional representative now to support the reinstatement of SAMHSA grants >>

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Internet addiction in adolescents with suicidal ideation: the role of self-esteem and school connectedness

BackgroundInternet addiction (IA) has become a growing concern, particularly among adolescents, due to its adverse effects on mental health, physical well-being, and future development. Adolescents with suicidal ideation (SI) are particularly vulnerable to IA, which may be associated with a higher risk of engaging in suicidal behaviors. However, the relationship and underlying mechanisms between SI and IA remain unclear. This study, grounded in the cognitive-behavioral model of pathological internet use, investigates the relationship and explores the roles of self-esteem (mediator) and school connectedness (moderator) in this association.MethodsIn this cross-sectional study, 462 Chinese adolescents with SI (79.0% female) were recruited from psychiatric outpatient clinics between June 2024 and September 2025. Validated instruments measured SI, self-esteem, school connectedness, and IA. Structural equation modeling with bootstrapping procedures was used to test the mediation effect of self-esteem on the relationship between SI and IA. The moderating role of school connectedness was examined using PROCESS Model 8.ResultsSI was positively associated with IA (β = 0.224, p < 0.001). SI was negatively associated with self-esteem (β = -0.464, p < 0.001), and self-esteem was further negatively associated with IA (β = -0.448, p < 0.001). Self-esteem partially mediated the relationship between SI and IA, with an indirect effect of 0.208 (95% CI: 0.154-0.271). School connectedness significantly moderated the direct association between SI and IA (β = -0.005, p = 0.001), but did not moderate the association between SI and the mediator, self-esteem (β = 0.004, p = 0.202).ConclusionThis study identifies a significant positive association between SI and IA among adolescents with SI, with self-esteem partially mediating this link. Furthermore, school connectedness showed a very weak buffering effect on the direct association between SI and IA, and it does not moderate the association between SI and self-esteem. These findings enhance our understanding of the mechanisms underlying IA in this vulnerable population and suggest potential targets for interventions.
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Canada Gets its First National Guidance on AI for Mental and Substance Use Health

Ottawa (ONTARIO) – In a first-of-its-kind initiative, national guidance for using artificial intelligence (AI) in the mental and substance use health field is being developed through a partnership between the Canadian Centre on Substance Use and Addiction (CCSA) and the Mental Health Commission of Canada.

AI is increasingly being used for healthcare triage, service navigation, service delivery, and communication, but developers and users have no guidelines specific to mental or substance use health to support its effective and safe use. The recently published E-Mental Health Strategy for Canada highlights the need for safety in this field.

The new National Guidance for Artificial Intelligence Use in Mental Health and Substance Use Health Care will provide guidance, tools, and resources  to help practitioners, organizations, and health leaders in efficiently evaluating and implementing AI-enabled mental health and substance use health care services and solutions. It will also support people with lived or living experience of mental health or substance use health concerns in making informed choices about these technologies, while helping technology companies design and improve such solutions to meet the needs of those who use them.

“People are excited about what AI can bring, but the saying ‘break it then fix it’ can take on new dangers when what is at risk is people’s lives. This guidance will allow innovators to move fast while working to ensure it’s done safely and in a way that increases impact and access,” says CCSA CEO Dr. Alexander Caudarella.

The Mental Health Commission of Canada President and CEO Lili-Anna Pereša adds, “Technology can be a powerful ally in transforming mental health care, but innovation must be matched with responsibility. Communities are the best problem-solvers. By working together with developers, providers, and people with lived experience, we’re creating guidance that ensures AI enhances care safely and meaningfully.”

The National Guidance team will share its early findings at several upcoming conferences, including the World Psychiatric Association’s World Congress of Psychiatry, the Canadian Centre on Substance Use and Addiction’s Issues of Substance conference, and the eMental Health International Collaborative (eMHIC) Congress.

In Canada, mental health and substance use health needs are highly common, yet many people continue to face significant barriers to care, including limited access, stigma, financial costs, and lack of tailored treatment options.

 
The National Guidance for Artificial Intelligence Use with Mental Health and Substance Use Health is expected to launch in 2026/2027.

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About CCSA:

CCSA was created by Parliament to provide national leadership to address substance use in Canada. A trusted counsel, we provide national guidance to decision makers by harnessing the power of research, curating knowledge and bringing together diverse perspectives. CCSA activities and products are made possible through a financial contribution from Health Canada. The views of CCSA do not necessarily represent the views of Health Canada.

About The Mental Health Commission of Canada:

The Commission leads the development and dissemination of innovative programs and tools to support the mental health and wellness of people in Canada. Through its unique mandate from the Government of Canada, the Commission supports federal, provincial, and territorial governments as well as organizations in the implementation of sound public policy. The Commission’s current mandate aims to deliver on priority areas identified in the Mental Health Strategy for Canada in alignment with the delivery of its strategic plan. 

Media contacts:

Canadian Centre on Substance Use and Addiction
Christine LeBlanc, Senior Strategic Communications Advisor
613-898-6343 | cleblanc@ccsa.ca

Mental Health Commission of Canada
media@mentalhealthcommission.ca

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