Local functional connectivity density is closely associated with the response of electroconvulsive therapy in major depressive disorder

Functional network characteristics in adolescent psychotic mood disorder: associations with symptom severity and treatment effects

Adolescent psychotic mood disorder (MDP) is a specific phenotype that characterized by more severe symptoms and prognosis compared to nonpsychotic mood disorder (MDNP). But the underlying neural mechanisms remain unknown, and graph theory analysis can help to understand possible mechanisms of psychotic symptoms from the perspective of functional networks. A total of 177 adolescent patients with mood disorders were recruited, including 61 MDP and 116 MDNP. Functional networks were constructed, and topological properties were compared between the two groups at baseline and after treatment, and the association between properties changes and symptom improvement was explored. Compared to the MDNP group, the MDP group exhibited higher small-world properties (FDR q = 0.003) and normalized clustering coefficients (FDR q = 0.008) but demonstrated decreased nodal properties in the superior temporal gyrus (STG), Heschl's gyrus, and medial cingulate gyrus (all FDR q < 0.05). These properties were found to be correlated with the severity of psychotic symptoms. Topological properties also changed with improvement of psychotic symptoms after treatment, and changes in degree centrality of STG in the MDP was significantly positive correlated with improvement of psychotic symptoms (r = 0.377, P = 0.031). This study indicated that functional networks are more severely impaired in patients with psychotic symptoms. Topological properties, particularly those associated with the STG, hold promise as emerging metrics for assessing symptoms and treatment efficacy in patients with psychotic symptoms.

Distance-related functional reorganization predicts motor outcome in stroke patients

BACKGROUND

Analyzing distance-dependent functional connectivity density (FCD) yields valuable insights into patterns of brain activity. Nevertheless, whether alterations of FCD in non-acute stroke patients are associated with the anatomical distance between brain regions remains unclear. This study aimed to explore the distance-related functional reorganization in non-acute stroke patients following left and right hemisphere subcortical lesions, and its relationship with clinical assessments.

METHODS

In this study, we used resting-state fMRI to calculate distance-dependent (i.e., short- and long-range) FCD in 25 left subcortical stroke (LSS) patients, 22 right subcortical stroke (RSS) patients, and 39 well-matched healthy controls (HCs). Then, we compared FCD differences among the three groups and assessed the correlation between FCD alterations and paralyzed motor function using linear regression analysis.

RESULTS

Our findings demonstrated that the left inferior frontal gyrus displayed distance-independent FCD changes, while the bilateral supplementary motor area, cerebellum, and left middle occipital gyrus exhibited distance-dependent FCD alterations in two patient subgroups compared with HCs. Furthermore, we observed a positive correlation between increased FCD in the bilateral supplementary motor area and the motor function of lower limbs, and a negative correlation between increased FCD in the left inferior frontal gyrus and the motor function of both upper and lower limbs across all stroke patients. These associations were validated by using a longitudinal dataset.

CONCLUSIONS

The FCD in the cerebral and cerebellar cortices shows distance-related changes in non-acute stroke patients with motor dysfunction, which may serve as potential biomarkers for predicting motor outcomes after stroke. These findings enhance our comprehension of the neurobiological mechanisms driving non-acute stroke.

TRIAL REGISTRATION

All data used in the present study were obtained from a research trial registered with the ClinicalTrials.gov database (NCT05648552, registered 05 December 2022, starting from 01 January 2022).

GCTNet: a graph convolutional transformer network for major depressive disorder detection based on EEG signals

Objective.Identifying major depressive disorder (MDD) using objective physiological signals has become a pressing challenge.Approach.Hence, this paper proposes a graph convolutional transformer network (GCTNet) for accurate and reliable MDD detection using electroencephalogram (EEG) signals. The developed framework integrates a residual graph convolutional network block to capture spatial information and a Transformer block to extract global temporal dynamics. Additionally, we introduce the contrastive cross-entropy (CCE) loss that combines contrastive learning to enhance the stability and discriminability of the extracted features, thereby improving classification performance.Main results. The effectiveness of the GCTNet model and CCE loss was assessed using EEG data from 41 MDD patients and 44 normal controls, in addition to a publicly available dataset. Utilizing a subject-independent data partitioning method and 10-fold cross-validation, the proposed method demonstrated significant performance, achieving an average Area Under the Curve of 0.7693 and 0.9755 across both datasets, respectively. Comparative analyses demonstrated the superiority of the GCTNet framework with CCE loss over state-of-the-art algorithms in MDD detection tasks.Significance. The proposed method offers an objective and effective approach to MDD detection, providing valuable support for clinical-assisted diagnosis.

Machine Learning in Electroconvulsive Therapy: A Systematic Review

ABSTRACT

Despite years of research, we are still not able to reliably predict who might benefit from electroconvulsive therapy (ECT) treatment. As we exhaust what is possible using traditional statistical analysis, ECT remains a good candidate for machine learning approaches due to the large data sets with data captured through electroencephalography (EEG) and other objective measures. A systematic review of 6 databases led to the full-text examination of 26 articles using machine learning approaches in examining data predicting response to ECT treatment. The identified articles used a wide variety of data types covering structural and functional imaging data (n = 15), clinical data (n = 5), a combination of clinical and imaging data (n = 2), EEG (n = 3), and social media posts (n = 1). The clinical indications in which response prediction was assessed were depression (n = 21) and psychosis (n = 4). Changes in multiple anatomical regions in the brain were identified as holding a predictive value for response to ECT. These primarily centered on the limbic system and associated networks. Clinical features predicting good response to ECT in depression included shorter duration, lower severity, higher medication dose, psychotic features, low cortisol levels, and positive family history. It has also been possible to predict the likelihood of relapse of readmission with psychosis after ECT treatment, including a better response if higher transfer entropy was calculated from EEG signals. A transdisciplinary approach with an international consortium collecting a wide range of retrospective and prospective data may help to refine and extend these outcomes and translate them into clinical practice.

Molecular basis underlying changes of brain entropy and functional connectivity in major depressive disorders after electroconvulsive therapy

INTRODUCTION

Electroconvulsive therapy (ECT) is widely used for treatment-resistant depression. However, it is unclear whether/how ECT can be targeted to affect brain regions and circuits in the brain to dynamically regulate mood and cognition.

METHODS

This study used brain entropy (BEN) to measure the irregular levels of brain systems in 46 major depressive disorder (MDD) patients before and after ECT treatment. Functional connectivity (FC) was further adopted to reveal changes of functional couplings. Moreover, transcriptomic and neurotransmitter receptor data were used to reveal genetic and molecular basis of the changes of BEN and functional connectivities.

RESULTS

Compared to pretreatment, the BEN in the posterior cerebellar lobe (PCL) significantly decreased and FC between the PCL and the right temporal pole (TP) significantly increased in MDD patients after treatment. Moreover, we found that these changes of BEN and FC were closely associated with genes' expression profiles involved in MAPK signaling pathway, GABAergic synapse, and dopaminergic synapse and were significantly correlated with the receptor/transporter density of 5-HT, norepinephrine, glutamate, etc. CONCLUSION: These findings suggest that loops in the cerebellum and TP are crucial for ECT regulation of mood and cognition, which provides new evidence for the antidepressant effects of ECT and the potential molecular mechanism leading to cognitive impairment.

Disrupted properties of functional brain networks in major depressive disorder during emotional face recognition: an EEG study via graph theory analysis

Previous neuroimaging studies have revealed abnormal brain networks in patients with major depressive disorder (MDD) in emotional processing. While any cognitive task consists of a series of stages, little is yet known about the topology of functional brain networks in MDD for these stages during emotional face recognition. To address this problem, electroencephalography (EEG)-based functional brain networks of MDD patients at different stages of facial information processing were investigated in this study. First, EEG signals were collected from 16 patients with MDD and 18 age-, gender-, and education-matched normal subjects when performing an emotional face recognition task. Second, the global field power (GFP) method was employed to divide group-averaged event-related potentials into different stages. Third, using the phase transfer entropy (PTE) approach, the brain networks of MDD patients and normal individuals were constructed for each stage in negative and positive face processing, respectively. Finally, we compared the topological properties of brain networks of each stage between the two groups using graph theory approaches. The results showed that the analyzed three stages of emotional face processing corresponded to specific neurophysiological phases, namely, visual perception, face recognition, and emotional decision-making. It was also demonstrated that depressed patients showed abnormally decreased characteristic path length at the visual perception stage of negative face recognition and normalized characteristic path length in the stage of emotional decision-making during positive face processing compared to healthy subjects. Furthermore, while both the MDD and normal groups' brain networks were found to exhibit small-world network characteristics, the brain network of patients with depression tended to be randomized. Moreover, for patients with MDD, the centro-parietal region may lose its status as a hub in the process of facial expression identification. Together, our findings suggested that altered emotional function in MDD patients might be associated with disruptions in the topological organization of functional brain networks during emotional face recognition, which further deepened our understanding of the emotion processing dysfunction underlying MDD.

Electroconvulsive therapy enhances degree centrality in the orbitofrontal cortex in depressive rumination

Depressive rumination has been implicated in the onset, duration, and treatment response of refractory depression. Electroconvulsive therapy (ECT) is remarkably effective in treatment of refractory depression by modulating the functional coordination between brain hubs. However, the mechanisms by which ECT regulates depressive rumination remain unsolved. We investigated degree centrality (DC) in 32 pre- and post-ECT depression patients as well as 38 matched healthy controls. An identified brain region was defined as the seed to calculate functional connectivity (FC) in whole brains. Rumination was measured by the Ruminative Response Scale (RRS) and its relationships with identified DC and FC alterations were examined. We found a significant negative correlation between DC of the right orbitofrontal cortex (rOFC) before ECT and brooding level before and after treatment. Moreover, rOFC DC increased after ECT. DC of the left superior temporal gyrus (lSTG) was positively correlated with reflective level before intervention, while lSTG DC decreased after ECT. Patients showed elevated FC in the rOFC with default mode network. No significant association was found between decreased RRS scores and changes in DC and FC. Our findings suggest that functional changes in rOFC and lSTG may be associated with the beneficial effects of ECT on depressive rumination.

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.

True grit and brain: Trait grit mediates the connection of DLPFC functional connectivity density to posttraumatic growth following COVID-19

BACKGROUND

There is increasing interest in identifying factors to predict posttraumatic growth (PTG), a positive psychological response following traumatic events (e.g., the COVID-19 pandemic). Grit, a psychological trait of perseverance and passion to pursue long-term goals, has emerged as a promising predictor for PTG. This study aimed to examine the functional connectivity markers of grit and the potential brain-grit mechanism in predicting PTG.

METHODS

Baseline brain imaging scans and grit scale and other controlling measures were administered in 100 normal young adults before the COVID-19 pandemic, and follow-up PTG measurement was obtained during the period of community-level outbreak. Whole-brain correlation analysis and prediction analysis were used to identify the brain regions whose functional connectivity density (FCD) related to individuals' grit scores. Mediation analyses were performed to explore the mediation relation between FCD, grit and PTG.

RESULTS

Grit was positively related to FCD in the right dorsolateral prefrontal cortex (DLPFC), a core hub implicated in self-regulation and reward-motivation processes. Furthermore, grit mediated the effect of right DLPFC FCD on COVID-related PTG. These results survived controlling for self-control and family socioeconomic status.

LIMITATIONS

Our study is limited by only one-session neuroimaging data and self-reported behavioral measures in a sample of normal adults.

CONCLUSIONS

This study indicates grit and right DLPFC FCD as neuropsychological contributors for the development of PTG. It deepens our understanding of the neural bases of grit, and may have clinical potential to develop targeted brain interventions aimed at improving grit to raise PTG and mental health during the pandemic.

Ensemble graph neural network model for classification of major depressive disorder using whole-brain functional connectivity

Major depressive disorder (MDD) is characterized by impairments in mood and cognitive functioning, and it is a prominent source of global disability and stress. A functional magnetic resonance imaging (fMRI) can aid clinicians in their assessments of individuals for the identification of MDD. Herein, we employ a deep learning approach to the issue of MDD classification. Resting-state fMRI data from 821 individuals with MDD and 765 healthy controls (HCs) is employed for investigation. An ensemble model based on graph neural network (GNN) has been created with the goal of identifying patients with MDD among HCs as well as differentiation between first-episode and recurrent MDDs. The graph convolutional network (GCN), graph attention network (GAT), and GraphSAGE models serve as a base models for the ensemble model that was developed with individual whole-brain functional networks. The ensemble's performance is evaluated using upsampling and downsampling, along with 10-fold cross-validation. The ensemble model achieved an upsampling accuracy of 71.18% and a downsampling accuracy of 70.24% for MDD and HC classification. While comparing first-episode patients with recurrent patients, the upsampling accuracy is 77.78% and the downsampling accuracy is 71.96%. According to the findings of this study, the proposed GNN-based ensemble model achieves a higher level of accuracy and suggests that our model produces can assist healthcare professionals in identifying MDD.

Abnormal dynamics of resting-state functional activity and couplings in postpartum depression with and without anxiety

Postpartum depression (PPD) and PPD comorbid with anxiety (PPD-A) are highly prevalent and severe mental health problems in postnatal women. PPD and PPD-A share similar pathopsychological features, leading to ongoing debates regarding the diagnostic and neurobiological uniqueness. This paper aims to delineate common and disorder-specific neural underpinnings and potential treatment targets for PPD and PPD-A by characterizing functional dynamics with resting-state functional magnetic resonance imaging in 138 participants (45 first-episode, treatment-naïve PPD; 31 PDD-A patients; and 62 healthy postnatal women [HPW]). PPD-A group showed specifically increased dynamic amplitude of low-frequency fluctuation in the subgenual anterior cingulate cortex (sgACC) and increased dynamic functional connectivity (dFC) between the sgACC and superior temporal sulcus. PPD group exhibited specifically increased static FC (sFC) between the sgACC and ventral anterior insula. Common disrupted sFC between the sgACC and middle temporal gyrus was found in both PPD and PPD-A patients. Interestingly, dynamic changes in dFC between the sgACC and superior temporal gyrus could differentiate PPD, PPD-A, and HPW. Our study presents initial evidence on specifically abnormal functional dynamics of limbic, emotion regulation, and social cognition systems in patients with PDD and PPD-A, which may facilitate understanding neurophysiological mechanisms, diagnosis, and treatment for PPD and PPD-A.

Changes in gray matter volume following electroconvulsive therapy in adolescent depression with suicidal ideation: A longitudinal structural magnetic resonance imaging study

Objective

We aimed to investigate changes in whole-brain gray matter volumes (GMVs) before and after electroconvulsive therapy (ECT) in adolescents with major depressive disorder (MDD) and suicidal ideation (SI).

Methods

Thirty adolescents with MDD and SI were observed, and structural magnetic resonance imaging (sMRI) was performed at baseline and after ECT for each patient. But Twenty-five healthy controls (HCs) were scanned only at baseline. The voxel-based morphometry (VBM) techniques were used to examine GMVs.

Results

Compared with HCs, MDDs at baseline showed decreased GMVs in the left middle temporal gyrus, right superior temporal gyrus, right middle temporal gyrus, left precuneus, right precuneus, and left superior frontal gyrus. After ECT, MDDs showed increased GMVs in the right superior frontal gyrus and right superior temporal gyrus. Pearson's correlation found that Beck Scale for Suicide Ideation (BSSI) scores at baseline were negatively correlated with GMVs in the left superior frontal gyrus and HAMD and BSSI scores after ECT were negatively correlated with GMVs in the right superior temporal gyrus.

Conclusion

Frontal-temporal-precuneus structure changes may be a potential cause of depressive and suicidal symptoms in adolescents. ECT may improve depressive and suicidal symptoms in adolescents by regulating brain structures to compensate original defects.

Neural signatures of default mode network in major depression disorder after electroconvulsive therapy

Functional abnormalities of default mode network (DMN) have been well documented in major depressive disorder (MDD). However, the association of DMN functional reorganization with antidepressant treatment and gene expression is unclear. Moreover, whether the functional interactions of DMN could predict treatment efficacy is also unknown. Here, we investigated the link of treatment response with functional alterations of DMN and gene expression with a comparably large sample including 46 individuals with MDD before and after electroconvulsive therapy (ECT) and 46 age- and sex-matched healthy controls. Static and dynamic functional connectivity (dFC) analyses showed increased intrinsic/static but decreased dynamic functional couplings of inter- and intra-subsystems and between nodes of DMN. The changes of static functional connections of DMN were spatially correlated with brain gene expression profiles. Moreover, static and dFC of the DMN before treatment as features could predict depressive symptom improvement following ECT. Taken together, these results shed light on the underlying neural and genetic basis of antidepressant effect of ECT and the intrinsic functional connectivity of DMN have the potential to serve as prognostic biomarkers to guide accurate personalized treatment.

Disrupted functional connectivity patterns of the left inferior frontal gyrus subregions in benign childhood epilepsy with centrotemporal spikes

BACKGROUND

Benign epilepsy with centrotemporal spikes (BECTS) is one of the most common pediatric epileptic syndromes. Recent studies have shown that BECTS can lead to significant language dysfunction. Although research supports the role of the left inferior frontal gyrus (LIFG) in BECTS, it is unclear whether the subregions of the LIFG show different change patterns in patients with this syndrome.

METHODS

Using resting-state functional magnetic resonance imaging (fMRI) data in a group of 49 BECTS patients and 49 healthy controls, we investigated whether the BECTS patients show abnormal connectivity patterns of the LIFG subregions.

RESULTS

Compared with healthy controls, the BECTS patients exhibited higher connectivity between the following: the inferior frontal sulcus (IFS) and the right anterior cingulate cortex (ACC), and the ventral area 44 (A44v) region and the left hippocampus/parahippocampus. Also, a decreased connectivity was found between the IFS and the left inferior temporal gyrus (ITG). No other significant differences in functional connectivity were found in the other 4 functional subregions of the LIFG in the BECTS.

CONCLUSIONS

These findings provide evidence for BECTS-related functional connectivity patterns of the LIFG subregions and suggest that different subregions may be involved in different neural circuits associated with language function in the BECTS.

Personalized Diagnosis and Treatment for Neuroimaging in Depressive Disorders

Depressive disorders are highly heterogeneous in nature. Previous studies have not been useful for the clinical diagnosis and prediction of outcomes of major depressive disorder (MDD) at the individual level, although they provide many meaningful insights. To make inferences beyond group-level analyses, machine learning (ML) techniques can be used for the diagnosis of subtypes of MDD and the prediction of treatment responses. We searched PubMed for relevant studies published until December 2021 that included depressive disorders and applied ML algorithms in neuroimaging fields for depressive disorders. We divided these studies into two sections, namely diagnosis and treatment outcomes, for the application of prediction using ML. Structural and functional magnetic resonance imaging studies using ML algorithms were included. Thirty studies were summarized for the prediction of an MDD diagnosis. In addition, 19 studies on the prediction of treatment outcomes for MDD were reviewed. We summarized and discussed the results of previous studies. For future research results to be useful in clinical practice, ML enabling individual inferences is important. At the same time, there are important challenges to be addressed in the future.

Detection of Abnormal Spontaneous Brain Activity Patterns in Patients With Orbital Fractures Using Fractional Amplitude of Low Frequency Fluctuation

Background

To date, no in-depth study has been conducted on the intrinsic pathological relationship between altered brain activity and related behavioral changes in patients with orbital fracture (OF).

Purpose

The present research aimed to explore the potential functional network cerebrum activities in patients with OF using resting state functional magnetic resonance imaging–fractional amplitude of low-frequency fluctuation (rsfMRI-fALFF). This technique can reveal dynamic functional changes in specific cerebrum areas.

Methods

Twenty patients with OF and 20 healthy controls (HCs) were included, closely matched in terms of gender, age, weight, and education level. To record spontaneous cerebral activity changes, the rsfMRI-fALFF tool was applied. Receiver operating characteristic (ROC) curves and Pearson's correlation analysis were used to analyze mean fALFF values in specific cerebrum regions and to explore changes of behavioral changes in patients with OF. The Hospital Depression and Anxiety scale was applied to reveal the relationship between emotional states and fALFF values of the right superior temporal gyrus in patients with OF.

Results

In comparison with HCs, significantly lower fALFF values were detected in the left anterior cingulate gyrus (LACG) and right superior temporal gyrus (RSTG) in patients with OF. ROC curve analysis showed excellent accuracy. The mean fALFF values of the RSTG negatively correlated with the depression score as well as the anxiety score.

Conclusion

The finding of abnormal spontaneous activities in cerebral regions may contribute to more comprehensive understanding of the potential neural network changes in patients with OF. The changes of fALFF value in patients with OF may help to gauge their emotional changes and clinical recovery levels.

Children's Neural Sensitivity to Prosodic Features of Natural Speech and Its Significance to Speech Development in Cochlear Implanted Children

Catchy utterances, such as proverbs, verses, and nursery rhymes (i.e., "No pain, no gain" in English), contain strong-prosodic (SP) features and are child-friendly in repeating and memorizing; yet the way those prosodic features encoded by neural activity and their influence on speech development in children are still largely unknown. Using functional near-infrared spectroscopy (fNIRS), this study investigated the cortical responses to the perception of natural speech sentences with strong/weak-prosodic (SP/WP) features and evaluated the speech communication ability in 21 pre-lingually deaf children with cochlear implantation (CI) and 25 normal hearing (NH) children. A comprehensive evaluation of speech communication ability was conducted on all the participants to explore the potential correlations between neural activities and children's speech development. The SP information evoked right-lateralized cortical responses across a broad brain network in NH children and facilitated the early integration of linguistic information, highlighting children's neural sensitivity to natural SP sentences. In contrast, children with CI showed significantly weaker cortical activation and characteristic deficits in speech perception with SP features, suggesting hearing loss at the early age of life, causing significantly impaired sensitivity to prosodic features of sentences. Importantly, the level of neural sensitivity to SP sentences was significantly related to the speech behaviors of all children participants. These findings demonstrate the significance of speech prosodic features in children's speech development.

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.

More Than Just Statics: Temporal Dynamic Changes in Inter- and Intrahemispheric Functional Connectivity in First-Episode, Drug-Naive Patients With Major Depressive Disorder

Several functional magnetic resonance imaging (fMRI) studies have demonstrated abnormalities in static intra- and interhemispheric functional connectivity among diverse brain regions in patients with major depressive disorder (MDD). However, the dynamic changes in intra- and interhemispheric functional connectivity patterns in patients with MDD remain unclear. Fifty-eight first-episode, drug-naive patients with MDD and 48 age-, sex-, and education level-matched healthy controls (HCs) underwent resting-state fMRI. Whole-brain functional connectivity, analyzed using the functional connectivity density (FCD) approach, was decomposed into ipsilateral and contralateral functional connectivity. We computed the intra- and interhemispheric dynamic FCD (dFCD) using a sliding window analysis to capture the dynamic patterns of functional connectivity. The temporal variability in functional connectivity was quantified as the variance of the dFCD over time. In addition, intra- and interhemispheric static FCD (sFCD) patterns were calculated. Associations between the dFCD variance and sFCD in abnormal brain regions and the severity of depressive symptoms were analyzed. Compared to HCs, patients with MDD showed lower interhemispheric dFCD variability in the inferior/middle frontal gyrus and decreased sFCD in the medial prefrontal cortex/anterior cingulate cortex and posterior cingulate cortex/precuneus in both intra- and interhemispheric comparisons. No significant correlations were found between any abnormal dFCD variance or sFCD at the intra- and interhemispheric levels and the severity of depressive symptoms. Our results suggest intra- and interhemispheric functional connectivity alterations in the dorsolateral prefrontal cortex (DLPFC) and default mode network regions involved in cognition, execution and emotion. Furthermore, our study emphasizes the essential role of altered interhemispheric communication dynamics in the DLPFC in patients with MDD. These findings contribute to our understanding of the pathophysiology of MDD.

Disassociated and concurrent structural and functional abnormalities in the drug-naïve first-episode early onset schizophrenia

Schizophrenia which is an abnormally developmental disease has been widely reported to show abnormal brain structure and function. Enhanced functional integration is a predominant neural marker for brain mature. Abnormal development of structure and functional integration may be a biomarker for early diagnosis of schizophrenia. Fifty-five patients with early onset schizophrenia (EOS) and 79 healthy controls were enrolled in this study. Voxel-based morphometry (VBM) and functional connectivity density (FCD) were performed to explore gray matter volume (GMV) lesion, abnormal functional integration, and concurrent structural and functional abnormalities in the brain. Furthermore, the relationships between abnormalities structural and function and clinical characteristics were evaluated in EOS. Compared with healthy controls, EOS showed significantly decreased GMV in the bilateral OFC, frontal, temporal, occipital, parietal and limbic system. EOS also showed decreased FCD in precuneus and increased FCD in cerebellum. Moreover, we found concurrent changes of structure and function in left lateral orbitofrontal cortex (lOFC). Finally, correlation analyses did not find significant correlation between abnormal neural measurements and clinical characteristic in EOS. The results reveal disassociated and bound structural and functional abnormalities patterns in EOS suggesting structural and functional measurements play different roles in delineating the abnormal patterns of EOS. The concurrent structural and functional changes in lOFC may be a biomarker for early diagnosis of schizophrenia. Our findings will deepen our understanding of the pathophysiological mechanisms in EOS.

Altered functional connectivity density in mild cognitive impairment with moxibustion treatment: A resting-state fMRI study

PURPOSE

Mild cognitive impairment (MCI) is a general neurodegenerative disease. Moxibustion has been shown to have remarkable effect on cognitive improvement, however, less is known about the effect of moxibustion on MCI and its underlying neural mechanism. This study aimed to investigate the ameliorative brain network in MCI after treatments of acupoint-related moxibustion.

METHODS

Resting-state functional MRI were derived from 47 MCI patients and 30 healthy controls (HCs). Patients were randomized as Tiaoshen YiZhi (TSYZ, n = 27) and sham (SHAM, n = 20) acupoint moxibustion groups. Functional connectivity density (FCD) method and repeated-measures two-way analysis of variance (ANOVA) were performed to ascertain the interaction effects between groups (TSYZ and SHAM) and time (baseline and post-treatment). Abnormal FCD was examined between baseline and post-treatment in TSYZ and SHAM groups, respectively.

RESULTS

Compared with HCs, MCI showed altered FCD in the middle frontal cortex (MFC), inferior frontal cortex, temporal pole, thalamus and middle cingulate cortex. After moxibustion treatment in MCI, 1) a significant time-by-groups interaction was observed in the medial prefrontal cortex (mPFC); 2) abnormal long-range FCD (lrFCD) in the mPFC and MFC were modulated in TSYZ group; 3) significantly improved clinical symptoms; 4) changed lrFCD in the MFC was significantly negatively correlated with the increased Montreal Cognitive Assessment scores in TSYZ group.

CONCLUSIONS

These imaging findings suggest that treatments of acupoint-related moxibustion could improve lrFCD in certain regions related to self-related cognitive and decision making. Our study might promote understanding of MCI neural mechanisms and expand the clinical application of moxibustion in MCI.

Noninvasive neuromodulation of the prefrontal cortex in mental health disorders

More than any other brain region, the prefrontal cortex (PFC) gives rise to the singularity of human experience. It is therefore frequently implicated in the most distinctly human of all disorders, those of mental health. Noninvasive neuromodulation, including electroconvulsive therapy (ECT), repetitive transcranial magnetic stimulation (rTMS), and transcranial direct current stimulation (tDCS) among others, can-unlike pharmacotherapy-directly target the PFC and its neural circuits. Direct targeting enables significantly greater on-target therapeutic effects compared with off-target adverse effects. In contrast to invasive neuromodulation approaches, such as deep-brain stimulation (DBS), noninvasive neuromodulation can reversibly modulate neural activity from outside the scalp. This combination of direct targeting and reversibility enables noninvasive neuromodulation to iteratively change activity in the PFC and its neural circuits to reveal causal mechanisms of both disease processes and healthy function. When coupled with neuronavigation and neurophysiological readouts, noninvasive neuromodulation holds promise for personalizing PFC neuromodulation to relieve symptoms of mental health disorders by optimizing the function of the PFC and its neural circuits. ClinicalTrials.gov Identifier: NCT03191058.

Dynamic changes of large-scale resting-state functional networks in major depressive disorder

Sliding window method is widely used to study the functional connectivity dynamics in brain networks. A key issue of this method is how to choose the window length and number of clusters across different window length. Here, we introduced a universal method to determine the optimal window length and number of clusters and applied it to study the dynamic functional network connectivity (FNC) in major depressive disorder (MDD). Specifically, we first extracted the resting-state networks (RSNs) in 27 medication-free MDD patients and 54 healthy controls using group independent component analysis (ICA), and constructed the dynamic FNC patterns for each subject in the window range of 10-80 repetition times (TRs) using sliding window method. Then, litekmeans algorithm was utilized to cluster the FNC patterns corresponding to each window length into 2-20 clusters. The optimal number of clusters was determined by voting method and the optimal window length was determined by identifying the most representative window length. Finally, 8 recurring FNC patterns regarded as FNC states were captured for further analyzing the dynamic attributes. Our results revealed that MDD patients showed increased mean dwell time and fraction of time spent in state #5, and the mean dwell time is correlated with depression symptom load. Additionally, compared with healthy controls, MDD patients had significantly reduced FNC within FPN in state #7. Our study reported a new approach to determine the optimal window length and number of clusters, which may facilitate the future study of the functional dynamics. These findings about MDD using dynamic FNC analyses provide new evidence to better understand the neuropathology of MDD.

Altered Functional Connectivity Density and Couplings in Postpartum Depression with and Without Anxiety

Postpartum depression (PPD) is the most common psychological health issue among women, which often comorbids with anxiety (PPD-A). PPD and PPD-A showed highly overlapping clinical symptoms. Identifying disorder-specific neurophysiological markers of PDD and PPD-A is important for better clinical diagnosis and treatments. Here, we performed functional connectivity density (FCD) and resting-state functional connectivity (rsFC) analyses in 138 participants (45 unmedicated patients with first-episode PPD, 31 PDD-A patients and 62 healthy postnatal women, respectively). FCD mapping revealed specifically weaker long-range FCD in right lingual gyrus (LG.R) for PPD patients and significantly stronger long-range FCD in left ventral striatum (VS.L) for PPD-A patients. The follow-up rsFC analyses further revealed reduced functional connectivity between dorsomedial prefrontal cortex (dmPFC) and VS.L in both PPD and PPD-A. PPD showed specific changes of rsFC between LG.R and dmPFC, right angular gyrus and left precentral gyrus, while PPD-A represented specifically abnormal rsFC between VS.L and left ventrolateral prefrontal cortex. Moreover, the altered FCD and rsFC were closely associated with depression and anxiety symptoms load. Taken together, our study is the first to identify common and disorder-specific neural circuit disruptions in PPD and PPD-A, which may facilitate more effective diagnosis and treatments.

Decreased Cortical Folding of the Fusiform Gyrus and Its Hypoconnectivity with Sensorimotor Areas in Major Depressive Disorder

BACKGROUND

Neuroimaging studies have revealed abnormal cortical folding pattern and disruptive functional connectivity in major depressive disorder (MDD). Combining structure and function in the same population may further our understanding of the neuropathological mechanisms of MDD.

METHOD

Sixty-two patients with MDD and 61 healthy controls (HCs) underwent structural and resting-state functional magnetic resonance imaging (MRI). Group differences in the cortical folding (measured by local gyrification index (LGI)) were analyzed in FreeSurfer. Taking the brain regions with significant group differences in LGI as seed regions, the resting-state functional connectivity analysis was further conducted to explore the corresponding functional connectivity alterations.

RESULTS

Comparing with HCs, patients with MDD showed significantly decreased LGI in the right fusiform gyrus (cohen's d = 0.70). In the seed-based functional connectivity analysis, we found that compared with HCs, patients with MDD showed decreased functional connections between the right fusiform gyrus with sensorimotor areas (precentral and postcentral gyrus) (cohen's d = 1.32) and right superior temporal gyrus (cohen's d = 0.94).

LIMITATIONS

Main limitations are the relatively small sample size and the cross-sectional study design.

CONCLUSION

Decreased LGI in the right fusiform gyrus, as well as decreased functional connectivity between the right fusiform gyrus and the sensorimotor area and right superior temporal gyrus, appears to play a role in the pathophysiology of MDD.

Establishment of Effective Biomarkers for Depression Diagnosis With Fusion of Multiple Resting-State Connectivity Measures

Major depressive disorder (MDD) is a severe mental disorder and is lacking in biomarkers for clinical diagnosis. Previous studies have demonstrated that functional abnormalities of the unifying triple networks are the underlying basis of the neuropathology of depression. However, whether the functional properties of the triple network are effective biomarkers for the diagnosis of depression remains unclear. In our study, we used independent component analysis to define the triple networks, and resting-state functional connectivities (RSFCs), effective connectivities (EC) measured with dynamic causal modeling (DCM), and dynamic functional connectivity (dFC) measured with the sliding window method were applied to map the functional interactions between subcomponents of triple networks. Two-sample t-tests with p < 0.05 with Bonferroni correction were used to identify the significant differences between healthy controls (HCs) and MDD. Compared with HCs, the MDD showed significantly increased intrinsic FC between the left central executive network (CEN) and salience network (SAL), increased EC from the right CEN to left CEN, decreased EC from the right CEN to the default mode network (DMN), and decreased dFC between the right CEN and SAL, DMN. Moreover, by fusion of the changed RSFC, EC, and dFC as features, support vector classification could effectively distinguish the MDD from HCs. Our results demonstrated that fusion of the multiple functional connectivities measures of the triple networks is an effective way to reveal functional disruptions for MDD, which may facilitate establishing the clinical diagnosis biomarkers for depression.

Analysis of Functional Brain Network in MDD Based on Improved Empirical Mode Decomposition With Resting State EEG Data

At present, most brain functional studies are based on traditional frequency bands to explore the abnormal functional connections and topological organization of patients with depression. However, they ignore the characteristic relationship of electroencephalogram (EEG) signals in the time domain. Therefore, this paper proposes a network decomposition model based on Improved Empirical Mode Decomposition (EMD), it is suitable for time-frequency analysis of brain functional network. On the one hand, it solves the problem of mode mixing on original EMD method, especially on high-density EEG data. On the other hand, by building brain function networks on different intrinsic mode function (IMF), we can perform time-frequency analysis of brain function connections. It provides a new insight for brain function connectivity analysis of major depressive disorder (MDD). Experimental results found that the IMFs waveform decomposed by Improved EMD was more stable and the difference between IMFs was obvious, it indicated that the mode mixing can be effectively solved. Besides, the analysis of the brain network, we found that the changes in MDD functional connectivity on different IMFs, it may be related to the pathological changes for MDD. More statistical results on three network metrics proved that there were significant differences between MDD and normal controls (NC) group. In addition, the aberrant brain network structure of MDDs was also confirmed in the hubs characteristic. These findings may provide potential biomarkers for the clinical diagnosis of MDD patients.

A multimetric systematic review of fMRI findings in patients with MDD receiving ECT

BACKGROUND

Electroconvulsive therapy (ECT) is considered the most effective treatment for major depressive disorder (MDD). In recent years, the pursuit of the neurobiological mechanisms of ECT action has generated a significant amount of functional magnetic resonance imaging (fMRI) research.

OBJECTIVE

In this systematic review, we integrated all fMRI research in patients with MDD receiving ECT and, importantly, evaluated the level of convergence and replicability across multiple fMRI metrics.

RESULTS

While according to most studies changes in patients with MDD after ECT appear to be widely distributed across the brain, our multimetric review revealed specific changes involving functional connectivity increases in the superior and middle frontal gyri as the most replicated and across-modality convergent findings. Although this modulation of prefrontal connectivity was associated to ECT outcome, we also identified fMRI measurements of the subgenual anterior cingulate cortex as the fMRI signals most significantly linked to clinical response.

CONCLUSION

We identified specific prefrontal and cingulate territories which activity and connectivity with other brain regions is modulated by ECT, critically accounting for its mechanism of action.

Altered functional connectivity patterns of insular subregions in major depressive disorder after electroconvulsive therapy

Although electroconvulsive therapy (ECT) is an efficient treatment for major depressive disorder (MDD), however, it also brings memory impairment. The insula is a critical brain structure for coordinating affective, cognitive memory, saliency processing, and attention switching suggesting functional activity of insula maybe an important indicator to delineate the treatment and side effects of ECT. Here, Resting-state functional connectivity analyses of insular subregions were performed to reveal the changes of connectivity in 23 MDD patients before and after ECT and 25 healthy control (HC) and identified significantly increased functional connectivity of the right ventral anterior insular subregion with bilateral caudate, angular gyrus, and dorsolateral prefrontal cortex after ECT. Granger causality analyses identified significantly increased effective connectivity from dorsolateral prefrontal cortex to right angular gyrus in MDD patients after ECT. Furthermore, increased effective connectivity from dorsolateral prefrontal cortex to right angular gyrus exhibited significantly positive correlation with changed Hamilton Rating Scale for Depression scores. These results showed that ECT can normalize abnormal functional connectivity and effective connectivity in MDD. Our findings also indicated that the right ventral anterior insula and effective connectivity from dorsolateral prefrontal cortex to right angular gyrus are biomarkers of antidepressant effects during ECT of MDD.

Dose-Response Association of Waist-to-Height Ratio Plus BMI and Risk of Depression: Evidence from the NHANES 05-16

Background

Obesity or underweight has been found to be associated with depression, but the relationship remains to be determined so that more precise prevention strategies can be implemented. Body mass index (BMI) and waist–height ratio (WHR) were used as indicators to study the dose–response relationship between depression and obesity or underweight.

Methods

We obtained basic information and disease-related data for 13,975 adults from the National Health and Nutrition Examination Survey (NHANES) 2005–2016 dataset. The depressive status was determined based on the PHQ-9 scale (>4). Logistic regression was used to analyze the association and risk of BMI, WHTR and depressive status. Based on the results of logistic regression, the dose–response relationship between BMI, WHTR and depressive state was analyzed using restricted cubic splines (RCS).

Results

The adjusted model showed that compared with the fourth quartile (Q4) of BMI, the odds ratios (ORs) of depression for Q1, Q2 and Q3 were 0.63 (0.56–0.71), 0.61 (0.54–0.68) and 0.74 (0.66–0.82), and compared with the fourth quartile (Q4) of WHtR, the odds ratios (ORs) of depression for Q1, Q2 and Q3 were 0.55 (0.49–0.62), 0.57 (0.51–0.64) and 0.64 (0.57–0.71), respectively. The restricted cubic spline regression depicted a U-shaped dose–response relationship between continuous changes of obesity indicators and the risk of depression (P1, P2 < 0.001). When the participants’ BMI reached approximately 25kg/m² with the reference value of BMI was 18.5kg/m², the risk of depression was minimized (OR=0.68, 95% Cl=0.56–0.83). When the WHtR reached approximately 0.52 with the reference value of WHtR was 0.40, the risk of depression was minimized (OR=0.69, 95% Cl=0.54–0.88).

Conclusion

We found a significant U-shape correlation between BMI, WHtR and depression. People with slight overweight have the lowest risk of depression. However, according to the International Obesity standards, the population at these levels of weight may have an obesity-chronic disease risk, and this is not recommended.

Resting State Functional Connectivity of Brain With Electroconvulsive Therapy in Depression: Meta-Analysis to Understand Its Mechanisms

Introduction: Electroconvulsive therapy (ECT) is a commonly used brain stimulation treatment for treatment-resistant or severe depression. This study was planned to find the effects of ECT on brain connectivity by conducting a systematic review and coordinate-based meta-analysis of the studies performing resting state fMRI (rsfMRI) in patients with depression receiving ECT. Methods: We systematically searched the databases published up to July 31, 2020, for studies in patients having depression that compared resting-state functional connectivity (rsFC) before and after a course of pulse wave ECT. Meta-analysis was performed using the activation likelihood estimation method after extracting details about coordinates, voxel size, and method for correction of multiple comparisons corresponding to the significant clusters and the respective rsFC analysis measure with its method of extraction. Results: Among 41 articles selected for full-text review, 31 articles were included in the systematic review. Among them, 13 articles were included in the meta-analysis, and a total of 73 foci of 21 experiments were examined using activation likelihood estimation in 10 sets. Using the cluster-level interference method, one voxel-wise analysis with the measure of amplitude of low frequency fluctuations and one seed-voxel analysis with the right hippocampus showed a significant reduction (p < 0.0001) in the left cingulate gyrus (dorsal anterior cingulate cortex) and a significant increase (p < 0.0001) in the right hippocampus with the right parahippocampal gyrus, respectively. Another analysis with the studies implementing network-wise (posterior default mode network: dorsomedial prefrontal cortex) resting state functional connectivity showed a significant increase (p < 0.001) in bilateral posterior cingulate cortex. There was considerable variability as well as a few key deficits in the preprocessing and analysis of the neuroimages and the reporting of results in the included studies. Due to lesser studies, we could not do further analysis to address the neuroimaging variability and subject-related differences. Conclusion: The brain regions noted in this meta-analysis are reasonably specific and distinguished, and they had significant changes in resting state functional connectivity after a course of ECT for depression. More studies with better neuroimaging standards should be conducted in the future to confirm these results in different subgroups of depression and with varied aspects of ECT.

Bifrontal electroconvulsive therapy changed regional homogeneity and functional connectivity of left angular gyrus in major depressive disorder

Electroconvulsive therapy (ECT) is a rapid and effective treatment for MDD. However, the mechanism of ECT for MDD has not been clarified. In this study, we used resting-state functional magnetic resonance imaging (rs-fMRI) to explore the mechanism of ECT. Two groups of subjects were recruited: healthy controls (HCs) and MDD patients who received bifrontal ECT. MDD patients and HCs underwent rs-fMRI scans and clinical assessments (Hamilton Depression Rating Scale, Rey-Auditory Verbal Learning Test (RAVLT), and the verbal fluency test). Regional homogeneity (ReHo) and functional connectivity were evaluated for the analysis of rs-fMRI data. The results showed that ReHo values in the left angular gyrus (LAG) significantly increased in MDD patients after ECT, and the functional connectivity of the LAG with bilateral inferior temporal gyrus, bilateral middle frontal gyrus, left superior frontal gyrus, left middle temporal gyrus, left precuneus, left posterior cingulate gyrus, and right angular gyrus was found to be strengthened after ECT. The scores of delayed recall trial in the RAVLT of MDD patients were related to the functional connectivity of the LAG with the left inferior temporal gyrus and the left posterior cingulate gyrus. It indicated LAG palyed an important role in the mechanism of ECT in MDD.

Habenula and left angular gyrus circuit contributes to response of electroconvulsive therapy in major depressive disorder

The habenula (Hb), one of the hottest structures in depression, has been widely demonstrated to be involved in the neurobiology of depression. Although the structural and functional abnormalities of Hb have been reported in major depressive disorders (MDD) patients, the role of Hb in treatment response in MDD remains unclear. In this study, resting-state functional connectivity (RSFC) and Granger causality analysis (GCA) were performed to investigate the intrinsic and causal changes of Hb in MDD after ECT. Moreover, support vector classification was applied to find out whether the changed functional and causal connections of Hb can effectively distinguish the MDD patients from healthy controls. The RSFC and GCA identified increased RSFC strength between bilateral Hb and left angular gyrus (AG), decreased causal connectivity strength from left AG to left Hb, from right Hb to left AG, and bidirectional interactions between left and right Hb in MDD patients after ECT. The changed causal connectivities from left AG to left Hb, and from right Hb to left AG were correlated with the changed depression symptoms and impaired delay memory recall performances. Furthermore, the functional and causal connectivities between left AG and bilateral Hb could serve as a biomarker to differentiate MDD from HCs. These results provided new evidence for the importance of Hb in depression and revealed that the interactions between Hb and left AG contribute to ECT response in MDD. Our findings will facilitate the future treatment of depression with the target of Hb in MDD and other brain disorders.

Concurrent alterations of white matter microstructure and functional activities in medication-free major depressive disorder

Although numerous studies have revealed the structural and functional alterations in major depressive disorder (MDD) using unimodal diffusion magnetic resonance imaging (MRI) or functional MRI, however, the potential associations between changed microstructure and corresponding functional activities in the MDD has been largely uninvestigated. Herein, 27 medication-free MDD patients and 54 gender-, age-, and educational level-matched healthy controls (HC) were used to investigate the concurrent alterations of white matter microstructure and functional activities using tract-based spatial statistics (TBSS) analyses, fractional amplitude of low-frequency fluctuation (fALFF), and degree centrality (DC). The TBSS analyses revealed significantly decreased fractional anisotropy (FA) in the superior longitudinal fasciculus (SLF I) in the MDD patients compared to HC. Correlation analyses showed that decreased FA in the SLF I was significantly correlated with fALFF in left pre/postcentral gyrus and binary, weighted DC in right posterior cerebellum. Moreover, the fALFF in left pre/postcentral gyrus significantly reduced in MDD patients while binary and weighted DC in right posterior cerebellum significantly increased in MDD patients. Our results revealed concurrent structural and functional changes in MDD patients suggesting that the underlying structural disruptions are an important indicator of functional abnormalities.

Prediction on treatment improvement in depression with resting state connectivity: A coordinate-based meta-analysis

BACKGROUND

Previous neuroimaging studies revealed abnormal resting-state functional connectivity between distributed brain areas in patients with major depressive disorder. Those abnormalities were normalized after treatment. Moreover, the functional connectivity could predict clinical response to those treatments. However, there has currently been no meta-analysis to verify these findings.

METHODS

The current study aimed to investigate how the resting-state connectivity patterns predict antidepressant response to various treatments across depressive studies by using coordinate-based meta-analysis named activation likelihood estimation. The relevant articles were obtained by searching on PubMed and Web of Science.

RESULTS

Following exclusion criteria of inappropriate studies, seventeen papers with 392 individual depressive patients were included. Those articles contained repetitive transcranial magnetic stimulation (rTMS) treatment, pharmacotherapy, cognitive behavioral therapy (CBT), electroconvulsive therapy (ECT) and transcutaneous vagus nerve stimulation in patients with depression. Meta-analysis revealed that clinical response to all treatments could be predicted by baseline default mode network connectivity in patients with depression. The rTMS treatment had larger effect size compared to other treatment strategies. Furthermore, subgroup meta-analysis showed that the baseline connectivity of perigenual anterior cingulate cortex (pgACC) and ventral medial prefrontal cortex could predict symptoms improvement of rTMS treatment.

LIMITATIONS

More resting-state connectivity studies of CBT and ECT treatment are needed.

CONCLUSIONS

This study highlighted crucial role of DMN, especially the pgACC, in understanding the underlying treatment mechanism of depression.

Evolutional and developmental anatomical architecture of the left inferior frontal gyrus

The left inferior frontal gyrus (IFG) including Broca's area is involved in the processing of many language subdomains, and thus, research on the evolutional and human developmental characteristics of the left IFG will shed light on how language emerges and maturates. In this study, we used diffusion magnetic resonance imaging (dMRI) and resting-state functional MRI (fMRI) to investigate the evolutional and developmental patterns of the left IFG in humans (age 6-8, age 11-13, and age 16-18 years) and macaques. Tractography-based parcellation was used to define the subcomponents of left IFG and consistently identified four subregions in both humans and macaques. This parcellation scheme for left IFG in human was supported by specific coactivation patterns and functional characterization for each subregion. During evolution and development, we found increased functional balance, amplitude of low frequency fluctuations, functional integration, and functional couplings. We also observed higher fractional anisotropy values, i.e. better myelination of dorsal and ventral white matter language pathways during evolution and development. We assume that the resting-state functional connectivity and task-related coactivation mapping are associated with hierarchical language processing. Our findings have shown the evolutional and human developmental patterns of left IFG, and will contribute to the understanding of how the human language evolves and how atypical language developmental disorders may occur.

Electroconvulsive therapy modulates functional interactions between submodules of the emotion regulation network in major depressive disorder

An increasing number of neuroimaging studies have consistently revealed that disrupted functional interactions within the cognitive emotion regulation network (ERN) contribute to the onset of major depressive disorders (MDD). To disentangle the functional reorganization of ERN after electroconvulsive therapy (ECT) in MDD is curial for understanding its neuropathology. Resting-state functional magnetic resonance imaging data was collected from 23 MDD patients before and after ECT, as well as 25 healthy controls. Network modularity analysis was used to identify the submodules and functional connectivity (FC) was used to investigate the functional reorganization of ERN in the MDD patients after ECT. Four submodules of ERN were identified, including emotion response module (ERM), emotion integration module (EIM), emotion generation module (EGM), and emotion execution module (EEM). The increased intra-modular FC of EEM and inter-modular FCs of EEM with EIM\ERM were found in MDD patients after ECT. Modular transition analysis revealed that left ventrolateral prefrontal cortex, supplementary motor area, posterior cingulate cortex, right angular gyrus, and right precentral gyrus were transferred across different submodules across the three groups. Further analyses showed correlations between changed FC and clinical symptoms in the MDD patients after ECT. Finally, we also identified 11 increased connections between nodes belonging to different submodules of ERN in MDD patients after ECT. These results showed that ECT could induce functional reorganization of intra- and inter-modules within the ERN, and the functional changes were related to therapeutic efficacy or memory impairments of ECT in MDD patients.

Static and Dynamic Changes of Amplitude of Low-Frequency Fluctuations in Cervical Discogenic Pain

Cervical discogenic pain (CDP) is a clinically common pain syndrome caused by cervical disk degeneration. A large number of studies have reported that CDP results in brain functional impairments. However, the detailed dynamic brain functional abnormalities in CDP are still unclear. In this study, using resting-state functional magnetic resonance imaging, we explored the neural basis of CDP with 40 CDP patients and 40 age-, gender-matched healthy controls to delineate the changes of the voxel-level static and dynamic amplitude of low frequency fluctuations (ALFF). We found increased static ALFF in left insula (INS) and posterior precuneus (PCu), and decreased static ALFF in left precentral/postcentral gyrus (PreCG/PoCG), thalamus (THA), and subgenual anterior cingulate cortex in CPD patients compared to healthy controls. We also found decreased dynamic ALFF in left PreCG/PoCG, right posterior middle temporal gyrus, and bilateral THA. Moreover, we found that static ALFF in left PreCG/PoCG and dynamic ALFF in THA were significantly negatively correlated with visual analog scale and disease duration, respectively. Our findings provide the neurophysiological basis for CDP and facilitate understanding the neuropathology of CDP.

Improved and residual functional abnormalities in major depressive disorder after electroconvulsive therapy

Electroconvulsive therapy (ECT) can induce fast remission of depression but still retain the residual functional impairments in major depressive disorder (MDD) patients. To delineate the different functional circuits of effective antidepressant treatment and residual functional impairments is able to better guide clinical therapy for depression. Herein, voxel-level whole brain functional connectivity homogeneity (FcHo), functional connectivity, multivariate pattern classification approaches were applied to reveal the specific circuits for treatment response and residual impairments in MDD patients after ECT. Increased FcHo values in right dorsomedial prefrontal cortex (dmPFC) and left angular gyrus (AG) and their corresponding functional connectivities between dmPFC and right AG, dorsolateral prefrontal cortex (dlPFC), superior frontal gyrus, precuneus (Pcu) and between left AG with dlPFC, bilateral AG, and left ventrolateral prefrontal cortex in MDD patients after ECT. Moreover, we found decreased FcHo values in left middle occipital gyrus (MOG) and lingual gyrus (LG) and decreased functional connectivities between MOG and dorsal postcentral gyrus (PCG) and between LG and middle PCG/anterior superior parietal lobule in MDD patients before and after ECT compared to healthy controls (HCs). The increased or normalized FcHo and functional connections may be related to effective antidepressant therapy, and the decreased FcHo and functional connectivities may account for the residual functional impairments in MDD patients after ECT. The different change patterns in MDD after ECT indicated a specific brain circuit supporting fast remission of depression, which was supported by the following multivariate pattern classification analyses. Finally, we found that the changed FcHo in dmPFC was correlated with changed depression scores. These results revealed a specific functional circuit supporting antidepressant effects of ECT and neuroanatomical basis for residual functional impairments. Our findings also highlighted the key role of dmPFC in antidepressant and will provide an important reference for depression treatment.

Two-week rTMS-induced neuroimaging changes measured with fMRI in depression

OBJECTIVE

To study the neuroimaging mechanisms of repetitive transcranial magnetic stimulation (rTMS) in treating major depressive disorder (MDD).

METHODS

Twenty-seven treatment-naive patients with major depressive disorder (MDD) and 27 controls were enrolled. All of them were scanned with resting-state functional magnetic resonance imaging (fMRI) at baseline, and 15 patients were rescanned after two-week rTMS. The amplitude of low frequency fluctuation (ALFF) and functional connection degree (FCD), based on voxels and 3 brain networks (default mode network [DMN], central executive network [CEN], salience network[SN]),were used as imaging indicators to analyze. The correlations of brain imaging changes after rTMS with clinical efficacy were calculated.

RESULTS

At baseline, patients groups showed increased ALFF in the right orbital frontal cortex (OFC) and decreased ALFF in the left striatal cortex and medial prefrontal cortex (PFC), while increased FCD in the right dorsal anterior cingulate cortex and OFC and decreased FCD in the right inferior parietal lobe and in the CEN. After rTMS, patients showed increased ALFF in the left dorsolateral prefrontal cortex (DLPFC)and superior frontal gyrus, FCD in the right dorsal anterior cingulate cortex, superior temporal gyrus and CEN, as well as decreased FCD in the bilateral lingual gyrus than pre-rTMS . These rTMS induced neuroimaging changes did not significantly correlated with clinical effecacy.

CONCLUSIONS

This study indicated that rTMS resulted in changes of ALFF and FCD in some brain regions and CEN. But we could not conclude this is the neuroimaging mechanism of rTMS according to the correlation analysis.

Advanced machine learning methods in psychiatry: an introduction

Mental health questions can be tackled through machine learning (ML) techniques. Apart from the two ML methods we introduced in our previous paper, we discuss two more advanced ML approaches in this paper: support vector machines and artificial neural networks. To illustrate how these ML methods have been employed in mental health, recent research applications in psychiatry were reported.

Abnormal dynamic functional connectivity density in patients with generalized anxiety disorder

BACKGROUND

Numerous studies have revealed the abnormal static functional connectivity (FC) among different brain regions in patients with generalized anxiety disorder (GAD). However, little is known about the dynamic changes of FC in patients with GAD.

METHODS

This study investigated the whole-brain dynamic changes of FC in patients with GAD by combining global FC density (FCD) and sliding window correlation analyses. The standard deviation of dynamic FCD (dFCD) was calculated to evaluate its temporal variability along time. Support vector regression was then employed to predict the symptom severity of patients based on abnormal dynamic connectivity patterns.

RESULTS

The abnormal dFCD variability between 81 GAD patients and 80 healthy controls showed that the patients had higher dFCD variability in the bilateral dorsomedial prefrontal cortex (dmPFC) and left hippocampus while lower dFCD variability in the right postcentral gyrus. The abnormal dFCD variability of the left dmPFC is an important feature for anxiety prediction.

LIMITATIONS

The selection of sliding window length remains controversial, and most of our patients have been treated with medications. Future studies are expected to rule out the potential confounding effects from applying different parameters of the sliding window and recruiting large samples of medication-free patients.

CONCLUSION

The altered patterns of time-varying brain connectivity in the frontolimbic and sensorimotor areas may reflect abnormal dynamic neural communication between these regions and other regions of the brain, which may deepen our understanding of the disease.

Dopamine Multilocus Genetic Profile, Spontaneous Activity of Left Superior Temporal Gyrus, and Early Therapeutic Effect in Major Depressive Disorder

Objectives: This study aimed to examine the interactive effects of dopamine (DA) pathway gene and disease on spontaneous brain activity and further to explore the relationship between spontaneous brain activity and the early antidepressant therapeutic effect in patients with major depressive disorder (MDD). Methods: A total of 104 patients with MDD and 64 healthy controls (HCs) were recruited. The Hamilton Depression Scale-24 (HAMD-24) was used to measure the depression severity. Both groups were given resting-state functional magnetic resonance imaging (rs-fMRI) scan. The amplitude of low-frequency fluctuation (ALFF) was calculated to reflect the spontaneous brain activity based on the rs-fMRI data. After treatment for 2 weeks, depression severity was evaluated again, and HAMD-24 reductive rate was used to measure the therapeutic effect of antidepressants. Multilocus genetic profile scores (MGPS) were used to assess the multi-site cumulative effect of DA pathway gene. The interactive effects of MDD and DA pathway gene on the ALFF of regional brain areas were measured by the multivariate linear regression analysis. Finally, partial correlation analysis (age, sex, education, and illness durations as covariates) was performed to identify the relationship between regional ALFF and therapeutic effect. Results: MDD and DA-MGPS had interactive effects on the left fusiform gyrus (FG_L), right calcarine sulcus (CS_R), left superior temporal gyrus (STG_L), bilateral cerebellum posterior lobe (CPL), bilateral inferior frontal gyrus (IFG), and bilateral superior frontal gyrus (SFG). Partial correlation analysis revealed that the ALFF of STG_L had a significant negative correlation with 2-week HAMD-24 reductive rate (r = -0.211, P = 0.035). Conclusions: The spontaneous activity of STG_L may be a potential biomarker of antidepressant-related early therapeutic effect underlying the influence of DA pathway genes in MDD.

Altered whole brain functional connectivity pattern homogeneity in medication-free major depressive disorder

BACKGROUND

Many previous studies have revealed abnormal functional connectivity patterns between brain areas underlying the onset of major depressive disorder (MDD) using resting-state functional magnetic resonance imaging (rs-fMRI). However, how to exactly characterize the voxel-wise whole brain functional connectivity pattern changes in MDD remains unclear, which will provide more convincing evidence for localizing the exactly functional connectivity abnormality in MDD.

METHODS

In this study, we employed our newly developed whole brain functional connectivity homogeneity (FcHo) method to identify the voxel-wise changes of functional connectivity patterns in 27 medication-free MDD patients and 34 gender-, age-, and education level-matched healthy controls (HC). Furthermore, seed-based functional connectivity analysis was then used to identify the alteration of corresponding functional connectivity.

RESULTS

Significantly decreased FcHo values in right ventral anterior insula (INS) and medial prefrontal cortex (MPFC) were identified in MDD patients. The ensuing functional connectivity analyses identified decreased functional connectivity between MPFC and left angular gyrus (AG) in MDD patients. Moreover, both decreased FcHo values in INS, MPFC and functional connectivity between MPFC and left AG showed significant negative correlations with Hamilton depression rating scale (HDRS) scores. The FcHo values in INS were also negatively correlated with disease duration. Finally, meta-analysis based functional characterization found that these brain areas are mainly involved in emotion, theory of mind and reward processing.

CONCLUSIONS

Our findings revealed abnormal whole brain FcHo in INS and MPFC and functional interactions between MPFC and AG in MDD and suggested that dysfunctions of INS and MPFC play an important role in the neuropathology of MDD.

Convergent neurobiological predictors of mood and anxiety symptoms and treatment response

Introduction: Mood and anxiety disorders are leading contributors to the global burden of diseases. Comorbid mood and anxiety disorders have a lifetime prevalence of ~20% globally and increases the risk for suicide, a leading cause of death. Areas covered: In this review, authors highlight recent advances in the understanding of multilevel-neurobiological mechanisms for normal/pathological human affective-functioning. The authors then address the complex interplay between environmental-adversity and molecular-genetic mediators of brain correlates of affective-symptoms. The molecular focus is strategically limited to GTF2i, BDNF, and FKBP5 genes that are, respectively, involved in transcriptional-, neurodevelopmental- and neuroendocrine-pathway mediation of affective-functions. The importance of these genes is illustrated with studies of copy-number-variants, genome-wide association (GWAS), and candidate gene-sequence variant associations with disease etiology. Authors concluded by highlighting the predictive values of integrative neurobiological processing of gene-environment interactions for affective disorder symptom management. Expert opinion: Given the transcriptional, neurodevelopmental and neuroimmune relevance of GTF2i, BDNF, and FKBP5 genes, respectively, authors reviewed the putative roles of these genes in neurobiological mediation of adaptive affective-responses. Authors discussed the importance of studying gene-dosage effects in understanding affective disorder risk biology, and how such targeted neurogenetic studies could guide precision identification of novel pharmacotherapeutic targets and aid in prediction of treatment response.

Neurognitive function and symptom remission 2 years after ECT in major depressive disorders

BACKGROUND

There is a lack of knowledge of possible cognitive side effects of electroconvulsive therapy (ECT) beyond the first few months after treatment. We aim to describe cognitive effects and symptom remission 2 years after ECT in major depressive disorders.

METHOD

Twenty-seven depression patients were assessed with the MATRICS Consensus Cognitive Battery (MCCB) and the Everyday Memory Questionnaire (EMQ) before and 2 years after ECT. Their scores were compared with those of healthy matches. Depression and remission status were assessed with the Montgomery-Åsberg Depression Rating Scale (MADRS). Main statistical analyses were ANOVAs and linear mixed model tests.

RESULTS

At baseline, the patient group was significantly impaired on 7 of 10 cognitive tests compared to the control group. Two years later, this gap was reduced to impairment on 5 of 10 tests. Within the patient group, neurocognitive function either increased significantly from baseline to follow-up, or there was no change. Two years after ECT, 62.9% of the patients were in remission. Those in remission reported better subjective memory function, but displayed no different neuropsychological test results, compared to the non-remitters.

LIMITATIONS

Major limitations were low sample size and lack of uniform ECT procedure.

CONCLUSIONS

We found improved neurocognitive function 2 years after ECT. This effect occurred regardless of remission status, suggesting that ECT induces unique cognitive boosting processes.

Ventral Visual Pathway-Cerebellar Circuit Deficits in Alcohol Dependence: Long- and Short-Range Functional Connectivity Density Study

Objective: To identify the underlying intrinsic functional connectome changes in patients with alcohol dependence. Methods: A functional connectivity density (FCD) analysis was used to report on the functional connectivity changes in 24 male patients with alcohol dependence (age, 47.83 ± 6.93 years) and 24 healthy male subjects (age, 47.67 ± 6.99 years). We defined the voxels with a correlated threshold of r > 0.25 inside their neighborhood (radius sphere ≤ 6 mm) as shortFCD, and radius sphere > 6 mm as longFCD. We repeated the network analysis using a range of correlation r thresholds (r = 0.30, 0.35, 0.40, 0.45, 0.50, 0.6, and 0.75) to determine whether between-group differences were substantially affected by the selection of the different R-value thresholds used. A ROC curve was used to test the ability of the FCD in discriminating between the two groups. Pearson's correlation was used to evaluate the relationships between the FCD differences in brain areas and demographic characteristics. Results: The covered differences in brain areas in binarized shortFCD were larger than binarized longFCD in both groups. The intra-group FCD differences did not depend on the selection of different thresholds used. Patients with alcohol dependence were associated with the longFCD deficit in the cerebellum posterior lobe, and shortFCD deficit in the ventral system of the visual pathway and increased shortFCD in the left precentral gyrus, right salience network and right cingulate gyrus. A ROC curve demonstrated that these specific brain areas alone discriminated between the two groups with a high degree of sensitivity and specificity. In the alcohol dependence group, the cerebellum posterior lobe, visual association cortex and the salience network displayed significant correlations with demographic characteristics. Conclusions: The shortFCD analysis was more sensitive than the longFCD analysis in finding differences in the brain areas. The ventral visual pathway-cerebellar circuit deficit appeared to be altered in patients with alcohol dependence.