Electroconvulsive Therapy Induces Cortical Morphological Alterations in Major Depressive Disorder Revealed with Surface-Based Morphometry Analysis

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.

Effects of electroconvulsive therapy on cortical thickness in depression: a systematic review

OBJECTIVE

Electroconvulsive therapy (ECT) is one of the most studied and validated available treatments for severe or treatment-resistant depression. However, little is known about the neural mechanisms underlying ECT. This systematic review aims to critically review all structural magnetic resonance imaging studies investigating longitudinal cortical thickness (CT) changes after ECT in patients with unipolar or bipolar depression.

METHODS

We performed a search on PubMed, Medline, and Embase to identify all available studies published before April 20, 2023. A total of 10 studies were included.

RESULTS

The investigations showed widespread increases in CT after ECT in depressed patients, involving mainly the temporal, insular, and frontal regions. In five studies, CT increases in a non-overlapping set of brain areas correlated with the clinical efficacy of ECT. The small sample size, heterogeneity in terms of populations, comorbidities, and ECT protocols, and the lack of a control group in some investigations limit the generalisability of the results.

CONCLUSIONS

Our findings support the idea that ECT can increase CT in patients with unipolar and bipolar depression. It remains unclear whether these changes are related to the clinical response. Future larger studies with longer follow-up are warranted to thoroughly address the potential role of CT as a biomarker of clinical response after ECT.

Autism Spectrum Disorder Detection by Hybrid Convolutional Recurrent Neural Networks from Structural and Resting State Functional MRI Images

This study aims to increase the accuracy of autism spectrum disorder (ASD) diagnosis based on cognitive and behavioral phenotypes through multiple neuroimaging modalities. We apply machine learning (ML) algorithms to classify ASD patients and healthy control (HC) participants using structural magnetic resonance imaging (s-MRI) together with resting state functional MRI (rs-f-MRI and f-MRI) data from the large multisite data repository ABIDE (autism brain imaging data exchange) and identify important brain connectivity features. The 2D f-MRI images were converted into 3D s-MRI images, and datasets were preprocessed using the Montreal Neurological Institute (MNI) atlas. The data were then denoised to remove any confounding factors. We show, by using three fusion strategies such as early fusion, late fusion, and cross fusion, that, in this implementation, hybrid convolutional recurrent neural networks achieve better performance in comparison to either convolutional neural networks (CNNs) or recurrent neural networks (RNNs). The proposed model classifies subjects as autistic or not according to how functional and anatomical connectivity metrics provide an overall diagnosis based on the autism diagnostic observation schedule (ADOS) standard. Our hybrid network achieved an accuracy of 96% by fusing s-MRI and f-MRI together, which outperforms the methods used in previous studies.

Clinical Improvement in Depression and Cognitive Deficit Following Electroconvulsive Therapy

Electroconvulsive therapy (ECT) is a long-standing treatment choice for disorders such as depression when pharmacological treatments have failed. However, a major drawback of ECT is its cognitive side effects. While numerous studies have investigated the therapeutic effects of ECT and its mechanism, much less research has been conducted regarding the mechanism behind the cognitive side effects of ECT. As both clinical remission and cognitive deficits occur after ECT, it is possible that both may share a common mechanism. This review highlights studies related to ECT as well as those investigating the mechanism of its outcomes. The process underlying these effects may lie within BDNF and NMDA signaling. Edema in the astrocytes may also be responsible for the adverse cognitive effects and is mediated by metabotropic glutamate receptor 5 and the protein Homer1a.

Decreased dorsal attention network homogeneity as a potential neuroimaging biomarker for major depressive disorder

BACKGROUND

Gaining insight into abnormal functional brain network homogeneity (NH) has the potential to aid efforts to target or otherwise study major depressive disorder (MDD). The NH of the dorsal attention network (DAN) in first-episode treatment-naive MDD patients, however, has yet to be studied. As such, the present study was developed to explore the NH of the DAN in order to determine the ability of this parameter to differentiate between MDD patients and healthy control (HC) individuals.

METHODS

This study included 73 patients with first-episode treatment-naive MDD and 73 age-, gender-, and educational level-matched healthy controls. All participants completed the attentional network test (ANT), Hamilton Rating Scale for Depression (HRSD), and resting-state functional magnetic resonance imaging (rs-fMRI) analyses. A group independent component analysis (ICA) was used to identify the DAN and to compute the NH of the DAN in patients with MDD. Spearman's rank correlation analyses were used to explore relationships between significant NH abnormalities in MDD patients, clinical parameters, and executive control reaction time.

RESULTS

Relative to HCs, patients exhibited reduced NH in the left supramarginal gyrus (SMG). Support vector machine (SVM) analyses and receiver operating characteristic curves indicated that the NH of the left SMG could be used to differentiate between HCs and MDD patients with respective accuracy, specificity, sensitivity, and AUC values of 92.47 %, 91.78 %, 93.15 %, and 65.39 %. A significant positive correlation was observed between the left SMG NH values and HRSD scores among MDD patients.

CONCLUSIONS

These results suggest that NH changes in the DAN may offer value as a neuroimaging biomarker capable of differentiating between MDD patients and healthy individuals.

Neuroplasticity-Related Genes and Dopamine Receptors Associated with Regional Cortical Thickness Increase Following Electroconvulsive Therapy for Major Depressive Disorder

Electroconvulsive therapy (ECT) is an effective neuromodulatory therapy for major depressive disorder (MDD). Treatment is associated with regional changes in brain structure and function, indicating activation of neuroplastic processes. To investigate the underlying neurobiological mechanism of macroscopic reorganization following ECT, we longitudinally (before and after ECT in two centers) collected magnetic resonance images for 96 MDD patients. Similar patterns of cortical thickness (CT) changes following ECT were observed in two centers. These CT changes were spatially colocalized with a weighted combination of genes enriched for neuroplasticity-related ontology terms and pathways (e.g., synaptic pruning) as well as with a higher density of D2/3 dopamine receptors. A multiple linear regression model indicated that the region-specific gene expression and receptor density patterns explained 40% of the variance in CT changes after ECT. In conclusion, these findings suggested that dopamine signaling and neuroplasticity-related genes are associated with the ECT-induced morphological reorganization.

Neuroinflammation mechanisms of neuromodulation therapies for anxiety and depression

Mood disorders are associated with elevated inflammation, and the reduction of symptoms after multiple treatments is often accompanied by pro-inflammation restoration. A variety of neuromodulation techniques that regulate regional brain activities have been used to treat refractory mood disorders. However, their efficacy varies from person to person and lack reliable indicator. This review summarizes clinical and animal studies on inflammation in neural circuits related to anxiety and depression and the evidence that neuromodulation therapies regulate neuroinflammation in the treatment of neurological diseases. Neuromodulation therapies, including transcranial magnetic stimulation (TMS), transcranial electrical stimulation (TES), electroconvulsive therapy (ECT), photobiomodulation (PBM), transcranial ultrasound stimulation (TUS), deep brain stimulation (DBS), and vagus nerve stimulation (VNS), all have been reported to attenuate neuroinflammation and reduce the release of pro-inflammatory factors, which may be one of the reasons for mood improvement. This review provides a better understanding of the effective mechanism of neuromodulation therapies and indicates that inflammatory biomarkers may serve as a reference for the assessment of pathological conditions and treatment options in anxiety and depression.

Gray matter atrophy and corresponding impairments in connectivity in patients with anti-N-methyl-D-aspartate receptor encephalitis

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is a severe autoimmune disease that is commonly accompanied by cognitive impairment and various neurological and psychiatric symptoms, advanced image analyses help explore the pathogenesis of this disease. Therefore, this study aimed to explore specific structural and functional alterations and their relationship with the clinical symptoms of anti-NMDAR encephalitis. In this study, twenty-two patients with anti-NMDAR encephalitis after the acute stage and 29 controls received cognitive assessments and magnetic resonance imaging. Grey matter atrophy was measured using voxel-based morphometry, and functional alterations in abnormal regions were subsequently investigated using resting state functional connectivity (RSFC). Finally, correlation analyses were performed to explore the associations between imaging alterations and cognitive assessments. The patients demonstrated significant gray matter atrophy in the bilateral triangle part of the inferior frontal gyrus (triIFG.L and triIFG.R) and right precuneus, decreased RSFC between triIFG.L and bilateral Heschl gyrus (HES), decreased RSFC between triIFG.R and HES.R, decreased RSFC between right precuneus and left cerebellum, and increased RSFC between triIFG.R and left superior frontal gyrus. Further correlation analyses showed that the gray matter volume in triIFG.R and decreased RSFC between triIFG.L and HES.R were associated with decreased memory scores, whereas decreased RSFC between triIFG.R and HES.R was marginally correlated with the disease course in patients. In conclusion, this study suggests that cognitive impairments in patients with anti-NMDAR encephalitis may be mainly associated with gray matter atrophy and abnormal RSFC in the triIFG. These findings provide new insights into anti-NMDAR encephalitis pathogenesis and help explore potential treatments.

The Neurobiological Effects of Electroconvulsive Therapy Studied Through Magnetic Resonance: What Have We Learned, and Where Do We Go?

Electroconvulsive therapy (ECT) is an established treatment choice for severe, treatment-resistant depression, yet its mechanisms of action remain elusive. Magnetic resonance imaging (MRI) of the human brain before and after treatment has been crucial to aid our comprehension of the ECT neurobiological effects. However, to date, a majority of MRI studies have been underpowered and have used heterogeneous patient samples as well as different methodological approaches, altogether causing mixed results and poor clinical translation. Hence, an association between MRI markers and therapeutic response remains to be established. Recently, the availability of large datasets through a global collaboration has provided the statistical power needed to characterize whole-brain structural and functional brain changes after ECT. In addition, MRI technological developments allow new aspects of brain function and structure to be investigated. Finally, more recent studies have also investigated immediate and long-term effects of ECT, which may aid in the separation of the therapeutically relevant effects from epiphenomena. The goal of this review is to outline MRI studies (T1, diffusion-weighted imaging, proton magnetic resonance spectroscopy) of ECT in depression to advance our understanding of the ECT neurobiological effects. Based on the reviewed literature, we suggest a model whereby the neurobiological effects can be understood within a framework of disruption, neuroplasticity, and rewiring of neural circuits. An improved characterization of the neurobiological effects of ECT may increase our understanding of ECT's therapeutic effects, ultimately leading to improved patient care.

Electroconvulsive therapy for obsessive-compulsive disorder: A retrospective study

BACKGROUND

Chronic mental diseases such as obsessive-compulsive disorder (OCD) are associated with a high disability rate. Some patients still do not improve their symptoms even with adequate cognitive-behavioral therapy and drug treatment. In the treatment of OCD, electroconvulsive therapy (ECT) is not considered a neuromodulation modality with sufficient evidence.

OBJECTIVE

This retrospective study aimed to determine the efficacy and associated risk factors of ECT in OCD patients.

MATERIALS AND METHODS

The study included 21 OCD patients who underwent ECT at a high-volume center in China between January 2009 and December 2020. The demographics and clinical characteristics of the patients were assessed using descriptive statistics. Based on Clinical Global Impressions-Improvement scale, patients were categorized into response and non-response groups. Clinical and demographic characteristics of two groups of patients were compared.

RESULTS

An analysis of 21 patients was conducted. In total, 12 patients (57.1%) responded to ECT, 11 patients (52.4%) reported side effects, and an average of 7 ECT sessions were administered. In terms of demographic, there was no statistically significant difference between the two groups. It is noteworthy that the non-response group reported more depression and schizophrenia related disorders comorbidities than the response group (χ² = 6.252, P = 0.041).

CONCLUSION

The effectiveness of ECT in treating OCD is limited, especially in patients with refractory symptoms. Comorbidity with other mental disorders may affect the efficacy of ECT.

Abnormal core functional connectivity on the pathology of MDD and antidepressant treatment: A systematic review

RATIONALE/IMPORTANCE

Researches have highlighted communication deficits between resting-state brain networks in major depressive disorder (MDD), as reflected in abnormal functional connectivity (FC). However, it is unclear whether impaired FC is associated with MDD pathology or is simply incidental to MDD symptoms. Moreover, there is no generalized theory to analyze the impact of treatment modalities on MDD.

OBJECTIVES

To address the issues, we conducted a systematic review of 49 eligible papers to provide insight into the pathological mechanisms of MDD patients by summarizing resting-state FC alterations involving mood and cognitive abnormalities and the effects of medications on them.

RESULTS

Mood disorders in MDD were characterized by abnormal FC between the amygdala, insula, anterior cingulate cortex (ACC), and prefrontal cortex (PFC). Cognitive impairment manifests as deficits in executive function, attention, memory, and rumination, primarily modulated by dysfunction between the fronto-parietal network and default mode network. Especially, we proposed the set of core abnormal FC (CA-FC) contributing to mood and cognitive impairment in MDD, currently including ACC-left precuneus/amygdala, rostral ACC-left dorsolateral PFC, left subgenual ACC-left cerebellar, left PFC- anterior subcallosal, and left precuneus-left pulvinar. After treatment, patients with normalized CA-FC showed remission of depressive symptoms.

CONCLUSIONS

We propose a CA-FC set for possible causative principle of MDD, which unifies the FC results from specific, difficult-to-analyze conditions into one outcome set for screening. Furthermore, CA-FC varies from person to person, and the low success rate of a single treatment may be due to the inability to cover too many CA-FC.

Progressive cortical and sub-cortical alterations in patients with anti-N-methyl-D-aspartate receptor encephalitis

BACKGROUND

Advanced structural analyses are increasingly being highly valued to uncover pathophysiological understanding of anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis. Therefore, we aimed to explore whether and how antibody-mediated NMDAR dysfunction affected cortical and sub-cortical brain morphology and their relationship with clinical symptoms.

METHODS

We performed surface-based morphometry analyses, hippocampal segmentation, and correlational analyses in 24 patients with anti-NMDAR encephalitis after acute disease stage and 30 normal controls (NC) in this case-control study.

RESULTS

Patients showed significantly decreased cortical alterations mainly in language network (LN) and default mode network (DMN), as well as decreased gray matter volume in left cornu ammonis 1 (CA1) body of hippocampus. Further correlation analyses showed that the decreased cortical thickness in the right superior frontier gyrus was associated with decreased cognitive scores, the decreased cortical volume in the right pars triangulari and decreased surface area in the right pars operculari were associated with decreased memory scores, whereas decreased gray matter volume in the left CA1 body was significantly correlated with longer time between first symptom and imaging in the patients.

CONCLUSION

These results suggested that cognitive impairments resulted from long-term sequelae of the encephalitis were mainly associated with cortical alterations in LN and DMN and sub-cortical atrophy of left CA1 body, which can be served as effective features to assess disease progression in clinical routine examination.

Altered Brain Structure and Spontaneous Functional Activity in Children With Concomitant Strabismus

Strabismus occurs in about 2% of children and may result in amblyopia or lazy eyes and loss of depth perception. However, whether/how long-term strabismus shapes the brain structure and functions in children with concomitant strabismus (CS) is still unclear. In this study, a total of 26 patients with CS and 28 age-, sex-, and education-matched healthy controls (HCs) underwent structural and resting-state functional magnetic resonance imaging examination. The cortical thickness and amplitude of low-frequency fluctuation (ALFF) were calculated to assess the structural and functional plasticity in children with CS. Compared with HCs group, patients with CS showed increased cortical thickness in the precentral gyrus and angular gyrus while decreased cortical thickness in the left intraparietal sulcus, parieto-occipital sulcus, superior and middle temporal gyrus, right ventral premotor cortex, anterior insula, orbitofrontal cortex, and paracentral lobule. Meanwhile, CS patients exhibited increased ALFF in the prefrontal cortex and superior temporal gyrus, and decreased ALFF in the caudate and hippocampus. These results show that children with CS have abnormal structure and function in brain regions subserving eye movement, controls, and high-order cognitive functions. Our findings revealed the structural and functional abnormalities induced by CS and may provide new insight into the underlying neural mechanisms for CS.

The Neurobiological Basis of Cognitive Side Effects of Electroconvulsive Therapy: A Systematic Review

Decades of research have consistently demonstrated the efficacy of electroconvulsive therapy (ECT) for the treatment of major depressive disorder (MDD), but its clinical use remains somewhat restricted because of its cognitive side effects. The aim of this systematic review is to comprehensively summarize current evidence assessing potential biomarkers of ECT-related cognitive side effects. Based on our systematic search of human studies indexed in PubMed, Scopus, and Web of Knowledge, a total of 29 studies evaluating patients with MDD undergoing ECT were reviewed. Molecular biomarkers studies did not consistently identify concentration changes in plasma S-100 protein, neuron-specific enolase (NSE), or Aβ peptides significantly associated with cognitive performance after ECT. Importantly, these findings suggest that ECT-related cognitive side effects cannot be explained by mechanisms of neural cell damage. Notwithstanding, S-100b protein and Aβ40 peptide concentrations, as well as brain-derived neurotrophic factor (BDNF) polymorphisms, have been suggested as potential predictive biomarkers of cognitive dysfunction after ECT. In addition, recent advances in brain imaging have allowed us to identify ECT-induced volumetric and functional changes in several brain structures closely related to memory performance such as the hippocampus. We provide a preliminary framework to further evaluate neurobiological cognitive vulnerability profiles of patients with MDD treated with ECT.

A lateralized model of the pain-depression dyad

Chronic pain and depression are two frequently co-occurring and debilitating conditions. Even though the former is treated as a physical affliction, and the latter as a mental illness, both disorders closely share neural substrates. Here, we review the association of pain with depression, especially when symptoms are lateralized on either side of the body. We also explore the overlapping regions in the forebrain implicated in these conditions. Finally, we synthesize these findings into a model, which addresses gaps in our understanding of comorbid pain and depression. Our lateralized pain-depression dyad model suggests that individuals diagnosed with depression should be closely monitored for pain symptoms in the left hemibody. Conversely, for patients in pain, with the exception of acute pain with a known source, referrals in today's pain centers for psychological evaluation should be part of standard practice, within the framework of an interdisciplinary approach to pain treatment.

Cortical Gyrification in Transgender Individuals

Gender incongruence (GI) is characterized by a feeling of estrangement from the own body in the context of self. GI is often described in people who identify as transgender. The underlying mechanisms are unknown. Data from MRI measurements and tests of own body perception triggered us to pose a model that GI in transgender persons (TGI) could be associated with a disconnection within the brain circuits mediating the perception of own body as self. This is a departure from a previous model of sex atypical cerebral dimorphism, introducing a concept that better accords with a core feature of TGI. The present MRI study of 54 hormone naive transmen (TrM), 38 transwomen (TrW), 44 cismen and 41 ciswomen show that cortical gyrification, a metric that reflects early maturation of cerebral cortex, is significantly lower in transgender compared with cisgender participants. This reduction is limited to the occipito-parietal cortex and the sensory motor cortex, regions encoding own body image and body ownership. Moreover, the cortical gyrification correlated inversely with own body-self incongruence in these regions. These novel data suggest that GI in TGI may originate in the neurodevelopment of body image encoding regions. The results add potentially to understanding neurobiological contributors to gender identity.

Systemic Review on Transcranial Electrical Stimulation Parameters and EEG/fNIRS Features for Brain Diseases

Background

Brain disorders are gradually becoming the leading cause of death worldwide. However, the lack of knowledge of brain disease’s underlying mechanisms and ineffective neuropharmacological therapy have led to further exploration of optimal treatments and brain monitoring techniques.

Objective

This study aims to review the current state of brain disorders, which utilize transcranial electrical stimulation (tES) and daily usable noninvasive neuroimaging techniques. Furthermore, the second goal of this study is to highlight available gaps and provide a comprehensive guideline for further investigation.

Method

A systematic search was conducted of the PubMed and Web of Science databases from January 2000 to October 2020 using relevant keywords. Electroencephalography (EEG) and functional near-infrared spectroscopy were selected as noninvasive neuroimaging modalities. Nine brain disorders were investigated in this study, including Alzheimer’s disease, depression, autism spectrum disorder, attention-deficit hyperactivity disorder, epilepsy, Parkinson’s disease, stroke, schizophrenia, and traumatic brain injury.

Results

Sixty-seven studies (1,385 participants) were included for quantitative analysis. Most of the articles (82.6%) employed transcranial direct current stimulation as an intervention method with modulation parameters of 1 mA intensity (47.2%) for 16–20 min (69.0%) duration of stimulation in a single session (36.8%). The frontal cortex (46.4%) and the cerebral cortex (47.8%) were used as a neuroimaging modality, with the power spectrum (45.7%) commonly extracted as a quantitative EEG feature.

Conclusion

An appropriate stimulation protocol applying tES as a therapy could be an effective treatment for cognitive and neurological brain disorders. However, the optimal tES criteria have not been defined; they vary across persons and disease types. Therefore, future work needs to investigate a closed-loop tES with monitoring by neuroimaging techniques to achieve personalized therapy for brain disorders.

Multisite Autism Spectrum Disorder Classification Using Convolutional Neural Network Classifier and Individual Morphological Brain Networks

Autism spectrum disorder (ASD) is a range of neurodevelopmental disorders with behavioral and cognitive impairment and brings huge burdens to the patients' families and the society. To accurately identify patients with ASD from typical controls is important for early detection and early intervention. However, almost all the current existing classification methods for ASD based on structural MRI (sMRI) mainly utilize the independent local morphological features and do not consider the covariance patterns of these features between regions. In this study, by combining the convolutional neural network (CNN) and individual structural covariance network, we proposed a new framework to classify ASD patients with sMRI data from the ABIDE consortium. Moreover, gradient-weighted class activation mapping (Grad-CAM) was applied to characterize the weight of features contributing to the classification. The experimental results showed that our proposed method outperforms the currently used methods for classifying ASD patients with the ABIDE data and achieves a high classification accuracy of 71.8% across different sites. Furthermore, the discriminative features were found to be mainly located in the prefrontal cortex and cerebellum, which may be the early biomarkers for the diagnosis of ASD. Our study demonstrated that CNN is an effective tool to build the framework for the diagnosis of ASD with individual structural covariance brain network.

Altered EEG Oscillatory Brain Networks During Music-Listening in Major Depression

To examine the electrophysiological underpinnings of the functional networks involved in music listening, previous approaches based on spatial independent component analysis (ICA) have recently been used to ongoing electroencephalography (EEG) and magnetoencephalography (MEG). However, those studies focused on healthy subjects, and failed to examine the group-level comparisons during music listening. Here, we combined group-level spatial Fourier ICA with acoustic feature extraction, to enable group comparisons in frequency-specific brain networks of musical feature processing. It was then applied to healthy subjects and subjects with major depressive disorder (MDD). The music-induced oscillatory brain patterns were determined by permutation correlation analysis between individual time courses of Fourier-ICA components and musical features. We found that (1) three components, including a beta sensorimotor network, a beta auditory network and an alpha medial visual network, were involved in music processing among most healthy subjects; and that (2) one alpha lateral component located in the left angular gyrus was engaged in music perception in most individuals with MDD. The proposed method allowed the statistical group comparison, and we found that: (1) the alpha lateral component was activated more strongly in healthy subjects than in the MDD individuals, and that (2) the derived frequency-dependent networks of musical feature processing seemed to be altered in MDD participants compared to healthy subjects. The proposed pipeline appears to be valuable for studying disrupted brain oscillations in psychiatric disorders during naturalistic paradigms.

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.

Cortical Alterations Are Associated with Depression in Subcortical Vascular Mild Cognitive Impairment Revealed by Surface-Based Morphometry

BACKGROUND

Late-life depression often coexists with vascular cognitive impairment and affects the quality of life for elders. However, little is known about cortical morphometric interactions between subcortical vascular mild cognitive impairment (svMCI) and concomitant mild depressive symptoms at the early stage.

OBJECTIVE

We aimed to investigate cortical alterations of svMCI with and without depressive symptoms and determine whether these parameters are associated with depression symptoms and/or cognitive impairments.

METHODS

Surface based morphometry was performed on 18 svMCI patients with depressive symptoms (svMCI + D), 16 svMCI patients without depressive symptoms (svMCI-D), and 23 normal controls (NC).

RESULTS

Compared to NC, both svMCI + D and svMCI-D patients exhibited significantly decreased surface area (SA) in many cortical areas. Interestingly, svMCI + D patients showed significantly increased rather than decreased SA in right lateral occipital gyrus (LOG.R), and a consistent trend of increased SA in these areas compared to svMCI-D. In addition, the svMCI + D showed increased gray matter volume of left pericalcarine (periCAL.L) than svMCI-D, whereas svMCI-D showed decreased gray matter volume of periCAL.L than NC. Further correlation analyses revealed that the SA of left superior temporal gyrus (STG.L) and right lateral orbital part of frontal gyrus (lorbFG.R) were significantly correlated with Hamilton depression rating scale of svMCI + D.

CONCLUSION

In conclusion, these results extend our insight into svMCI and add weight to reevaluation of concomitant early stage depressive symptoms. Moreover, we suggest that LOG.R∖periCAL.L∖STG.L∖lorbFG.R might serve as sensitive and trait-dependent biomarkers to detect concomitant depressive symptoms in svMCI patients.

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.

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.

Deep Support Vector Machines for the Identification of Stress Condition from Electrodermal Activity

Early detection of stress condition is beneficial to prevent long-term mental illness like depression and anxiety. This paper introduces an accurate identification of stress/calm condition from electrodermal activity (EDA) signals. The acquisition of EDA signals from a commercial wearable as well as their storage and processing are presented. Several time-domain, frequency-domain and morphological features are extracted over the skin conductance response of the EDA signals. Afterwards, a classification is undergone by using several classical support vector machines (SVMs) and deep support vector machines (D-SVMs). In addition, several binary classifiers are also compared with SVMs in the stress/calm identification task. Moreover, a series of video clips evoking calm and stress conditions have been viewed by 147 volunteers in order to validate the classification results. The highest F1-score obtained for SVMs and D-SVMs are 83% and 92%, respectively. These results demonstrate that not only classical SVMs are appropriate for classification of biomarker signals, but D-SVMs are very competitive in comparison to other classification techniques. In addition, the results have enabled drawing useful considerations for the future use of SVMs and D-SVMs in the specific case of stress/calm identification.

Differences in Cerebral Structure Associated With Depressive Symptoms in the Elderly With Alzheimer's Disease

Background: Alzheimer's disease (AD) is characterized by global deterioration in multiple cognitive domains. In addition to cognitive impairment, depressive symptoms are common issues that trouble AD patients. The neuroanatomical basis of depressive symptoms in AD patients has yet to be elucidated. Method: Twenty AD patients and 22 healthy controls (HCs) were recruited for the present study. Depressive symptoms in AD patients and HCs were assessed according to the Hamilton Depression Rating Scale (HDRS). Anatomical structural differences were assessed between AD patients and HCs using voxel-based morphometry (VBM) and surface-based morphometry (SBM). Correlation analyses were conducted to investigate relationships between depressive symptoms and structural altered regions. Multiple pattern analysis using linear support vector machine (SVM) was performed in another independent cohort, which was collected from Alzheimer's Disease Neuroimaging Initiative (ADNI) data and contained 20 AD patients and 20 HCs, to distinguish AD patients from HCs. Results: Compared with HCs, AD patients exhibited global cerebral atrophy in gray matter volume (GMV) and cortical thickness, including frontal, parietal, temporal, occipital, and insular lobes. In addition, insular GMV was negatively correlated with depressive symptoms. Moreover, SVM-based classification achieved an accuracy of 77.5%, a sensitivity of 70%, and a specificity of 85% by leave-one-out cross-validation. Conclusion: GMV of the insula displayed atrophy among AD patients, which is associated with depressive symptoms. Our observations provide a potential neural substrate for analysis to examine the co-occurrence of AD with depressive symptoms.