Changes in genome architecture and transcriptional dynamics progress independently of sensory experience during post-natal brain development.

Both transcription and three-dimensional (3D) architecture of the mammalian genome play critical roles in neurodevelopment and its disorders. However, 3D genome structures of single brain cells have not been solved; little is known about the dynamics of single-cell transcriptome and 3D genome after birth. Here, we generated a transcriptome (3,517 cells) and 3D genome (3,646 cells) atlas of the developing mouse cortex and hippocampus by using our high-resolution multiple annealing and looping-based amplification cycles for digital transcriptomics (MALBAC-DT) and diploid chromatin conformation capture (Dip-C) methods and developing multi-omic analysis pipelines. In adults, 3D genome "structure types" delineate all major cell types, with high correlation between chromatin A/B compartments and gene expression. During development, both transcriptome and 3D genome are extensively transformed in the first post-natal month. In neurons, 3D genome is rewired across scales, correlated with gene expression modules, and independent of sensory experience. Finally, we examine allele-specific structure of imprinted genes, revealing local and chromosome (chr)-wide differences. These findings uncover an unknown dimension of neurodevelopment.

Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations.

Genome-wide association studies (GWASs) have focused primarily on populations of European descent, but it is essential that diverse populations become better represented. Increasing diversity among study participants will advance our understanding of genetic architecture in all populations and ensure that genetic research is broadly applicable. To facilitate and promote research in multi-ancestry and admixed cohorts, we outline key methodological considerations and highlight opportunities, challenges, solutions, and areas in need of development. Despite the perception that analyzing genetic data from diverse populations is difficult, it is scientifically and ethically imperative, and there is an expanding analytical toolbox to do it well.

The Psychiatric Cell Map Initiative: A Convergent Systems Biological Approach to Illuminating Key Molecular Pathways in Neuropsychiatric Disorders.

Although gene discovery in neuropsychiatric disorders, including autism spectrum disorder, intellectual disability, epilepsy, schizophrenia, and Tourette disorder, has accelerated, resulting in a large number of molecular clues, it has proven difficult to generate specific hypotheses without the corresponding datasets at the protein complex and functional pathway level. Here, we describe one path forward-an initiative aimed at mapping the physical and genetic interaction networks of these conditions and then using these maps to connect the genomic data to neurobiology and, ultimately, the clinic. These efforts will include a team of geneticists, structural biologists, neurobiologists, systems biologists, and clinicians, leveraging a wide array of experimental approaches and creating a collaborative infrastructure necessary for long-term investigation. This initiative will ultimately intersect with parallel studies that focus on other diseases, as there is a significant overlap with genes implicated in cancer, infectious disease, and congenital heart defects.

Smartphones and the Neuroscience of Mental Health.

Improvements in understanding the neurobiological basis of mental illness have unfortunately not translated into major advances in treatment. At this point, it is clear that psychiatric disorders are exceedingly complex and that, in order to account for and leverage this complexity, we need to collect longitudinal data sets from much larger and more diverse samples than is practical using traditional methods. We discuss how smartphone-based research methods have the potential to dramatically advance our understanding of the neuroscience of mental health. This, we expect, will take the form of complementing lab-based hard neuroscience research with dense sampling of cognitive tests, clinical questionnaires, passive data from smartphone sensors, and experience-sampling data as people go about their daily lives. Theory- and data-driven approaches can help make sense of these rich data sets, and the combination of computational tools and the big data that smartphones make possible has great potential value for researchers wishing to understand how aspects of brain function give rise to, or emerge from, states of mental health and illness.

Effort Foraging Task reveals positive correlation between individual differences in the cost of cognitive and physical effort in humans.

Effort-based decisions, in which people weigh potential future rewards against effort costs required to achieve those rewards involve both cognitive and physical effort, though the mechanistic relationship between them is not yet understood. Here, we use an individual differences approach to isolate and measure the computational processes underlying effort-based decisions and test the association between cognitive and physical domains. Patch foraging is an ecologically valid reward rate maximization problem with well-developed theoretical tools. We developed the Effort Foraging Task, which embedded cognitive or physical effort into patch foraging, to quantify the cost of both cognitive and physical effort indirectly, by their effects on foraging choices. Participants chose between harvesting a depleting patch, or traveling to a new patch that was costly in time and effort. Participants' exit thresholds (reflecting the reward they expected to receive by harvesting when they chose to travel to a new patch) were sensitive to cognitive and physical effort demands, allowing us to quantify the perceived effort cost in monetary terms. The indirect sequential choice style revealed effort-seeking behavior in a minority of participants (preferring high over low effort) that has apparently been missed by many previous approaches. Individual differences in cognitive and physical effort costs were positively correlated, suggesting that these are perceived and processed in common. We used canonical correlation analysis to probe the relationship of task measures to self-reported affect and motivation, and found correlations of cognitive effort with anxiety, cognitive function, behavioral activation, and self-efficacy, but no similar correlations with physical effort.

Transdiagnostic inflexible learning dynamics explain deficits in depression and schizophrenia.

Deficits in reward learning are core symptoms across many mental disorders. Recent work suggests that such learning impairments arise by a diminished ability to use reward history to guide behaviour, but the neuro-computational mechanisms through which these impairments emerge remain unclear. Moreover, limited work has taken a transdiagnostic approach to investigate whether the psychological and neural mechanisms that give rise to learning deficits are shared across forms of psychopathology. To provide insight into this issue, we explored probabilistic reward learning in patients diagnosed with major depressive disorder (n = 33) or schizophrenia (n = 24) and 33 matched healthy controls by combining computational modelling and single-trial EEG regression. In our task, participants had to integrate the reward history of a stimulus to decide whether it is worthwhile to gamble on it. Adaptive learning in this task is achieved through dynamic learning rates that are maximal on the first encounters with a given stimulus and decay with increasing stimulus repetitions. Hence, over the course of learning, choice preferences would ideally stabilize and be less susceptible to misleading information. We show evidence of reduced learning dynamics, whereby both patient groups demonstrated hypersensitive learning (i.e. less decaying learning rates), rendering their choices more susceptible to misleading feedback. Moreover, there was a schizophrenia-specific approach bias and a depression-specific heightened sensitivity to disconfirmational feedback (factual losses and counterfactual wins). The inflexible learning in both patient groups was accompanied by altered neural processing, including no tracking of expected values in either patient group. Taken together, our results thus provide evidence that reduced trial-by-trial learning dynamics reflect a convergent deficit across depression and schizophrenia. Moreover, we identified disorder distinct learning deficits.

Assessment of the excitation-inhibition ratio in the Fmr1 KO2 mouse using neuronal oscillation dynamics.

In vitro and ex vivo studies have shown consistent indications of hyperexcitability in the Fragile X Messenger Ribonucleoprotein 1 (Fmr1) knockout mouse model of autism spectrum disorder. We recently introduced a method to quantify network-level functional excitation-inhibition ratio from the neuronal oscillations. Here, we used this measure to study whether the implicated synaptic excitation-inhibition disturbances translate to disturbances in network physiology in the Fragile X Messenger Ribonucleoprotein 1 (Fmr1) gene knockout model. Vigilance-state scoring was used to extract segments of inactive wakefulness as an equivalent behavioral condition to the human resting-state and, subsequently, we performed high-frequency resolution analysis of the functional excitation-inhibition biomarker, long-range temporal correlations, and spectral power. We corroborated earlier studies showing increased high-frequency power in Fragile X Messenger Ribonucleoprotein 1 (Fmr1) knockout mice. Long-range temporal correlations were higher in the gamma frequency ranges. Contrary to expectations, functional excitation-inhibition was lower in the knockout mice in high frequency ranges, suggesting more inhibition-dominated networks. Exposure to the Gamma-aminobutyric acid (GABA)-agonist clonazepam decreased the functional excitation-inhibition in both genotypes, confirming that increasing inhibitory tone results in a reduction of functional excitation-inhibition. In addition, clonazepam decreased electroencephalogram power and increased long-range temporal correlations in both genotypes. These findings show applicability of these new resting-state electroencephalogram biomarkers to animal for translational studies and allow investigation of the effects of lower-level disturbances in excitation-inhibition balance.

NASP gene contributes to autism by epigenetic dysregulation of neural and immune pathways.

BACKGROUND: Epigenetics makes substantial contribution to the aetiology of autism spectrum disorder (ASD) and may harbour a unique opportunity to prevent the development of ASD. We aimed to identify novel epigenetic genes involved in ASD aetiology. METHODS: Trio-based whole exome sequencing was conducted on ASD families. Genome editing technique was used to knock out the candidate causal gene in a relevant cell line. ATAC-seq, ChIP-seq and RNA-seq were performed to investigate the functional impact of knockout (KO) or mutation in the candidate gene. RESULTS: We identified a novel candidate gene NASP (nuclear autoantigenic sperm protein) for epigenetic dysregulation in ASD in a Chinese nuclear family including one proband with autism and comorbid atopic disease. The de novo likely gene disruptive variant tNASP(Q289X) subjects the expression of tNASP to nonsense-mediated decay. tNASP KO increases chromatin accessibility, promotes the active promoter state of genes enriched in synaptic signalling and leads to upregulated expression of genes in the neural signalling and immune signalling pathways. Compared with wild-type tNASP, tNASP(Q289X) enhances chromatin accessibility of the genes with enriched expression in the brain. RNA-seq revealed that genes involved in neural and immune signalling are affected by the tNASP mutation, consistent with the phenotypic impact and molecular effects of nasp-1 mutations in Caenorhabditis elegans. Two additional patients with ASD were found carrying deletion or deleterious mutation in the NASP gene. CONCLUSION: We identified novel epigenetic mechanisms mediated by tNASP which may contribute to the pathogenesis of ASD and its immune comorbidity.

Further delineation of the rare GDACCF (global developmental delay, absent or hypoplastic corpus callosum, dysmorphic facies syndrome): genotype and phenotype of 22 patients with ZNF148 mutations.

BACKGROUND: Pathogenic variants in the zinc finger protein coding genes are rare causes of intellectual disability and congenital malformations. Mutations in the ZNF148 gene causing GDACCF syndrome (global developmental delay, absent or hypoplastic corpus callosum, dysmorphic facies; MIM #617260) have been reported in five individuals so far. METHODS: As a result of an international collaboration using GeneMatcher Phenome Central Repository and personal communications, here we describe the clinical and molecular genetic characteristics of 22 previously unreported individuals. RESULTS: The core clinical phenotype is characterised by developmental delay particularly in the domain of speech development, postnatal growth retardation, microcephaly and facial dysmorphism. Corpus callosum abnormalities appear less frequently than suggested by previous observations. The identified mutations concerned nonsense or frameshift variants that were mainly located in the last exon of the ZNF148 gene. Heterozygous deletion including the entire ZNF148 gene was found in only one case. Most mutations occurred de novo, but were inherited from an affected parent in two families. CONCLUSION: The GDACCF syndrome is clinically diverse, and a genotype-first approach, that is, exome sequencing is recommended for establishing a genetic diagnosis rather than a phenotype-first approach. However, the syndrome may be suspected based on some recurrent, recognisable features. Corpus callosum anomalies were not as constant as previously suggested, we therefore recommend to replace the term 'GDACCF syndrome' with 'ZNF148-related neurodevelopmental disorder'.

Reward-Mediated, Model-Free Reinforcement-Learning Mechanisms in Pavlovian and Instrumental Tasks Are Related.

Model-free and model-based computations are argued to distinctly update action values that guide decision-making processes. It is not known, however, if these model-free and model-based reinforcement learning mechanisms recruited in operationally based instrumental tasks parallel those engaged by pavlovian-based behavioral procedures. Recently, computational work has suggested that individual differences in the attribution of incentive salience to reward predictive cues, that is, sign- and goal-tracking behaviors, are also governed by variations in model-free and model-based value representations that guide behavior. Moreover, it is not appreciated if these systems that are characterized computationally using model-free and model-based algorithms are conserved across tasks for individual animals. In the current study, we used a within-subject design to assess sign-tracking and goal-tracking behaviors using a pavlovian conditioned approach task and then characterized behavior using an instrumental multistage decision-making (MSDM) task in male rats. We hypothesized that both pavlovian and instrumental learning processes may be driven by common reinforcement-learning mechanisms. Our data confirm that sign-tracking behavior was associated with greater reward-mediated, model-free reinforcement learning and that it was also linked to model-free reinforcement learning in the MSDM task. Computational analyses revealed that pavlovian model-free updating was correlated with model-free reinforcement learning in the MSDM task. These data provide key insights into the computational mechanisms mediating associative learning that could have important implications for normal and abnormal states.SIGNIFICANCE STATEMENT Model-free and model-based computations that guide instrumental decision-making processes may also be recruited in pavlovian-based behavioral procedures. Here, we used a within-subject design to test the hypothesis that both pavlovian and instrumental learning processes were driven by common reinforcement-learning mechanisms. Sign-tracking and goal-tracking behaviors were assessed in rats using a pavlovian conditioned approach task, and then instrumental behavior was characterized using an MSDM task. We report that sign-tracking behavior was associated with greater model-free, but not model-based, learning in the MSDM task. These data suggest that pavlovian and instrumental behaviors may be driven by conserved reinforcement-learning mechanisms.

NeuroimaGene: an R package for assessing the neurological correlates of genetically regulated gene expression.

BACKGROUND: We present the NeuroimaGene resource as an R package designed to assist researchers in identifying genes and neurologic features relevant to psychiatric and neurological health. While recent studies have identified hundreds of genes as potential components of pathophysiology in neurologic and psychiatric disease, interpreting the physiological consequences of this variation is challenging. The integration of neuroimaging data with molecular findings is a step toward addressing this challenge. In addition to sharing associations with both molecular variation and clinical phenotypes, neuroimaging features are intrinsically informative of cognitive processes. NeuroimaGene provides a tool to understand how disease-associated genes relate to the intermediate structure of the brain. RESULTS: We created NeuroimaGene, a user-friendly, open access R package now available for public use. Its primary function is to identify neuroimaging derived brain features that are impacted by genetically regulated expression of user-provided genes or gene sets. This resource can be used to (1) characterize individual genes or gene sets as relevant to the structure and function of the brain, (2) identify the region(s) of the brain or body in which expression of target gene(s) is neurologically relevant, (3) impute the brain features most impacted by user-defined gene sets such as those produced by cohort level gene association studies, and (4) generate publication level, modifiable visual plots of significant findings. We demonstrate the utility of the resource by identifying neurologic correlates of stroke-associated genes derived from pre-existing analyses. CONCLUSIONS: Integrating neurologic data as an intermediate phenotype in the pathway from genes to brain-based diagnostic phenotypes increases the interpretability of molecular studies and enriches our understanding of disease pathophysiology. The NeuroimaGene R package is designed to assist in this process and is publicly available for use.

Precision Medicine Approaches to Mental Health Care.

Developing a more comprehensive understanding of the physiological underpinnings of mental illness, precision medicine has the potential to revolutionize psychiatric care. With recent breakthroughs in next-generation multi-omics technologies and data analytics, it is becoming more feasible to leverage multimodal biomarkers, from genetic variants to neuroimaging biomarkers, to objectify diagnostics and treatment decisions in psychiatry and improve patient outcomes. Ongoing work in precision psychiatry will parallel progress in precision oncology and cardiology to develop an expanded suite of blood- and neuroimaging-based diagnostic tests, empower monitoring of treatment efficacy over time, and reduce patient exposure to ineffective treatments. The emerging model of precision psychiatry has the potential to mitigate some of psychiatry's most pressing issues, including improving disease classification, lengthy treatment duration, and suboptimal treatment outcomes. This narrative-style review summarizes some of the emerging breakthroughs and recurring challenges in the application of precision medicine approaches to mental health care.

The neoliberal leaning of the neuroscience discourse when it deals with mental health and learning disorders.

Neuroscience attracted increasing attention in mass media during the last decades. Indeed, neuroscience advances raise high expectations in society concerning major societal issues such as mental health and learning difficulties. Unfortunately, according to leading experts, neuroscience advances have not yet benefited patients, students and socially deprived families. Yet, neuroscience findings are widely overstated and misrepresented in the media. Academic studies, briefly described here, showed that most data misrepresentations were already present in the neuroscience literature before spreading in mass media. This triumphalist neuroscience discourse reinforces a neuro-essentialist conception of mental disorders and of learning difficulties. By emphasizing brain plasticity, this discourse fuels the neoliberal ethics that overvalue autonomy, rationality, flexibility and individual responsibility. According to this unrealistic rhetoric, neuroscience-based techniques will soon bring inexpensive private solutions to enduring social problems. When considering the social consequences of this rhetoric, neuroscientists should refrain from overstating the interpretation of their observations in their scientific publications and in their exchanges with journalists.

Using unsupervised clustering approaches to identify common mental health profiles and associated mental health-care service-use patterns in Ontario, Canada.

Mental health is a complex, multidimensional concept that goes beyond clinical diagnoses, including psychological distress, life stress, and well-being. In this study, we aimed to use unsupervised clustering approaches to identify multidimensional mental health profiles that exist in the population, and their associated service-use patterns. The data source was the 2012 Canadian Community Health Survey-Mental Health, linked to administrative health-care data; all Ontario, Canada, adult respondents were included. We used a partitioning around medoids clustering algorithm with Gower's proximity to identify groups with distinct combinations of mental health indicators and described them according to their sociodemographic and service-use characteristics. We identified 4 groups with distinct mental health profiles, including 1 group that met the clinical threshold for a depressive diagnosis, with the remaining 3 groups expressing differences in positive mental health, life stress, and self-rated mental health. The 4 groups had different age, employment, and income profiles and exhibited differential access to mental health-care services. This study represents the first step in identifying complex profiles of mental health at the population level in Ontario. Further research is required to better understand the potential causes and consequences of belonging to each of the mental health profiles identified. This article is part of a Special Collection on Mental Health.

Let's fail better: Using philosophical tools to improve neuroscientific research in psychiatry.

Despite predictions that neuroscientific discoveries would revolutionize psychiatry, decades of research have not yet led to clinically significant advances in psychiatric care. For this reason, an increasing number of researchers are recognizing the limitations of a purely biomedical approach in psychiatric research. These researchers call for reevaluating the conceptualization of mental disorders and argue for a non-reductionist approach to mental health. The aim of this paper is to discuss philosophical assumptions that underly neuroscientific research in psychiatry and offer practical tools to researchers for overcoming potential conceptual problems that are derived from those assumptions. Specifically, we will discuss: the analogy problem, questioning whether mental health problems are equivalent to brain disorders, the normativity problem, addressing the value-laden nature of psychiatric categories and the priority problem, which describes the level of analysis (e.g., biological, psychological, social, etc.) that should be prioritized when studying psychiatric conditions. In addition, we will explore potential strategies to mitigate practical problems that might arise due to these implicit assumptions. Overall, the aim of this paper is to suggest philosophical tools of practical use for neuroscientists, demonstrating the benefits of a closer collaboration between neuroscience and philosophy.

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity.

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.

Thinking computationally in translational psychiatry. A commentary on Neville et al. (2024).

There is a growing focus on the computational aspects of psychiatric disorders in humans. This idea also is gaining traction in nonhuman animal studies. Commenting on a new comprehensive overview of the benefits of applying this approach in translational research by Neville et al. (Cognitive Affective & Behavioral Neuroscience 1-14, 2024), we discuss the implications for translational model validity within this framework. We argue that thinking computationally in translational psychiatry calls for a change in the way that we evaluate animal models of human psychiatric processes, with a shift in focus towards symptom-producing computations rather than the symptoms themselves. Further, in line with Neville et al.'s adoption of the reinforcement learning framework to model animal behaviour, we illustrate how this approach can be applied beyond simple decision-making paradigms to model more naturalistic behaviours.

A hierarchical reinforcement learning model explains individual differences in attentional set shifting.

Attentional set shifting refers to the ease with which the focus of attention is directed and switched. Cognitive tasks, such as the widely used CANTAB IED, reveal great variation in set shifting ability in the general population, with notable impairments in those with psychiatric diagnoses. The attentional and learning processes underlying this cognitive ability and how they lead to the observed variation remain unknown. To directly test this, we used a modelling approach on two independent large-scale online general-population samples performing CANTAB IED, with one including additional psychiatric symptom assessment. We found a hierarchical model that learnt both feature values and dimension attention best explained the data and that compulsive symptoms were associated with slower learning and higher attentional bias to the first relevant stimulus dimension. These data showcase a new methodology to analyse data from the CANTAB IED task, as well as suggest a possible mechanistic explanation for the variation in set shifting performance, and its relationship to compulsive symptoms.

Prolonged increase in psychotropic drug use among young women following the COVID-19 pandemic: a French nationwide retrospective study.

BACKGROUND: The COVID-19 pandemic has had a significant impact on mental health, with evidence suggesting an enduring mental health crisis. Studies worldwide observed increased usage of antidepressants, anxiolytics, and hypnotics during the pandemic, notably among young people and women. However, few studies tracked consumption post-2021. Our study aimed to fill this gap by investigating whether the surge in the number psychotropic drug consumers in France persisted 2 years after the first lockdown, particularly focusing on age and gender differences. METHODS: We conducted a national retrospective observational study based on the French national insurance database. We retrieved all prescriptions of anxiolytics, hypnotics, and antidepressants dispensed in pharmacies in France for the period 2015-2022. We performed interrupted time series analyses based on Poisson models for five age classes (12-18; 19-25; 26-50; 51-75; 76 and more) to assess the trend before lockdown, the gap induced and the change in trend after. RESULTS: In the overall population, the number of consumers remained constant for antidepressants while it decreased for anxiolytics and hypnotics. Despite this global trend, a long-term increase was observed in the 12-18 and 19-25 groups for the three drug classes. Moreover, for these age classes, the increases were more pronounced for women than men, except for hypnotics where the trends were similar. CONCLUSIONS: The number of people using antidepressants continues to increase more than 2 years after the first lockdown, showing a prolonged effect on mental health. This effect is particularly striking among adolescents and young adults confirming the devastating long-term impact of the pandemic on their mental health.

Effectiveness of home treatment in children and adolescents with psychiatric disorders-systematic review and meta-analysis.

BACKGROUND: Home treatment in child and adolescent psychiatry offers an alternative to conventional inpatient treatment by involving the patient's family, school, and peers more directly in therapy. Although several reviews have summarised existing home treatment programmes, evidence of their effectiveness remains limited and data synthesis is lacking. METHODS: We conducted a meta-analysis on the effectiveness of home treatment compared with inpatient treatment in child and adolescent psychiatry, based on a systematic search of four databases (PubMed, CINAHL, PsychINFO, Embase). Primary outcomes were psychosocial functioning and psychopathology. Additional outcomes included treatment satisfaction, duration, costs, and readmission rates. Group differences were expressed as standardised mean differences (SMD) in change scores. We used three-level random-effects meta-analysis and meta-regression and conducted both superiority and non-inferiority testing. RESULTS: We included 30 studies from 13 non-overlapping samples, providing data from 1795 individuals (mean age: 11.95 ± 2.33 years; 42.5% female). We found no significant differences between home and inpatient treatment for postline psychosocial functioning (SMD = 0.05 [- 0.18; 0.30], p = 0.68, I2 = 98.0%) and psychopathology (SMD = 0.10 [- 0.17; 0.37], p = 0.44, I2 = 98.3%). Similar results were observed from follow-up data and non-inferiority testing. Meta-regression showed better outcomes for patient groups with higher levels of psychopathology at baseline and favoured home treatment over inpatient treatment when only randomised controlled trials were considered. CONCLUSIONS: This meta-analysis found no evidence that home treatment is less effective than conventional inpatient treatment, highlighting its potential as an effective alternative in child and adolescent psychiatry. The generalisability of these findings is reduced by limitations in the existing literature, and further research is needed to better understand which patients benefit most from home treatment. TRIAL REGISTRATION: Registered at PROSPERO (CRD42020177558), July 5, 2020.