Frequency-dependent effective connections between local signals and the global brain signal during resting-state.

The psychological and physiological meanings of resting-state global brain signal (GS) and GS topography have been well confirmed. However, the causal relationship between GS and local signals was largely unknown. Based on the Human Connectome Project dataset, we investigated the effective GS topography using the Granger causality (GC) method. In consistent with GS topography, both effective GS topographies from GS to local signals and from local signals to GS showed greater GC values in sensory and motor regions in most frequency bands, suggesting that the unimodal superiority is an intrinsic architecture of GS topography. However, the significant frequency effect for GC values from GS to local signals was primarily located in unimodal regions and dominated at slow 4 frequency band whereas that from local signals to GS was mainly located in transmodal regions and dominated at slow 6 frequency band, consisting with the opinion that the more integrated the function, the lower the frequency. These findings provided valuable insight for the frequency-dependent effective GS topography, improving the understanding of the underlying mechanism of GS topography. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-022-09831-0.

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.

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.

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.

Prediction of childhood maltreatment and subtypes with personalized functional connectome of large-scale brain networks.

Childhood maltreatment (CM) has a long impact on physical and mental health of children. However, the neural underpinnings of CM are still unclear. In this study, we aimed to establish the associations between functional connectome of large-scale brain networks and influences of CM evaluated through Childhood Trauma Questionnaire (CTQ) at the individual level based on resting-state functional magnetic resonance imaging data of 215 adults. A novel individual functional mapping approach was employed to identify subject-specific functional networks and functional network connectivities (FNCs). A connectome-based predictive modeling (CPM) was used to estimate CM total and subscale scores using individual FNCs. The CPM established with FNCs can well predict CM total scores and subscale scores including emotion abuse, emotion neglect, physical abuse, physical neglect, and sexual abuse. These FNCs primarily involve default mode network, fronto-parietal network, visual network, limbic network, motor network, dorsal and ventral attention networks, and different networks have distinct contributions to predicting CM and subtypes. Moreover, we found that CM showed age and sex effects on individual functional connections. Taken together, the present findings revealed that different types of CM are associated with different atypical neural networks which provide new clues to understand the neurobiological consequences of childhood adversity.

Enhanced default mode network functional connectivity links with electroconvulsive therapy response in major depressive disorder.

BACKGROUND: Electroconvulsive therapy (ECT) is an effective neuromodulatory treatment for major depressive disorder (MDD), especially for cases resistant to antidepressant drugs. While the precise mechanisms underlying ECT efficacy are still unclear, it is speculated that ECT modulates brain connectivity. The current study aimed to investigate the longitudinal effects of ECT on resting-state functional connectivity (FC) in MDD patients and test if baseline FC can be used to predict therapeutic response. METHOD: Resting-state functional magnetic resonance imaging data were collected at baseline and following ECT from 33 MDD patients. Whole-brain multi-voxel pattern analysis (MVPA) and region of interest-wise FC analysis were employed to fully investigate ECT effects on brain connectivity. Linear support vector regression was further utilized to predict the improvement in depressive symptoms based on baseline connectivity. RESULTS: MVPA revealed a significant ECT effect on FC in the default mode network (DMN), central executive network (CEN), sensorimotor network (SMN), and cerebellar posterior lobe. The FCs within the DMN and between DMN and CEN were enhanced in patients after ECT, and the changed FC between the medial prefrontal cortex and ventrolateral prefrontal cortex was negatively correlated with depressive symptom improvement. Moreover, baseline FC within the DMN and between the DMN and CEN could effectively predict the improvement of depressive symptoms. CONCLUSIONS: The findings suggest that the FCs within the DMN and between DMN and CEN may be critical therapeutic targets for effective antidepressant treatment as well as neuromarkers for predicting treatment response.

Enduring effect of abuse: Childhood maltreatment links to altered theory of mind network among adults.

Childhood maltreatment (CM) confers a great risk of maladaptive development outcomes later in life, however, the neurobiological mechanism underlying this vulnerability is still unclear. The present study aimed to investigate the long-term consequences of CM on neural connectivity while controlling for psychiatric conditions, medication, and, substance abuse. A sample including adults with (n = 40) and without CM (n = 50) completed Childhood Trauma Questionnaire (CTQ), personality questionnaires, and resting-state functional magnetic resonance imaging scan were recruited for the current study. The whole-brain functional connectivity (FC) was evaluated using an unbiased, data-driven, multivariate pattern analysis method. Relative to controls, adults with CM suffered a higher level of temperament and impulsivity and showed decreased FC between the insula and superior temporal gyrus (STG) and between inferior parietal lobule (IPL) and middle frontal gyrus, STG, and dorsal anterior cingulate cortex (dACC), while increased FC between IPL and cuneus and superior frontal gyrus (SFG) regions. The FCs of IPL with dACC and SFG were correlated with the anxious and cyclothymic temperament and attentional impulsivity. Moreover, these FCs partially mediated the relationship between CM and attentional impulsivity. Our results suggest that CM has a significant effect on the modulation of FC within theory of mind (ToM) network even decades later in adulthood, and inform a new framework to account for how CM results in the development of impulsivity. The novel findings reveal the neurobiological consequences of CM and provide new clues to the prevention and intervention strategy to reduce the risk of the development of psychopathology.

Dimensional Analysis of Atypical Functional Connectivity of Major Depression Disorder and Bipolar Disorder.

Literatures have reported considerable heterogeneity with atypical functional connectivity (FC) pattern of psychiatric disorders. However, traditional statistical methods are hard to explore this heterogeneity pattern. We proposed a "brain dimension" method to describe the atypical FC patterns of major depressive disorder and bipolar disorder (BD). The approach was firstly applied to a simulation dataset. It was then utilized to a real resting-state functional magnetic resonance imaging dataset of 47 individuals with major depressive disorder, 32 individuals with BD, and 52 well matched health controls. Our method showed a better ability to extract the FC dimensions than traditional methods. The results of the real dataset revealed atypical FC dimensions for major depressive disorder and BD. Especially, an atypical FC dimension which exhibited decreased FC strength of thalamus and basal ganglia was found with higher severity level of individuals with BD than the ones with major depressive disorder. This study provided a novel "brain dimension" method to view the atypical FC patterns of major depressive disorder and BD and revealed shared and specific atypical FC patterns between major depressive disorder and BD.

Tracking Positive and Negative Symptom Improvement in First-Episode Schizophrenia Treated with Risperidone Using Individual-Level Functional Connectivity.

Background: To improve treatment outcomes of patients with schizophrenia, research efforts have focused on identifying brain-based markers of treatment response. Personal characteristics regarding disease-related behaviors likely stem from interindividual variability in the organization of brain functional systems. This study aimed to track dimension-specific changes in psychotic symptoms following risperidone treatment using individual-level functional connectivity (FC). Methods: A reliable cortical parcellation approach that accounts for individual heterogeneity in cortical functional anatomy was used to localize functional regions in a longitudinal cohort consisting of 42, drug-naive, first-episode schizophrenia (FES) patients at baseline and after 8 weeks of risperidone treatment. FC was calculated in individually specified brain regions and used to predict the baseline severity and improvement of positive and negative symptoms in FES. Results: Distinct sets of individual-specific FC were separately associated with the positive and negative symptom burden at baseline, which could be used to track the corresponding symptom resolution in FES patients following risperidone treatment. Between-network connections of the fronto-parietal network (FPN) contributed the most to predicting the positive symptom domain. A combination of between-network connections of the default mode network, FPN, and within-network connections of the FPN contributed markedly to the prediction model of negative symptoms. Conclusion: This novel study, which accounts for individual brain variation, takes a step toward establishing individual-specific theranostic biomarkers in schizophrenia. Impact statement This study revealed a theranostic marker for personalized medicine in schizophrenia and may aid in circuit-specific therapies for this disorder.

Psychological Crisis Intervention of College Sports Majors Based on Big Data Analysis / Intervención en Crisis Psicológicas de Mayores del Deporte Universitario Basada en Análisis de Big Data

In recent years, the psychological problems of college students have attracted extensive attention. It is of great practical significance to timely and arcuately intervene in college sports majors faced with psychological crisis (PC). However, the existing studies mainly analyze the mood and psychological state at a certain moment, but rarely track the psychological health state of different types of college students. This paper proposes a way to intervene and predict the PC of college sports majors based on big data analysis. Firstly, the massive evaluation data were collected from a psychological census database on PC of college sports majors and subjected to data mining. Besides, a PC evaluation model was established based on the decision tree (DT) algorithm. Next, the behavior big data of college sports majors in social network were fully utilized, and a PC intervention and prediction method was developed based on social network readme texts. Further, the authors extracted features from readme texts, evaluated the level of PC risk, and analyzed the longitudinal features. Finally, the proposed model was proved valid through experiments. This paper effectively applies new technologies to the data mining of the typical behaviors of college sports majors, and thereby realizes accurate PC warning. The research results are of great significance to improving the psychological health of college students.(AU)

Prefrontal-limbic-striatum dysconnectivity associated with negative emotional endophenotypes in bipolar disorder during depressive episodes.

BACKGROUND: The prefrontal-limbic-subcortical network has been suggested as an important circuitry in the pathophysiology underlying bipolar disorder during depressive episodes (BDD). However, the relationships between disrupted prefrontal-limbic-subcortical connection and the emotional endophenotypes in BDD patients remain largely unclear. METHODS: Forty-three BDD patients and 63 matched healthy controls (HCs) underwent the resting-state functional magnetic resonance imaging scan. The altered clusters were first identified by using a spatial pairwise clustering method and then were extracted as regions of interest to calculate the functional connectivity (FC). Group comparisons were conducted to identify the abnormal FCs. Classification analysis was employed to examine whether the altered FCs could distinguish BDD from HCs. The relationships between FC alterations and the emotional endophenotypes as measured by the Affective Neuroscience Personality Scales (ANPS) were further detected in BDD. RESULTS: Compared with HCs, BDD patients showed abnormal FCs in the prefrontal-limbic-striatum circuit. Importantly, the altered FCs yielded 84.91% accuracy (p< 1/5000) with 93.65% sensitivity and 72.09% specificity in differentiating between BDD and HCs. Moreover, the decreased FCs in the prefrontal-striatum and prefrontal-limbic systems were positively correlated with negative emotional endophenotypes of Sadness and Fear scores. CONCLUSIONS: The findings demonstrated that prefrontal-limbic-striatum disconnection may be identified as a potential effective biomarker for BDD, which could help further explain the neurobiological mechanisms underlying BDD.

Anomalous neurovascular coupling in patients with generalized anxiety disorder evaluated by combining cerebral blood flow and functional connectivity strength.

Coupling between neuronal activity and blood perfusion is termed neurovascular coupling, and it provides a new mechanistic perspective into understanding numerous brain diseases. Although abnormal brain activity and blood supply have been separately reported in generalized anxiety disorder (GAD), whether anomalous neurovascular coupling would still be presented in such disease is hitherto unknown. In this study, the neuronal activity and blood supply were measured using the functional connectivity strength (FCS) and cerebral blood flow (CBF). The voxel-wise CBF-FCS correlations and CBF/FCS ratio were separately used to assess global and local neurovascular coupling in participants. Patients with GAD showed decreased voxel-wise CBF-FCS correlation, implicating global neurovascular decoupling. They also exhibited increased CBF/FCS ratio in the right superior parietal gyrus (SPG), and the enhanced CBF/FCS ratio in this region was negatively correlated with the self-esteem scores of GAD. The abnormal neurovascular coupling of GAD may indicate the disrupted balance between the intrinsic functional organization of the brain and corresponding blood perfusion of patients, and the abnormally increased local neurovascular coupling of the right SPG may be correlated with the abnormal self in GAD. These findings provide new information in understanding the brain dysfunction and abnormal cognition of GAD from the perspective of neurovascular coupling.

Default mode network subsystem alterations in bipolar disorder during major depressive episode.

BACKGROUND: Default mode network (DMN) has been widely reported to be altered in bipolar disorder during major depressive episode (BDD). Recent studies supported the idea that DMN is not an unitary system, but composed of smaller and distinct functional subsystems. The current study aimed to investigate the abnormal functional connectivity (FC) patterns of DMN subsystems in BDD. METHODS: Seed-based FC method was applied to extract 11 DMN components in resting-state functional magnetic resonance imaging data acquired from 40 patients with BDD and 63 demographically matched healthy controls. RESULTS: Patients showed reduced FC between precuneus and all three DMN subsystems. Additionally, in the midline core, patients revealed increased FC between posterior cingutate cortex (PCC) seed and lateral orbitofrontal cortex. In the dorsomedial prefrontal cortex and medial temporal lobe subsystems, patients demonstrated increased FC with sensorimotor, visual, and salience network regions. Furthermore, the abnormal FC between the PCC seed and precuneus was correlated with high pessimism. LIMITATIONS: Our sample size is relatively small, limiting the power to detect subtle group differences and significant correlations between brain connectivity and clinical variables. In addition, most of our patients have been treated with medications. CONCLUSION: Our findings revealed the abnormal FC patterns of DMN subdivision circuits and highlighted these abnormalities were associated the pathological mechanisms in BDD.

Parcellation of the thalamus by using a dual-segment method based on resting-state functional connectivity: An application on autism spectrum disorder.

BACKGROUND: Evidence suggests thalamus is a key "information relay" center and all cortical areas receive inputs from the thalamus and each of the main nuclei of thalamus connects a single one or a few cortical areas. The traditional "winner-takes-all" thalamus parcellation method was then proposed based on this assumption. However, this method is based on the structural segments of the cortex which is not suitable for the functional parcellation of the thalamus. METHOD: Here we proposed a dual-segment method for thalamus functional parcellation based on the resting-state fMRI data. The traditional "winner-takes-all" and the proposed dual-segment methods were both applied to the dataset of 76 healthy controls (HCs) and 34 subjects with autism spectrum disorder. RESULTS: The results showed that the thalamus was subdivided into two sub-regions by using the dual-segment method: one is located in the dorsomedial part of thalamus which connects the high-level cognitive cortical regions; the other is located in the ventrolateral part of thalamus which connects the low-level sensory cortical areas. The functional connectivity strength between thalamus sub-regions and the corresponding cortical regions based on the dual-segment method was higher than that of results from the traditional "winner-takes-all" method. The thalamo-cortical functional connectivity based on our proposed method also showed higher classification ability to distinguish subjects with autism spectrum disorder from HCs. CONCLUSION: Our study will provide a new method for functional thalamus parcellation which might help understand the sub-regions functions of thalamus in neuroscience studies.

Frequency-specific alteration of functional connectivity density in bipolar disorder depression.

Functional dysconnectivity has been widely reported in bipolar disorder during depressive episodes (BDD). However, the frequency-specific alterations of functional connectivity (FC) in BDD remain poorly understood. To address this issue, the FC patterns across slow-5 (0.01-0.027 Hz) and slow-4 (0.027-0.073 Hz) bands were computed using resting-state functional magnetic resonance imaging data from 37 BDD patients and 56 healthy controls (HCs). Short-range (local) FC density (lfcd) and long-range FC density (lrfcd) were calculated, and two-way analysis of variance was performed to ascertain the main effect of diagnosis and interaction effects between diagnosis and frequency. The BDD patients showed increased lfcd in the midline cerebelum. Meanwhile, the BDD patients showed increased lrfcd in the left supplementary motor cortex and right striatum and decreased lrfcd in the bilateral inferior temporal gyrus and left angular gyrus (AG) compared with the HCs. A significant frequency-by-diagnosis interaction was observed. In the slow-4 band, the BDD patients showed increased lfcd in the left pre-/postcentral gyrus and left fusiform gyrus (FG) and increased lrfcd in the left lingual gyrus (LG). In the slow-5 band, the BDD patients showed decreased lrfcd in the left LG. Moreover, the increased lfcd in the left FG in the slow-4 band was correlated with clinical progression and decreased lrfcd in the left AG was correlated with depressive severity. These results suggest that the presence of aberrant communication in the default mode network, sensory network, and subcortical and limbic modulating regions (striatum and midline cerebelum), which may offer a new framework for the understanding of the pathophysiological mechanisms of BDD.

Spatial variability of low frequency brain signal differentiates brain states.

Temporal variability of the neural signal has been demonstrated to be closely related to healthy brain function. Meanwhile, the evolving brain functions are supported by dynamic relationships among brain regions. We hypothesized that the spatial variability of brain signal might provide important information about brain function. Here we used the spatial sample entropy (SSE) to investigate the spatial variability of neuroimaging signal during a steady-state presented face detection task. Lower SSE was found during task state than during resting state, associating with more repetitive functional interactions between brain regions. The standard deviation (SD) of SSE during the task was negatively related to the SD of reaction time, suggesting that the spatial pattern of neural activity is reorganized according to particular cognitive function and supporting the previous theory that greater variability is associated with better task performance. These results were replicated with reordered data, implying the reliability of SSE in measuring the spatial organization of neural activity. Overall, the present study extends the research scope of brain signal variability from the temporal dimension to the spatial dimension, improving our understanding of the spatiotemporal characteristics of brain activities and the theory of brain signal variability.

Differences in brain structure and function in children with the FTO obesity-risk allele.

OBJECTIVE: Noncoding alleles of the fat mass and obesity-associated (FTO) gene have been associated with obesity risk, yet the underlying mechanisms remain unknown. Risk allele carriers show alterations in brain structure and function, but previous studies have not disassociated the effects of genotype from those of body mass index (BMI). METHODS: Differences in brain structure and function were examined in children without obesity grouped by their number of copies (0,1,2) of the FTO obesity-risk single-nucleotide polymorphism (SNP) rs1421085. One hundred five 5- to 10-year-olds (5th-95th percentile body fat) were eligible to participate. Usable scans were obtained from 93 participants (15 CC [homozygous risk], 31 CT [heterozygous] and 47 TT [homozygous low risk]). RESULTS: Homozygous C allele carriers (CCs) showed greater grey matter volume in the cerebellum and temporal fusiform gyrus. CCs also demonstrated increased bilateral cerebellar white matter fibre density and increased resting-state functional connectivity between the bilateral cerebellum and regions in the frontotemporal cortices. CONCLUSIONS: This is the first study to examine brain structure and function related to FTO alleles in young children not yet manifesting obesity. This study lends support to the notion that the cerebellum may be involved in FTO-related risk for obesity, yet replication and further longitudinal study are required.

Combined static and dynamic functional connectivity signatures differentiating bipolar depression from major depressive disorder.

OBJECTIVE: Bipolar disorder in the depressive phase (BDd) may be misdiagnosed as major depressive disorder (MDD), resulting in poor treatment outcomes. To identify biomarkers distinguishing BDd from MDD is of substantial clinical significance. This study aimed to characterize specific alterations in intrinsic functional connectivity (FC) patterns in BDd and MDD by combining whole-brain static and dynamic FC. METHODS: A total of 40 MDD and 38 BDd patients, and 50 age-, sex-, education-, and handedness-matched healthy controls (HCs) were included in this study. Static and dynamic FC strengths (FCSs) were analyzed using complete time-series correlations and sliding window correlations, respectively. One-way analysis of variance was performed to test group effects. The combined static and dynamic FCSs were then used to distinguish BDd from MDD and to predict clinical symptom severity. RESULTS: Compared with HCs, BDd patients showed lower static FCS in the medial orbitofrontal cortex and greater static FCS in the caudate, while MDD patients exhibited greater static FCS in the medial orbitofrontal cortex. BDd patients also demonstrated greater static and dynamic FCSs in the thalamus compared with both MDD patients and HCs, while MDD patients exhibited greater dynamic FCS in the precentral gyrus compared with both BDd patients and HCs. Combined static and dynamic FCSs yielded higher accuracy than either static or dynamic FCS analysis alone, and also predicted anhedonia severity in BDd patients and negative mood severity in MDD patients. CONCLUSION: Altered FC within frontal-striatal-thalamic circuits of BDd patients and within the default mode network/sensorimotor network of MDD patients accurately distinguishes between these disorders. These unique FC patterns may serve as biomarkers for differential diagnosis and provide clues to the pathogenesis of mood disorders.

Abnormal functional connectivity as neural biological substrate of trait and state characteristics in major depressive disorder.

OBJECTIVE: Major depressive disorder (MDD) is a neuropsychiatric disorder associated with functional dysconnectivity in emotion regulation system. State characteristics which measure the current presence of depressive symptoms, and trait characteristics which indicate the long-term vulnerability to depression are two important features of MDD. However, the relationships between trait and state characteristics of MDD and functional connectivity (FC) within the emotion regulation system still remain unclear. METHODS: This study aims to examine the neural biological mechanisms of trait characteristics measured by the Affective Neuroscience Personality Scale (ANPS) and state anhedonia measured by the Snaith-Hamilton Pleasure Scale (SHAPS) in MDD. Sixty-three patients with MDD and 63 well-matched healthy controls (HCs) underwent resting-state functional magnetic resonance imaging. A spatial pairwise clustering and the network-based analysis approaches were adopted to identify the abnormal FC networks. Support vector regression was utilized to predict the trait and state characteristics based on abnormal FCs. RESULTS: Four disrupted subnetworks mainly involving the prefrontal-limbic-striatum system were observed in MDD. Importantly, the abnormal FC between the left amygdala (AMYG)/hippocampus (HIP) and right AMYG/HIP could predict the SADNESS scores of ANPS (trait characteristics) in MDD. While the aberrant FC between the medial prefrontal cortex (mPFC)/anterior cingulate gyrus (ACC) and AMYG/parahippocampal gyrus could predict the state anhedonia scores (state characteristics). CONCLUSIONS: The present findings give first insights into the neural biological basis underlying the trait and state characteristics associated with functional dysconnectivity within the emotion regulation system in MDD.

Anomalous intrinsic connectivity within and between visual and auditory networks in major depressive disorder.

OBJECTIVE: Major depressive disorder (MDD) is a ubiquitous mental illness with heterogeneous symptoms, however, the pathophysiology mechanisms are still not fully understood. Clinical and preclinical studies suggested that depression could cause disturbances in sensory perception systems, disruptions in auditory and visual functions may serve as an essential clinical features underlying MDD. METHODS: The current study investigated the abnormal intrinsic connectivity within and between visual and auditory networks in 95 MDD patients and 97 age-, gender-, education level-matched healthy controls (HCs) by using resting-state functional magnetic resonance imaging (fMRI). One auditory network (AN) and three visual components including visual component 1 (VC1), VC2, and VC3 were identified by using independent component analysis method based on the fMRI networks during the resting state with the largest spatial correlations, combining with brain regions and specific network templates. RESULTS: We found that MDD could be characterized by the following disrupted network model relative to HCs: (i) reduced within-network connectivity in the AN, VC2, and VC3; (ii) reduced between-network connectivity between the AN and the VC3. Furthermore, aberrant functional connectivity (FC) within the visual network was linked to the clinical symptoms. CONCLUSIONS: Overall, our results demonstrated that abnormalities of FC in perception systems including intrinsic visual and auditory networks may explain neurobiological mechanisms underlying MDD and could serve as a potential effective biomarker.