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.
Background: Clozapine is superior to all other antipsychotics in treating schizophrenia in terms of its curative efficacy; however, this drug is prescribed only as a last resort in the treatment of schizophrenia, given its potential to induce cardiac arrest. The mechanism of clozapine-induced cardiac arrest remains unclear, so we aimed to elucidate the potential mechanisms of clozapine-induced cardiac arrest using network pharmacology and molecular docking.
Methods: We identified and analyzed the overlap between potential cardiac arrest-related target genes and clozapine target genes. We conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. We then constructed a protein-protein interaction (PPI) network and screened the core targets. We used molecular docking to evaluate the binding energy between clozapine and core targets.
Results: We identified a total of 2405 target genes related to cardiac arrest and 107 target genes for clozapine. Among these, we found 41 overlapping target genes. The main enriched GO biological processes included the upregulation of the mitogen-activated protein kinase (MAPK) cascade and the adenylate cyclase-activating adrenergic receptor signalling pathway. The KEGG enrichment analysis showed that the neuroactive ligand-receptor interaction and the forkhead box O (FoxO) signalling pathway seemed to be the key signalling pathways involved in clozapine-induced cardiac arrest. The 7 core targets identified in the established PPI network were G-protein-coupled receptor kinase 2, 5-hydroxytryptamine 2A receptor, dopamine D2 receptor, glycogen synthase kinase 3β, cyclin-dependent kinase 2, CREB-binding protein, and signal transducer and activator of transcription 3. The molecular docking results indicated a high affinity between clozapine and all of these core targets.
Limitations: The relatively small scope of the predictive and modelling methods, which predominantly comprised network pharmacology and molecular docking strategies, is a limitation of this study.
Conclusion: Network pharmacology and molecular docking approaches unveiled target genes for clozapine and potential mechanisms by which it may cause cardiac arrest, including the MAPK cascade, neuroactive ligand-receptor interactions, and the FoxO signalling pathway.
Background: The default mode network (DMN) is not a single system, but rather is composed of smaller and distinct functional subsystems that interact with each other. The functional relevance of these subsystems in tobacco use disorder (TUD) and the neurobiological features associated with smoking motivation are still unclear; thus, we sought to assess causal or direct connectivity alterations within 3 subsystems of the DMN among people with TUD.
Methods: We recruited male smokers and nonsmokers. We conducted resting-state functional magnetic resonance imaging (rs-fMRI) and collected ratings on smoking-related clinical scales. We applied dynamic causal modelling (DCM) to rs-fMRI to characterize changes of effective connectivity in TUD from 3 DMN subsystems, including the midline core network (i.e., the posterior cingulate cortex and the anterior medial prefrontal cortex [PCC-aMPFC] core DMN), the medial temporal subsystem (MTL-DMN), and the dorsal medial prefrontal cortex subsystem (dMPFC-DMN). We used leave-one-out cross-validation to investigate whether the neural response could predict smoking reasons, evaluated using the Russell Reason for Smoking Questionnaire).
Results: We recruited 88 smokers and 54 nonsmokers. Among people with TUD, the parahippocampal cortex (PHC) region showed enhanced self-connection, which was associated with the severity of TUD after nighttime withdrawal. Compared with nonsmokers, people with TUD displayed significant increased effective connectivity within the dMPFC-DMN, and decreased effective connectivity from the dMPFC-DMN to the PCC-aMPFC core DMN. Moreover, decreased effective connectivity from the lateral temporal cortex to the dMPFC could predict the smoking reason related to automatic behaviour.
Limitations: Although we found aberrance in causal connections in DMN subsystems among people with TUD, our cross-sectional study could not be used to investigate changes in effective connectivity over time and their relationship with clinical features.
Conclusion: This study emphasized the aberrant causal connections of different functional subsystems of the DMN in TUD and revealed the neural correlates of automatic smoking behaviours. These findings suggested DMN subsystem-derived indicators could be a potential biomarker for TUD and could be used to identify the heterogeneity in motivation for smoking behaviour.
Background: The intricate interplay between peripheral adaptive immune cells and the central nervous system (CNS) has garnered increasing recognition. Given that alterations in cell quantities often translate into modifications in metabolite profiles and that these metabolic changes can potentially traverse the bloodstream and enter the CNS, thereby modulating the progression of mental illnesses, we sought to explore the metabolic profiles of peripheral immune cells in a ketamine-treated mouse model of schizophrenia.
Methods: We used flow cytometry to scrutinize the alterations in peripheral adaptive immune cells in a ketamine-induced schizophrenia mouse model. Subsequently, we implemented an untargeted metabolomic approach with ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) to detect the metabolite profiles of peripheral abnormal lymphocytes and identify differential metabolites present in plasma. We then employed targeted metabolomics using UPLC-MS/MS to quantify the common differential metabolites detected in mouse plasma.
Results: Flow cytometry analysis detected a notable increase in the count of peripheral CD3+ T cells in a ketamine-induced schizophrenia mouse model. Subsequent untargeted metabolomics analysis revealed that the amino acid metabolism pathway underwent substantial alterations. A detailed quantification of 22 amino acid profiles in the peripheral plasma indicated significant elevation in the levels of glycine, alanine, asparagine, and aspartic acid.
Limitations: Our ongoing research has yet to conclusively identify the precise amino acid metabolism pathway that serves as the pivotal factor in the manifestation of the schizophrenia-like phenotype induced by ketamine.
Conclusion: The peripheral amino acid metabolism pathway is involved in the ketamine-induced schizophrenia-like phenotype. The metabolic profile of peripheral immune cells could provide accurate biomarkers for the diagnosis and treatment of psychiatric diseases.
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