Abnormal resting-state effective connectivity in large-scale networks among obsessive-compulsive disorder.

BACKGROUND: Obsessive-compulsive disorder (OCD) is a chronic mental illness characterized by abnormal functional connectivity among distributed brain regions. Previous studies have primarily focused on undirected functional connectivity and rarely reported from network perspective. METHODS: To better understand between or within-network connectivities of OCD, effective connectivity (EC) of a large-scale network is assessed by spectral dynamic causal modeling with eight key regions of interests from default mode (DMN), salience (SN), frontoparietal (FPN) and cerebellum networks, based on large sample size including 100 OCD patients and 120 healthy controls (HCs). Parametric empirical Bayes (PEB) framework was used to identify the difference between the two groups. We further analyzed the relationship between connections and Yale-Brown Obsessive Compulsive Scale (Y-BOCS). RESULTS: OCD and HCs shared some similarities of inter- and intra-network patterns in the resting state. Relative to HCs, patients showed increased ECs from left anterior insula (LAI) to medial prefrontal cortex, right anterior insula (RAI) to left dorsolateral prefrontal cortex (L-DLPFC), right dorsolateral prefrontal cortex (R-DLPFC) to cerebellum anterior lobe (CA), CA to posterior cingulate cortex (PCC) and to anterior cingulate cortex (ACC). Moreover, weaker from LAI to L-DLPFC, RAI to ACC, and the self-connection of R-DLPFC. Connections from ACC to CA and from L-DLPFC to PCC were positively correlated with compulsion and obsession scores (r = 0.209, p = 0.037; r = 0.199, p = 0.047, uncorrected). CONCLUSIONS: Our study revealed dysregulation among DMN, SN, FPN, and cerebellum in OCD, emphasizing the role of these four networks in achieving top-down control for goal-directed behavior. There existed a top-down disruption among these networks, constituting the pathophysiological and clinical basis.

Gray matter atrophy is constrained by normal structural brain network architecture in depression.

BACKGROUND: There is growing evidence that gray matter atrophy is constrained by normal brain network (or connectome) architecture in neuropsychiatric disorders. However, whether this finding holds true in individuals with depression remains unknown. In this study, we aimed to investigate the association between gray matter atrophy and normal connectome architecture at individual level in depression. METHODS: In this study, 297 patients with depression and 256 healthy controls (HCs) from two independent Chinese dataset were included: a discovery dataset (105 never-treated first-episode patients and matched 130 HCs) and a replication dataset (106 patients and matched 126 HCs). For each patient, individualized regional atrophy was assessed using normative model and brain regions whose structural connectome profiles in HCs most resembled the atrophy patterns were identified as putative epicenters using a backfoward stepwise regression analysis. RESULTS: In general, the structural connectome architecture of the identified disease epicenters significantly explained 44% (±16%) variance of gray matter atrophy. While patients with depression demonstrated tremendous interindividual variations in the number and distribution of disease epicenters, several disease epicenters with higher participation coefficient than randomly selected regions, including the hippocampus, thalamus, and medial frontal gyrus were significantly shared by depression. Other brain regions with strong structural connections to the disease epicenters exhibited greater vulnerability. In addition, the association between connectome and gray matter atrophy uncovered two distinct subgroups with different ages of onset. CONCLUSIONS: These results suggest that gray matter atrophy is constrained by structural brain connectome and elucidate the possible pathological progression in depression.

Resolving heterogeneity in depression using individualized structural covariance network analysis.

BACKGROUND: Elucidating individual aberrance is a critical first step toward precision medicine for heterogeneous disorders such as depression. The neuropathology of depression is related to abnormal inter-regional structural covariance indicating a brain maturational disruption. However, most studies focus on group-level structural covariance aberrance and ignore the interindividual heterogeneity. For that reason, we aimed to identify individualized structural covariance aberrance with the help of individualized differential structural covariance network (IDSCN) analysis. METHODS: T1-weighted anatomical images of 195 first-episode untreated patients with depression and matched healthy controls (n = 78) were acquired. We obtained IDSCN for each patient and identified subtypes of depression based on shared differential edges. RESULTS: As a result, patients with depression demonstrated tremendous heterogeneity in the distribution of differential structural covariance edges. Despite this heterogeneity, altered edges within subcortical-cerebellum network were often shared by most of the patients. Two robust neuroanatomical subtypes were identified. Specifically, patients in subtype 1 often shared decreased motor network-related edges. Patients in subtype 2 often shared decreased subcortical-cerebellum network-related edges. Functional annotation further revealed that differential edges in subtype 2 were mainly implicated in reward/motivation-related functional terms. CONCLUSIONS: In conclusion, we investigated individualized differential structural covariance and identified that decreased edges within subcortical-cerebellum network are often shared by patients with depression. The identified two subtypes provide new insights into taxonomy and facilitate potential clues to precision diagnosis and treatment of depression.

Progressive brain structural abnormality in depression assessed with MR imaging by using causal network analysis.

BACKGROUND: As a neuroprogressive illness, depression is accompanied by brain structural abnormality that extends to many brain regions. However, the progressive structural alteration pattern remains unknown. METHODS: To elaborate the progressive structural alteration of depression according to illness duration, we recruited 195 never-treated first-episode patients with depression and 130 healthy controls (HCs) undergoing T1-weighted MRI scans. Voxel-based morphometry method was adopted to measure gray matter volume (GMV) for each participant. Patients were first divided into three stages according to the length of illness duration, then we explored stage-specific GMV alterations and the causal effect relationship between them using causal structural covariance network (CaSCN) analysis. RESULTS: Overall, patients with depression presented stage-specific GMV alterations compared with HCs. Regions including the hippocampus, the thalamus and the ventral medial prefrontal cortex (vmPFC) presented GMV alteration at onset of illness. Then as the illness advanced, others regions began to present GMV alterations. These results suggested that GMV alteration originated from the hippocampus, the thalamus and vmPFC then expanded to other brain regions. The results of CaSCN analysis revealed that the hippocampus and the vmPFC corporately exerted causal effect on regions such as nucleus accumbens, the precuneus and the cerebellum. In addition, GMV alteration in the hippocampus was also potentially causally related to that in the dorsolateral frontal gyrus. CONCLUSIONS: Consistent with the neuroprogressive hypothesis, our results reveal progressive morphological alteration originating from the vmPFC and the hippocampus and further elucidate possible details about disease progression of depression.

Identification of shared and distinct patterns of brain network abnormality across mental disorders through individualized structural covariance network analysis.

BACKGROUND: Mental disorders, including depression, obsessive compulsive disorder (OCD), and schizophrenia, share a common neuropathy of disturbed large-scale coordinated brain maturation. However, high-interindividual heterogeneity hinders the identification of shared and distinct patterns of brain network abnormalities across mental disorders. This study aimed to identify shared and distinct patterns of altered structural covariance across mental disorders. METHODS: Subject-level structural covariance aberrance in patients with mental disorders was investigated using individualized differential structural covariance network. This method inferred structural covariance aberrance at the individual level by measuring the degree of structural covariance in patients deviating from matched healthy controls (HCs). T1-weighted anatomical images of 513 participants (105, 98, 190 participants with depression, OCD and schizophrenia, respectively, and 130 age- and sex-matched HCs) were acquired and analyzed. RESULTS: Patients with mental disorders exhibited notable heterogeneity in terms of altered edges, which were otherwise obscured by group-level analysis. The three disorders shared high difference variability in edges attached to the frontal network and the subcortical-cerebellum network, and they also exhibited disease-specific variability distributions. Despite notable variability, patients with the same disorder shared disease-specific groups of altered edges. Specifically, depression was characterized by altered edges attached to the subcortical-cerebellum network; OCD, by altered edges linking the subcortical-cerebellum and motor networks; and schizophrenia, by altered edges related to the frontal network. CONCLUSIONS: These results have potential implications for understanding heterogeneity and facilitating personalized diagnosis and interventions for mental disorders.

Abnormal voxel-wise whole-brain functional connectivity in first-episode, drug-naïve adolescents with major depression disorder.

Major depression disorder (MDD) is one of the most common psychiatric disorders. Previous studies have demonstrated structural and functional abnormalities in adult depression. However, the neurobiology of adolescent depression has not been fully understood. The aim of this study was to investigate the intrinsic dysconnectivity pattern of voxel-level whole-brain functional networks in first-episode, drug-naïve adolescents with MDD. Resting-state functional magnetic resonance imaging data were acquired from 66 depressed adolescents and 47 matched healthy controls. Voxel-wise degree centrality (DC) analysis was performed to identify voxels that showed altered whole-brain functional connectivity (FC) with other voxels. We further conducted seed-based FC analysis to investigate in more detail the connectivity patterns of the identified DC changes. The relationship between altered DC and clinical variables in depressed adolescents was also analyzed. Compared with controls, depressed adolescents showed lower DC in the bilateral hippocampus, left superior temporal gyrus and right insula. Seed-based analysis revealed that depressed adolescents, relative to controls, showed hypoconnectivity between the hippocampus to the medial prefrontal regions and right precuneus. Furthermore, the DC values in the bilateral hippocampus were correlated with the Hamilton Depression Rating Scale score and duration of disease (all P < 0.05, false discovery rate corrected). Our study indicates abnormal intrinsic dysconnectivity patterns of whole-brain functional networks in drug-naïve, first-episode adolescents with MDD, and abnormal DC in the hippocampus may affect the association of prefrontal-hippocampus circuit. These findings may provide new insights into the pathophysiology of adolescent-onset MDD.

Abnormal dynamic functional connectivity in first-episode, drug-naïve adolescents with major depressive disorder.

Previous neuroimaging studies have identified disrupted large-scale functional brain networks in major depressive disorder (MDD); however, most of them focused on adult patients and were based on static functional connectivity (FC). Thus, we aimed to investigate the patterns of change in dynamic FC in depressed adolescents. Resting-state functional magnetic resonance imaging data were acquired from 60 first-episode, drug-naïve adolescents with MDD and 60 matched healthy controls (HCs). Then, the dynamic FC properties were analyzed using a sliding windows approach, k-means clustering, and graph theory methods. The intrinsic brain FC were clustered into two configuration states-a more frequent and relatively sparsely connected State 1 and a less frequent and more strongly interconnected State 2. Compared with HCs, depressed adolescents had higher reoccurrence fraction and dwell time in State 1, and lower reoccurrence fraction and dwell time in State 2, and higher total number of transitions between the two states. Depressed adolescents showed decreased FC within the default mode network (DMN) and between the DMN and other networks in State 1. Additionally, the MDD group showed higher variances in the global and local efficiency. Furthermore, the duration of illness was positively correlated with the number of state transitions, and the 17-item Hamilton Depression Rating Scale score was positively correlated with the mean dwell time in State 1. This study demonstrated abnormal dynamic FC in depressed adolescents, which provided new insights into the pathophysiological mechanisms of adolescent-onset depression.

The stage-specifically accelerated brain aging in never-treated first-episode patients with depression.

Depression associated with structural brain abnormalities is hypothesized to be related with accelerated brain aging. However, there is far from a unified conclusion because of clinical variations such as medication status, cumulative illness burden. To explore whether brain age is accelerated in never-treated first-episode patients with depression and its association with clinical characteristics, we constructed a prediction model where gray matter volumes measured by voxel-based morphometry derived from T1-weighted MRI scans were treated as features. The prediction model was first validated using healthy controls (HCs) in two Chinese Han datasets (Dataset 1, N = 130 for HCs and N = 195 for patients with depression; Dataset 2, N = 270 for HCs) separately or jointly, then the trained prediction model using HCs (N = 400) was applied to never-treated first-episode patients with depression (N = 195). The brain-predicted age difference (brain-PAD) scores defined as the difference between predicted brain age and chronological age, were calculated for all participants and compared between patients with age-, gender-, educational level-matched HCs in Dataset 1. Overall, patients presented higher brain-PAD scores suggesting patients with depression having an "older" brain than expected. More specially, this difference occurred at illness onset (illness duration <3 months) and following 2 years then disappeared as the illness further advanced (>2 years) in patients. This phenomenon was verified by another data-driven method and significant correlation between brain-PAD scores and illness duration in patients. Our results reveal that accelerated brain aging occurs at illness onset and suggest it is a stage-dependent phenomenon in depression.

More than just statics: Temporal dynamic changes of intrinsic brain activity in cigarette smoking.

Smoking is companied with altered intrinsic activity of the brain measured by amplitude of low-frequency fluctuation. Evidence has revealed that human brain activity is a highly dynamic and rapidly changing system. How exactly cigarette smoking affect temporal dynamic intrinsic brain activity is not fully understood nor is it clear how smoking severity influences spontaneous brain activity. Dynamic amplitude of low-frequency fluctuation (dALFF) was used to examine the dynamic temporal variability in 93 participants (63 smokers, 30 nonsmokers). We further divided smokers into light and heavy smokers. The temporal variability in intrinsic brain activity among these groups was compared. Correlation analyses were performed between dALFF in areas showing group differences and smoking behaviour (e.g., the Fagerström Test for Nicotine Dependence [FTND] scores and pack-years). Smokers showed significantly increased dALFF in the left inferior/middle frontal gyrus, right orbitofrontal gyrus, right insula, left superior/medial frontal gyrus and right middle frontal gyrus than nonsmokers. Light smokers showed increased dALFF variability in the left prefrontal cortex. Heavy smokers showed increased dynamics in specific brain regions, including the right postcentral gyrus, right insula and left precentral gyrus. Furthermore, the temporal variability in dALFF in the left superior/medial frontal gyrus, left superior/middle frontal gyrus, right middle frontal gyrus and right insula was positively correlated with pack-years or FTND. Combined, these results suggest that smokers increase stable and persistent spontaneous brain activity in prefrontal cortex, involved impaired gold-directed action and value-based decision-making. In addition, individuals with heavier smoking severity show increased perturbance on spontaneous brain activity of perception and sensorimotor, related to increased reliance.

Serum uric acid and incident atrial fibrillation: A systematic review and dose-response meta-analysis.

The exposure-effect association between serum uric acid and atrial fibrillationis not well known. We conduct a meta-analysis to quantitatively examine the exposure-effect relationship between serum uric acid and atrial fibrillation. Prospective studies (including cohort or nested case-control) that reported the serum uric acid and atrial fibrillation were identified through electronic searches using EMBASE, PubMed, and the Cochrane Library database. The exposure-effect analysis was performed using a stage robust error meta-regression. Eleven studies were included, with a total of 6831 cases of atrial fibrillation among 527 908 individuals. Both the highest (risk ratio (RR), 1.9; 95% confidence interval (CI), 1.64-2.23; I2  = 0%) and intermediate (RR, 1.36; 95% CI, 1.16-1.59; I2  = 36%) level of serum uric acid were associated with increased risks of atrial fibrillation compared to the patients with the lowest level of serum uric acid. In the exposure-effect analysis, for each 1 mg/dL increase in serum uric acid level, the incidence of atrial fibrillation increased by 21% (RR, 1.21; 95% CI, 1.12-1.32; I2  = 78%). Furthermore, a significant positive linear relationship between serum uric acid and the risk of atrial fibrillation, Pnonlinearity  = 0.47 was found. The exposure-effect analysis demonstrated that serum uric acid over 5.0 mg/dL significantly increased the risk of atrial fibrillation. There was a positive linear association between serum uric acid and risk of atrial fibrillation, both in subjects with noruricaemia and hyperuricaemia. More studies are needed to explore the impact of serum uric acid reduction on the incidence of atrial fibrillation.

A WD-repeat protein stabilizes ORC binding to chromatin.

Origin recognition complex (ORC) plays critical roles in the initiation of DNA replication and cell-cycle progression. In metazoans, ORC associates with origin DNA during G1 and with heterochromatin in postreplicated cells. However, what regulates the binding of ORC to chromatin is not understood. We have identified a highly conserved, leucine-rich repeats and WD40 repeat domain-containing protein 1 (LRWD1) or ORC-associated (ORCA) in human cells that interacts with ORC and modulates chromatin association of ORC. ORCA colocalizes with ORC and shows similar cell-cycle dynamics. We demonstrate that ORCA efficiently recruits ORC to chromatin. Depletion of ORCA in human primary cells and embryonic stem cells results in loss of ORC association to chromatin, concomitant reduction of MCM binding, and a subsequent accumulation in G1 phase. Our results suggest ORCA-mediated association of ORC to chromatin is critical to initiate preRC assembly in G1 and chromatin organization in post-G1 cells.

Identifying two distinct neuroanatomical subtypes of first-episode depression using heterogeneity through discriminative analysis.

BACKGROUND: Neurobiological heterogeneity in depression remains largely unknown, leading to inconsistent neuroimaging findings. METHODS: Here, we adopted a novel proposed machine learning method ground on gray matter volumes (GMVs) to investigate neuroanatomical subtypes of first-episode treatment-naïve depression. GMVs were obtained from high-resolution T1-weighted images of 195 patients with first-episode, treatment-naïve depression and 78 matched healthy controls (HCs). Then we explored distinct subtypes of depression by employing heterogeneity through discriminative analysis (HYDRA) with regional GMVs as features. RESULTS: Two prominently divergent subtypes of first-episode depression were identified, exhibiting opposite structural alterations compared with HCs but no different demographic features. Subtype 1 presented widespread increased GMVs mainly located in frontal, parietal, temporal cortex and partially located in limbic system. Subtype 2 presented widespread decreased GMVs mainly located in thalamus, cerebellum, limbic system and partially located in frontal, parietal, temporal cortex. Subtype 2 had smaller TIV and longer illness duration than Subtype 1. And TIV in Subtype 1 was positively correlated with age of onset while not in Subtype 2, probably implying the different potential neuropathological mechanisms. LIMITATIONS: Despite results obtained in this study were validated by employing another brain atlas, the conclusions were acquired from a single dataset. CONCLUSIONS: This study revealed two distinguishing neuroanatomical subtypes of first-episode depression, which provides new insights into underlying biological mechanisms of the heterogeneity in depression and might be helpful for accurate clinical diagnosis and future treatment.

Decreased intrinsic neural timescale in treatment-naïve adolescent depression.

BACKGROUND: Major depressive disorder (MDD) is mainly characterized by its core dysfunction in higher-order brain cortices involved in emotional and cognitive processes, whose neurobiological basis remains unclear. In this study, we applied a relatively new developed resting-state functional magnetic resonance imaging (rs-fMRI) method of intrinsic neural timescale (INT), which reflects how long neural information is stored in a local brain area and reflects an ability of information integration, to investigate the local intrinsic neural dynamics using univariate and multivariate analyses in adolescent depression. METHOD: Based on the rs-fMRI data of sixty-six treatment-naïve adolescents with MDD and fifty-two well-matched healthy controls (HCs), we calculated an INT by assessing the magnitude of autocorrelation of the resting-state brain activity, and then compared the difference of INT between the two groups. Correlation between abnormal INT and clinical features was performed. We also utilized multivariate pattern analysis to determine whether INT could differentiate MDD patients from HCs at the individual level. RESULT: Compared with HCs, patients with MDD showed shorter INT widely distributed in cortical and partial subcortical regions. Interestingly, the decreased INT in the left hippocampus was related to disease severity of MDD. Furthermore, INT can distinguish MDD patients from HCs with the most discriminative regions located in the dorsolateral prefrontal cortex, angular, middle occipital gyrus, and cerebellar posterior lobe. CONCLUSION: Our research aids in advancing understanding the brain abnormalities of treatment-naïve adolescents with MDD from the perspective of the local neural dynamics, highlighting the significant role of INT in understanding neurophysiological mechanisms. This study shows that the altered intrinsic timescales of local neural signals widely distributed in higher-order brain cortices regions may be the neurodynamic basis of cognitive and emotional disturbances in MDD patients, and provides preliminary support for the suggestion that these could be used to aid the identification of MDD patients in clinical practice.

Two distinct neuroanatomical subtypes of migraine without aura revealed by heterogeneity through discriminative analysis.

The neurobiological heterogeneity in migraine is poorly studied, resulting in conflicting neuroimaging findings. This study used a newly proposed method based on gray matter volumes (GMVs) to investigate objective neuroanatomical subtypes of migraine. Structural MRI and clinical measures of 31 migraine patients without aura and 33 matched healthy controls (HCs) were explored. Firstly, we investigated whether migraine patients exhibited higher interindividual variability than HCs in terms of GMVs. Then, heterogeneity through discriminative analysis (HYDRA) was applied to categorize migraine patients into distinct subtypes by regional volumetric measures of GMVs. Voxel-wise volume and clinical characteristics among different subtypes were also explored. Migraine patients without aura exhibited higher interindividual GMVs variability. Two distinct and reproducible neuroanatomical subtypes of migraine were revealed. These two subtypes exhibited opposite neuroanatomical aberrances compared to HCs. Subtype 1 showed widespread decreased GMVs, while Subtype 2 showed increased GMVs in limited regions. The total intracranial volume was significantly positively correlated with cognitive function in Subtype 2. Subtype 1 showed significantly longer illness duration and less cognitive scores compared to Subtype 2. The present study shows that migraine patients without aura have high structural heterogeneity and uncovers two distinct and robust neuroanatomical subtypes, which provide a possible explanation for conflicting neuroimaging findings.

Structural and functional deficits and couplings in severe and moderate OCD.

Changes in gray matter volume and functional connections have been frequently observed in patients with obsessive compulsive disorder. However, different grouping may cause diverse volume alterations and could draw more adverse conclusions about the pathophysiology of obsessive compulsive disorder(OCD). Most of them preferred to divide subjects into patients and healthy controls, rather than a detailed subgroup. Moreover, multimodal neuroimaging studies about structural-functional defects and couplings are rather rare. Our aim was to explore gray matter volume(GMV) and functional networks abnormalities induced by structural deficits based on severity of Yale-Brown Obsessive Compulsive Scale(Y-BOCS) symptom including OCD patients with severe(S-OCD, n = 31) and moderate symptoms(M-OCD, n = 42) and healthy controls (HCs, n = 54); Voxel-based morphometry(VBM) method was used to detect GMV differences among three groups, then used as masks according to one-way analysis of variance(ANOVA) results for the subsequent resting-state functional connectivity(rs-FC) analysis. Besides, correlation and subgroup analysis were performed to detect the potential roles of structural deficits between every two groups. ANOVA analysis showed that both S-OCD and M-OCD had increased volume in anterior cingulate cortex(ACC), left precuneus(L-Pre) and paracentral lobule(PCL), postcentral gyrus, left inferior occipital gyrus(L-IOG) and right superior occipital gyrus(R-SOG) and bilateral cuneus, middle occipital gyrus(MOG), and calcarine. Additionally, increased connections between Pre and angular gyrus(AG) and inferior parietal lobule(IPL) have been found. Moreover, connections between the left cuneus and lingual gyrus, between IOG and left lingual gyrus, fusiform and between L-MOG and cerebellum were also included. Subgroup analysis showed that decreased GMV in left caudate was negatively correlated with compulsion and total score in patients with moderate symptom compared to HCs. Our findings indicated that altered GMV in occipital-related regions, Pre, ACC and PCL and the disrupted FC networks including MOG-cerebellum and Pre-AG and IPL. Moreover, subgroup GMV analysis furtherly revealed negative associations between GMV changes and Y-BOCS symptom, offering preparatory proof for the involvement of structural and functional deficits in cortical-subcortical circuitry. Thus, they could provide insights into the neurobiological basis.

Decreased intrinsic neural timescales in obsessive compulsive disorder and two distinct subtypes revealed by heterogeneity through discriminative analysis.

BACKGROUND: OCD is featured as the destruction of information storage and processing. The cognition of neurobiological and clinical heterogeneity is in suspense and poorly studied. METHODS: Ninety-nine patients and matched HCs(n = 104) were recruited and underwent resting-state functional MRI scans. We applied INT to evaluate altered local neural dynamics representing the ability of information integration. Moreover, considering OCD was a highly heterogeneous disorder, we investigated putative OCD subtypes from INT using a novel semi-supervised machine learning, named HYDRA. RESULTS: Compared with HCs, patients with OCD showed decreased INTs in extensive brain regions, including bilateral cerebellum and precuneus, STG/MTG and PCC, hippocampus in DMN; right IFG/MFG/SFG, SPL and bilateral angular gyrus in CEN and insula, SMA in SN. Moreover, many other regions involved in visual processing also had disrupted dynamics of local neural organization, consisting of bilateral CUN, LING and fusiform gyrus and occipital lobe. HYDRA divided patients into two distinct neuroanatomical subtypes from INT. Subtype 1 showed decreased INTs in distributed networks, while subtype 2 presented increased in several common regions which were also found to be decreased in subtype 1, such as STG, IPL, postcentral gyrus and left insula, supramarginal gyrus. CONCLUSION: This study showed distinct abnormalities from the perspective of dynamics of local neural organization in OCD. Such alteration and dimensional approach may provide a new insight into the prior traditional cognition of this disorder and to some extent do favor of more precise diagnosis and treatment response in the future.

Convergent molecular and structural neuroimaging signatures of first-episode depression.

BACKGROUND: Convergent studies have demonstrated morphological abnormalities in various brain regions in depression patients. However, the molecular underpinnings of the structural impairments remain largely unknown, despite a pressing need for treatment targets and mechanisms. Here, we investigated the gray matter volume (GMV) alteration in patients with depression and its underlying molecular architecture. METHODS: We recruited 195 first-episode, treatment-naïve depression patients and 78 gender-, age-, and education level-matched healthy controls (HCs) who underwent high-resolution T1-weighted magnetic resonance scans. Voxel-based morphometry (VBM) was adopted to calculate the GMV differences between two groups. Then we analyzed the spatial correlation between depression-induced alteration in GMV and density maps of 10 receptors/transporters deriving from prior molecular imaging in healthy people. RESULTS: Compared to HCs, the depression group had significantly increased GMV in the left ventral portions of the ventral medial prefrontal cortex, parahippocampal gyrus, amygdala, the right superior parietal lobule and precuneus while decreased GMV in the bilateral hippocampus extending to the thalamus and cerebellum. The GMV alteration introduced by depression was spatially correlated with serotonin receptors (5-HT1a, 5-HT1b, and 5-HT2a), dopamine receptors (D1 and D2) and GABAergic receptor (GABAa) densities. LIMITATIONS: The conclusions drawn in this study were obtained from a single dataset. CONCLUSIONS: This study reveals abnormal GMV alteration and provides a series of neurotransmitters receptors possibly related to GMV alteration in depression, which facilitates an integrative understanding of the molecular mechanism underlying the structural abnormalities in depression and may provide clues to new treatment strategies.

Alteration of static and dynamic intrinsic brain activity induced by short-term spinal cord stimulation in postherpetic neuralgia patients.

Introduction: Short-term spinal cord stimulation (stSCS) is an effective treatment for postherpetic neuralgia (PHN). However, how exactly stSCS affects time-dynamic intrinsic brain activity in PHN patients is not clear. The purpose of this study was to examine the static and dynamic variability of neural activity in PHN patients after stSCS. Methods: In this study, 10 patients with PHN underwent resting-state functional magnetic resonance imaging (rs-fMRI) at baseline and after SCS. The amplitude of low-frequency fluctuations (ALFF) and dynamic ALFF (dALFF) were used to investigate the static and dynamic variability of neural activity in PHN patients after stSCS. We additionally examined the associations between clinical parameters and functional changes in the brain. Results: There was a significant increase in dALFF in the left precuneus and right superior parietal gyrus, and a decrease in dALFF in the left inferior temporal gyrus, right gyrus rectus, left superior temporal gyrus, right orbitofrontal cortex, and left orbitofrontal cortex. There was significantly increased ALFF in the right inferior temporal gyrus, and decreased ALFF in the right lingual gyrus, left superior parietal gyrus, right superior parietal gyrus, and left precuneus. Furthermore, Pittsburgh sleep quality index scores were positively associated with dALFF changes in the left superior temporal gyrus and left orbitofrontal cortex. Hospital anxiety and depression scale scores and continuous pain scores exhibited significant negative correlation with dALFF changes in the right superior parietal gyrus. Conclusion: This study indicated that stSCS is able to cause dALFF changes in PHN patients, thus stSCS might alter brain functions to relieve pain, sleep, and mood symptoms. The findings provide new insights into the mechanisms of stSCS efficacy in the treatment of patients with PHN.

Altered structural covariance network of nucleus accumbens is modulated by illness duration and severity of symptom in depression.

The differential structural covariance of nucleus accumbens (NAcc), playing a vital role in etiology and treatment, remains unclear in depression. We aimed to investigate whether structural covariance of NAcc was altered and how it was modulated by illness duration and severity of symptom measured with Hamilton Depression scale (HAMD). T1-weighted anatomical images of never-treated first-episode patients with depression (n = 195) and matched healthy controls (HCs, n = 78) were acquired. Gray matter volumes were calculated using voxel-based morphometry analysis for each subject. Then, we explored abnormal structural covariance of NAcc and how the abnormality was modulated by illness duration and severity of symptom. Patients with depression exhibited altered structural covariance of NAcc connected to key brain regions in reward system including the medial orbitofrontal cortex, amygdala, insula, parahippocampa gyrus, precuneus, thalamus, hippocampus and cerebellum. In addition, the structural covariance of the NAcc was distinctly modulated by illness duration and the severity of symptom in patients with depression. What is more, the structural covariance of the NAcc connected to hippocampus was modulated by these two factors at the same time. These results elucidate altered structural covariance of the NAcc and its distinct modulation of illness duration and severity of symptom.

Parsing altered gray matter morphology of depression using a framework integrating the normative model and non-negative matrix factorization.

The high inter-individual heterogeneity in individuals with depression limits neuroimaging studies with case-control approaches to identify promising biomarkers for individualized clinical decision-making. We put forward a framework integrating the normative model and non-negative matrix factorization (NMF) to quantitatively assess altered gray matter morphology in depression from a dimensional perspective. The proposed framework parses altered gray matter morphology into overlapping latent disease factors, and assigns patients distinct factor compositions, thus preserving inter-individual variability. We identified four robust disease factors with distinct clinical symptoms and cognitive processes in depression. In addition, we showed the quantitative relationship between the group-level gray matter morphological differences and disease factors. Furthermore, this framework significantly predicted factor compositions of patients in an independent dataset. The framework provides an approach to resolve neuroanatomical heterogeneity in depression.