Biological psychiatry. Cognitive neuroscience and neuroimaging最新文献

Background: The underlying neurobiology of a recently described subtype of major depressive disorder (MDD), immunometabolic depression (IMD), characterized by low-grade inflammation and metabolic dysregulation, remains unclear.

Methods: We integrated multimodal neuroimaging (structural and functional magnetic resonance imaging [MRI]) and demographic data from 145 patients with MDD and 68 healthy control (HC) participants. After defining a composite IMD score derived from C-reactive protein, body mass index, triglycerides, and high-density lipoprotein cholesterol levels by principal component analysis, we implemented a binary classification task using machine learning to distinguish high IMD score (IMD group, n = 37) from low IMD score (non-IMD group, n = 37) subgroups. Structural MRI (cortical thickness and gray matter volume), resting-state functional MRI (regional homogeneity [ReHo]/fractional amplitude of low-frequency fluctuations [fALFF]), and demographic covariates were integrated as predictors.

Results: The multimodal model showed promise in distinguishing the IMD group from the non-IMD group (mean ± SD cross-validated area under the receiver operating characteristic curve [AUC] = 0.826 ± 0.098). Furthermore, its performance appeared somewhat more pronounced for within-MDD subtyping compared with differentiating MDD from HC participants (mean cross-validated AUCs of 0.647 ± 0.151 for non-IMD group vs. HC group and 0.741 ± 0.111 for IMD group vs. HC group), indicating subtype specificity. Key predictors included right amygdala volume and functional activity (ReHo/fALFF) in the hippocampus and midcingulate cortex. Clinically, the IMD group exhibited significantly higher anhedonia (p = .04), but lower somatic symptom scores (p < .05), compared with the non-IMD group.

Conclusions: Our analysis shows that IMD is characterized by a distinct, multimodal neurodemographic signature involving corticolimbic circuitry. This signature demonstrates high specificity for unraveling MDD heterogeneity and is clinically linked to anhedonia, supporting the potential for biologically informed patient stratification.

Common mental health conditions such as depression, anxiety, and substance use disorders are important contributors to disability and reduced quality of life. Efforts to address these conditions have been hindered by an inadequate clinician workforce capacity. Furthermore, first-line treatments (medications and clinician-delivered counseling) have modest efficacy, and there is a paucity of data to guide treatment decisions. As a result, it takes years for many patients to find a treatment that works, and the large and growing proportion of patients needing care face long wait times. To overcome these challenges, we need scalable, innovative solutions that both increase access and tailor care to the unique needs of each patient at a specific point in time. Because of their low cost and scalability, digital interventions are a potential tool to increase treatment capacity. However, these interventions, typically delivered by apps, have not achieved robust user engagement and have produced only modest effects across a range of mental health symptoms and conditions, and as a result they have not meaningfully closed the treatment gap. Here, we outline the potential for precision approaches for the delivery of mental health interventions, both digital and conventional, to improve population-level outcomes. Mobile technology, genetics, and electronic health records provide data that capture constructs central to mental health. These data sources provide key inputs for modern data science methods that have the potential to effectively match patients to treatments as well as tailor the timing, dosage, and content within specific digital interventions.

Background: Individualized brain systems mapping is a recently developed method for understanding person-specific functional brain organization using human functional magnetic resonance imaging (fMRI) data. Abnormal structure and function in the subcortex have been previously implicated in individuals with major depressive disorder (MDD). However, the systems-level functional organization of the subcortex in MDD has yet to be investigated. Moreover, almost all prior studies of brain systems in MDD have used group-level functional brain mapping that assumes organizational homogeneity across individuals.

Methods: In the current study, the functional systems organization of the subcortex was mapped in individuals with MDD (n=288, 67% female) and healthy controls (n=40, 63% female). Individualized subcortical system metrics were then related to psychiatric diagnosis and symptoms, and related behavioral measures. We evaluate hypothesis-driven comparisons in the size of subcortical systems representation of the control, default, affective, and salience systems.

Results: Results include significant differences between depressed and healthy participants in subcortical control system representation (Z=2.77, p=0.006, d=0.46). Specifically, among the MDD group, the control system was more represented in the thalamus (Z=2.99, p=0.003, d=0.51). Total subcortical control system representation was associated with behavioral indices of cognitive control (i.e., A-not-B total correct response; r=0.13, p=0.029).

Conclusions: Taken together these findings provide the first evidence that mental illness is related to individualized subcortical system representation and thus provide new insight for neural models of MDD and related neuropsychiatric conditions.

Background: Major depressive disorder (MDD) is a highly prevalent psychiatric disorder with limited treatment success in both youth and adults. Functional magnetic resonance imaging (fMRI) studies have reported partially differential patterns of activation in youth and adults diagnosed with MDD that may be attributable to age or length of illness.

Methods: Following PRISMA guidelines, we searched PubMed through November 2023 and selected 135 primary studies (N = 6,391) based on the following inclusion criteria: (1)task-based fMRI activation, (2) voxel-wise whole-brain analyses, and (3) compared participants diagnosed with MDD to age-matched healthy controls (HCs). We used multilevel kernel density analysis with ensemble thresholding (P < .05 - .0001; FDR-corrected) to find activation differences between (1) youth with MDD and HCs, (2) youth and adults with MDD, (3) youth and adults with shorter-duration MDD, and (4) adults with longer and shorter-duration MDD.

Results: Relative to adults with MDD, youth with MDD demonstrated significant patterns of differential activation in regions such as the subgenual anterior cingulate cortex (sgACC) and dorsolateral prefrontal cortex (dlPFC; P < .0025). Second, after controlling for length of illness, relative to adults with shorter-duration MDD, youth demonstrated hypoactivation in regions such as the sgACC (P < .01). Lastly, when controlling for age, relative to adults with shorter-duration MDD, adults with longer-duration MDD demonstrated hypoactivation in regions such as the dlPFC (P < .05).

Conclusions: These findings underscore the importance of considering age and length of illness in developmental models of MDD and inform neural models and clinical interventions for depression.

Background: Adolescent substance use is associated with increased risk of suicide, overdose, and executive dysfunction. Alcohol and marijuana are the two most-used substances among young people. Substance use is associated with dysfunctional resting-state network connectivity between the default mode network (DMN), executive control network (ECN), and salience network (SN) and executive dysfunction. There is limited research on triple-network connectivity (DMN, ECN, SN) in adolescents who use substances.

Methods: In typically developing youth from the Philadelphia Neurodevelopmental Cohort (PNC), regression models were used to examine relationships between alcohol and marijuana use and triple-network connectivity (n = 520), while adjusting for age, sex, maternal education, and head motion. In individuals with neurocognitive and substance use data (n = 4197), regression models were used to examine relationships with executive control.

Results: Alcohol use was not associated with any connectivity measures after false discovery rate correction. Higher marijuana use was associated with higher DMN-ECN connectivity (F_2,507 = 5.08, p = .0066, q = .039). Higher alcohol use was associated with better working memory (p = .020), mental flexibility (p < .0001), attention (p = .019), and executive efficiency (p = .0015) and accuracy (p = .00044), which may have been due to other socioeconomic factors. Marijuana use was not associated with neurocognitive performance.

Conclusions: In typically developing youth, marijuana use was associated with DMN-ECN connectivity, while alcohol use was associated with neurocognitive performance. Future research should use interventions targeting the DMN, ECN, and SN to interrogate relationships between connectivity, cognitive performance, and substance use.

Background: Differences in sensory processing are a defining characteristic of autism, affecting up to 87% of autistic individuals. These differences cause widespread perceptual changes that can negatively impact cognition, development, and daily functioning. Our research identified five sensory processing 'phenotypes' with varied behavioural presentations; however, their neural basis remains unclear. This study aims to ground these sensory phenotypes in unique patterns of functional connectivity.

Methods: We analyzed data from 146 autistic participants from the Province of Ontario Neurodevelopmental Network. We classified participants into five sensory phenotypes using k-means clustering of scores from the Short Sensory Profile. We then computed a connectivity matrix from 200 cortical and 32 subcortical regions and calculated graph-theoretic measures (betweenness centrality, strength, local efficiency, and clustering coefficient) to assess information exchange between these regions. We then trained machine learning models to use these measures to classify between all pairs of sensory phenotypes.

Results: Our sample was clustered into five sensory phenotypes. The machine learning models distinguished seven of the ten total pairs of sensory phenotypes using graph-theoretic measures (p < 0.005). Information exchange within and between the somatomotor network, orbitofrontal cortex, posterior parietal cortex, prefrontal cortex and subcortical areas was predictive of sensory phenotype.

Conclusions: Sensory phenotypes in autism correspond to differences in functional connectivity across cortical, subcortical, and network levels. These findings support the view that variability in sensory processing is reflected in measurable neural patterns and motivate continued work to refine models of sensory processing, with the goal of better understanding and capturing the heterogeneity implicit in autism.

Background: Major depressive disorder (MDD) is a highly prevalent psychiatric disorder marked by disrupted brain dynamics. However, the neural mechanisms underlying remission remain poorly understood, particularly regarding common neural markers across diverse therapeutic interventions. Emerging evidence suggests that temporal brain dynamics and their hierarchical organization, referred to as metastates, serve as sensitive markers of individual variability across cognitive functions. In this study, we evaluated whether metastate dynamics derived from resting-state functional magnetic resonance imaging (rs-fMRI) differed according to remission status across pharmacotherapy, psychotherapy, and neuromodulation.

Methods: This multicenter observational study with 370 participants included 229 individuals with depression and 141 healthy control participants. The depression cohort comprised individuals undergoing cognitive behavioral therapy (n = 92), pharmacotherapy (n = 59), electroconvulsive therapy (n = 50), and repetitive transcranial magnetic stimulation (n = 28). rs-fMRI data were analyzed to derive metastate dynamics, and comparisons were made according to remission status across treatment modalities.

Results: Two distinct metastates were identified, one associated with higher-order cognitive brain regions and another linked to sensory and motor systems. Participants who achieved remission exhibited greater predictability in transitions between brain states within metastates, supporting higher-order cognitive functions. This altered transition pattern was accompanied by alterations in the anticorrelation between the default mode and executive function networks, which may underlie the increased predictability.

Conclusions: Remission from MDD may involve a reorganization of hierarchical brain dynamics-particularly in systems supporting cognitive control-and may offer a potential treatment modality-independent biomarker of remission.

Background: Low socioeconomic status (SES) is associated with alterations in brain development and youth psychopathology risk. However, the mechanisms linking SES to neurodevelopment remain unclear. We tested whether pubertal timing and tempo mediate the association between SES and cortical thinning in adolescence and whether these neurobiological processes predict socioeconomic disparities in internalizing symptoms.

Methods: Participants (N = 2949) (1474 females) were drawn from the Adolescent Brain Cognitive Development (ABCD) Study (ages 10-14 years). Latent growth models tested whether pubertal development mediated the relationship between SES (operationalized as household income-to-needs ratio) and cortical thickness development. A second model tested associations with internalizing symptoms at age 14. These pathways were investigated for males and females separately in both global and region-specific models.

Results: In females, low SES was associated with earlier pubertal timing and slower tempo (standardized β = -0.23 and β = 0.30, p < .001), which predicted faster and slower cortical thinning, respectively. Overall, low SES was associated with faster cortical thinning (β = 0.33, p < .012), partially mediated through earlier timing (β = 0.20, p < .001) and slower tempo (β = -0.18, p = .001) of pubertal development. These opposing pathways were observed for both global and regional cortical measures in areas associated with social cognition, emotion regulation, and self-referential processing. Earlier pubertal timing and faster cortical thinning partially mediated the link between SES and internalizing problems. In males, no significant indirect effects were observed globally, with few regional effects.

Conclusions: Findings suggest that pubertal development mediates the link between disadvantage and cortical development, in turn predicting adolescent psychopathology. These pathways may represent targets for early intervention in socioeconomically disadvantaged youth.

Background: Cognitive behavioral therapy (CBT) for pediatric anxiety targets heightened emotional sensitivity and impaired cognitive control over emotion, core neurocognitive features of anxiety. Exposure is considered the most active component of CBT, but its efficacy varies. Pretreatment function of substrates for emotion processing and cognitive control may predict response to exposure relative to other CBT subcomponents, such as relaxation.

Methods: Youth with clinical anxiety (N = 118, 7-17 years) completed a functional magnetic resonance imaging task probing emotion processing and cognitive control and then were randomized to 12 sessions of exposure-focused CBT (EF-CBT) or relaxation management training (RMT). Voxelwise linear mixed-effects models tested how pretreatment whole-brain activation was associated with symptom reduction over the course of treatment.

Results: EF-CBT led to greater symptom reduction than RMT. Better EF-CBT response was predicted by lower pretreatment activation in the left dorsolateral prefrontal cortex during emotion processing and in the bilateral dorsal anterior cingulate cortex and inferior parietal lobe during cognitive control of emotion. Conversely, these patterns were associated with poorer outcomes of RMT. Direct comparisons revealed that EF-CBT was more effective than RMT for youth with low and mean activation in all regions, but not high activation.

Conclusions: EF-CBT outperformed RMT for youth with low-to-mean activation in regions supporting emotional appraisal, cognitive control, and threat attention, indicating that these patterns may be biomarkers of exposure readiness. Conversely, high activation in these regions did not confer differential benefit and may reflect hypervigilance or overcontrol that could interfere with exposure-based learning. These findings support the value of preparatory interventions to optimize treatment readiness and personalize delivery of exposure-based CBT.

Background: Bipolar disorder coincides with one of the highest rates of suicide among all psychiatric conditions. Individual differences in stress reactivity may contribute to increased susceptibility to suicide-related thoughts and behaviors (STBs). Research examining the stress response and its relationship with STBs in bipolar disorder is limited. The purpose of this study was to investigate associations between recent perceived stress and neurophysiological response to acute psychosocial stress in anterior-paralimbic system in young adults with bipolar disorder with and without a suicide attempt history.

Methods: Seventy-two young adults (21 with bipolar disorder and a history of suicide attempt(s) [BD-SA], 22 diagnostic control participants without a suicide attempt history [BD-noSA], and 29 typically developing [TD] individuals) were assessed for past-month perceived stress (Perceived Stress Scale [PSS]) and completed a functional magnetic resonance imaging Stress Math Task. Stress-related functional changes in anterior-paralimbic regions of interest were examined in relation to PSS scores. Effects of lifetime alcohol/cannabis use disorder and nicotine use on stress reactivity were explored.

Results: In the BD-SA group, recent perceived stress was associated with greater reactivity to psychosocial stress in the medial orbitofrontal cortex, anterior insula, amygdala, and anterior cingulate cortex (group-by-PSS interactions: ps ≤ .008). These patterns were not observed in the BD-noSA or TD groups. Lifetime cannabis use disorder and recent nicotine use were related to greater anterior-paralimbic responses to stress in bipolar disorder (ps ≤ .002).

Conclusions: Heightened anterior-paralimbic reactivity to cumulative stress may represent a risk factor for STBs. Cannabis and nicotine use may exacerbate stress-related anterior-paralimbic dysregulation. Future longitudinal research is needed to extend findings and investigate temporal relationships between stress reactivity, cannabis/nicotine use, and STBs.