Altered effective connectivity in sensorimotor cortices is a signature of severity and clinical course in depression

Depression vulnerability and gray matter integrity of interoceptive networks in remitted depressed outpatients

BACKGROUND

Interoception, the representation of internal body states, plays an important role in mental health. While functional neuroimaging links Major Depressive Disorder (MDD) relapse vulnerability to stress-induced inhibition of sensorimotor regions, its association with structural changes in interoceptive networks remains unclear.

METHODS

A secondary analysis explored relationships between gray matter volume and relapse vulnerability in remitted MDD patients (N = 85), with two data acquisitions surrounding eight-weeks of prophylactic psychotherapy followed by a two-year follow-up. Participants were randomly assigned to either Cognitive Behavioral Therapy or Mindfulness-Based Cognitive Therapy (MBCT). Mixed-effects models were applied to study the relationships between cortical thickness, time, and intervention type with clinical variables such as relapse status, residual symptoms, and decentering, adjusting for relevant covariates. Analyses were conducted at whole brain levels as well as in pre-defined regions of interest, focusing on sensory regions implicated by prior research.

RESULTS

Relapse was consistently linked to greater cortical thickness in the left superior circular sulcus of the insula and the left anterior occipital sulcus. Residual symptoms correlated with increased cortical thickness in the left insula and right precentral regions, while decentering was linked to reduced thickness in the middle temporal and inferior parietal regions. MBCT participants showed greater cortical thickness increases in the right superior temporal gyrus over time.

CONCLUSIONS

MDD vulnerability was unexpectedly linked to greater cortical thickness in sensory and prefrontal brain regions, suggesting that depression vulnerability may reflect maladaptive skill acquisition. MBCT may promote gray matter growth in the right superior temporal region.

TRIAL REGISTRATION

ClinicalTrials.govNCT01178424.

Predicting depression severity using effective and functional brain connectivity of the electroencephalography signals

Depression, also known as major depressive disorder (MDD), is a mental health condition that can lead to self-injury and suicide with significant effects on individuals and communities. Recent studies suggest that analysing functional connectivity (FC) from electroencephalography (EEG) signals provides insights into brain network integration in depressive states. Effective connectivity (EC) assesses the directional influence between brain regions, offering deeper insights into neural circuit dynamics. This study aimed to capture the subtle changes in brain dynamics, identify predictive biomarkers of MDD, and elucidate its neurophysiological basis. Resting-state EEG signals from 44 subjects with MDD were used to extract connectivity features. Graph-theoretical-based EC features from the phase slope index (PSI) and FC features from the weighted phase lag index (WPLI) were analysed. Correlation analysis showed significant associations between EC features (diameter) and depression severity, as well as between FC features (global efficiency) and severity, both in the alpha frequency band. These significant features were fed into several machine learning regression models, which demonstrated comparable performance in predicting depression scores. FC features performed slightly better (4.71 root mean square error and 3.93 mean absolute error) than EC features (5.01 root mean square error and 4.29 mean absolute error). These findings indicate that alterations in functional and effective connectivity are linked to depression severity and could improve diagnostic accuracy and therapeutic strategies, while offering new avenues for research into brain connectivity in MDD.

Depression is marked by differences in structural covariance between deep-brain nuclei and sensorimotor cortex

BACKGROUND

Depression impacts nearly 3% of the global adult population. Symptomatology is likely related to regions encompassing frontoparietal, somatosensory, and salience networks. Questions regarding deep brain nuclei (DBN), including the substantia nigra (STN), subthalamic nucleus (STN), and red nucleus (RN) remain unanswered.

METHODS

Using an existing structural neuroimaging dataset including 86 individuals (Baranger et al., 2021; nDEP = 39), frequentist and Bayesian logistic regressions assessed whether DBN volumes predict diagnosis, then structural covariance analyses in FreeSurfer tested diagnostic differences in deep brain volume and cortical morphometry covariance. Exploratory correlations tested relationships between implicated cortical regions and Hamilton Depression Rating Scale (HAM-D) scores.

RESULTS

Group differences emerged in deep brain/cortical covariance. Right RN volume covaried with left parietal operculum volume and central sulcus thickness, while left RN and right STN volumes covaried with right occipital pole volume. Positive relationships were observed within the unaffected group and negative relationships among those with depression. These cortical areas did not correlate with HAM-D scores. Simple DBN volumes did not predict diagnostic group.

CONCLUSION

Structural codependence between DBN and cortical regions may be important in depression, potentially for sensorimotor features. Future work should focus on causal mechanisms of DBN involvement with sensory integration.

Aberrant Structural-Functional Coupling of Large-Scale Brain Networks in Older Women With Subthreshold Depression

OBJECTIVES

Subthreshold depression (SD) is common in the older population, more so in females than males, and can lead to serious physical and mental ill-health. However, the underlying neurobiology remains unclear. This study used multimodal magnetic resonance imaging (MRI) to investigate the topological organization and coupling of the structural and functional brain networks in older women with SD.

METHODS

We constructed the structural network from diffusion MRI and the functional network from resting-state functional MRI in 50 older women with SD and 52 demographically matched older women healthy controls (HC). We used graph theory analysis to examine the topological properties of functional and structural networks, and structural-functional connectivity (SC-FC) coupling, and their potential relationship to depressive symptoms.

RESULTS

Globally, compared with older women HC, the older women with SD showed lower local efficiency in the structural network but not the functional network. Locally, older women with SD showed altered convergent nodal metrics in the default mode, salience, and sensorimotor network regions in both structural and functional networks. Moreover, SC-FC coupling reduced in older women with SD compared to older women HC. These network metric alterations were correlated with depressive symptoms.

DISCUSSION

Older women with SD showed alterations in both structural and functional networks, and in their coupling, which throw light on the role of large-scale brain networks in older female SD.

Dysregulated connectivity configuration of triple-network model in obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) is signified by altered functional network connectivity (FNC), particularly within the default mode network (DMN), salience network (SAL), and fronto-parietal network (FPN). While previous studies suggest disruptions within triple networks, dynamic causal interactions across networks remain unaddressed. This study seeks to validate previous findings of static dysconnectivity between triple networks and further delineate the time-varying interactions and causal relationships among these networks in OCD. A resting-state functional magnetic resonance imaging study was performed on a relatively large and well-characterized clinical sample, comprising 88 medication-free OCD patients and 93 healthy controls (HC). Group independent component analysis, combined with a sliding window approach and k-means clustering analysis, was used to assess static and dynamic time-varying FNC within triple networks. Spectral dynamic causal modelling and parametric empirical Bayes framework were utilized to explore the abnormal effective connectivity among these networks in OCD patients. Our results proposed a novel dysregulated connectivity configuration of the triple-network model for OCD. With the self-inhibition increase in the left FPN, the excitatory effect onto the right FPN decrease, resulting in a weakened static connectivity between the left and right FPNs. Concurrently, time-varying hypoconnectivity patterns are observed between the left FPN and DMN, as well as the right FPN and SAL in OCD. Additionally, the excitatory influence from the DMN to the SAL suggests an atypical modulation within OCD's network pathology. These findings advance our understanding of the dysregulated information transfer and the complex interplay of brain networks in OCD, potentially guiding future therapeutic strategies.

Higher amplitudes of visual networks are associated with trait- but not state-depression

Despite depression being a leading cause of global disability, neuroimaging studies have struggled to identify replicable neural correlates of depression or explain limited variance. This challenge may, in part, stem from the intertwined state (current symptoms; variable) and trait (general propensity; stable) experiences of depression.Here, we sought to disentangle state from trait experiences of depression by leveraging a longitudinal cohort and stratifying individuals into four groups: those in remission ('trait depression group'), those with large longitudinal severity changes in depression symptomatology ('state depression group'), and their respective matched control groups (total analytic n = 1030). We hypothesized that spatial network organization would be linked to trait depression due to its temporal stability, whereas functional connectivity between networks would be more sensitive to state-dependent depression symptoms due to its capacity to fluctuate.We identified 15 large-scale probabilistic functional networks from resting-state fMRI data and performed group comparisons on the amplitude, connectivity, and spatial overlap between these networks, using matched control participants as reference. Our findings revealed higher amplitude in visual networks for the trait depression group at the time of remission, in contrast to controls. This observation may suggest altered visual processing in individuals predisposed to developing depression over time. No significant group differences were observed in any other network measures for the trait-control comparison, nor in any measures for the state-control comparison. These results underscore the overlooked contribution of visual networks to the psychopathology of depression and provide evidence for distinct neural correlates between state and trait experiences of depression.

Assessment of Resting-state functional Magnetic Resonance Imaging Connectivity Among Patients with Major Depressive Disorder: A Comparative Study

Background

Resting-state functional connectivity analysis has a potential to unearth the putative neuronal underpinnings of various disorders of the brain. Major depressive disorder (MDD) is regarded as a disorder arising from alterations in functional networks of the brain.

Purpose

There is paucity of literature on resting-state functional magnetic resonance imaging (Rs-fMRI) in MDD, especially from the Indian subcontinent. The purpose of our study was to elucidate the differences in Rs-fMRI connectivity between MDD patients and age and gender matched healthy controls (HC).

Methods

In this prospective single institute-based study, the patients were recruited consecutively based on Hamilton depression rating scale (HAM-D). Age and gender matched HC were also recruited. Rs-fMRI and anatomical MRI images were acquired for all the subjects (MDD and HC group) and subsequent analysis was done using the CONN toolbox.

Results

A total of 49 subjects were included in the final analysis (MDD = 28 patients, HC = 21). HAM-D score was noted to be 24.4 ± 4.8 in the MDD group. There was no significant difference between MDD and HC groups as far as age, gender, employment status, and level of education is concerned. Region-of-interest-based analysis of Rs-fMRI data showed a significantly lower connectivity between the left insula and left nucleus accumbens and between left paracingulate gyrus and bilateral posterior middle temporal gyri in MDD group as compared to HC group.

Conclusion

There is reduced connectivity between certain key regions of the brain in MDD patients, that is, between the left insular cortex and the left nucleus accumbens and between the left paracingulate gyrus and the bilateral posterior middle temporal gyrus. These findings could explain the basis of clinical features of MDD such as anhedonia, rumination of thoughts, reduced visuo-spatial comprehension, reduced language function, and response to external stimuli.

Resting-state dynamic functional connectivity in major depressive disorder: A systematic review

As a novel measure, dynamic functional connectivity (dFC) provides insight into the dynamic nature of brain networks and their interactions in resting-state, surpassing traditional static functional connectivity in pathological conditions such as depression. Since a comprehensive review is still lacking, we then reviewed forty-five eligible papers to explore pathological mechanisms of major depressive disorder (MDD) from perspectives including abnormal brain regions and functional networks, brain state, topological properties, relevant recognition, along with longitudinal studies. Though inconsistencies could be found, common findings are: (1) From different perspectives based on dFC, default-mode network (DMN) with its subregions exhibited a close relation to the pathological mechanism of MDD. (2) With a corrupted integrity within large-scale functional networks and imbalance between them, longer fraction time in a relatively weakly-connected state may be a possible property of MDD concerning its relation with DMN. Abnormal transition frequencies between states were correlated to the severity of MDD. (3) Including dynamic properties in topological network metrics enhanced recognition effect. In all, this review summarized its use for clinical diagnosis and treatment, elucidating the non-stationary of MDD patients' aberrant brain activity in the absence of stimuli and bringing new views into its underlying neuro mechanism.

Brain-based correlates of depression and traumatic brain injury: a systematic review of structural and functional magnetic resonance imaging studies

Introduction

Depression is prevalent after traumatic brain injury (TBI). However, there is a lack of understanding of the brain-based correlates of depression post-TBI. This systematic review aimed to synthesize findings of structural and functional magnetic resonance imaging (MRI) studies to identify consistently reported neural correlates of depression post-TBI.

Methods

A search for relevant published studies was conducted through OVID (MEDLINE, APA PsycINFO, and Embase), with an end date of August 3rd, 2023. Fourteen published studies were included in this review.

Results

TBI patients with depression exhibited distinct changes in diffusion- based white matter fractional anisotropy, with the direction of change depending on the acuteness or chronicity of TBI. Decreased functional connectivity (FC) of the salience and default mode networks was prominent alongside the decreased volume of gray matter within the insular, dorsomedial prefrontal, and ventromedial prefrontal cortices. Seven studies reported the correlation between observed neuroimaging and depression outcomes. Of these studies, 42% indicated that FC of the bilateral medial temporal lobe subregions was correlated with depression outcomes in TBI.

Discussion

This systematic review summarizes existing neuroimaging evidence and reports brain regions that can be leveraged as potential treatment targets in future studies examining depression post-TBI.

Depression and metabolic connectivity: insights into the locus coeruleus, HF-rTMS, and anxiety

The use of repetitive Transcranial Magnetic Stimulation (rTMS) in treating major depressive disorder (MDD) is increasingly being explored in precision medicine. However, there's a notable lack of understanding of the underlying neurobiological effects, which limits our ability to correlate specific imaging features with treatment efficacy. As one possible neurobiological mechanism, clinical research has already shown that in MDD, lower norepinephrine release in the locus coeruleus (LC) triggers depressive symptoms, and pharmacological approaches that block norepinephrine reuptake boost its levels, easing depression. Surprisingly, the LC has not received a more pronounced focus in contemporary rTMS research. This study investigates the role of the LC in MDD and its response to high-frequency (HF)-rTMS using 18FDG-PET imaging. We compared LC metabolic connectivity between MDD patients (n = 43) and healthy controls (n = 32). Additionally, we evaluated the predictive value of LC connectivity for HF-rTMS treatment outcomes and examined post-treatment changes in LC metabolic connectivity. Our findings revealed significant differences in LC metabolic connectivity between MDD patients and controls. Baseline LC metabolic connectivity did not predict HF-rTMS treatment outcomes. However, post-treatment analyses showed a significant correlation between improved clinical outcomes and attenuation of LC metabolic connectivity in regions associated with cognitive control and the default mode network. Notably, a reduction in state anxiety moderated this relationship, highlighting the role of anxiety in HF-rTMS efficacy for MDD treatment. Our findings suggest that LC metabolic connectivity, influenced by state anxiety levels, may be crucial in HF-rTMS efficacy, offering further insights for personalized MDD treatment strategies.

Multimodal nonlinear correlates of behavioural symptoms in frontotemporal dementia

Studies exploring the brain correlates of behavioral symptoms in the frontotemporal dementia spectrum (FTD) have mainly searched for linear correlations with single modality neuroimaging data, either structural magnetic resonance imaging (MRI) or fluoro-deoxy-D-glucose positron emission tomography (FDG-PET). We aimed at studying the two imaging modalities in combination to identify nonlinear co-occurring patterns of atrophy and hypometabolism related to behavioral symptoms. We analyzed data from 93 FTD patients who underwent T1-weighted MRI, FDG-PET imaging, and neuropsychological assessment including the Neuropsychiatric Inventory, Frontal Systems Behavior Scale, and Neurobehavioral Rating Scale. We used a data-driven approach to identify the principal components underlying behavioral variability, then related the identified components to brain variability using a newly developed method fusing maps of grey matter volume and FDG metabolism. A component representing apathy, executive dysfunction, and emotional withdrawal was associated with atrophy in bilateral anterior insula and putamen, and with hypometabolism in the right prefrontal cortex. Another component representing the disinhibition versus depression/mutism continuum was associated with atrophy in the right striatum and ventromedial prefrontal cortex for disinhibition, and hypometabolism in the left fronto-opercular region and sensorimotor cortices for depression/mutism. A component representing psychosis was associated with hypometabolism in the prefrontal cortex and hypermetabolism in auditory and visual cortices. Behavioral symptoms in FTD are associated with atrophy and altered metabolism of specific brain regions, especially located in the frontal lobes, in a hierarchical way: apathy and disinhibition are mostly associated with grey matter atrophy, whereas psychotic symptoms are mostly associated with hyper-/hypo-metabolism.

Altered effective connectivity of the default mode network in juvenile myoclonic epilepsy

Juvenile myoclonic epilepsy (JME) is associated with brain dysconnectivity in the default mode network (DMN). Most previous studies of patients with JME have assessed static functional connectivity in terms of the temporal correlation of signal intensity among different brain regions. However, more recent studies have shown that the directionality of brain information flow has a more significant regional impact on patients' brains than previously assumed in the present study. Here, we introduced an empirical approach incorporating independent component analysis (ICA) and spectral dynamic causal modeling (spDCM) analysis to study the variation in effective connectivity in DMN in JME patients. We began by collecting resting-state functional magnetic resonance imaging (rs-fMRI) data from 37 patients and 37 matched controls. Then, we selected 8 key nodes within the DMN using ICA; finally, the key nodes were analyzed for effective connectivity using spDCM to explore the information flow and detect patient abnormalities. This study found that compared with normal subjects, patients with JME showed significant changes in the effective connectivity among the precuneus, hippocampus, and lingual gyrus (p < 0.05 with false discovery rate (FDR) correction) with most of the effective connections being strengthened. In addition, previous studies have found that the self-connection of normal subjects' nodes showed strong inhibition, but the self-connection inhibition of the anterior cingulate cortex and lingual gyrus of the patient was decreased in this experiment (p < 0.05 with FDR correction); as the activity in these areas decreased, the nodes connected to them all appeared abnormal. We believe that the changes in the effective connectivity of nodes within the DMN are accompanied by changes in information transmission that lead to changes in brain function and impaired cognitive and executive function in patients with JME. Overall, our findings extended the dysconnectivity hypothesis in JME from static to dynamic causal and demonstrated that aberrant effective connectivity may underlie abnormal brain function in JME patients at early phase of illness, contributing to the understanding of the pathogenesis of JME.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-023-09994-4.

Predicting treatment outcome based on resting-state functional connectivity in internalizing mental disorders: A systematic review and meta-analysis

Predicting treatment outcome in internalizing mental disorders prior to treatment initiation is pivotal for precision mental healthcare. In this regard, resting-state functional connectivity (rs-FC) and machine learning have often shown promising prediction accuracies. This systematic review and meta-analysis evaluates these studies, considering their risk of bias through the Prediction Model Study Risk of Bias Assessment Tool (PROBAST). We examined the predictive performance of features derived from rs-FC, identified features with the highest predictive value, and assessed the employed machine learning pipelines. We searched the electronic databases Scopus, PubMed and PsycINFO on the 12th of December 2022, which resulted in 13 included studies. The mean balanced accuracy for predicting treatment outcome was 77% (95% CI: [72%- 83%]). rs-FC of the dorsolateral prefrontal cortex had high predictive value in most studies. However, a high risk of bias was identified in all studies, compromising interpretability. Methodological recommendations are provided based on a comprehensive exploration of the studies' machine learning pipelines, and potential fruitful developments are discussed.

Abnormally decreased functional connectivity of the right nucleus basalis of Meynert in Alzheimer's disease patients with depression symptoms

Dysfunction of the basal forebrain is the main pathological feature in patients with Alzheimer's disease (AD). The aim of this study was to explore whether depressive symptoms cause changes in the functional network of the basal forebrain in AD patients. We collected MRI data from depressed AD patients (n = 24), nondepressed AD patients (n = 14) and healthy controls (n = 20). Resting-state functional magnetic resonance imaging data and functional connectivity analysis were used to study the characteristics of the basal forebrain functional network of the three groups of participants. The functional connectivity differences among the three groups were compared using ANCOVA and post hoc analyses. Compared to healthy controls, depressed AD patients showed reduced functional connectivity between the right nucleus basalis of Meynert and the left supramarginal gyrus and the supplementary motor area. These results increase our understanding of the neural mechanism of depressive symptoms in AD patients.

Dynamic reconfigurations of brain networks in depressive and anxiety disorders: The influence of antidepressants

Major Depressive Disorder (MDD) and anxiety disorders are highly comorbid recurrent psychiatric disorders. Reduced dynamic reconfiguration of brain regions across subnetworks may play a critical role underlying these deficits, with indications of normalization after treatment with antidepressants. This study investigated dynamic reconfigurations in controls and individuals with a current MDD and/or anxiety disorder including antidepressant users and non-users in a large sample (N = 207) of adults. We quantified the number of subnetworks a region switched to (promiscuity) as well as the total number of switches (flexibility). Average whole-brain (i.e., global) values and subnetwork-specific values were compared between diagnosis and antidepressant groups. No differences in reconfiguration dynamics were found between individuals with a current MDD (N = 49), anxiety disorder (N = 46), comorbid MDD and anxiety disorder (N = 55), or controls (N = 57). Global and sensorimotor network (SMN) promiscuity and flexibility were higher in antidepressant users (N = 49, regardless of diagnosis) compared to non-users (N = 101) and controls. Dynamic reconfigurations were considerably higher in antidepressant users relative to non-users and controls, but not significantly altered in individuals with a MDD and/or anxiety disorder. The increase in antidepressant users was apparent across the whole brain and in the SMN when investigating subnetworks. These findings help disentangle how antidepressants improve symptoms.

Altered dynamic functional network connectivity in rheumatoid arthritis associated with peripheral inflammation and neuropsychiatric disorders

OBJECTIVE

This study explored the dynamic functional connective (DFC) alterations in patients with rheumatoid arthritis (RA) and investigated the correlation between the neuropsychiatric symptoms, peripheral inflammation and DFC alterations.

METHOD

Using resting-state functional MRI, we investigated the DFC based on spatial independent component analysis and sliding window method for 30 patients with RA and 30 healthy controls (HCs). The Spearman correlation was calculated between aberrant DFC alterations, Montreal Cognitive Assessment (MoCA), Hospital Anxiety and Depression Scale (HAD), C reactive protein (CRP) and erythrocyte sedimentation rate (ESR). Diagnostic efficacy of indicators was assessed using receiver operating characteristic analysis (ROC).

RESULTS

Three dynamic functional states were identified. Compared with HC, patients with RA showed reduced FC variabilities between sensorimotor network (SMN) and insula, SMN and orbitofrontal cortex, which were the crucial regions of sensory processing network. The above FC variabilities were correlated with the MoCA, HAD, CRP and ESR in patients with RA. Additionally, the CRP and ESR were negatively correlated to MoCA and positively related to HAD in patients with RA. The ROC analysis results showed that MoCA, HAD and FC variabilities of the sensory processing network could distinguish patients with RA from HC and also identify patients with RA with high ESR.

CONCLUSION

Our findings demonstrated that abnormal DFC patterns in sensory processing networks in patients with RA were closely associated with peripheral inflammation and neuropsychiatric symptoms. This indicates that the dynamic temporal characteristics of the brain functional network may be potential neuroimaging biomarkers for revealing the pathological mechanism of RA.

Common and distinct functional brain network abnormalities in adolescent, early-middle adult, and late adult major depressive disorders

BACKGROUND

The age-related heterogeneity in major depressive disorder (MDD) has received significant attention. However, the neural mechanisms underlying such heterogeneity still need further investigation. This study aimed to explore the common and distinct functional brain abnormalities across different age groups of MDD patients from a large-sample, multicenter analysis.

METHODS

The analyzed sample consisted of a total of 1238 individuals including 617 MDD patients (108 adolescents, 12-17 years old; 411 early-middle adults, 18-54 years old; and 98 late adults, > = 55 years old) and 621 demographically matched healthy controls (60 adolescents, 449 early-middle adults, and 112 late adults). MDD-related abnormalities in brain functional connectivity (FC) patterns were investigated in each age group separately and using the whole pooled sample, respectively.

RESULTS

We found shared FC reductions among the sensorimotor, visual, and auditory networks across all three age groups of MDD patients. Furthermore, adolescent patients uniquely exhibited increased sensorimotor-subcortical FC; early-middle adult patients uniquely exhibited decreased visual-subcortical FC; and late adult patients uniquely exhibited wide FC reductions within the subcortical, default-mode, cingulo-opercular, and attention networks. Analysis of covariance models using the whole pooled sample further revealed: (1) significant main effects of age group on FCs within most brain networks, suggesting that they are decreased with aging; and (2) a significant age group × MDD diagnosis interaction on FC within the default-mode network, which may be reflective of an accelerated aging-related decline in default-mode FCs.

CONCLUSIONS

To summarize, these findings may deepen our understanding of the age-related biological and clinical heterogeneity in MDD.

Characteristic patterns of functional connectivity-mediated cerebral small vessel disease-related cognitive impairment and depression

Cerebral small vessel disease is common in most individuals aged 60 years or older, and it is associated with cognitive dysfunction, depression, anxiety disorder, and mobility problems. Currently, many cerebral small vessel disease patients have both cognitive impairment and depressive symptoms, but the relationship between the 2 is unclear. The present research combined static and dynamic functional network connectivity methods to explore the patterns of functional networks in cerebral small vessel disease individuals with cognitive impairment and depression (cerebral small vessel disease-mild cognitive impairment with depression) and their relationship. We found specific functional network patterns in the cerebral small vessel disease-mild cognitive impairment with depression individuals (P < 0.05). The cerebral small vessel disease individuals with depression exhibited unstable dynamic functional network connectivity states (transitions likelihood: P = 0.040). In addition, we found that the connections within the lateral visual network between the sensorimotor network and ventral attention network could mediate white matter hyperintensity-related cognitive impairment (indirect effect: 0.064; 95% CI: 0.003, 0.170) and depression (indirect effect: -0.415; 95% CI: -1.080, -0.011). Cognitive function can negatively regulate white matter hyperintensity-related depression. These findings elucidate the association between cognitive impairment and depression and provide new insights into the underlying mechanism of cerebral small vessel disease-related cognitive dysfunction and depression.

Distinct resting-state effective connectivity of large-scale networks in first-episode and recurrent major depression disorder: evidence from the REST-meta-MDD consortium

Introduction

Previous studies have shown disrupted effective connectivity in the large-scale brain networks of individuals with major depressive disorder (MDD). However, it is unclear whether these changes differ between first-episode drug-naive MDD (FEDN-MDD) and recurrent MDD (R-MDD).

Methods

This study utilized resting-state fMRI data from 17 sites in the Chinese REST-meta-MDD project, consisting of 839 patients with MDD and 788 normal controls (NCs). All data was preprocessed using a standardized protocol. Then, we performed a granger causality analysis to calculate the effectivity connectivity (EC) within and between brain networks for each participant, and compared the differences between the groups.

Results

Our findings revealed that R-MDD exhibited increased EC in the fronto-parietal network (FPN) and decreased EC in the cerebellum network, while FEDN-MDD demonstrated increased EC from the sensorimotor network (SMN) to the FPN compared with the NCs. Importantly, the two MDD subgroups displayed significant differences in EC within the FPN and between the SMN and visual network. Moreover, the EC from the cingulo-opercular network to the SMN showed a significant negative correlation with the Hamilton Rating Scale for Depression (HAMD) score in the FEDN-MDD group.

Conclusion

These findings suggest that first-episode and recurrent MDD have distinct effects on the effective connectivity in large-scale brain networks, which could be potential neural mechanisms underlying their different clinical manifestations.

Dysconnectivity of the parafascicular nucleus in Parkinson's disease: A dynamic causal modeling analysis

BACKGROUND

Recent animal model studies have suggested that the parafascicular nucleus has the potential to be an effective deep brain stimulation target for Parkinson's disease. However, our knowledge on the role of the parafascicular nucleus in Parkinson's disease patients remains limited.

OBJECTIVE

We aimed to investigate the functional alterations of the parafascicular nucleus projections in Parkinson's disease patients.

METHODS

We enrolled 72 Parkinson's disease patients and 60 healthy controls, then utilized resting-state functional MRI and spectral dynamic causal modeling to explore the effective connectivity of the bilateral parafascicular nucleus to the dorsal putamen, nucleus accumbens, and subthalamic nucleus. The associations between the effective connectivity of the parafascicular nucleus projections and clinical features were measured with Pearson partial correlations.

RESULTS

Compared with controls, the effective connectivity from the parafascicular nucleus to dorsal putamen was significantly increased, while the connectivity to the nucleus accumbens and subthalamic nucleus was significantly reduced in Parkinson's disease patients. There was a significantly positive correlation between the connectivity of parafascicular nucleus-dorsal putamen projection and motor deficits. The connectivity from the parafascicular nucleus to the subthalamic nucleus was negatively correlated with motor deficits and apathy, while the connectivity from the parafascicular nucleus to the nucleus accumbens was negatively associated with depression.

CONCLUSION

The present study demonstrates that the parafascicular nucleus-related projections are damaged and associated with clinical symptoms of Parkinson's disease. Our findings provide new insights into the impaired basal ganglia-thalamocortical circuits and give support for the parafascicular nucleus as a potential effective neuromodulating target of the disease.

Functional dysconnectivity in youth depression: Systematic review, meta-analysis, and network-based integration

Youth depression has been associated with heterogenous patterns of aberrant brain connectivity. To make sense of these divergent findings, we conducted a systematic review encompassing 19 resting-state fMRI seed-to-whole-brain studies (1400 participants, comprising 795 youths with major depression and 605 matched healthy controls). We incorporated separate meta-analyses of connectivity abnormalities across the levels of the most commonly seeded brain networks (default-mode and limbic networks) and, based on recent additions to the literature, an updated meta-analysis of amygdala dysconnectivity in youth depression. Our findings indicated broad and distributed findings at an anatomical level, which could not be captured by conventional meta-analyses in terms of spatial convergence. However, we were able to parse the complexity of region-to-region dysconnectivity by considering constituent regions as components of distributed canonical brain networks. This integration revealed dysconnectivity centred on central executive, default mode, salience, and limbic networks, converging with findings from the adult depression literature and suggesting similar neurobiological underpinnings of youth and adult depression.

Aberrant single-subject morphological brain networks in first-episode, treatment-naive adolescents with major depressive disorder

Background

Neuroimaging-based connectome studies have indicated that major depressive disorder (MDD) is associated with disrupted topological organization of large-scale brain networks. However, the disruptions and their clinical and cognitive relevance are not well established for morphological brain networks in adolescent MDD.

Objective

To investigate the topological alterations of single-subject morphological brain networks in adolescent MDD.

Methods

Twenty-five first-episode, treatment-naive adolescents with MDD and 19 healthy controls (HCs) underwent T1-weighted magnetic resonance imaging and a battery of neuropsychological tests. Single-subject morphological brain networks were constructed separately based on cortical thickness, fractal dimension, gyrification index, and sulcus depth, and topologically characterized by graph-based approaches. Between-group differences were inferred by permutation testing. For significant alterations, partial correlations were used to examine their associations with clinical and neuropsychological variables in the patients. Finally, a support vector machine was used to classify the patients from controls.

Results

Compared with the HCs, the patients exhibited topological alterations only in cortical thickness-based networks characterized by higher nodal centralities in parietal (left primary sensory cortex) but lower nodal centralities in temporal (left parabelt complex, right perirhinal ectorhinal cortex, right area PHT and right ventral visual complex) regions. Moreover, decreased nodal centralities of some temporal regions were correlated with cognitive dysfunction and clinical characteristics of the patients. These results were largely reproducible for binary and weighted network analyses. Finally, topological properties of the cortical thickness-based networks were able to distinguish the MDD adolescents from HCs with 87.6% accuracy.

Conclusion

Adolescent MDD is associated with disrupted topological organization of morphological brain networks, and the disruptions provide potential biomarkers for diagnosing and monitoring the disease.

Theories of consciousness and psychiatric disorders - A comparative analysis

Disorders of consciousness represent an efficient way to test theories of consciousness' (ToCs) predictions. So far, ToCs have mostly focused on disorders of quantitative awareness such as coma, vegetative state, spatial neglect and hemianopia. Psychiatric disorders, by contrast, have received little attention, leaving their contribution to consciousness research almost unexplored. Therefore, this paper aims to assess the relation between ToCs and psychiatric disorders - that is, the extent to which current ToCs can account for psychiatric symptomatology. First, I review direct and indirect evidence linking each ToC to psychiatry disorders. Next, I differentiate ToCs based on their theoretical and methodological ground, highlighting how they distinctively address neural, cognitive, and phenomenological aspects of conscious experience and, in turn, psychiatric symptoms. Finally, I refer to one specific symptom to directly compare ToCs' explanatory power. Overall, Temporospatial Theory of Consciousness (TTC) appears to provide a more comprehensive account of psychiatric disorders, suggesting that a novel dimension of consciousness (i.e., form of consciousness) may be needed to address more qualitative alterations in conscious experience.

Investigating the heterogeneity within the somatosensory-motor network and its relationship with the attention and default systems

The somatosensory-motor network (SMN) not only plays an important role in primary somatosensory and motor processing but is also central to many disorders. However, the SMN heterogeneity related to higher-order systems still remains unclear. Here, we investigated SMN heterogeneity from multiple perspectives. To characterize the SMN substructures in more detail, we used ultra-high-field functional MRI to delineate a finer-grained cortical parcellation containing 430 parcels that is more homogenous than the state-of-the-art parcellation. We personalized the new parcellation to account for individual differences and identified multiscale individual-specific brain structures. We found that the SMN subnetworks showed distinct resting-state functional connectivity (RSFC) patterns. The Hand subnetwork was central within the SMN and exhibited stronger RSFC with the attention systems than the other subnetworks, whereas the Tongue subnetwork exhibited stronger RSFC with the default systems. This two-fold differentiation was observed in the temporal ordering patterns within the SMN. Furthermore, we characterized how the distinct attention and default streams were carried forward into the functions of the SMN using dynamic causal modeling and identified two behavioral domains associated with this SMN fractionation using meta-analytic tools. Overall, our findings provided important insights into the heterogeneous SMN organization at the system level and suggested that the Hand subnetwork may be preferentially involved in exogenous processes, whereas the Tongue subnetwork may be more important in endogenous processes.

Multimodal nonlinear correlates of behavioural symptoms in frontotemporal dementia

Background

Studies exploring the brain correlates of behavioural symptoms in the frontotemporal dementia spectrum (FTD) have mainly searched for linear correlations with single modality neuroimaging data, either structural magnetic resonance imaging (MRI) or fluoro-deoxy-D-glucose positron emission tomography (FDG-PET). We aimed at studying the two imaging modalities in combination to identify nonlinear co-occurring patterns of atrophy and hypometabolism related to behavioural symptoms.

Methods

We analysed data from 93 FTD patients who underwent T1-weighted MRI, FDG-PET imaging, and neuropsychological assessment including the Neuropsychiatric Inventory, Frontal Systems Behaviour Scale, and Neurobehavioral Rating Scale. We used a data-driven approach to identify the principal components underlying behavioural variability, then related the identified components to brain variability using a newly developed method fusing maps of grey matter volume and FDG metabolism.

Results

A component representing apathy, executive dysfunction, and emotional withdrawal was associated with atrophy in bilateral anterior insula and putamen, and with hypometabolism in the right prefrontal cortex. Another component representing the disinhibition versus depression/mutism continuum was associated with atrophy in the right striatum and ventromedial prefrontal cortex for disinhibition, and hypometabolism in the left fronto-opercular region and sensorimotor cortices for depression/mutism. A component representing psychosis was associated with hypometabolism in the prefrontal cortex and hypermetabolism in auditory and visual cortices.

Discussion

Behavioural symptoms in FTD are associated with atrophy and altered metabolism of specific brain regions, especially located in the frontal lobes, in a hierarchical way: apathy and disinhibition are mostly associated with grey matter atrophy, whereas psychotic symptoms are mostly associated with hyper-/hypo-metabolism.

Effective resting-state connectivity in severe unipolar depression before and after electroconvulsive therapy

BACKGROUND

Electroconvulsive therapy (ECT) is one of the most effective treatments for severe depressive disorders. A recent multi-center study found no consistent changes in correlation-based (undirected) resting-state connectivity after ECT. Effective (directed) connectivity may provide more insight into the working mechanism of ECT.

OBJECTIVE

We investigated whether there are consistent changes in effective resting-state connectivity.

METHODS

This multi-center study included data from 189 patients suffering from severe unipolar depression and 59 healthy control participants. Longitudinal data were available for 81 patients and 24 healthy controls. We used dynamic causal modeling for resting-state functional magnetic resonance imaging to determine effective connectivity in the default mode, salience and central executive networks before and after a course of ECT. Bayesian general linear models were used to examine differences in baseline and longitudinal effective connectivity effects associated with ECT and its effectiveness.

RESULTS

Compared to controls, depressed patients showed many differences in effective connectivity at baseline, which varied according to the presence of psychotic features and later treatment outcome. Additionally, effective connectivity changed after ECT, which was related to ECT effectiveness. Notably, treatment effectiveness was associated with decreasing and increasing effective connectivity from the posterior default mode network to the left and right insula, respectively. No effects were found using correlation-based (undirected) connectivity.

CONCLUSIONS

A beneficial response to ECT may depend on how brain regions influence each other in networks important for emotion and cognition. These findings further elucidate the working mechanisms of ECT and may provide directions for future non-invasive brain stimulation research.

Biotypes of major depressive disorder identified by a multiview clustering framework

BACKGROUND

The advances in resting-state functional magnetic resonance imaging techniques motivate parsing heterogeneity in major depressive disorder (MDD) through neurophysiological subtypes (i.e., biotypes). Based on graph theories, researchers have observed the functional organization of the human brain as a complex system with modular structures and have found wide-spread but variable MDD-related abnormality regarding the modules. The evidence implies the possibility of identifying biotypes using high-dimensional functional connectivity (FC) data in ways that suit the potentially multifaceted biotypes taxonomy.

METHODS

We proposed a multiview biotype discovery framework that involves theory-driven feature subspace partition (i.e., "view") and independent subspace clustering. Six views were defined using intra- and intermodule FC regarding three MDD focal modules (i.e., the sensory-motor system, default mode network, and subcortical network). For robust biotypes, the framework was applied to a large multisite sample (805 MDD participants and 738 healthy controls).

RESULTS

Two biotypes were stably obtained in each view, respectively characterized by significantly increased and decreased FC compared to healthy controls. These view-specific biotypes promoted the diagnosis of MDD and showed different symptom profiles. By integrating the view-specific biotypes into biotype profiles, a broad spectrum in the neural heterogeneity of MDD and its separation from symptom-based subtypes was further revealed.

LIMITATIONS

The power of clinical effects is limited and the cross-sectional nature cannot predict the treatment effects of the biotypes.

CONCLUSIONS

Our findings not only contribute to the understanding of heterogeneity in MDD, but also provide a novel subtyping framework that could transcend current diagnostic boundaries and data modality.

Information flow and dynamic functional connectivity during electroconvulsive therapy in patients with depression

BACKGROUND

Electroconvulsive therapy is effectively used for treatment-resistant depression; however, its neural mechanism is largely unknown. Resting-state functional magnetic resonance imaging is promising for monitoring outcomes of electroconvulsive therapy for depression. This study aimed to explore the imaging correlates of the electroconvulsive therapy effects on depression using Granger causality analysis and dynamic functional connectivity analyses.

METHODS

We performed advanced analyses of resting-state functional magnetic resonance imaging data at the beginning and intermediate stages and end of the therapeutic course to identify neural markers that reflect or predict the therapeutic effects of electroconvulsive therapy on depression.

RESULTS

We demonstrated that information flow between the functional networks analyzed by Granger causality changes during electroconvulsive therapy, and this change was correlated with the therapeutic outcome. Information flow and the dwell time (an index reflecting the temporal stability of functional connectivity) before electroconvulsive therapy are correlated with depressive symptoms during and after treatment.

LIMITATIONS

First, the sample size was small. A larger group is needed to confirm our findings. Second, the influence of concomitant pharmacotherapy on our results was not fully addressed, although we expected it to be minimal because only minor changes in pharmacotherapy occurred during electroconvulsive therapy. Third, different scanners were used the groups, although the acquisition parameters were the same; a direct comparison between patient and healthy participant data was not possible. Thus, we presented the data of the healthy participants separately from that of the patients as a reference.

CONCLUSIONS

These results show the specific properties of functional brain connectivity.

Cortical hierarchy disorganization in major depressive disorder and its association with suicidality

Objectives

To explore the suicide risk-specific disruption of cortical hierarchy in major depressive disorder (MDD) patients with diverse suicide risks.

Methods

Ninety-two MDD patients with diverse suicide risks and 38 matched controls underwent resting-state functional MRI. Connectome gradient analysis and stepwise functional connectivity (SFC) analysis were used to characterize the suicide risk-specific alterations of cortical hierarchy in MDD patients.

Results

Relative to controls, patients with suicide attempts (SA) had a prominent compression from the sensorimotor system; patients with suicide ideations (SI) had a prominent compression from the higher-level systems; non-suicide patients had a compression from both the sensorimotor system and higher-level systems, although it was less prominent relative to SA and SI patients. SFC analysis further validated this depolarization phenomenon.

Conclusion

This study revealed MDD patients had suicide risk-specific disruptions of cortical hierarchy, which advance our understanding of the neuromechanisms of suicidality in MDD patients.

Attenuated interoceptive processing in individuals with major depressive disorder and high repetitive negative thinking

Repetitive negative thinking (RNT) is a transdiagnostic symptom associated with poor outcomes in major depressive disorder (MDD). MDD is characterized by altered interoception, which has also been associated with poor outcomes. The present study investigated whether RNT is directly associated with altered interoceptive processing. Interoceptive awareness toward the heart and stomach was probed on the Visceral Interoceptive Attention (VIA) task with fMRI in MDD individuals who were propensity-matched on the severity of depression and anxiety symptoms and relevant demographics but different in RNT intensity (High RNT [H-RNT, n = 48] & Low RNT [L-RNT, n = 49]), and in matched healthy volunteers (HC, n = 27). Both H-RNT and L-RNT MDD individuals revealed reduced stomach interoceptive processing compared to HC in the left medial frontal region and insular cortex (H-RNT: β = -1.04, L-RNT: β = -0.97), perirhinal cortex (H-RNT: β = -0.99, L-RNT: β = -1.03), and caudate nucleus (H-RNT: β = -1.06, L-RNT: β = -0.89). However, H-RNT was associated with decreased right medial temporal lobe activity including the hippocampus and amygdala during stomach interoceptive trials (β = -0.61) compared to L-RNT. Insular interoceptive processing was similar in H-RNT and L-RNT participants (β = -0.07, p = 0.92). MDD individuals with high RNT exhibited altered gastric interoceptive responses in brain areas that are important for associating the information with specific contexts and emotions. Attenuated interoceptive processing may contribute to RNT generation, non-adaptive information processing, action selection, and thus poor treatment outcome.

Astrocyte dysfunction drives abnormal resting-state functional connectivity in depression

Major depressive disorder (MDD) is a devastating mental disorder that affects up to 17% of the population worldwide. Although brain-wide network-level abnormalities in MDD patients via resting-state functional magnetic resonance imaging (rsfMRI) exist, the mechanisms underlying these network changes are unknown, despite their immense potential for depression diagnosis and management. Here, we show that the astrocytic calcium-deficient mice, inositol 1,4,5-trisphosphate-type-2 receptor knockout mice (Itpr2^-/- mice), display abnormal rsfMRI functional connectivity (rsFC) in depression-related networks, especially decreased rsFC in medial prefrontal cortex (mPFC)-related pathways. We further uncover rsFC decreases in MDD patients highly consistent with those of Itpr2^-/- mice, especially in mPFC-related pathways. Optogenetic activation of mPFC astrocytes partially enhances rsFC in depression-related networks in both Itpr2^-/- and wild-type mice. Optogenetic activation of the mPFC neurons or mPFC-striatum pathway rescues disrupted rsFC and depressive-like behaviors in Itpr2^-/- mice. Our results identify the previously unknown role of astrocyte dysfunction in driving rsFC abnormalities in depression.

Resting state effective connectivity abnormalities of the Papez circuit and cognitive performance in multiple sclerosis

The Papez circuit is central to memory and emotional processes. However, little is known about its involvement in multiple sclerosis (MS). We aimed to investigate abnormalities of resting state (RS) effective connectivity (EC) between regions of the Papez circuit in MS and their relationship with cognitive performances. Sixty-two MS patients and 64 healthy controls (HC) underwent neuropsychological assessment, 3D T1-weighted, and RS functional MRI. RS EC analysis was performed using SPM12 and dynamic causal modeling. RS EC abnormalities were investigated using parametric empirical Bayes models and were correlated with cognitive scores. Compared to HC, MS patients showed (posterior probability > 0.95) higher EC between the right entorhinal cortex and right subiculum, and lower EC from the anterior cingulate cortex (ACC) to the posterior cingulate cortex (PCC), from left to right subiculum, from left anterior thalamus to ACC, and within ACC and PCC. Lower RS EC from the ACC to the PCC correlated with worse global cognitive scores (rho = 0.19; p = 0.03), worse visuospatial memory (rho = 0.19; p = 0.03) and worse semantic fluency (rho = 0.21; p = 0.02). Lower RS EC from the left to the right subiculum correlated with worse verbal memory (rho = 0.20; p = 0.02), lower RS EC within the ACC correlated with worse attention (rho = -0.19; p = 0.04) and more severe brain atrophy (rho = -0.26; p = 0.003). Higher EC from the right entorhinal cortex to right subiculum correlated with worse semantic fluency (rho = 0.21; p = 0.02). In conclusion, MS patients showed altered RS EC within the Papez circuit. Abnormal RS EC involving cingulate cortices and hippocampal formation contributed to explain cognitive deficits.

Static and treatment-responsive brain biomarkers of depression relapse vulnerability following prophylactic psychotherapy: Evidence from a randomized control trial

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A prospective study of neural biomarkers of relapse in remitted depressed patients.

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Assessed neural response to dysphoric mood-induction before and after psychotherapy.

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Relapse over a 2-year follow-up linked to dysphoria-evoked sensory inhibition.

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Relapse risk was lower when dorsolateral prefrontal reactivity decreased over time.

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Depression prophylaxis may involve reducing dysphoria-evoked sensory inhibition.

Background

Neural reactivity to dysphoric mood induction indexes the tendency for distress to promote cognitive reactivity and sensory avoidance. Linking these responses to illness prognosis following recovery from Major Depressive Disorder informs our understanding of depression vulnerability and provides engagement targets for prophylactic interventions.

Methods

A prospective fMRI neuroimaging design investigated the relationship between dysphoric reactivity and relapse following prophylactic intervention. Remitted depressed outpatients (N = 85) were randomized to 8 weeks of Cognitive Therapy with a Well-Being focus or Mindfulness Based Cognitive Therapy. Participants were assessed before and after therapy and followed for 2 years to assess relapse status. Neural reactivity common to both assessment points identified static biomarkers of relapse, whereas reactivity change identified dynamic biomarkers.

Results

Dysphoric mood induction evoked prefrontal activation and sensory deactivation. Controlling for past episodes, concurrent symptoms and medication status, somatosensory deactivation was associated with depression recurrence in a static pattern that was unaffected by prophylactic treatment, HR 0.04, 95% CI [0.01, 0.14], p < .001. Treatment-related prophylaxis was linked to reduced activation of the left lateral prefrontal cortex (LPFC), HR 3.73, 95% CI [1.33, 10.46], p = .013. Contralaterally, the right LPFC showed dysphoria-evoked inhibitory connectivity with the right somatosensory biomarker

Conclusions

These findings support a two-factor model of depression relapse vulnerability, in which: enduring patterns of dysphoria-evoked sensory deactivation contribute to episode return, but vulnerability may be mitigated by targeting prefrontal regions responsive to clinical intervention. Emotion regulation during illness remission may be enhanced by reducing prefrontal cognitive processes in favor of sensory representation and integration.

A single oral dose of citalopram increases interoceptive insight in healthy volunteers

RATIONALE

Interoception is the signalling, perception, and interpretation of internal physiological states. Many mental disorders associated with changes of interoception, including depressive and anxiety disorders, are treated with selective serotonin reuptake inhibitors (SSRIs). However, the causative link between SSRIs and interoception is not yet clear.

OBJECTIVES

To ascertain the causal effect of acute changes of serotonin levels on cardiac interoception.

METHODS

Using a within-participant placebo-controlled design, forty-seven healthy human volunteers (31 female, 16 male) were tested on and off a 20 mg oral dose of the commonly prescribed SSRI, citalopram. Participants made judgements on the synchrony between their heartbeat and auditory tones and then expressed confidence in each judgement. We measured three types of interoceptive cognition.

RESULTS

Citalopram increased cardiac interoceptive insight, measured as correspondence of self-reported confidence to the likelihood that interoceptive judgements were actually correct. This effect was driven by enhanced confidence for correct interoceptive judgements and was independent of measured cardiac and reported subjective effects of the drug.

CONCLUSIONS

An acute change of serotonin levels can increase insight into the reliability of inferences made from cardiac interoceptive sensations.

Alteration of whole-brain amplitude of low-frequency fluctuation and degree centrality in patients with mild to moderate depression: A resting-state functional magnetic resonance imaging study

BACKGROUND

Mild to moderate depressive disorder has a high risk of progressing to major depressive disorder.

METHODS

Low-frequency amplitude and degree centrality were calculated to compare 49 patients with mild to moderate depression and 21 matched healthy controls. Correlation analysis was conducted to explore the correlation between the amplitude of low-frequency fluctuation (ALFF) and the degree centrality (DC) of altered brain region and the scores of clinical scale. Receiver operating characteristic (ROC) curves were further analyzed to evaluate the predictive value of above altered ALFF and DC areas as image markers for mild to moderate depression.

RESULTS

Compared with healthy controls, patients with mild to moderate depression had lower ALFF values in the left precuneus and posterior cingulate gyrus [voxel p < 0.005, cluster p < 0.05, Gaussian random field correction (GRF) corrected] and lower DC values in the left insula (voxel p < 0.005, cluster p < 0.05, GRF corrected). There was a significant negative correlation between DC in the left insula and scale scores of Zung's Depression Scale (ZungSDS), Beck Self-Rating Depression Scale (BDI), Toronto Alexithymia Scale (TAS26), and Ruminative Thinking Response Scale (RRS_SUM, RRS_REFLECTION, RRS_DEPR). Finally, ROC analysis showed that the ALFF of the left precuneus and posterior cingulate gyrus had a sensitivity of 61.9% and a specificity of 79.6%, and the DC of the left insula had a sensitivity of 81% and a specificity of 85.7% in differentiating mild to moderate depression from healthy controls.

CONCLUSION

Intrinsic abnormality of the brain was mainly located in the precuneus and insular in patients with mild to moderate depression, which provides insight into potential neurological mechanisms.