Journal of Psychiatry & Neuroscience最新文献

Background: Patients with late-life depression (LLD) with suicidal ideation (SI) often have more explicit suicide plans, and suicide attempts among older adults are more highly lethal than in other age groups. Increasing evidence suggests that people with SI in depression exhibit abnormal brain network connectivity; however, the relationship between suicidal ideation in LLD and brain network dynamics is still unclear.

Methods: We recruited patients with LLD and SI (LLD-SI), patients with LLD without SI (LLD-NSI), and age-matched healthy older adults. We collected 64-channel resting state electroencephalography (EEG) recordings of all participants and used microstate analysis to explore large-scale brain network dynamics.

Results: We included 33 patients with LLD-SI, 29 patients with LLD-NSI, and 31 controls. We observed abnormal microstate parameters in the LLD-SI group, characterized by higher duration (p = 0.04), occurrence (p = 0.009), and contribution (p = 0.001) of microstate C (reflecting activity of the salience network), compared with the LLD-NSI group, as well as higher occurrence (p = 0.03) and contribution (p = 0.009) of microstate C compared with the control group. Furthermore, transition probabilities from microstate class A to D (r = -0.466, p = 0.04) and class D to A (r = -0.506, p = 0.02) (involving coupling and sequential activation of auditory and executive control network) were negatively correlated with completion time of Stroop Colour and Word Test Part C (a neuropsychological test of executive function) in the LLD-SI group.

Limitations: The sample size was relatively small, the cross-sectional nature of this study prohibited exploring the causal relationship between abnormal microstate dynamics and suicidal ideation, and we did not include medication-naive patients with first-episode LLD.

Conclusion: The study reveals altered microstate dynamics among patients with LLD-SI, compared with patients with LLD-NSI and controls. Our findings suggest that microstate dynamics could serve as potential neurobiomarkers for identifying SI in LLD.

Background: Cardiometabolic risk factors - including diabetes, hypertension, and obesity - have long been linked with adverse health outcomes such as strokes, but more subtle brain changes in regional brain volumes and cortical thickness associated with these risk factors are less understood. Computer models can now be used to estimate brain age based on structural magnetic resonance imaging data, and subtle brain changes related to cardiometabolic risk factors may manifest as an older-appearing brain in prediction models; thus, we sought to investigate the relationship between cardiometabolic risk factors and machine learning-predicted brain age.

Methods: We performed a systematic search of PubMed and Scopus. We used the brain age gap, which represents the difference between one's predicted and chronological age, as an index of brain structural integrity. We calculated the Cohen d statistic for mean differences in the brain age gap of people with and without diabetes, hypertension, or obesity and performed random effects meta-analyses.

Results: We identified 185 studies, of which 14 met inclusion criteria. Among the 3 cardiometabolic risk factors, diabetes had the highest effect size (12 study samples; d = 0.275, 95% confidence interval [CI] 0.198-0.352; n = 47 436), followed by hypertension (10 study samples; d = 0.113, 95% CI 0.063-0.162; n = 45 102) and obesity (5 study samples; d = 0.112, 95% CI 0.037-0.187; n = 15 678). These effects remained significant in sensitivity analyses that included only studies that controlled for confounding effects of the other cardiometabolic risk factors.

Limitations: Our study tested effect sizes of only categorically defined cardiometabolic risk factors and is limited by inconsistencies in diabetes classification, a smaller pooled sample in the obesity analysis, and limited age range reporting.

Conclusion: Our findings show that each of the cardiometabolic risk factors uniquely contributes to brain structure, as captured by brain age. The effect size for diabetes was more than 2 times greater than the independent effects of hypertension and obesity. We therefore highlight diabetes as a primary target for the prevention of brain structural changes that may lead to cognitive decline and dementia.

Background: Nonsuicidal self-injury (NSSI) is posited to arise from a complex interaction of biopsychosocial factors, with impulsivity playing a critical role. Given that current research on the neural mechanisms underlying this hypothesis remains inconsistent and limited in scope, we sought to explore how NSSI behaviours are associated with impulsivity resulting from structural brain alterations.

Methods: We recruited patients with NSSI behaviours and healthy controls from 11 psychiatric hospitals. We assessed the differences in impulse control between the 2 groups using the Barratt Impulsiveness Scale version 11 and the Attention Network Test. We also conducted T ₁-weighted magnetic resonance imaging (MRI) and diffusion tensor imaging. Finally, we analyzed the associations among brain structure, psychological characteristics, and self-injurious behaviour among patients with NSSI.

Results: We included 293 patients with NSSI behaviours and 140 healthy controls. Among them, 182 patients with NSSI and 95 controls underwent the T ₁-weighted MRI and diffusion tensor imaging. Patients with NSSI showed increased impulsivity and alerting function, with the strongest correlation between NSSI frequency and motor impulsivity. Compared with controls, patients with NSSI exhibited decreased grey matter volume and increased white matter volume, with no significant difference in cortical thickness. Pathway analysis demonstrated that motor impulsivity significantly mediated the association between white matter volume and the NSSI frequency in the right superior frontal gyrus and right inferior parietal lobe. When examining the connecting fibre tracts in the right frontoparietal area, patients with NSSI showed decreased integrity of white matter microstructure in the right cingulum, right superior corona radiata, and the splenium of the corpus callosum.

Limitations: Accurately measuring executive control linked to NSSI is challenging in cognitive behavioural tasks, as impulsive tendencies during NSSI occurrence are not effectively captured.

Conclusion: Our findings suggested that motor impulsivity, a prominent psychopathological characteristic of NSSI, is primarily modulated by the frontoparietal regions. These results provide empirical neuroimaging evidence for the impaired impulse control observed in NSSI.

Background: Phenomenology in anorexia nervosa (AN) appears to be subject to epigenetic regulation via DNA methylation. The micronutrients B₁₂ and betaine contribute directly to DNA methylation and have been shown to be abnormally elevated in blood samples from people with AN.

Methods: We measured plasma B₁₂ and betaine levels, as well as leukocyte DNA methylation levels, among women with active AN (AN-active group), those in 1-year remission from AN (AN-remitted group), and those who had never experienced an eating disorder (NED group). We compared the groups on micronutrient levels and on the strength of association between micronutrients and methylation.

Results: We included 64 women in the AN-active group, 49 in the AN-remitted group, and 49 in the NED group. Relative to those with NED (B₁₂: mean 339.6 [standard deviation (SD) 224.3] μmol/L; betaine: mean 33.74 [SD 17.10] μmol/L), participants with active AN showed high B₁₂ and betaine (B₁₂: mean 571.0 [SD 505.2] μmol/L; betaine: mean 43.73 [SD 22.50] μmol/L); AN-remitted participants had elevated B₁₂ alone (B₁₂: mean 588.2 [SD 379.9] μmol/L; betaine: mean 33.50 [SD 19.20] μmol/L). There were also group-based differences in the strength of association between B₁₂ and site-specific DNA methylation at genes regulating insulin function, glucose metabolism, cell regulation, and neurotransmitter function. These associations between B₁₂ and methylation levels were generally stronger among those without an ED than among those with either active or remitted AN.

Limitations: The extent to which plasma nutrient levels provide a meaningful proxy to cellular processes affecting DNA methylation is uncertain and the sample size limits the stability of results. We included only biological females in this investigation.

Conclusion: Elevated B₁₂ levels in AN resemble elevations reported among people with autoimmune, neoplastic, or other disorders. Such elevations imply that plasma B₁₂ levels may misrepresent nutritional status among people with AN. Observed associations between levels of B₁₂ and methylation at certain gene regions have ambiguous importance, but may indicate an influence of nutritional status on epigenetic mechanisms or may be the coincidence of separate processes that independently affect levels of micronutrients and DNA methylation.

Background: Schizophrenia is hypothesized to involve a disturbance in the temporal dynamics of self-processing, specifically within the interoceptive, exteroceptive, and cognitive layers of the self. This study aimed to investigate the intrinsic neural timescales (INTs) within these self-processing layers among people with schizophrenia.

Methods: We conducted a functional magnetic resonance imaging (fMRI) study to investigate INTs, as measured by the autocorrelation window, among people with schizophrenia and healthy controls during both resting-state and task (memory encoding and retrieval) conditions. We obtained data from the UCLA Consortium for Neuropsychiatric Phenomics data set and preprocessed using fMRIPrep.

Results: We included 45 people with schizophrenia and 65 healthy controls. Compared with controls, participants with schizophrenia exhibited significantly shorter INTs across all 3 self-processing layers during rest (p < 0.05). In addition, those with schizophrenia showed less INT shortening during task states, leading to reduced rest-task differences in INT across all self-processing layers (p < 0.05). We observed similar patterns of shortened INTs in primary sensory and motor regions.

Limitations: We included people with schizophrenia taking medication, which may influence INTs; our study was also limited by the relatively slow temporal resolution of the fMRI data and the higher variability of the autocorrelation function in the schizophrenia group, compared with the control group.

Conclusion: Our findings suggest that schizophrenia is characterized by a global temporal disturbance of the self, manifesting as shorter and inflexible INTs across self-processing and sensorimotor regions. These results support the hypothesis that schizophrenia involves a fundamental disruption in the temporal integration of neural signals, contributing to the core self-disturbance observed in the disorder.

Background: Genetic variants may confer risk for depression by modulating brain structure and function; evidence has underscored the key role of the subgenual anterior cingulate cortex (sgACC) in depression. We sought to examine how the resting-state functional connectivity (rsFC) of the sgACC was associated with polygenic risk for depression in a subclinical population.

Methods: Following published protocols, we computed seed-based whole-brain sgACC rsFC and calculated polygenic risk scores (PRS) using data from healthy young adults from the Human Connectome Project. We performed whole-brain regression against PRS and severity of depression symptoms in a single model for all participants and by sex, controlling for age, sex, race or ethnicity, alcohol use severity, and household income. We evaluated the results at a corrected threshold.

Results: We included data for 717 healthy young adults. We found lower rsFC between the sgACC and the default mode network and frontal regions in association with PRS and lower sgACC-cerebellar rsFC in association with depression severity. We also noted differences by sex in the connectivity correlates of PRS and depression severity. In an additional set of analyses, we observed a significant correlation between PRS and somatic complaints, as well as altered sgACC-somatosensory cortical connectivity in association with the severity of somatic complaints.

Limitations: The current findings should be considered specific to subclinical depression and may not generalize to patients with depressive disorders.

Conclusion: Our findings highlight the pivotal role of distinct sgACC-based networks in the genetic predisposition for depression and the manifestation of depression among young adults with subclinical depression. Distinguishing the risk from severity markers of depression may have implications in developing early and effective treatments for people at risk for depression.

Background: Both depressive symptoms and neurotransmitter changes affect the characteristics of functional brain networks in clinical patients. We sought to explore how brain functional grading is organized among patients with mild cognitive impairment and depressive symptoms (D-MCI) and whether changes in brain organization are related to neurotransmitter distribution.

Methods: Using 3 T magnetic resonance imaging (MRI) we acquired functional MRI (fMRI) data from patients with D-MCI, patients with mild cognitive impairment without depression (nD-MCI), and healthy controls. We used resting-state fMRI and diffusion embedding to examine the pattern of functional connectivity gradients. We used analysis of covariance and post hoc t tests to compare the difference in functional connectivity gradients among the 3 groups. We examined the correlation between variations in functional connectivity gradients and neurotransmitter maps using the JuSpace toolbox.

Results: We included 105 participants, including 31 patients with D-MCI, 40 patients with nD-MCI, and 34 healthy controls. Compared with healthy controls, both the nD-MCI and D-MCI groups showed abnormalities in the principal unimodal-transmodal gradient pattern. Compared with controls, the D-MCI group showed an increased secondary gradient in the default mode network. Differences in the functional connectivity gradients between the D-MCI and nD-MCI groups were significantly correlated with the distribution of 5-hydroxytryptamine receptor subtype 1A.

Limitations: The small sample size affects the generalizability of the results, and the neurotransmitter template is based on healthy participants, not patients.

Conclusion: Our results suggest that depressive symptoms cause abnormalities in the hierarchical segregation of functional brain organization among patients with MCI. Such abnormal changes may be related to the distribution of neurotransmitters.

Background: Cortical morphometry is an intermediate phenotype that is closely related to the genetics and onset of major depressive disorder (MDD), and cortical morphometric networks are considered more relevant to disease mechanisms than brain regions. We sought to investigate changes in cortical morphometric networks in MDD and their relationship with genetic risk in healthy controls.

Methods: We recruited healthy controls and patients with MDD of Han Chinese descent. Participants underwent DNA extraction and magnetic resonance imaging, including T ₁-weighted and diffusion tensor imaging. We calculated polygenic risk scores (PRS) based on previous summary statistics from a genome-wide association study of the Chinese Han population. We used a novel method based on Kullback-Leibler divergence to construct the morphometric inverse divergence (MIND) network, and we included the classic morphometric similarity network (MSN) as a complementary approach. Considering the relationship between cortical and white matter networks, we also constructed a streamlined density network. We conducted group comparison and PRS correlation analyses at both the regional and network level.

Results: We included 130 healthy controls and 195 patients with MDD. The results indicated enhanced connectivity in the MIND network among patients with MDD and people with high genetic risk, particularly in the somatomotor (SMN) and default mode networks (DMN). We did not observe significant findings in the MSN. The white matter network showed disruption among people with high genetic risk, also primarily in the SMN and DMN. The MIND network outperformed the MSN network in distinguishing MDD status.

Limitations: Our study was cross-sectional and could not explore the causal relationships between cortical morphological changes, white matter connectivity, and disease states. Some patients had received antidepressant treatment, which may have influenced brain morphology and white matter network structure.

Conclusion: The genetic mechanisms of depression may be related to white matter disintegration, which could also be associated with decoupling of the SMN and DMN. These findings provide new insights into the genetic mechanisms and potential biomarkers of MDD.