Biological psychiatry. Cognitive neuroscience and neuroimaging最新文献

Background: Major Depressive Disorder (MDD) and Bipolar Disorder (BD) are associated with persistent cognitive deficits, yet the biological mechanisms underlying these impairments remain unclear. Metabolic dysfunction, particularly insulin resistance (IR), may contribute to brain structural alterations and cognitive decline. However, diagnosis-specific metabolic effects on gray matter volumes (GMV) and cognition were not fully explored. Partial Least Squares Path Modeling was applied to examine associations among metabolic biomarkers, GMV, and cognitive performance in mood disorders, stratifying by diagnosis.

Methods: 81 BD (F=55, M=26) and 78 MDD (F=45, M=33) inpatients underwent neuropsychological evaluation with the Brief Assessment of Cognition in Schizophrenia. T1-weighted MRI images were processed to extract GMV. Blood samples were collected to assess metabolic markers.

Results: In the whole sample, the metabolism latent construct negatively predicted both GMV and cognition, with the GMV factor positively affecting cognition. A significant diagnostic difference emerged for the metabolism-to-cognition path (p = 0.0196). Stratified analyses showed that in BD, metabolism was significantly associated with both reduced GMV and poorer cognition, whereas in MDD no significant structural paths were identified. IR markers and leptin were the strongest positive contributors to the metabolism factor in both the full sample and BD group. Brain regions most affected encompassed areas central to cognitive and emotional regulation, characterized by a high density of insulin and leptin receptors.

Conclusion: These findings highlight the role of IR and leptin in shaping cognition in mood disorders and underscore the potential of insulin-related pathways as therapeutic targets, especially in BD with metabolic comorbidities.

Background: Ibogaine was recently found to result in significant functional improvements in treating the sequelae of traumatic brain injury (TBI) among Special Operations Forces veterans (SOVs). In the present article, we use multimodal neuroimaging to elucidate the neural correlates of ibogaine in 30 male SOVs who received ibogaine treatment.

Methods: Arterial spin labeling and blood oxygen level-dependent functional magnetic resonance imaging data were collected before, immediately after ibogaine treatment, and at 1-month follow-up. A whole-brain exploratory analysis was conducted to examine the effects of ibogaine on resting-state regional cerebral blood flow (rCBF) and functional connectivity.

Results: The results revealed gradual increases in rCBF in the cortical, limbic, and striatal subregions, and changes in functional connectivity across a wide range of functional networks. The magnitude of treatment-induced rCBF changes in the left insula and left anterior cingulate cortex correlated significantly with improvements in TBI-related disability symptoms.

Conclusion: Our results suggest that ibogaine may involve widespread reorganization of functional connections in the brain, and that persisting regional changes in metabolic activity after ibogaine treatment, particularly within paralimbic brain regions, might be related to the observed therapeutic effects of ibogaine. Our findings serve to generate future hypotheses for larger, controlled neuroimaging studies of ibogaine in humans, necessary to validate these initial findings.

Background: Bipolar disorder (BD) has been associated with accelerated aging, but studies investigating Brain Age have yielded mixed results. This may reflect differences in methodology and model sensitivity.

Methods: We compared three publicly available Brain Age models (ENIGMA, PyBrainAge, Pyment) using T1-weighted MRI scans from 352 individuals with BD Type I and 327 controls across four sites. Predicted age difference (PAD) was calculated as Brain Age minus chronological age. We examined group differences, medication effects, and age-related patterns using linear mixed-effects models controlling for chronological age, sex, and scanner.

Results: PAD was higher in BD than controls across all models (PyBrainAge: +3.03 years; ENIGMA: +2.78 years; Pyment: +1.43 years; Cohen's d=0.26-0.36; all p<.001) with group differences more pronounced in participants ≥40 years. In region-based models, thalamic and ventricular volumes contributed most consistently to elevated PAD in BD. Across all models, lithium-treated BD participants showed no significant PAD elevation compared to controls (all p>.5), while non-lithium-treated participants exhibited significant elevation (+1.47-3.24 years all: p<.01). Within BD participants, mixed modeling of current any medication status, lithium treatment, and illness duration/severity measures showed current any medication status to be associated with increased PAD (+2.03-4.48 years; all: p<.05) whereas lithium use was associated with a 1.87-3.67-year reduction in PAD (all p<.05) and no associations were found with duration/severity metrics.

Conclusions: Our findings support the presence of elevated Brain Age in BD, lithium's suggested neuroprotective profile, and highlight the influence of the Brain Age model, MRI scanner, and other confounders on predictions.

Background: Magnetic resonance-guided focused ultrasound capsulotomy (MRgFUS-AC) is an experimental incisionless intervention for refractory obsessive-compulsive disorder (OCD) and major depressive disorder (MDD). Although its clinical efficacy has been demonstrated, the longitudinal structural and microstructural brain changes it induces remain incompletely characterized.

Methods: A total of 33 patients with treatment-resistant obsessive-compulsive disorder (OCD, n = 14) or major depressive disorder (MDD, n = 19) underwent MRgFUS-AC targeting the ventral anterior limb of the internal capsule (ALIC). Diffusion and structural MRI were acquired preoperatively and at follow-up. White matter integrity was evaluated using probabilistic tractography, and cortical and subcortical volumes were quantified using the FreeSurfer longitudinal pipeline. Longitudinal changes were assessed with linear mixed-effects models.

Results: MRgFUS-AC produced small lesions (∼117 mm³) that regressed by over 80% within three months. Despite this, significant reductions in fractional anisotropy were observed along multiple cortico-thalamic and cortico-striatal tracts. Volume loss was detected in both cortical (medial orbitofrontal, inferior temporal, fusiform) and subcortical (bilateral caudate) regions, with more widespread effects in OCD. Smaller preoperative volume of the left pars triangularis predicted greater clinical improvement.

Conclusions: MRgFUS-AC induces measurable white matter and gray matter changes within fronto-striatal-thalamic circuits, even with small, regressing lesions. Structural effects were more pronounced in OCD than MDD. Preoperative imaging features may aid in stratifying response and optimizing individualized targeting strategies for psychiatric neurosurgery.

Background: Cognitive symptoms are among the core features of schizophrenia, but their underlying mechanisms remain unclear. Current hypotheses suggest that alterations in the frontal cortex cause network dysfunction, contributing to cognitive symptoms. Growing evidence links reactive astrocytes with cognitive function and the pathophysiology of schizophrenia. We aimed to investigate in vivo reactive astrocyte signals in the dysconnected networks underlying cognitive symptoms in patients with schizophrenia.

Methods: [¹⁸F]THK5351 positron emission tomography (PET) and resting-state functional MRI data were obtained from 32 patients with schizophrenia and 32 age- and sex-matched healthy controls. [¹⁸F]THK5351 PET was used to measure monoamine oxidase B, a marker of reactive astrocytes. We performed network analysis to identify dysconnected subnetworks related to cognitive symptoms and examined reactive astrocyte signals in these subnetwork regions.

Results: Patients showed impaired verbal learning (F = 18.97, p < 0.001) and memory (F = 24.31, p <0.001). In patients, reduced left medial orbitofrontal cortex (mOFC)-left dorsolateral prefrontal cortex and left mOFC-right dorsal anterior cingulate cortex connectivity predicted impaired verbal learning (β = 0.45, p = 0.011) and memory (β = 0.56, p = 0.001), respectively. The PET standardized uptake value ratio was greater in the left mOFC in patients than in controls (t = -2.61, p = 0.011).

Conclusions: We found evidence of increased reactive astrocyte activity in the key region of the dysconnected network underlying cognitive impairments in schizophrenia. These results suggest a potential link between reactive astrocytes in the mOFC and the pathophysiology underlying cognitive symptoms in schizophrenia.

Background: Effort-based decision-making (EBDM) is a key component of motivation. Impairments in EBDM have been consistently linked to amotivation in individuals with schizophrenia (SZ). Similar deficits are seen in SZ and bipolar disorder (BD), despite striking differences in motivational profiles between the two disorders. Similar task behavior, but distinct motivational profiles, may arise from functional differences in brain regions supporting EBDM.

Methods: 28 Veterans with SZ, 21 with BD, and 30 controls completed a cognitive EBDM task during fMRI scanning. Participants chose between an easy and a more effortful working memory task. Reward values were manipulated to bias the participant toward choosing the more effortful task, the easier task, or were relatively unbiased.

Results: Participants with SZ spent less time deliberating on unbiased trials and exhibited reduced task-related activation in the ACC, anterior insula, and dlPFC compared with control and BD groups. Greater activation to hard-task biased trials relative to unbiased or biased toward easy task trials in the striatum, vmPFC, and PCC was associated with motivation deficits in SZ, but lower amotivation in BD.

Conclusions: Hypoactivation to the task in the ACC and other regions was found in the SZ group. Associations found between activation in several brain regions underlying EBDM and clinical amotivation suggest distinct neurobehavioral processes contributing to motivational deficits in SZ and BD.

Background: Adverse Childhood Experiences (ACE) represent a strong influence on the developing brain, profoundly affect corticolimbic circuits, contributing to vulnerability for mental disorders. Individual differences in resilience-related behavior, such as physical activity, may mitigate these effects.

Methods: This retrospective study examined whether self-reported lifetime physical activity (LPA) modulates the relationship between ACE and resting-state functional connectivity (rs-FC) of key limbic regions among 75 adults (mean age = 31.8 years, 82.7% female). Interaction models (ACE × LPA) were constructed for seed-to-voxel analyses, using, the amygdala, hippocampus, and anterior cingulate cortex, as seeds. Significant clusters were extracted and subjected moderation analyses, and The Johnson-Neyman technique was used to determine sample-specific LPA ranges where the association between ACE and connectivity became statistically significant.

Results: Significant ACE × LPA interactions were observed across all three seed regions, with robust clusters located in subcortical-cerebellar, visual association, and motor networks. Across clusters, greater ACE exposure was associated with reduced connectivity at lower LPA levels, but increased connectivity at high levels, indicating a crossover moderation pattern. The Johnson-Neyman technique identified LPA ranges (∼150-390 min/week) where ACE effects on connectivity were statistically significant.

Conclusions: LPA moderated the association between ACE and rs-FC within emotion- and sensorimotor-related networks. Higher activity levels were linked to connectivity profiles consistent with potential neural resilience to early adversity. These findings highlight physical activity as a modifiable lifestyle factor associated with neurobiological adaptation following early adversity.

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

Methods: We integrated multimodal neuroimaging (structural/functional MRI) and demographic data from 145 MDD patients and 68 healthy controls (HC). After defining a composite IMD score derived from C-reactive protein, BMI, 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 (nonIMD group, n=37) subgroups. Structural MRI (cortical thickness and gray matter volume), resting-state functional MRI (ReHo/fALFF), and demographic covariates were integrated as predictors.

Results: The multimodal model showed promise in classifying IMD group from nonIMD group (mean cross-validated AUC = 0.826 ± 0.098). Furthermore, its performance appeared somewhat more pronounced for within-MDD subtyping compared to differentiating MDD from HC (mean cross-validated AUCs of 0.647 ± 0.151 for nonIMD group vs. HC and 0.741 ± 0.111 for IMD group vs. HC), indicating subtype specificity. Key predictors included right amygdala volume and functional activity (ReHo/fALFF) in the hippocampus and mid-cingulate cortex. Clinically, the IMD group exhibited significantly higher anhedonia (p = 0.04), but lower somatic symptom scores (p < 0.05) compared to nonIMD group.

Conclusions: Our analysis shows that IMD is characterized by a distinct, multimodal neuro-demographic signature involving cortico-limbic 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 like 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. Further, 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, many patients take years before they 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, only produced modest effects across a range of mental health symptoms and conditions and, as a result have not meaningfully closed the treatment gap. Here, we outline the potential for precision approaches to the delivery of mental health interventions, both digital and conventional, to improve population-level outcomes. Mobile technology, genetics and electronic health records (EHR) 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.