Brain structural and functional signatures of multi-generational family history of suicidal behaviors in preadolescent children.

Parent-child transmission of suicidal behaviors has been extensively studied, but the investigation of a three-generation family suicide risk paradigm remains limited. In this study, we aimed to explore the behavioral and brain signatures of multi-generational family history of suicidal behaviors (FHoS) in preadolescents, utilizing a longitudinal design and the dataset from Adolescent Brain and Cognitive DevelopmentSM Study (ABCD Study®), which comprised 4 years of data and includes a total of 9,653 preadolescents. Our findings revealed that multi-generational FHoS was significantly associated with an increased risk of problematic behaviors and suicidal behaviors (suicide ideation and suicide attempt) in offspring. Interestingly, the problematic behaviors were further identified as a mediator in the multi-generational transmission of suicidal behaviors. Additionally, we observed alterations in brain structure within superior temporal gyrus (STG), precentral/postcentral cortex, posterior parietal cortex (PPC), cingulate cortex (CC), and planum temporale (PT), as well as disrupted functional connectivity of default mode network (DMN), ventral attention network (VAN), dorsal attention network (DAN), fronto-parietal network (FPN), and cingulo-opercular network (CON) among preadolescents with FHoS. These results provide compelling longitudinal evidence at the population level, highlighting the associations between multi-generational FHoS and maladaptive behavioral and neurodevelopmental outcomes in offspring. These findings underscore the need for early preventive measures aimed at mitigating the familial transmission of suicide risk and reducing the global burden of deaths among children and adolescents.

Metabolic and functional substrates of impulsive decision-making in individuals with heroin addiction after prolonged methadone maintenance treatment.

Elevated impulsivity has been frequently reported in individuals with opioid addiction receiving methadone maintenance therapy (MMT), but the underlying neural mechanisms and cognitive subprocesses are not fully understood. We acquired functional magnetic resonance imaging (fMRI) data from 37 subjects with heroin addiction receiving long-term MMT and 33 healthy controls who performed a probabilistic reversal learning task, and measured their resting-state brain glucose using fluorine-18-fluorodeoxyglucose positron emission tomography (18F-FDG PET). Subjects receiving MMT exhibited significantly elevated self-reported impulsivity, and computational modeling revealed a marked impulsive decision bias manifested as switching more frequently without available evidence. Moreover, this impulsive decision bias was associated with the dose and duration of methadone use, irrelevant to the duration of heroin use. During the task, the switch-related hypoactivation in the left rostral middle frontal gyrus was correlated with the impulsive decision bias while the function of reward sensitivity was intact in subjects receiving MMT. Using prior brain-wide receptor density data, we found that the highest variance of regional metabolic abnormalities was explained by the spatial distribution of µ-opioid receptors among 10 types of neurotransmitter receptors. Heightened impulsivity in individuals receiving prolonged MMT is manifested as atypical choice bias and noise in decision-making processes, which is further driven by deficits in top-down cognitive control, other than reward sensitivity. Our findings uncover multifaceted mechanisms underlying elevated impulsivity in subjects receiving MMT, which might provide insights for developing complementary therapies to improve retention during MMT.

Quantitative Expression of Latent Disease Factors in Individuals Associated with Psychopathology Dimensions and Treatment Response.

Psychiatric comorbidity is common in symptom-based diagnoses like autism spectrum disorder (ASD), attention/deficit hyper-activity disorder (ADHD), and obsessive-compulsive disorder (OCD). However, these co-occurring symptoms mediated by shared and/or distinct neural mechanisms are difficult to profile at the individual level. Capitalizing on unsupervised machine learning with a hierarchical Bayesian framework, we derived latent disease factors from resting-state functional connectivity data in a hybrid cohort of ASD and ADHD and delineated individual associations with dimensional symptoms based on canonical correlation analysis. Models based on the same factors generalized to previously unseen individuals in a subclinical cohort and one local OCD database with a subset of patients undergoing neurosurgical intervention. Four factors, identified as variably co-expressed in each patient, were significantly correlated with distinct symptom domains (r = -0.26-0.53, P < 0.05): behavioral regulation (Factor-1), communication (Factor-2), anxiety (Factor-3), adaptive behaviors (Factor-4). Moreover, we demonstrated Factor-1 expressed in patients with OCD and Factor-3 expressed in participants with anxiety, at the degree to which factor expression was significantly predictive of individual symptom scores (r = 0.18-0.5, P < 0.01). Importantly, peri-intervention changes in Factor-1 of OCD were associated with variable treatment outcomes (r = 0.39, P < 0.05). Our results indicate that these data-derived latent disease factors quantify individual factor expression to inform dimensional symptom and treatment outcomes across cohorts, which may promote quantitative psychiatric diagnosis and personalized intervention.

Dissecting Psychiatric Heterogeneity and Comorbidity with Core Region-Based Machine Learning.

Machine learning approaches are increasingly being applied to neuroimaging data from patients with psychiatric disorders to extract brain-based features for diagnosis and prognosis. The goal of this review is to discuss recent practices for evaluating machine learning applications to obsessive-compulsive and related disorders and to advance a novel strategy of building machine learning models based on a set of core brain regions for better performance, interpretability, and generalizability. Specifically, we argue that a core set of co-altered brain regions (namely 'core regions') comprising areas central to the underlying psychopathology enables the efficient construction of a predictive model to identify distinct symptom dimensions/clusters in individual patients. Hypothesis-driven and data-driven approaches are further introduced showing how core regions are identified from the entire brain. We demonstrate a broadly applicable roadmap for leveraging this core set-based strategy to accelerate the pursuit of neuroimaging-based markers for diagnosis and prognosis in a variety of psychiatric disorders.

Brain-wide and cell-specific transcriptomic insights into MRI-derived cortical morphology in macaque monkeys.

Integrative analyses of transcriptomic and neuroimaging data have generated a wealth of information about biological pathways underlying regional variability in imaging-derived brain phenotypes in humans, but rarely in nonhuman primates due to the lack of a comprehensive anatomically-defined atlas of brain transcriptomics. Here we generate complementary bulk RNA-sequencing dataset of 819 samples from 110 brain regions and single-nucleus RNA-sequencing dataset, and neuroimaging data from 162 cynomolgus macaques, to examine the link between brain-wide gene expression and regional variation in morphometry. We not only observe global/regional expression profiles of macaque brain comparable to human but unravel a dorsolateral-ventromedial gradient of gene assemblies within the primate frontal lobe. Furthermore, we identify a set of 971 protein-coding and 34 non-coding genes consistently associated with cortical thickness, specially enriched for neurons and oligodendrocytes. These data provide a unique resource to investigate nonhuman primate models of human diseases and probe cross-species evolutionary mechanisms.