A shared neural basis underlying psychiatric comorbidity

An early enriched experience drives targeted microglial engulfment of miswired neural circuitry during a restricted postnatal period

Brain function is critically dependent on correct circuit assembly. Microglia are well-known for their important roles in immunological defense and neural plasticity, but whether they can also mediate experience-induced correction of miswired circuitry is unclear. Ten-m3 knockout (KO) mice display a pronounced and stereotyped visuotopic mismapping of ipsilateral retinal inputs in their visual thalamus, providing a useful model to probe circuit correction mechanisms. Environmental enrichment (EE) commenced around birth, but not later in life, can drive a partial correction of the most mismapped retinal inputs in Ten-m3 KO mice. Here, we assess whether enrichment unlocks the capacity for microglia to selectively engulf and remove miswired circuitry, and the timing of this effect. Expression of the microglial-associated lysosomal protein CD68 showed a clear enrichment-driven, spatially restricted change which had not commenced at postnatal day (P)18, was evident at P21, more robust at P25, and had ceased by P30. This was observed specifically at the corrective pruning site and was absent at a control site. An engulfment assay at the corrective pruning site in P25 mice showed EE-driven microglial-uptake of the mismapped axon terminals. This was temporally and spatially specific, as no enrichment-driven microglial engulfment was seen in P18 KO mice, nor the control locus. The timecourse of the EE-driven corrective pruning as determined anatomically, aligned with this pattern of microglia reactivity and engulfment. Collectively, these findings show experience can drive targeted microglial engulfment of miswired neural circuitry during a restricted postnatal window. This may have important therapeutic implications for neurodevelopmental conditions involving aberrant neural connectivity.

Air pollution from biomass burning disrupts early adolescent cortical microarchitecture development

Exposure to outdoor particulate matter (PM2.5) represents a ubiquitous threat to human health, and particularly the neurotoxic effects of PM2.5 from multiple sources may disrupt neurodevelopment. Studies addressing neurodevelopmental implications of PM exposure have been limited by small, geographically limited samples and largely focus either on macroscale cortical morphology or postmortem histological staining and total PM mass. Here, we leverage residentially assigned exposure to six, data-driven sources of PM2.5 and neuroimaging data from the longitudinal Adolescent Brain Cognitive Development Study (ABCD Study®), collected from 21 different recruitment sites across the United States. To contribute an interpretable and actionable assessment of the role of air pollution in the developing brain, we identified alterations in cortical microstructure development associated with exposure to specific sources of PM2.5 using multivariate, partial least squares analyses. Specifically, average annual exposure (i.e., at ages 8-10 years) to PM2.5 from biomass burning was related to differences in neurite development across the cortex between 9 and 13 years of age.

Increasing the representation of minoritized youth for inclusive and reproducible brain-behavior associations

Population neuroscience datasets allow researchers to estimate reliable effect sizes for brain-behavior associations because of their large sample sizes. However, these datasets undergo strict quality control to mitigate sources of noise, such as head motion. This practice often excludes a disproportionate number of minoritized individuals. We employ motion-ordering and motion-ordering+resampling (bagging) to test if these methods preserve functional MRI (fMRI) data in the Adolescent Brain Cognitive Development Study ( N = 5,733 ). Black and Hispanic youth exhibited excess head motion relative to data collected from White youth, and were discarded disproportionately when using conventional approaches. Both methods retained more than 99% of Black and Hispanic youth. They produced reproducible brain-behavior associations across low-/high-motion racial/ethnic groups based on motion-limited fMRI data. The motion-ordering and bagging methods are two feasible approaches that can enhance sample representation for testing brain-behavior associations and fulfill the promise of consortia datasets to produce generalizable effect sizes across diverse populations.

Dynamic structure-function coupling across three major psychiatric disorders

BACKGROUND

Convergent evidence has suggested atypical relationships between brain structure and function in major psychiatric disorders, yet how the abnormal patterns coincide and/or differ across different disorders remains largely unknown. Here, we aim to investigate the common and/or unique dynamic structure-function coupling patterns across major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ).

METHODS

We quantified the dynamic structure-function coupling in 452 patients with psychiatric disorders (MDD/BD/SZ = 166/168/118) and 205 unaffected controls at three distinct brain network levels, such as global, meso-, and local levels. We also correlated dynamic structure-function coupling with the topological features of functional networks to examine how the structure-function relationship facilitates brain information communication over time.

RESULTS

The dynamic structure-function coupling is preserved for the three disorders at the global network level. Similar abnormalities in the rich-club organization are found in two distinct functional configuration states at the meso-level and are associated with the disease severity of MDD, BD, and SZ. At the local level, shared and unique alterations are observed in the brain regions involving the visual, cognitive control, and default mode networks. In addition, the relationships between structure-function coupling and the topological features of functional networks are altered in a manner indicative of state specificity.

CONCLUSIONS

These findings suggest both transdiagnostic and illness-specific alterations in the dynamic structure-function relationship of large-scale brain networks across MDD, BD, and SZ, providing new insights and potential biomarkers into the neurodevelopmental basis underlying the behavioral and cognitive deficits observed in these disorders.

The power of many brains: Catalyzing neuropsychiatric discovery through open neuroimaging data and large-scale collaboration

Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders. By pooling images from various cohorts, statistical power has increased, enabling the detection of subtle abnormalities and robust associations, and fostering new research methods. Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment. Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric studies. We also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders. However, challenges such as data harmonization across different sites, privacy protection, and effective data sharing must be addressed. With proper governance and open science practices, we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis, treatment selection, and outcome prediction, contributing to optimal brain health.

Air pollution from biomass burning disrupts early adolescent cortical microarchitecture development

Exposure to outdoor particulate matter (PM 2.5 ) represents a ubiquitous threat to human health, and particularly the neurotoxic effects of PM 2.5 from multiple sources may disrupt neurodevelopment. Studies addressing neurodevelopmental implications of PM exposure have been limited by small, geographically limited samples and largely focus either on macroscale cortical morphology or postmortem histological staining and total PM mass. Here, we leverage residentially assigned exposure to six, data-driven sources of PM 2.5 and neuroimaging data from the longitudinal Adolescent Brain Cognitive Development Study (ABCD Study®), collected from 21 different recruitment sites across the United States. To contribute an interpretable and actionable assessment of the role of air pollution in the developing brain, we identified alterations in cortical microstructure development associated with exposure to specific sources of PM 2.5 using multivariate, partial least squares analyses. Specifically, average annual exposure (i.e., at ages 8-10 years) to PM 2.5 from biomass burning was related to differences in neurite development across the cortex between 9 and 13 years of age.

A framework for a brain-derived nosology of psychiatric disorders

Current psychiatric diagnoses are not defined by neurobiological measures which hinders the development of therapies targeting mechanisms underlying mental illness 1,2 . Research confined to diagnostic boundaries yields heterogeneous biological results, whereas transdiagnostic studies often investigate individual symptoms in isolation. There is currently no paradigm available to comprehensively investigate the relationship between different clinical symptoms, individual disorders, and the underlying neurobiological mechanisms. Here, we propose a framework that groups clinical symptoms derived from ICD-10/DSM-V according to shared brain mechanisms defined by brain structure, function, and connectivity. The reassembly of existing ICD-10/DSM-5 symptoms reveal six cross-diagnostic psychopathology scores related to mania symptoms, depressive symptoms, anxiety symptoms, stress symptoms, eating pathology, and fear symptoms. They were consistently associated with multimodal neuroimaging components in the training sample of young adults aged 23, the independent test sample aged 23, participants aged 14 and 19 years, and in psychiatric patients. The identification of symptom groups of mental illness robustly defined by precisely characterized brain mechanisms enables the development of a psychiatric nosology based upon quantifiable neurobiological measures. As the identified symptom groups align well with existing diagnostic categories, our framework is directly applicable to clinical research and patient care.

Ketogenic Diet Intervention on Metabolic and Psychiatric Health in Bipolar and Schizophrenia: A Pilot Trial

The ketogenic diet (KD, also known as metabolic therapy) has been successful in the treatment of obesity, type 2 diabetes, and epilepsy. More recently, this treatment has shown promise in the treatment of psychiatric illness. We conducted a 4-month pilot study to investigate the effects of a KD on individuals with schizophrenia or bipolar disorder with existing metabolic abnormalities. Twenty-three participants were enrolled in a single-arm trial. Results showcased improvements in metabolic health, with no participants meeting metabolic syndrome criteria by study conclusion. Adherent individuals experienced significant reduction in weight (12 %), BMI (12 %), waist circumference (13 %), and visceral adipose tissue (36 %). Observed biomarker enhancements in this population include a 27 % decrease in HOMA-IR, and a 25 % drop in triglyceride levels. In psychiatric measurements, participants with schizophrenia showed a 32 % reduction in Brief Psychiatric Rating Scale scores. Overall Clinical Global Impression (CGI) severity improved by an average of 31 %, and the proportion of participants that started with elevated symptomatology improved at least 1-point on CGI (79 %). Psychiatric outcomes across the cohort encompassed increased life satisfaction (17 %) and enhanced sleep quality (19 %). This pilot trial underscores the potential advantages of adjunctive ketogenic dietary treatment in individuals grappling with serious mental illness.

The impact of psychosocial adversity on brain and behaviour: an overview of existing knowledge and directions for future research

Environmental experiences play a critical role in shaping the structure and function of the brain. Its plasticity in response to different external stimuli has been the focus of research efforts for decades. In this review, we explore the effects of adversity on brain's structure and function and its implications for brain development, adaptation, and the emergence of mental health disorders. We are focusing on adverse events that emerge from the immediate surroundings of an individual, i.e., microenvironment. They include childhood maltreatment, peer victimisation, social isolation, affective loss, domestic conflict, and poverty. We also take into consideration exposure to environmental toxins. Converging evidence suggests that different types of adversity may share common underlying mechanisms while also exhibiting unique pathways. However, they are often studied in isolation, limiting our understanding of their combined effects and the interconnected nature of their impact. The integration of large, deep-phenotyping datasets and collaborative efforts can provide sufficient power to analyse high dimensional environmental profiles and advance the systematic mapping of neuronal mechanisms. This review provides a background for future research, highlighting the importance of understanding the cumulative impact of various adversities, through data-driven approaches and integrative multimodal analysis techniques.

How much or how often? Examining the screening properties of the DSM cross-cutting symptom measure in a youth population-based sample

BACKGROUND

The DSM Level 1 Cross-Cutting Symptom Measure (DSM-XC) allows for assessing multiple psychopathological domains. However, its capability to screen for mental disorders in a population-based sample and the impact of adverbial framings (intensity and frequency) on its performance are unknown.

METHODS

The study was based on cross-sectional data from the 1993 Pelotas birth cohort in Brazil. Participants with completed DSM-XC and structured diagnostic interviews (n = 3578, aged 22, 53.6% females) were included. Sensitivity, specificity, positive (LR+), and negative (LR-) likelihood ratios for each of the 13 DSM-XC domains were estimated for detecting five internalizing disorders (bipolar, generalized anxiety, major depressive, post-traumatic stress, and social anxiety disorders) and three externalizing disorders (antisocial personality, attention-deficit/hyperactivity, and alcohol use disorders). Sensitivities and specificities >0.75, LR+ > 2 and LR- < 0.5 were considered meaningful. Values were calculated for the DSM-XC's original scoring and for adverbial framings.

RESULTS

Several DSM-XC domains demonstrated meaningful screening properties. The anxiety domain exhibited acceptable sensitivity and LR- values for all internalizing disorders. The suicidal ideation, psychosis, memory, repetitive thoughts and behaviors, and dissociation domains displayed acceptable specificity for all disorders. Domains also yielded small but meaningful LR+ values for internalizing disorders. However, LR+ and LR- values were not generally meaningful for externalizing disorders. Frequency-framed questions improved screening properties.

CONCLUSIONS

The DSM-XC domains showed transdiagnostic screening properties, providing small but meaningful changes in the likelihood of internalizing disorders in the community, which can be improved by asking frequency of symptoms compared to intensity. The DSM-XC is currently lacking meaningful domains for externalizing disorders.

Unveiling the Core Functional Networks of Cognition: An Ontology-Guided Machine Learning Approach

Deciphering the functional architecture that underpins diverse cognitive functions is fundamental quest in neuroscience. In this study, we employed an innovative machine learning framework that integrated cognitive ontology with functional connectivity analysis to identify brain networks essential for cognition. We identified a core assembly of functional connectomes, primarily located within the association cortex, which showed superior predictive performance compared to two conventional methods widely employed in previous research across various cognitive domains. Our approach achieved a mean prediction accuracy of 0.13 across 16 cognitive tasks, including working memory, reading comprehension, and sustained attention, outperforming the traditional methods' accuracy of 0.08. In contrast, our method showed limited predictive power for sensory, motor, and emotional functions, with a mean prediction accuracy of 0.03 across 9 relevant tasks, slightly lower than the traditional methods' accuracy of 0.04. These cognitive connectomes were further characterized by distinctive patterns of resting-state functional connectivity, structural connectivity via white matter tracts, and gene expression, highlighting their neurogenetic underpinnings. Our findings reveal a domain-general functional network fingerprint that pivotal to cognition, offering a novel computational approach to explore the neural foundations of cognitive abilities.

The missing hallmark of health: psychosocial adaptation

The eight biological hallmarks of health that we initially postulated (Cell. 2021 Jan 7;184(1):33-63) include features of spatial compartmentalization (integrity of barriers, containment of local perturbations), maintenance of homeostasis over time (recycling & turnover, integration of circuitries, rhythmic oscillations) and an array of adequate responses to stress (homeostatic resilience, hormetic regulation, repair & regeneration). These hallmarks affect all eight somatic strata of the human body (molecules, organelles, cells, supracellular units, organs, organ systems, systemic circuitries and meta-organism). Here we postulate that mental and socioeconomic factors must be added to this 8×8 matrix as an additional hallmark of health ("psychosocial adaptation") and as an additional stratum ("psychosocial interactions"), hence building a 9×9 matrix. Potentially, perturbation of each of the somatic hallmarks and strata affects psychosocial factors and vice versa. Finally, we discuss the (patho)physiological bases of these interactions and their implications for mental health improvement.

Efficacy and executive function of solution-focused brief therapy on adolescent depression

Objective

To investigate the efficacy and impact on executive function of Solution-Focused Brief Therapy (SFBT) in treating Major Depressive Disorder (MDD) in adolescents.

Methods

A total of 129 adolescents diagnosed with MDD were enrolled in the study. Out of these, 28 adolescents were assigned to the SFBT group, while 25 were part of the Active Control group (AC group), receiving psychodynamic psychotherapy. Executive function, depressive and anxiety symptoms were assessed at baseline, at the time of the third intervention, the sixth intervention, and the 10th intervention.

Results

After the third intervention, the scores of the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) of the participants in the SFBT group decreased significantly, which had the cumulative effect at the 6th and 10th interventions. The verbal fluency task (VFT) performances of the SFBT group participants yielded significantly higher scores after the third intervention and remained increasing at the 6th and 10th interventions. The AC group steadily decreased after the intervention. Analysis of functional near-infrared spectroscopy (fNIRS) data revealed a progressive and significant increase in the average oxyhemoglobin (oxy-Hb) levels in the dorsolateral prefrontal cortex (DLPFC) in the SFBT group compared to the AC group after the 10th intervention.

Conclusions

SFBT might improve depressive and anxiety symptoms as well as executive function of adolescent depression.

Clinical trial registration

https://www.chictr.org.cn, identifier ChiCTR2300067909.

Transdiagnostic cognitive biases in psychiatric disorders: A systematic review and network meta-analysis

Psychiatric disorders are characterized by cognitive deficits, which have been proposed as a transdiagnostic feature of psychopathology ("C" factor). Similarly, cognitive biases (e.g., in attention, memory, and interpretation) represent common tendencies in information processing that are often associated with psychiatric symptoms. However, the question remains whether cognitive biases are also transdiagnostic or are specific to certain psychiatric disorders/symptoms. The current systematic review sought to address whether the proposed "C" factor of transdiagnostic cognitive dysfunction in psychopathology can be extended to cognitive biases. Overall, 31 studies comprising 4401 participants (2536 patients, 1865 non-clinical controls) met inclusion criteria, assessing 19 cognitive biases across 20 diagnostic categories, with most studies focusing on interpretation (k = 22) and attention (k = 11) biases and only 2 assessing memory biases. Traditional meta-analyses found a moderate effect size (g = 0.32) for more severe cognitive biases in all patients relative to non-clinical controls, as well as small but significant associations between interpretation biases and transdiagnostic symptom categories (general psychopathology: r = 0.20, emotion dysfunction: r = 0.17, psychotic symptoms: r = 0.25). Network meta-analyses revealed significant patient versus non-clinical control differences on attention and interpretation biases across diagnoses, as well as significant differences between diagnoses, with highest severity in panic disorder for attention biases and obsessive-compulsive disorder for interpretation biases. The current findings extend the big "C" interpretation of transdiagnostic cognitive dysfunction in psychiatric disorders to cognitive biases and transdiagnostic symptom dimensions. Results also suggest that while the presence of cognitive biases is transdiagnostic, bias severity differs across diagnoses, as in traditional neurocognitive deficits.

Machine learning models for diagnosis and risk prediction in eating disorders, depression, and alcohol use disorder

This study uses machine learning models to uncover diagnostic and risk prediction markers for eating disorders (EDs), major depressive disorder (MDD), and alcohol use disorder (AUD). Utilizing case-control samples (ages 18-25 years) and a longitudinal population-based sample (n=1,851), the models, incorporating diverse data domains, achieved high accuracy in classifying EDs, MDD, and AUD from healthy controls. The area under the receiver operating characteristic curves (AUC-ROC [95% CI]) reached 0.92 [0.86-0.97] for AN and 0.91 [0.85-0.96] for BN, without relying on body mass index as a predictor. The classification accuracies for MDD (0.91 [0.88-0.94]) and AUD (0.80 [0.74-0.85]) were also high. Each data domain emerged as accurate classifiers individually, with personality distinguishing AN, BN, and their controls with AUC-ROCs ranging from 0.77 to 0.89. The models demonstrated high transdiagnostic potential, as those trained for EDs were also accurate in classifying AUD and MDD from healthy controls, and vice versa (AUC-ROCs, 0.75-0.93). Shared predictors, such as neuroticism, hopelessness, and symptoms of attention-deficit/hyperactivity disorder, were identified as reliable classifiers. For risk prediction in the longitudinal population sample, the models exhibited moderate performance (AUC-ROCs, 0.64-0.71), highlighting the potential of combining multi-domain data for precise diagnostic and risk prediction applications in psychiatry.

Neurovascular coupling and cerebrovascular hemodynamics are modified by exercise training status at different stages of maturation during youth

Neurovascular coupling (NVC) is mediated via nitric oxide signaling, which is independently influenced by sex hormones and exercise training. Whether exercise training differentially modifies NVC pre- versus postpuberty, where levels of circulating sex hormones will differ greatly within and between sexes, remains to be determined. Therefore, we investigated the influence of exercise training status on resting intracranial hemodynamics and NVC at different stages of maturation. Posterior and middle cerebral artery velocities (PCAv and MCAv) and pulsatility index (PCAPI and MCAPI) were assessed via transcranial Doppler ultrasound at rest and during visual NVC stimuli. N = 121 exercise-trained (males, n = 32; females, n = 32) and untrained (males, n = 28; females, n = 29) participants were characterized as pre (males, n = 33; females, n = 29)- or post (males, n = 27; females, n = 32)-peak height velocity (PHV). Exercise-trained youth demonstrated higher resting MCAv (P = 0.010). Maturity and training status did not affect the ΔPCAv and ΔMCAv during NVC. However, pre-PHV untrained males (19.4 ± 13.5 vs. 6.8 ± 6.0%; P ≤ 0.001) and females (19.3 ± 10.8 vs. 6.4 ± 7.1%; P ≤ 0.001) had a higher ΔPCAPI during NVC than post-PHV untrained counterparts, whereas the ΔPCAPI was similar in pre- and post-PHV trained youth. Pre-PHV untrained males (19.4 ± 13.5 vs. 7.9 ± 6.0%; P ≤ 0.001) and females (19.3 ± 10.8 vs. 11.1 ± 7.3%; P = 0.016) also had a larger ΔPCAPI than their pre-PHV trained counterparts during NVC, but the ΔPCAPI was similar in trained and untrained post-PHV youth. Collectively, our data indicate that exercise training elevates regional cerebral blood velocities during youth, but training-mediated adaptations in NVC are only attainable during early stages of adolescence. Therefore, childhood provides a unique opportunity for exercise-mediated adaptations in NVC.NEW & NOTEWORTHY We report that the change in cerebral blood velocity during a neurovascular coupling task (NVC) is similar in pre- and postpubertal youth, regardless of exercise-training status. However, prepubertal untrained youth demonstrated a greater increase in cerebral blood pulsatility during the NVC task when compared with their trained counterparts. Our findings highlight that childhood represents a unique opportunity for exercise-mediated adaptations in cerebrovascular hemodynamics during NVC, which may confer long-term benefits in cerebrovascular function.

Understanding the causal relationships of attention-deficit/hyperactivity disorder with mental disorders and suicide attempt: a network Mendelian randomisation study

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is a lifespan neurodevelopmental condition resulting from complex interactions between genetic and environmental risk factors. There is evidence that ADHD is associated with other mental disorders, but it remains unclear whether and in what way a causal relationship exists.

OBJECTIVE

To investigate the direct and indirect causal paths between ADHD and seven common mental disorders.

METHODS

Two-sample network Mendelian randomisation analysis was performed to identify psychiatric disorders causally related to ADHD. Total and direct effects were estimated in an univariable and multivariable setting, respectively. Robustness of results was ensured in three ways: a range of pleiotropy-robust methods, an iterative approach identifying and excluding outliers, and use of up to two genome-wide association studies per outcome to replicate results and calculate subsequently pooled meta-estimates.

RESULTS

Genetic liability to ADHD was independently associated with the risk of anorexia nervosa (OR 1.28 (95% CI 1.11 to 1.47); p=0.001). A bidirectional association was found with major depressive disorder (OR 1.09 (95% CI 1.03 to 1.15); p=0.003 in the forward direction and OR 1.76 (95% CI 1.50 to 2.06); p=4×10-12 in the reverse direction). Moreover, after adjustment for major depression disorder, a direct association with both suicide attempt (OR 1.30 (95% CI 1.16 to 1.547); p=2×10-5) and post-traumatic stress disorder (OR 1.18 (95% CI 1.05 to 1.33); p=0.007) was observed. There was no evidence of a relationship with anxiety, bipolar disorder or schizophrenia.

CONCLUSIONS

This study suggests that ADHD is an independent risk factor for a number of common psychiatric disorders.

CLINICAL IMPLICATIONS

The risk of comorbid psychiatric disorders in individuals with ADHD needs to be considered both in diagnosis and treatment.