An investigation into the abnormal dynamic connection mechanism of generalized anxiety disorders based on non-homogeneous Markov models.

BACKGROUND: The dynamic and hierarchical nature of the functional brain network. The neural dynamical systems tend to converge to multiple attractors (stable fixed points or dynamical states) in long run. Little is known about how the changes in this brain dynamic "long-term" behavior of the connectivity flow of brain network in generalized anxiety disorder (GAD). METHODS: This study recruited 92 patients with GAD and 77 healthy controls (HC). We applied a reachable probability approach combining a Non-homogeneous Markov model with transition probability to quantify all possible connectivity flows and the hierarchical structure of brain functional systems at the dynamic level and the stationary probability vector (10-step transition probabilities) to describe the steady state of the system in the long run. A random forest algorithm was conducted to predict the severity of anxiety. RESULTS: The dynamic functional patterns in distributed brain networks had larger possibility to converge in bilateral thalamus, posterior cingulate cortex (PCC), right superior occipital gyrus (SOG) and smaller possibility to converge in bilateral superior temporal gyrus (STG) and right parahippocampal gyrus (PHG) in patients with GAD compared to HC. The abnormal transition probability pattern could predict anxiety severity in patients with GAD. LIMITATIONS: Small samples and subjects taking medications may have influenced our results. Future studies are expected to rule out the potential confounding effects. CONCLUSION: Our results have revealed abnormal dynamic neural communication and integration in emotion regulation in patients with GAD, which give new insights to understand the dynamics of brain function of patients with GAD.

Enhanced group-level dorsolateral prefrontal cortex subregion parcellation through functional connectivity-based distance-constrained spectral clustering with application to autism spectrum disorder.

The dorsolateral prefrontal cortex (DLPFC) assumes a central role in cognitive and behavioral control, emerging as a crucial target region for interventions in autism spectrum disorder neuroregulation. Consequently, we endeavor to unravel the functional subregions within the DLPFC to shed light on the intricate functions of the brain. We introduce a distance-constrained spectral clustering (SC-DW) methodology that leverages functional connection to identify distinctive functional subregions within the DLPFC. Furthermore, we verify the relationship between the functional characteristics of these subregions and their clinical implications. Our methodology begins with principal component analysis to extract the salient features. Subsequently, we construct an adjacency matrix, which is constrained by the spatial properties of the brain, by linearly combining the distance matrix and a similarity matrix. The quality of spectral clustering is further optimized through multiple cluster evaluation coefficient. The results from SC-DW revealed four uniform and contiguous subregions within the bilateral DLPFC. Notably, we observe a substantial positive correlation between the functional characteristics of the third and fourth subregions in the left DLPFC with clinical manifestations. These findings underscore the unique insights offered by our proposed methodology in the realms of brain subregion delineation and therapeutic targeting.

Comprehensive exploration of multi-modal and multi-branch imaging markers for autism diagnosis and interpretation: insights from an advanced deep learning model.

Autism spectrum disorder is a complex neurodevelopmental condition with diverse genetic and brain involvement. Despite magnetic resonance imaging advances, autism spectrum disorder diagnosis and understanding its neurogenetic factors remain challenging. We propose a dual-branch graph neural network that effectively extracts and fuses features from bimodalities, achieving 73.9% diagnostic accuracy. To explain the mechanism distinguishing autism spectrum disorder from healthy controls, we establish a perturbation model for brain imaging markers and perform a neuro-transcriptomic joint analysis using partial least squares regression and enrichment to identify potential genetic biomarkers. The perturbation model identifies brain imaging markers related to structural magnetic resonance imaging in the frontal, temporal, parietal, and occipital lobes, while functional magnetic resonance imaging markers primarily reside in the frontal, temporal, occipital lobes, and cerebellum. The neuro-transcriptomic joint analysis highlights genes associated with biological processes, such as "presynapse," "behavior," and "modulation of chemical synaptic transmission" in autism spectrum disorder's brain development. Different magnetic resonance imaging modalities offer complementary information for autism spectrum disorder diagnosis. Our dual-branch graph neural network achieves high accuracy and identifies abnormal brain regions and the neuro-transcriptomic analysis uncovers important genetic biomarkers. Overall, our study presents an effective approach for assisting in autism spectrum disorder diagnosis and identifying genetic biomarkers, showing potential for enhancing the diagnosis and treatment of this condition.

Cortical thickness reductions associate with brain network architecture in major depressive disorder.

BACKGROUND: Cortical thickness reductions in major depressive disorder are distributed across multiple regions. Research has indicated that cortical atrophy is influenced by connectome architecture on a range of neurological and psychiatric diseases. However, whether connectome architecture contributes to changes in cortical thickness in the same manner as it does in depression is unclear. This study aims to explain the distribution of cortical thickness reductions across the cortex in depression by brain connectome architecture. METHODS: Here, we calculated a differential map of cortical thickness between 110 depression patients and 88 age-, gender-, and education level-matched healthy controls by using T1-weighted images and a structural network reconstructed through the diffusion tensor imaging of control group. We then used a neighborhood deformation model to explore how cortical thickness change in an area is influenced by areas structurally connected to it. RESULTS: We found that cortical thickness in the frontoparietal and default networks decreased in depression, regional cortical thickness changes were related to reductions in their neighbors and were mainly limited by the frontoparietal and default networks, and the epicenter was in the prefrontal lobe. CONCLUSION: Current findings suggest that connectome architecture contributes to the irregular topographic distribution of cortical thickness reductions in depression and cortical atrophy is restricted by and dependent on structural foundation.

Effects of rTMS Intervention on Functional Neuroimaging Activities in Adolescents with Major Depressive Disorder Measured Using Resting-State fMRI.

Repetitive transcranial magnetic stimulation (rTMS) to the left dorsolateral prefrontal cortex (L-DLPFC) is commonly used for the clinical treatment of major depressive disorder (MDD). The neuroimaging biomarkers and mechanisms of rTMS are still not completely understood. This study aimed to explore the functional neuroimaging changes induced by rTMS in adolescents with MDD. A total of ten sessions of rTMS were administrated to the L-DLPFC in thirteen adolescents with MDD once a day for two weeks. All of them were scanned using resting-state functional magnetic resonance imaging at baseline and after rTMS treatment. The regional homogeneity (ReHo), amplitude of low-frequency fluctuation (ALFF), and the subgenual anterior cingulate cortex (sgACC)-based functional connectivity (FC) were computed as neuroimaging indicators. The correlation between changes in the sgACC-based FC and the improvement in depressive symptoms was also analyzed. After rTMS treatment, ReHo and ALFF were significantly increased in the L-DLPFC, the left medial prefrontal cortex, bilateral medial orbital frontal cortex, and the left ACC. ReHo and ALFF decreased mainly in the left middle occipital gyrus, the right middle cingulate cortex (MCC), bilateral calcarine, the left cuneus, and the left superior occipital gyrus. Furthermore, the FCs between the left sgACC and the L-DLPFC, the right IFGoper, the left MCC, the left precuneus, bilateral post-central gyrus, the left supplementary motor area, and the left superior marginal gyrus were enhanced after rTMS treatment. Moreover, the changes in the left sgACC-left MCC FC were associated with an improvement in depressive symptoms in early improvers. This study showed that rTMS treatment in adolescents with MDD causes changes in brain activities and sgACC-based FC, which may provide basic neural biomarkers for rTMS clinical trials.

Widely targeted metabolomics and HPLC analysis elaborated the quality formation of Yunnan pickled tea during the whole process at an industrial scale.

Yunnan pickled tea is produced from fresh tea-leaves through fixation, rolling, anaerobic fermentation and sun-drying. In this study, widely targeted metabolomics using UHPLC-QQQ-MS/MS and HPLC analysis were carried out to elaborate its quality formation during the whole process. Results confirmed the contribution of preliminary treatments and anaerobic fermentation to the quality formation. A total of 568 differential metabolites (VIP > 1.0, P < 0.05, FC > 1.50 or < 0.67) were screened through OPLS-DA. (-)-Epigallocatechin and (-)-epicatechin significantly (P < 0.05) increased from the hydrolyzation of ester catechins, such as (-)-epigallocatechin gallate and (-)-epicatechin gallate in anaerobic fermentation. Additionally, the anaerobic fermentation promoted vast accumulations of seven essential amino acids, four phenolic acids, three flavones and flavone glycosides, pelargonidin and pelargonidin glycosides, flavonoids and flavonoid glycosides (i.e. kaempferol, quercetin, taxifolin, apigenin, myricetin, luteolin and their glycosides) through relevant N-methylation, O-methylation, hydrolyzation, glycosylation and oxidation.

Functional connectivity alterations in reward-related circuits associated with non-suicidal self-injury behaviors in drug-naïve adolescents with depression.

Non-suicidal self-injury (NSSI) behaviors are a major public health concern among adolescents with depression. Such behaviors may be associated with the reward system. However, the underlying mechanism in patients with depression and NSSI still remains unclear. A total of 56 drug-naïve adolescents with depression, including 23 patients with NSSI (the NSSI group) and 33 patients without NSSI (the nNSSI group), and 25 healthy controls (HCs) were recruited in this study. Seed-based functional connectivity (FC) was used to explore the NSSI-related FC alterations in the reward circuit. Correlation analysis was conducted between the altered FCs and clinical data. Compared with the nNSSI group, the NSSI group showed greater FC between left nucleus accumbens (NAcc) and right lingual gyrus and between right putamen accumbens and right angular gyrus (ANG). The NSSI group also had declined FC between right NAcc and left inferior cerebellum, between left cingulate gyrus (CG) and right ANG, between left CG and left middle temporal gyrus (MTG), and between right CG and bilateral MTG (voxel-wise p < 0.01, cluster-wise p < 0.05, Gaussian random field correction). The FC between right NAcc and left inferior cerebellum was found positively correlated with the score of addictive features of NSSI (r = 0.427, p = 0.042). Our findings indicated that the regions in the reward circuit with NSSI-related FC alterations included bilateral NAcc, right putamen and bilateral CG, which may provide new evidence on the neural mechanisms of NSSI behaviors in adolescents with depression.

Disrupted functional networks within white-matter served as neural features in adolescent patients with conduct disorder.

BACKGROUND: Conduct disorder (CD) has been conceptualized as a psychiatric disorder associated with white-matter (WM) structural abnormalities. Although diffusion tensor imaging could identify WM structural architecture changes, it cannot characterize functional connectivity (FC) within WM. Few studies have focused on disentangling the WM dysfunctions in CD patients by using functional magnetic resonance imaging (fMRI). METHODS: The resting-state fMRI data were first obtained from both adolescent CD and typically developing (TD) controls. A voxel-based clustering analysis was utilized to identify the large-scale WM FC networks. Then, we examined the disrupted WM network features in CD, and further investigated whether these features could predict the impulsive symptoms in CD using support vector regression prediction model. RESULTS: We identified 11 WM functional networks. Compared with TDs, CD patients showed increased FCs between occipital network (ON) and superior temporal network (STN), between orbitofrontal network (OFN) and corona radiate network (CRN), as well as between deep network and CRN. Further, the disrupted FCs between ON and STN and between OFN and CRN were significantly negatively associated with non-planning impulsivity scores in CD. Moreover, the disrupted WM networks could be served as features to predict the motor impulsivity scores in CD. CONCLUSIONS: Our results provided further support on the existence of WM functional networks and could extended our knowledge about the WM functional abnormalities related with emotional and perception processing in CD patients from the view of WM dysfunction.

Depression mediated the relationships between precentral-subcortical causal links and motor recovery in spinal cord injury patients.

Depression after brain damage may impede the motivation and consequently influence the motor recovery after spinal cord injury (SCI); however, the neural mechanism underlying the psychological effects remains unclear. This study aimed to examine the casual connectivity changes of the emotion-motivation-motor circuit and the potential mediating effects of depression on motor recovery after SCI. Using the resting-state functional magnetic resonance imaging data of 35 SCI patients (24 good recoverers, GR and 11 poor recoverers, PR) and 32 healthy controls (HC), the results from the conditional Granger causality (GC) analysis demonstrated that the GR group exhibited sparser emotion-motivation-motor GC network compared with the HC and PR groups, though the in-/out-degrees of the emotion subnetwork and the motor subnetwork were relatively balanced in the HC and GR group. The PR group showed significantly inhibitory causal links from amygdala to supplementary motor area and from precentral gyrus to nucleus accumbens compared with GR group. Further mediation analysis revealed the indirect effect of the 2 causal connections on motor function recovery via depression severity. Our findings provide further evidence of abnormal causal connectivity in emotion-motivation-motor circuit in SCI patients and highlight the importance of emotion intervention for motor function recovery after SCI.

Effect of intermittent theta burst stimulation on suicidal ideation and depressive symptoms in adolescent depression with suicide attempt: A randomized sham-controlled study.

BACKGROUND: Suicidal ideation is a serious symptom of major depressive disorder (MDD). Intermittent theta burst stimulation (iTBS) is a safe, effective brain stimulation treatment for alleviating suicidal ideation in adults with MDD. This study aimed to examine the clinical efficacy of iTBS on reducing suicidal ideation in adolescent MDD with suicide attempt. METHODS: In a randomized, sham-controlled protocol, a total of 10 sessions of iTBS was administrated to the left dorsolateral prefrontal cortex (DLPFC) in patients once a day for two weeks. The suicidal ideation and depressive symptoms were assessed using Beck Scale for Suicide Ideation-Chinese Version (BSI-CV), Hamilton Rating Scale for Depression (HAMD-24), and Self-rating Depression Scale (SDS) at baseline and after 10 treatment sessions. RESULTS: Forty-five patients were randomized assigned to either active iTBS (n = 23) or sham group (n = 22). The suicidal ideation and depressive symptoms of the active iTBS group were significantly ameliorated over 2 weeks of treatment. Further, higher baseline SDS, HAMD-24 and BSI-CV scores in the active iTBS group were associated with greater reductions. LIMITATIONS: A larger sample size and double-blinded clinical trial should be conducted to verify the reliability and reproducibility. CONCLUSIONS: The current study suggested that daily iTBS of the left DLPFC for 2 weeks could effectively and safely alleviate suicidal ideation and mitigate depression in adolescent MDD, especially for individuals with relatively more severe symptoms. Although caution is warranted, the findings could provide further evidence for the effectiveness and safety of iTBS in clinical practice.

Shared and distinct patterns of dynamic functional connectivity variability of thalamo-cortical circuit in bipolar depression and major depressive disorder.

Evidence has indicated abnormalities of thalamo-cortical functional connectivity (FC) in bipolar disorder during a depressive episode (BDD) and major depressive disorder (MDD). However, the dynamic FC (dFC) within this system is poorly understood. We explored the thalamo-cortical dFC pattern by dividing thalamus into 16 subregions and combining with a sliding-window approach. Correlation analysis was performed between altered dFC variability and clinical data. Classification analysis with a linear support vector machine model was conducted. Compared with healthy controls (HCs), both patients revealed increased dFC variability between thalamus subregions with hippocampus (HIP), angular gyrus and caudate, and only BDD showed increased dFC variability of the thalamus with superior frontal gyrus (SFG), HIP, insula, middle cingulate gyrus, and postcentral gyrus. Compared with MDD and HCs, only BDD exhibited enhanced dFC variability of the thalamus with SFG and superior temporal gyrus. Furthermore, the number of depressive episodes in MDD was significantly positively associated with altered dFC variability. Finally, the disrupted dFC variability could distinguish BDD from MDD with 83.44% classification accuracy. BDD and MDD shared common disrupted dFC variability in the thalamo-limbic and striatal-thalamic circuitries, whereas BDD exhibited more extensive and broader aberrant dFC variability, which may facilitate distinguish between these 2 mood disorders.

Brain functional activity-based classification of autism spectrum disorder using an attention-based graph neural network combined with gene expression.

Autism spectrum disorder (ASD) is a complex brain neurodevelopmental disorder related to brain activity and genetics. Most of the ASD diagnostic models perform feature selection at the group level without considering individualized information. Evidence has shown the unique topology of the individual brain has a fundamental impact on brain diseases. Thus, a data-constructing method fusing individual topological information and a corresponding classification model is crucial in ASD diagnosis and biomarker discovery. In this work, we trained an attention-based graph neural network (GNN) to perform the ASD diagnosis with the fusion of graph data. The results achieved an accuracy of 79.78%. Moreover, we found the model paid high attention to brain regions mainly involved in the social-brain circuit, default-mode network, and sensory perception network. Furthermore, by analyzing the covariation between functional magnetic resonance imaging data and gene expression, current studies detected several ASD-related genes (i.e. MUTYH, AADAT, and MAP2), and further revealed their links to image biomarkers. Our work demonstrated that the ASD diagnostic framework based on graph data and attention-based GNN could be an effective tool for ASD diagnosis. The identified functional features with high attention values may serve as imaging biomarkers for ASD.

An individual-level weighted artificial neural network method to improve the systematic bias in BrainAGE analysis.

BrainAGE is a commonly used machine learning technique to measure the accelerated/delayed development pattern of human brain structure/function with neuropsychiatric disorders. However, recent studies have shown a systematic bias ("regression toward mean" effect) in the BrainAGE method, which indicates that the prediction error is not uniformly distributed across Chronological Ages: for the older individuals, the Brain Ages would be under-estimated but would be over-estimated for the younger individuals. In the present study, we propose an individual-level weighted artificial neural network method and apply it to simulation datasets (containing 5000 simulated subjects) and a real dataset (containing 135 subjects). Results show that compared with traditional machine learning methods, the individual-level weighted strategy can significantly reduce the "regression toward mean" effect, while the prediction performance can achieve the comparable level with traditional machine learning methods. Further analysis indicates that the sigmoid active function for artificial neural network shows better performance than the relu active function. The present study provides a novel strategy to reduce the "regression toward mean" effect of BrainAGE analysis, which is helpful to improve accuracy in exploring the atypical brain structure/function development pattern of neuropsychiatric disorders.

Different functional connectivity optimal frequency in autism compared with healthy controls and the relationship with social communication deficits: Evidence from gene expression and behavior symptom analyses.

Studies have reported that different brain regions/connections possess distinct frequency properties, which are related to brain function. Previous studies have proposed altered brain activity frequency and frequency-specific functional connectivity (FC) patterns in autism spectrum disorder (ASD), implying the varied dominant frequency of FC in ASD. However, the difference of the dominant frequency of FC between ASD and healthy controls (HCs) remains unclear. In the present study, the dominant frequency of FC was measured by FC optimal frequency, which was defined as the intermediate of the frequency bin at which the FC strength could reach the maximum. A multivariate pattern analysis was conducted to determine whether the FC optimal frequency in ASD differs from that in HCs. Partial least squares regression (PLSR) and enrichment analyses were conducted to determine the relationship between the FC optimal frequency difference of ASD/HCs and cortical gene expression. PLSR analyses were also performed to explore the relationship between FC optimal frequency and the clinical symptoms of ASD. Results showed a significant difference of FC optimal frequency between ASD and HCs. Some genes whose cortical expression patterns are related to the FC optimal frequency difference of ASD/HCs were enriched for social communication problems. Meanwhile, the FC optimal frequency in ASD was significantly related to social communication symptoms. These results may help us understand the neuro-mechanism of the social communication deficits in ASD.

Insular-associated causal network of structural covariance evaluating progressive gray matter changes in major depressive disorder.

BACKGROUND: Morphometric studies demonstrated wide-ranging distribution of brain structural abnormalities in major depressive disorder (MDD). OBJECTIVE: This study explored the progressive gray matter volume (GMV) changes pattern of structural network in 108 MDD patients throughout the illness duration by using voxel-based morphometric analysis. METHODS: The causal structural covariance network method was applied to map the causal effects of GMV alterations between the original source of structural changes and other brain regions as the illness duration prolonged in MDD. This was carried out by utilizing the Granger causality analysis to T1-weighted data ranked based on the disease progression information. RESULTS: With greater illness duration, the GMV reduction was originated from the right insula and progressed to the frontal lobe, and then expanded to the occipital lobe, temporal lobe, dorsal striatum (putamen and caudate) and the cerebellum. Importantly, results revealed that the right insula was the prominent node projecting positive causal influences (i.e., GMV decrease) to frontal lobe, temporal lobe, postcentral gyrus, putamen, and precuneus. While opposite causal effects were detected from the right insula to the angular, parahippocampus, supramarginal gyrus and cerebellum. CONCLUSIONS: This work may provide further information and vital evidence showing that MDD is associated with progressive brain structural alterations.

Classification of major depressive disorder using an attention-guided unified deep convolutional neural network and individual structural covariance network.

Major depressive disorder (MDD) is the second leading cause of disability worldwide. Currently, the structural magnetic resonance imaging-based MDD diagnosis models mainly utilize local grayscale information or morphological characteristics in a single site with small samples. Emerging evidence has demonstrated that different brain structures in different circuits have distinct developmental timing, but mature coordinately within the same functional circuit. Thus, establishing an attention-guided unified classification framework with deep learning and individual structural covariance networks in a large multisite dataset could facilitate developing an accurate diagnosis strategy. Our results showed that attention-guided classification could improve the classification accuracy from primary 75.1% to ultimate 76.54%. Furthermore, the discriminative features of regional covariance connectivities and local structural characteristics were found to be mainly located in prefrontal cortex, insula, superior temporal cortex, and cingulate cortex, which have been widely reported to be closely associated with depression. Our study demonstrated that our attention-guided unified deep learning framework may be an effective tool for MDD diagnosis. The identified covariance connectivities and structural features may serve as biomarkers for MDD.

Multidisciplinary collaboration in diagnosis and treatment of neuroleptic malignant syndrome complicated with pneumonia: report of three cases.

BACKGROUND: Neuroleptic malignant syndrome (NMS) is a relatively rare and a potentially fatal syndrome. It is a serious complication associated with antipsychotic therapy. NMS is easily prone to pneumonia, rhabdomyolysis and other problems. However, the clinical features of NMS complicated with pneumonia remains largely unclear. CASE PRESENTATION: Here, we described three female adult patients of NMS complicated with pneumonia in our own hospital. The symptoms of the patients were controlled with antipsychotic drugs at admission. Symptoms such as high fever, high muscle tone, difficulty in eating, phlegm in the throat, anhelation, rhabdomyolysis and autonomic nervous dysfunction occurred 2 days after the treatment, which mainly concentrated within 1 week. In addition, they are all healed. CONCLUSIONS: NMS is a rare and serious complication in psychiatric department, which is easy to be complicated with pneumonia and respiratory failure. Timely identification and early intervention could help achieve a good prognosis.

Disorder-specific impaired neurocognitive function in major depression and generalized anxiety disorder.

BACKGROUND: Generalized anxiety disorder (GAD) and major depressive disorder (MDD) are both highly prevalent and comorbid psychiatric disorders. Neurocognitive dysfunction has been commonly found in MDD, but the findings in GAD are inconsistent. Few studies have directly compared cognitive performance between GAD and MDD. Therefore, the present study aimed to reveal the similar and distinct cognitive impairments between both disorders. METHODS: Three non-overlapping and non-comorbid groups were enrolled in the current study including patients with GAD (n = 37), MDD (n = 107) and healthy controls (n = 74). Levels of anxiety and depression were assessed using the Hamilton Anxiety Rating Scale (HAMA) and the Hamilton Depression Rating Scale (HAMD) respectively. The Cambridge Neuropsychological Test Automated Battery (CANTAB) was used to compare the cognitive performance, including sustained attention, visual memory, executive functions and learning. RESULTS: Both MDD and GAD groups demonstrated common significant deficits in sustained attention, visual memory, working memory and learning when compared to healthy controls. Despite the similarities, the MDD group had significantly greater impairment in learning, particularly generalization, while the GAD group demonstrated more pronounced deficits in visual memory. LIMITATIONS: Patients involved were medicated and the sample size for GAD was relatively small. CONCLUSIONS: The significant differences in visual memory and learning between MDD and GAD groups might be indicators to distinguishing both disorders. These results confirm that cognitive function is of great importance as a future target for treatment in order to improve wellbeing, quality of life and functionality in both GAD and MDD.