Biological psychiatry. Cognitive neuroscience and neuroimaging最新文献

Neurodevelopmental disorders such as attention deficit hyperactivity disorder, autism spectrum disorder, and schizophrenia are classically defined by cognitive and sensorimotor impairments. However, dysregulated motivation is a core yet underrecognized feature of these conditions, with significant implications for quality of life. We present a heuristic, hypothesis-generating framework that distinguishes two interacting and partially dissociable subdomains of motivation: self-initiated motivation, defined as goal-directed behavior that arises in the absence of immediate external prompting, and stimulus-driven motivation, defined as responses elicited by environmental cues or physiological states. Unlike traditional distinctions such as intrinsic versus extrinsic motivation or the liking-wanting dichotomy, this framework emphasizes the initiation of motivated action as its organizing axis, focusing on whether behaviors are generated internally or triggered by external stimuli, rather than on reward valuation or hedonic impact. These subdomains are implemented by overlapping, dynamically interacting neural circuits that follow relatively distinct developmental trajectories and may exhibit differential sensitivity to early-life adversity. Our model provides a transdiagnostic conceptual scaffold that bridges categorical diagnoses and aligns with the Research Domain Criteria (RDoC) Motivation Systems framework. We focus on disorders with early-emerging circuit vulnerability and developmental onset, while recognizing that the framework is applicable more broadly across psychiatric conditions. Rather than offering a definitive nosology, the model supports mechanistic phenotyping, hypothesis-driven experimental design, and translational inference across neurodevelopmental disorders. To illustrate its translational utility, we highlight behavioral assays in animal models that differentially engage each subdomain and propose circuit-informed, testable strategies to guide future intervention development.

Background: The brain age biomarker estimates biological age from brain structure and is discussed as a potential screening tool for clinically relevant brain aging patterns in individuals. For brain age estimates to be of clinical utility, they must be meaningful for individual patients and free from systematic bias. Here, we investigate how biases from training data age-skewness, which we call distribution bias, impact the reliability and biological interpretability of this promising biomarker.

Methods: Using Monte Carlo simulations with data from 9,305 individuals and external validation in neuropsychiatric cohorts (1,345 individuals), we trained 100 brain age models for each of four differently age-skewed training distributions, respectively. For each model, we evaluated predictive performance, conducted standard group-level analyses for different neurodegenerative and psychiatric diseases and evaluated clinical utility of the prediction as an individual risk marker.

Results: Training data age-distribution significantly influenced model predictions, causing substantial fluctuations in predicted brain ages across the aging continuum. Statistical analyses revealed that these fluctuations impact effect sizes and statistical significance across all diseases. Moreover, we found limited effectiveness of the brain age gap as an individual risk marker and different levels of disease-associated brain age across the aging continuum.

Conclusions: Skewed training data age distributions significantly impact brain age model predictions and may compromise scientific results. Based on our findings, we want to raise awareness for distribution bias and propose age-wise interpretation of brain age gaps as a practical solution for robust research and meaningful clinical application.

Background: Trauma-related intrusive memories (TR-IMs) are spontaneous and emotionally intense sensory recollections experienced as occurring in the here-and-now, and a core feature of posttraumatic stress disorder (PTSD). Although central to symptom burden, the neurobiological substrates of specific phenomenological properties of TR-IMs remain poorly understood. Prior work has linked dynamic hippocampus (HPC) co-activation with default mode and visual networks to TR-IM properties, but whether structural connectivity tracks with these properties is unknown.

Methods: In 114 symptomatic trauma-exposed adults (87 female), two weeks of ecological momentary assessment surveys captured TR-IM properties (intrusiveness, reliving, visual detail, vividness, emotional intensity). Diffusion-weighted imaging (DWI) was used to quantify fractional anisotropy (FA) in two HPC-posterior cortical pathways: the parahippocampal-parietal cingulum, connecting the HPC to posterior default mode regions, and the inferior longitudinal fasciculus (ILF), linking the medial temporal regions to visual cortex.

Results: Bayesian models incorporating functional co-activation priors revealed that lower FA in the parahippocampal-parietal cingulum was associated with greater TR-IM intrusiveness, while lower FA in the ILF was linked to heightened reliving. Associations were strongest when weighted by prior dynamic connectivity findings in this sample.

Conclusions: These findings suggest that separable HPC-posterior cortical white matter pathways support different TR-IM properties, with the cingulum related to their intrusiveness and the ILF with reliving. This study identifies white matter correlates of TR-IM phenomenology and demonstrates the value of integrating real-world memory sampling with anatomically informed Bayesian modeling, advancing mechanistic understanding of PTSD reexperiencing.

Background: Pregnancy represents a critical period of neuroplasticity when environmental stressors can disrupt the neurobiological foundations of parent-infant bonding, with lasting consequences for perinatal mental health. While chronic poverty has been linked to adverse parenting outcomes, the specific impact of income instability during pregnancy on parental brain adaptation remains unknown.

Methods: We examined whether prenatal income changes prospectively influence neurobiological responses to infant distress cues among birthing individuals (n = 120) in the early postpartum period. Monthly income data across pregnancy were used to compute income-to-needs ratio (INR), income losses, and income gains. Participants underwent fMRI while listening to their own and a control infant's cry and matched white noise.

Results: Income losses during pregnancy were associated with dampened brain responses to infant cry across motor, auditory, and empathy-related cortices, a pattern consistent with impaired caregiving sensitivity and known risk factors for postpartum depression. These neurobiological alterations were paralleled by elevated prenatal depression and anxiety symptoms. In contrast, income gains were associated with greater activation to one's own infant's cry in prefrontal regions involved in cognitive empathy and emotion regulation, and were linked to stronger postnatal attachment bonds.

Discussion: These findings suggest that prenatal income instability is associated with variation in postpartum brain responses within circuits relevant to caregiving and parent-infant bonding. The associations between prenatal income loss and prenatal mood symptoms also suggest that screening for economic instability during routine prenatal care may help identify families who could benefit from additional supports, including mental health resources.

Background: Altered prediction error (PE) signaling has been implicated in psychosis risk, yet its expression in daily life and modulation by stress remain unclear. This study examined how momentary stress and PE jointly shape threat anticipation and psychotic-like experiences (PLEs).

Methods: Ninety-nine individuals with PLEs and 102 controls completed a seven-day experience sampling protocol. PE was defined as the signed and absolute deviation between anticipated and experienced stress.

Results: Individuals with PLEs exhibited significantly larger absolute and smaller signed PE. Lagged analyses showed significant associations between signed PE and PLEs, such that lower signed PE predicted higher subsequent PLEs and higher PLEs predicted greater subsequent signed PE, whereas absolute PE was only predicted by prior PLEs. In models predicting next-moment threat anticipation, higher absolute PE was associated with lower threat anticipation overall; however, in individuals with PLEs this association differed under high stress, such that higher absolute PE predicted increased subsequent threat anticipation. Signed PE showed a significant stress- and group-dependent association with threat anticipation, attenuated under high stress, particularly in controls.

Conclusions: These findings indicate that PLEs are associated with unstable prediction - outcome relationships in daily life and dynamic coupling between signed PE and symptom expression. Stress might modulate how PE shapes threat anticipation, such that surprise fails to consistently down-regulate perceived threat in individuals with PLEs. Together, the results suggest that stress-sensitive disruption of predictive processes in everyday contexts may contribute to persistent threat anticipation and vulnerability across the psychosis spectrum.

Background: Depression is one of the most common mental health problems among adolescents. Both group cognitive behavioral therapy (GCBT) and transcranial direct current stimulation (tDCS) have shown certain efficacy in treating adolescent depression, but they often have slow onset and insufficient effectiveness. This study aims to explore the effectiveness of combining GCBT with tDCS as a novel treatment approach for adolescent depression.

Methods: In this randomized, single-blind, sham-controlled, parallel-group trial, 67 adolescents with depression received either active or sham tDCS for five weeks in combination with GCBT. Depressive symptoms were assessed before and after treatment using the Hamilton Depression Rating Scale (HDRS-24) and the Self-Rating Depression Scale (SDS).

Results: After treatment, both the GCBT + active tDCS and GCBT + sham tDCS groups showed significant improvements in SDS and HDRS-24 total scores. However, the GCBT + active tDCS group exhibited greater reductions in SDS (p < 0.001,Cohen's d =0.51) and HDRS-24 (p < 0.05, Cohen's d =0.67) total scores compared with the GCBT + sham tDCS group. Among HDRS-24 sub-dimensions, significant between-group differences were observed only in retardation and hopelessness. The clinical response rate was higher in the GCBT + active tDCS group (48.49%) than in the sham group (25.00%, p < 0.05), whereas remission rates did not differ significantly.

Conclusions: GCBT combined with tDCS is a feasible intervention for adolescent depression and can significantly improve depressive symptoms, indicating its potential for future clinical application.

The rapidly evolving field of computational psychiatry enables quantification of specific cognitive processes, and their underlying mechanisms, in a translational and potentially scalable manner, using a combination of data collection via mechanistically informed behavioral tasks and theory-driven mathematical modeling. In parallel, transdiagnostic, dimensional approaches to psychiatric diagnostics, such as RDoC and HiTOP, seek to facilitate links between clinical research and real-world clinical reality, which rarely respects traditional diagnostic boundaries. These two approaches are seldom combined. In addition, while most psychiatric disorders are defined by their longitudinal course, our ability to predict symptom trajectories and tailor treatments to the individual remains limited, in part due to a dearth of longitudinal data collected using assessments sensitive to individual change over time. To address these gaps, the recently launched 'Individually Measured Phenotypes to Advance Computational Translation at Yale' (IMPACT-Y) study is collecting longitudinal data from a transdiagnostic cohort of 2400 individuals, using a combination of 'traditional' clinical research methods (e.g., health records, standardized assessments) and more novel computational approaches (e.g., behavioral tasks with demonstrated sensitivity to latent constructs and to within-person change, spoken narrative data). Here, we discuss unique challenges and opportunities in study design and analysis considerations of IMPACT-Y. Incorporating both theory- and data-driven analytics, we hope that IMPACT-Y will provide an unprecedented resource for characterizing longitudinal trajectories of core computational psychiatry constructs (e.g., reward learning) within and between individuals, for parsing heterogeneity beyond traditional diagnostic categories, and for linking inter- and intra-individual clinical variability to underlying mechanisms.

Background: Maternal mental health during pregnancy has been linked to early neurodevelopment, but the unique contributions of maternal and paternal psychosocial risk and protective factors to neonatal brain structure remain unclear. This study examined associations between prenatal parental psychosocial factors and neonatal brain morphometry (intracranial and subcortical volumes) and white matter microstructure.

Methods: Structural and diffusion MRI data were acquired at 2-5 weeks postnatal age in n=174 neonates (M gestational age=39.9 ± 1.2 weeks) from the FinnBrain Birth Cohort. Psychosocial data were collected via questionnaires from n=173 mothers and n=116 fathers during pregnancy. Latent risk and protective constructs were derived using exploratory factor analysis. Associations with neonatal brain metrics: intracranial volume, bilateral hippocampal/amygdala volumes, and white matter microstructure (fractional anisotropy and mean diffusivity in key tracts) were tested using structural equation modeling, adjusted for covariates and FDR correction.

Results: Four maternal (mental health and well-being, early relationships, pregnancy-related anxiety, attachment) and two paternal (mental health and well-being, social bonding) latent factors were identified. Greater maternal mental health and well-being was associated with larger neonatal intracranial volume. Greater paternal mental health and well-being was associated with lower fractional anisotropy in the hippocampal cingulum and inferior fronto-occipital fasciculus, and higher mean diffusivity in the latter.

Conclusions: Findings suggest that prenatal parental psychosocial health is associated with subtle deviations in neonatal brain architecture. These results underscore the need for holistic research on parental mental health, paving the way for care models that integrate psychosocial well-being to promote better health outcomes across generations.