Resting state functional connectivity predictors of treatment response to electroconvulsive therapy in depression

Neural and behavioral markers of inhibitory control predict symptom improvement during internet-delivered cognitive behavioral therapy for depression

Poor inhibitory control contributes to deficits in emotion regulation, which are often targeted by treatments for major depressive disorder (MDD), including cognitive behavioral therapy (CBT). Brain regions that contribute to inhibitory control and emotion regulation overlap; thus, inhibitory control might relate to response to CBT. In this study, we examined whether baseline inhibitory control and resting state functional connectivity (rsFC) within overlapping emotion regulation-inhibitory control regions predicted treatment response to internet-based CBT (iCBT). Participants with MDD were randomly assigned to iCBT (N = 30) or a monitored attention control (MAC) condition (N = 30). Elastic net regression was used to predict post-treatment Patient Health Questionnaire-9 (PHQ-9) scores from baseline variables, including demographic variables, PHQ-9 scores, Flanker effects (interference, sequential dependency, post-error slowing), and rsFC between the dorsal anterior cingulate cortex, bilateral anterior insula (AI), and right temporoparietal junction (TPJ). Essential prognostic predictor variables retained in the elastic net regression included treatment group, gender, Flanker interference response time (RT), right AI-TPJ rsFC, and left AI-right AI rsFC. Prescriptive predictor variables retained included interactions between treatment group and baseline PHQ-9 scores, age, gender, Flanker RT, sequential dependency effects on accuracy, post-error accuracy, right AI-TPJ rsFC, and left AI-right AI rsFC. Inhibitory control and rsFC within inhibitory control-emotion regulation regions predicted reduced symptom severity following iCBT, and these effects were stronger in the iCBT group than in the MAC group. These findings contribute to a growing literature indicating that stronger inhibitory control at baseline predicts better outcomes to psychotherapy, including iCBT.

Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression

Treatment outcomes widely vary for individuals diagnosed with major depressive disorder, implicating a need for deeper understanding of the biological mechanisms conferring a greater likelihood of response to a particular treatment. Our improved understanding of intrinsic brain networks underlying depression psychopathology via magnetic resonance imaging and other neuroimaging modalities has helped reveal novel and potentially clinically meaningful biological markers of response. And while we have made considerable progress in identifying such biomarkers over the last decade, particularly with larger, multisite trials, there are significant methodological and practical obstacles that need to be overcome to translate these markers into the clinic. The aim of this review is to review current literature on brain network structural and functional biomarkers of treatment response or selection in depression, with a specific focus on recent large, multisite trials reporting predictive accuracy of candidate biomarkers. Regarding pharmaco- and psychotherapy, we discuss candidate biomarkers, reporting that while we have identified candidate biomarkers of response to a single intervention, we need more trials that distinguish biomarkers between first-line treatments. Further, we discuss the ways prognostic neuroimaging may help to improve treatment outcomes to neuromodulation-based therapies, such as transcranial magnetic stimulation and deep brain stimulation. Lastly, we highlight obstacles and technical developments that may help to address the knowledge gaps in this area of research. Ultimately, integrating neuroimaging-derived biomarkers into clinical practice holds promise for enhancing treatment outcomes and advancing precision psychiatry strategies for depression management. By elucidating the neural predictors of treatment response and selection, we can move towards more individualized and effective depression interventions, ultimately improving patient outcomes and quality of life.

Predicting the outcome of psilocybin treatment for depression from baseline fMRI functional connectivity

BACKGROUND

Psilocybin is a serotonergic psychedelic drug under assessment as a potential therapy for treatment-resistant and major depression. Heterogeneous treatment responses raise interest in predicting the outcome from baseline data.

METHODS

A machine learning pipeline was implemented to investigate baseline resting-state functional connectivity measured with functional magnetic resonance imaging (fMRI) as a predictor of symptom severity in psilocybin monotherapy for treatment-resistant depression (16 patients administered two 5 mg capsules followed by 25 mg, separated by one week). Generalizability was tested in a sample of 22 patients who participated in a psilocybin vs. escitalopram trial for moderate-to-severe major depression (two separate doses of 25 mg of psilocybin 3 weeks apart plus 6 weeks of daily placebo vs. two separate doses of 1 mg of psilocybin 3 weeks apart plus 6 weeks of daily oral escitalopram). The analysis was repeated using both samples combined.

RESULTS

Functional connectivity of visual, default mode and executive networks predicted early symptom improvement, while the salience network predicted responders up to 24 weeks after treatment (accuracy≈0.9). Generalization performance was borderline significant. Consistent results were obtained from the combined sample analysis. Fronto-occipital and fronto-temporal coupling predicted early and late symptom reduction, respectively.

LIMITATIONS

The number of participants and differences between the two datasets limit the generalizability of the findings, while the lack of a placebo arm limits their specificity.

CONCLUSIONS

Baseline neurophysiological measurements can predict the outcome of psilocybin treatment for depression. Future research based on larger datasets should strive to assess the generalizability of these predictions.

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

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

Functional connectivity changes between amygdala and prefrontal cortex after ECT are associated with improvement in distinct depressive symptoms

Electroconvulsive therapy (ECT) is one of the most effective treatments for treatment-resistant depression. However, the underlying mechanisms of action are not yet fully understood. The investigation of depression-specific networks using resting-state fMRI and the relation to differential symptom improvement might be an innovative approach providing new insights into the underlying processes. In this naturalistic study, we investigated the relationship between changes in resting-state functional connectivity (rsFC) and symptom improvement after ECT in 21 patients with treatment-resistant depression. We investigated rsFC before and after ECT and focused our analyses on FC changes directly related to symptom reduction and on FC at baseline to identify neural targets that might predict individual clinical responses to ECT. Additional analyses were performed to identify the direct relationship between rsFC change and symptom dimensions such as sadness, negative thoughts, detachment, and neurovegetative symptoms. An increase in rsFC between the left amygdala and left dorsolateral prefrontal cortex (DLPFC) after ECT was related to overall symptom reduction (Bonferroni-corrected p = 0.033) as well as to a reduction in specific symptoms such as sadness (r = 0.524, uncorrected p = 0.014), negative thoughts (r = 0.700, Bonferroni-corrected p = 0.002) and detachment (r = 0.663, p = 0.004), but not in neurovegetative symptoms. Furthermore, high baseline rsFC between the left amygdala and the right frontal pole (FP) predicted treatment outcome (uncorrected p = 0.039). We conclude that changes in FC in regions of the limbic-prefrontal network are associated with symptom improvement, particularly in affective and cognitive dimensions. Frontal-limbic connectivity has the potential to predict symptom improvement after ECT. Further research combining functional imaging biomarkers and a symptom-based approach might be promising.

Sex differences of brain cortical structure in major depressive disorder

Background

Major depressive disorder (MDD) has different clinical presentations in males and females. However, the neuroanatomical mechanisms underlying these sex differences are not fully understood.

Objective

The purpose of present study was to explore the sex differences in brain cortical thickness (CT) and surface area (SA) of MDD and the relationship between these differences and clinical manifestations in different gender.

Methods

High-resolution T1-weighted images were acquired from 61 patients with MDD and 61 healthy controls (36 females and 25 males, both). The sex differences in CT and SA were obtained using the FreeSurfer software and compared between every two groups by post hoc test. Spearman correlation analysis was also performed to explore the relationships between these regions and clinical characteristics.

Results

In male patients with MDD, the CT of the right precentral was thinner compared to female patients, although this did not survive Bonferroni correction. The SA of several regions, including right superior frontal, medial orbitofrontal gyrus, inferior frontal gyrus triangle, superior temporal, middle temporal, lateral occipital gyrus, and inferior parietal lobule in female patients with MDD was smaller than that in male patients (P < 0.01 after Bonferroni correction). In female patients, the SA of the right superior temporal (r = 0.438, P = 0.008), middle temporal (r = 0.340, P = 0.043), and lateral occipital gyrus (r = 0.372, P = 0.025) were positively correlated with illness duration.

Conclusion

The current study provides evidence of sex differences in CT and SA in patients with MDD, which may improve our understanding of the sex-specific neuroanatomical changes in the development of MDD.

Functional connectomics in depression: insights into therapies

Depression is a common mental disorder characterized by heterogeneous cognitive and behavioral symptoms. The emerging research paradigm of functional connectomics has provided a quantitative theoretical framework and analytic tools for parsing variations in the organization and function of brain networks in depression. In this review, we first discuss recent progress in depression-associated functional connectome variations. We then discuss treatment-specific brain network outcomes in depression and propose a hypothetical model highlighting the advantages and uniqueness of each treatment in relation to the modulation of specific brain network connectivity and symptoms of depression. Finally, we look to the future promise of combining multiple treatment types in clinical practice, using multisite datasets and multimodal neuroimaging approaches, and identifying biological depression subtypes.

State-specific modulation of mood using intracranial electrical stimulation of the orbitofrontal cortex

BACKGROUND

Orbitofrontal cortex (OFC) is a promising target for intracranial electrical stimulation (iES) aimed at improving mood states. However, knowledge gaps remain regarding the underlying neural mechanisms of iES effects, such as the effect of the OFC target in comparison with other emotional network targets, the impact of brain state at the time of stimulation, and the neural response induced by iES at both local and network scales.

OBJECTIVE

Our study aims to address the neural mechanisms underlying the effects of iES in improving mood states.

METHODS

We conducted a study in 24 epilepsy patients who received iES through implanted electrodes in the emotional network and compared the effects of iES on multiple targets in the emotional network.

RESULTS

We found that only iES applied to the orbitofrontal cortex (OFC) led to mood improvement and changes in neural activity. We also observed that iES to the OFC suppressed the delta-theta power when the brain was in a low mood state. Moreover, the iES to the OFC decreased delta-theta power and increased gamma power at local regions within the emotional network, and enhanced the information flow through the frequency domain among regions within the emotional network.

CONCLUSIONS

These findings provide insight into the neural correlates of iES-induced mood improvement and support the potential of iES as a therapeutic intervention for mood disorders.

Alterations in EEG functional connectivity in individuals with depression: A systematic review

The brain works as an organised, network-like structure of functionally interconnected regions. Disruptions to interconnectivity in certain networks have been linked to symptoms of depression and impairments in cognition. Electroencephalography (EEG) is a low-burden tool by which differences in functional connectivity (FC) can be assessed. This systematic review aims to provide a synthesis of evidence relating to EEG FC in depression. A comprehensive electronic literature search for terms relating to depression, EEG, and FC was conducted on studies published before the end of November 2021, according to PRISMA guidelines. Studies comparing EEG measures of FC of individuals with depression to that of healthy control groups were included. Data was extracted by two independent reviewers, and the quality of EEG FC methods was assessed. Fifty-two studies assessing EEG FC in depression were identified: 36 assessed resting-state FC, and 16 assessed task-related or other (i.e., sleep) FC. Somewhat consistent findings in resting-state studies suggest for no differences between depression and control groups in EEG FC in the delta and gamma frequencies. However, while most resting-state studies noted a difference in alpha, theta, and beta, no clear conclusions could be drawn about the direction of the difference, due to considerable inconsistencies between study design and methodology. This was also true for task-related and other EEG FC. More robust research is needed to understand the true differences in EEG FC in depression. Given that the FC between brain regions drives behaviour, cognition, and emotion, characterising how FC differs in depression is essential for understanding the aetiology of depression.

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.

Adolescent neurodevelopment and psychopathology: The interplay between adversity exposure and genetic risk for accelerated brain ageing

In adulthood, stress exposure and genetic risk heighten psychological vulnerability by accelerating neurobiological senescence. To investigate whether molecular and brain network maturation processes play a similar role in adolescence, we analysed genetic, as well as longitudinal task neuroimaging (inhibitory control, incentive processing) and early life adversity (i.e., material deprivation, violence) data from the Adolescent Brain and Cognitive Development study (N = 980, age range: 9-13 years). Genetic risk was estimated separately for Major Depressive Disorder (MDD) and Alzheimer's Disease (AD), two pathologies linked to stress exposure and allegedly sharing a causal connection (MDD-to-AD). Adversity and genetic risk for MDD/AD jointly predicted functional network segregation patterns suggestive of accelerated (GABA-linked) visual/attentional, but delayed (dopamine [D2]/glutamate [GLU5R]-linked) somatomotor/association system development. A positive relationship between brain maturation and psychopathology emerged only among the less vulnerable adolescents, thereby implying that normatively maladaptive neurodevelopmental alterations could foster adjustment among the more exposed and genetically more stress susceptible youths. Transcriptomic analyses suggested that sensitivity to stress may underpin the joint neurodevelopmental effect of adversity and genetic risk for MDD/AD, in line with the proposed role of negative emotionality as a precursor to AD, likely to account for the alleged causal impact of MDD on dementia onset.

Predictive signature of static and dynamic functional connectivity for ECT clinical outcomes

Introduction: Electroconvulsive therapy (ECT) remains one of the most effective approaches for treatment-resistant depressive episodes, despite the potential cognitive impairment associated with this treatment. As a potent stimulator of neuroplasticity, ECT might normalize aberrant depression-related brain function via the brain's reconstruction by forming new neural connections. Multiple lines of evidence have demonstrated that functional connectivity (FC) changes are reliable indicators of antidepressant efficacy and cognitive changes from static and dynamic perspectives. However, no previous studies have directly ascertained whether and how different aspects of FC provide complementary information in terms of neuroimaging-based prediction of clinical outcomes. Methods: In this study, we implemented a fully automated independent component analysis framework to an ECT dataset with subjects (n = 50, age = 65.54 ± 8.92) randomized to three treatment amplitudes (600, 700, or 800 milliamperes [mA]). We extracted the static functional network connectivity (sFNC) and dynamic FNC (dFNC) features and employed a partial least square regression to build predictive models for antidepressant outcomes and cognitive changes. Results: We found that both antidepressant outcomes and memory changes can be robustly predicted by the changes in sFNC (permutation test p < 5.0 × 10^-3). More interestingly, by adding dFNC information, the model achieved higher accuracy for predicting changes in the Hamilton Depression Rating Scale 24-item (HDRS₂₄, t = 9.6434, p = 1.5 × 10^-21). The predictive maps of clinical outcomes show a weakly negative correlation, indicating that the ECT-induced antidepressant outcomes and cognitive changes might be associated with different functional brain neuroplasticity. Discussion: The overall results reveal that dynamic FC is not redundant but reflects mechanisms of ECT that cannot be captured by its static counterpart, especially for the prediction of antidepressant efficacy. Tracking the predictive signatures of static and dynamic FC will help maximize antidepressant outcomes and cognitive safety with individualized ECT dosing.

Computational approaches to treatment response prediction in major depression using brain activity and behavioral data: A systematic review

Major depressive disorder is a heterogeneous diagnostic category with multiple available treatments. With the goal of optimizing treatment selection, researchers are developing computational models that attempt to predict treatment response based on various pretreatment measures. In this paper, we review studies that use brain activity data to predict treatment response. Our aim is to highlight and clarify important methodological differences between various studies that relate to the incorporation of domain knowledge, specifically within two approaches delineated as data-driven and theory-driven. We argue that theory-driven generative modeling, which explicitly models information processing in the brain and thus can capture disease mechanisms, is a promising emerging approach that is only beginning to be utilized in treatment response prediction. The predictors extracted via such models could improve interpretability, which is critical for clinical decision-making. We also identify several methodological limitations across the reviewed studies and provide suggestions for addressing them. Namely, we consider problems with dichotomizing treatment outcomes, the importance of investigating more than one treatment in a given study for differential treatment response predictions, the need for a patient-centered approach for defining treatment outcomes, and finally, the use of internal and external validation methods for improving model generalizability.

Evaluation of functional MRI-based human brain parcellation: a review

Brain parcellations play a crucial role in the analysis of brain imaging data sets, as they can significantly affect the outcome of the analysis. In recent years, several novel approaches for constructing MRI-based brain parcellations have been developed with promising results. In the absence of ground truth, several evaluation approaches have been used to evaluate currently available brain parcellations. In this article, we review and critique methods used for evaluating functional brain parcellations constructed using fMRI data sets. We also describe how some of these evaluation methods have been used to estimate the optimal parcellation granularity. We provide a critical discussion of the current approach to the problem of identifying the optimal brain parcellation that is suited for a given neuroimaging study. We argue that the criteria for an optimal brain parcellation must depend on the application the parcellation is intended for. We describe a teleological approach to the evaluation of brain parcellations, where brain parcellations are evaluated in different contexts and optimal brain parcellations for each context are identified separately. We conclude by discussing several directions for further research that would result in improved evaluation strategies.

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.

Rostral anterior cingulate network effective connectivity in depressed adolescents and associations with treatment response in a randomized controlled trial

The rostral anterior cingulate cortex (rACC) is consistently implicated in the neurobiology of depression. While the functional connectivity of the rACC has been previously associated with treatment response, there is a paucity of work investigating the specific directional interactions underpinning these associations. We compared the fMRI resting-state effective connectivity of 94 young people with major depressive disorder and 91 healthy controls. Following the fMRI scan, patients were randomized to receive cognitive behavioral therapy for 12 weeks, plus either fluoxetine or a placebo. Using spectral dynamic causal modelling, we examined the effective connectivity of the rACC with eight other regions implicated in depression: the left and right anterior insular cortex (AIC), amygdalae, and dorsolateral prefrontal cortex (dlPFC); and in the midline, the subgenual (sgACC) and dorsal anterior cingulate cortex (dACC). Parametric empirical Bayes was used to compare baseline differences between controls and patients and responders and non-responders to treatment. Depressed patients demonstrated greater inhibitory connectivity from the rACC to the dlPFC, AIC, dACC and left amygdala. Moreover, treatment responders illustrated greater inhibitory connectivity from the rACC to dACC, greater excitatory connectivity from the dACC to sgACC and reduced inhibitory connectivity from the sgACC to amygdalae at baseline. The inhibitory hyperconnectivity of the rACC in depressed patients aligns with hypotheses concerning the dominance of the default mode network over other intrinsic brain networks. Surprisingly, treatment responders did not demonstrate connectivity which was more similar to healthy controls, but rather distinct alterations that may have predicated their enhanced treatment response.

Depression Classification Using Frequent Subgraph Mining Based on Pattern Growth of Frequent Edge in Functional Magnetic Resonance Imaging Uncertain Network

The brain network structure is highly uncertain due to the noise in imaging signals and evaluation methods. Recent works have shown that uncertain brain networks could capture uncertain information with regards to functional connections. Most of the existing research studies covering uncertain brain networks used graph mining methods for analysis; for example, the mining uncertain subgraph patterns (MUSE) method was used to mine frequent subgraphs and the discriminative feature selection for uncertain graph classification (DUG) method was used to select discriminant subgraphs. However, these methods led to a lack of effective discriminative information; this reduced the classification accuracy for brain diseases. Therefore, considering these problems, we propose an approximate frequent subgraph mining algorithm based on pattern growth of frequent edge (unFEPG) for uncertain brain networks and a novel discriminative feature selection method based on statistical index (dfsSI) to perform graph mining and selection. Results showed that compared with the conventional methods, the unFEPG and dfsSI methods achieved a higher classification accuracy. Furthermore, to demonstrate the efficacy of the proposed method, we used consistent discriminative subgraph patterns based on thresholding and weighting approaches to compare the classification performance of uncertain networks and certain networks in a bidirectional manner. Results showed that classification performance of the uncertain network was superior to that of the certain network within a defined sparsity range. This indicated that if a better classification performance is to be achieved, it is necessary to select a certain brain network with a higher threshold or an uncertain brain network model. Moreover, if the uncertain brain network model was selected, it is necessary to make full use of the uncertain information of its functional connection.

Whole-Brain Functional Connectivity Dynamics Associated With Electroconvulsive Therapy Treatment Response

BACKGROUND

Depressive episodes (DEPs), characterized by abnormalities in cognitive functions and mood, are a leading cause of disability. Electroconvulsive therapy (ECT), which involves a brief electrical stimulation of the anesthetized brain, is one of the most effective treatments used in patients with DEP due to its rapid efficacy.

METHODS

In this work, we investigated how dynamic brain functional connectivity responds to ECT and whether the dynamic responses are associated with treatment outcomes and side effects in patients. We applied a fully automated independent component analysis-based pipeline to 110 patients with DEP (including diagnosis of unipolar depression or bipolar depression) and 60 healthy control subjects. The dynamic functional connectivity was analyzed by a combination of the sliding window approach and clustering analysis.

RESULTS

Five recurring connectivity states were identified, and patients with DEPs had fewer occurrences in one brain state (state 1) with strong positive and negative connectivity. Patients with DEP changed the occupancy of two states (states 3 and 4) after ECT, resulting in significantly different occurrences of one additional state (state 3) compared with healthy control subjects. We further found that patients with DEP had diminished global metastate dynamism, two of which recovered to normal after ECT. The changes in dynamic connectivity characteristics were associated with the changes in memory recall and Hamilton Depression Rating Scale of DEP after ECT.

CONCLUSIONS

These converging results extend current findings on subcortical-cortical dysfunction and dysrhythmia in DEP and demonstrate that ECT might cause remodeling of brain functional dynamics that enhance the neuroplasticity of the diseased brain.

Exploring Oscillatory Dysconnectivity Networks in Major Depression during Resting State Using Coupled Tensor Decomposition

Dysconnectivity of large-scale brain networks has been linked to major depression disorder (MDD) during resting state. Recent researches show that the temporal evolution of brain networks regulated by oscillations reveals novel mechanisms and neural characteristics of MDD. Our study applied a novel coupled tensor decomposition model to investigate the dysconnectivity networks characterized by spatio-temporal-spectral modes of covariation in MDD using resting electroencephalography. The phase lag index is used to calculate the functional connectivity within each time window at each frequency bin. Then, two adjacency tensors with the dimension of time frequency connectivity subject are constructed for the healthy group and the major depression group. We assume that the two groups share the same features for group similarity and retain individual characteristics for group differences. Considering that the constructed tensors are nonnegative and the components in spectral and adjacency modes are partially consistent among the two groups, we formulate a double-coupled nonnegative tensor decomposition model. To reduce computational complexity, we introduce the lowrank approximation. Then, the fast hierarchical alternative least squares algorithm is applied for model optimization. After clustering analysis, we summarize four oscillatory networks characterizing the healthy group and four oscillatory networks characterizing the major depression group, respectively. The proposed model may reveal novel mechanisms of pathoconnectomics in MDD during rest, and it can be easily extended to other psychiatric disorders.

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.

The Human Connectome Project: A retrospective

The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances in human neuroimaging, particularly for measures of brain connectivity; apply these advances to study a large number of healthy young adults; and freely share the data and tools with the scientific community. NIH awarded grants to two consortia; this retrospective focuses on the "WU-Minn-Ox" HCP consortium centered at Washington University, the University of Minnesota, and University of Oxford. In just over 6 years, the WU-Minn-Ox consortium succeeded in its core objectives by: 1) improving MR scanner hardware, pulse sequence design, and image reconstruction methods, 2) acquiring and analyzing multimodal MRI and MEG data of unprecedented quality together with behavioral measures from more than 1100 HCP participants, and 3) freely sharing the data (via the ConnectomeDB database) and associated analysis and visualization tools. To date, more than 27 Petabytes of data have been shared, and 1538 papers acknowledging HCP data use have been published. The "HCP-style" neuroimaging paradigm has emerged as a set of best-practice strategies for optimizing data acquisition and analysis. This article reviews the history of the HCP, including comments on key events and decisions associated with major project components. We discuss several scientific advances using HCP data, including improved cortical parcellations, analyses of connectivity based on functional and diffusion MRI, and analyses of brain-behavior relationships. We also touch upon our efforts to develop and share a variety of associated data processing and analysis tools along with detailed documentation, tutorials, and an educational course to train the next generation of neuroimagers. We conclude with a look forward at opportunities and challenges facing the human neuroimaging field from the perspective of the HCP consortium.

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

Research into neurobiology of depression primarily focuses on its complex psychological aspects. Here we propose an alternative approach and target sensorimotor alterations—a prominent but often neglected feature of depression. We demonstrated using resting-state functional MRI data and computational modeling that top-down and bottom-up information flow in sensory and motor cortices is altered with increasing depression severity in a way that is consistent with depression symptoms. Depression-associated changes were found to be consistent across sessions, amenable to treatment and of effect size sufficiently large to predict whether somebody has mild or severe depression. These results pave the way for an avenue of research into the neural underpinnings of mental health conditions.

Functional neuroimaging research on depression has traditionally targeted neural networks associated with the psychological aspects of depression. In this study, instead, we focus on alterations of sensorimotor function in depression. We used resting-state functional MRI data and dynamic causal modeling (DCM) to assess the hypothesis that depression is associated with aberrant effective connectivity within and between key regions in the sensorimotor hierarchy. Using hierarchical modeling of between-subject effects in DCM with parametric empirical Bayes we first established the architecture of effective connectivity in sensorimotor cortices. We found that in (interoceptive and exteroceptive) sensory cortices across participants, the backward connections are predominantly inhibitory, whereas the forward connections are mainly excitatory in nature. In motor cortices these parities were reversed. With increasing depression severity, these patterns are depreciated in exteroceptive and motor cortices and augmented in the interoceptive cortex, an observation that speaks to depressive symptomatology. We established the robustness of these results in a leave-one-out cross-validation analysis and by reproducing the main results in a follow-up dataset. Interestingly, with (nonpharmacological) treatment, depression-associated changes in backward and forward effective connectivity partially reverted to group mean levels. Overall, altered effective connectivity in sensorimotor cortices emerges as a promising and quantifiable candidate marker of depression severity and treatment response.

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.

Translating Interventional Neuroscience to Suicide: It's About Time

Despite significant advances in psychiatric and psychological treatment over the last 30 years, suicide deaths have increased. Unfortunately, neuroscience insights have yielded few translational interventions that specifically target suicidal thoughts and behaviors. In our view, this is attributable to two factors. The first factor is our limited integration of neurocircuitry models with contemporary suicide theory. The second challenge is inherent to the variable nature of suicide risk over time. Few interventional neuroscience studies evaluate how temporal fluctuations in risk affect treatment, despite evidence that temporality is a key component distinguishing suicide phenotypes. To wit, individual variability in risk trajectories may provide different treatment targets to engage as a patient moves between suicidal ideation and attempt. Here, we first review contemporary ideation-to-action theories of suicide from a neurobiological perspective, focusing on valence and executive function circuits and the key role of state-induced (e.g., within stressful contexts) functional modulation on longitudinal risk trajectories. We then describe neural correlates of suicide reduction following various interventions, ranging from circuit specific (i.e., transcranial magnetic stimulation) to broader pharmacological (i.e., ketamine, lithium) to psychological (i.e., brief cognitive therapy). We then introduce novel strategies for tracking risk in naturalistic settings and real time using ecological momentary interventions. We provide a critical integration of the literature focusing on the intersection between targets and temporality, and we conclude by proposing novel research designs integrating real-time and biologically based interventions to generate novel strategies for future suicide reduction research.

Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysis

No tools are currently available to predict whether a patient suffering from major depressive disorder (MDD) will respond to a certain treatment. Machine learning analysis of magnetic resonance imaging (MRI) data has shown potential in predicting response for individual patients, which may enable personalized treatment decisions and increase treatment efficacy. Here, we evaluated the accuracy of MRI-guided response prediction in MDD. We conducted a systematic review and meta-analysis of all studies using MRI to predict single-subject response to antidepressant treatment in patients with MDD. Classification performance was calculated using a bivariate model and expressed as area under the curve, sensitivity, and specificity. In addition, we analyzed differences in classification performance between different interventions and MRI modalities. Meta-analysis of 22 samples including 957 patients showed an overall area under the bivariate summary receiver operating curve of 0.84 (95% CI 0.81-0.87), sensitivity of 77% (95% CI 71-82), and specificity of 79% (95% CI 73-84). Although classification performance was higher for electroconvulsive therapy outcome prediction (n = 285, 80% sensitivity, 83% specificity) than medication outcome prediction (n = 283, 75% sensitivity, 72% specificity), there was no significant difference in classification performance between treatments or MRI modalities. Prediction of treatment response using machine learning analysis of MRI data is promising but should not yet be implemented into clinical practice. Future studies with more generalizable samples and external validation are needed to establish the potential of MRI to realize individualized patient care in MDD.

Functional connectivity between the amygdala and subgenual cingulate gyrus predicts the antidepressant effects of ketamine in patients with treatment-resistant depression

Aim

Approximately one‐third of patients with major depressive disorder develop treatment‐resistant depression. One‐third of patients with treatment‐resistant depression demonstrate resistance to ketamine, which is a novel antidepressant effective for this disorder. The objective of this study was to examine the utility of resting‐state functional magnetic resonance imaging for the prediction of treatment response to ketamine in treatment‐resistant depression.

Methods

An exploratory seed‐based resting‐state functional magnetic resonance imaging analysis was performed to examine baseline resting‐state functional connectivity differences between ketamine responders and nonresponders before treatment with multiple intravenous ketamine infusions.

Results

Fifteen patients with treatment‐resistant depression received multiple intravenous subanesthetic (0.5 mg/kg/40 minutes) ketamine infusions, and nine were identified as responders. The exploratory resting‐state functional magnetic resonance imaging analysis identified a cluster of significant baseline resting‐state functional connectivity differences associating ketamine response between the amygdala and subgenual anterior cingulate gyrus in the right hemisphere. Using anatomical region of interest analysis of the resting‐state functional connectivity, ketamine response was predicted with 88.9% sensitivity and 100% specificity. The resting‐state functional connectivity of significant group differences between responders and nonresponders retained throughout the treatment were considered a trait‐like feature of heterogeneity in treatment‐resistant depression.

Conclusion

This study suggests the possible clinical utility of resting‐state functional magnetic resonance imaging for predicting the antidepressant effects of ketamine in treatment‐resistant depression patients and implicated resting‐state functional connectivity alterations to determine the trait‐like pathophysiology underlying treatment response heterogeneity in treatment‐resistant depression.

This study illustrates that the alteration in the RSFC within the right AN in TRD patients reflects the antidepressant response to ketamine at baseline. The alteration remained throughout the 2‐week treatment with multiple ketamine infusions and seemed to reflect the trait‐like features underlying treatment heterogeneity in TRD. By employing an anatomical ROI of the sc/sgACC, the present study also suggests the possible clinical utility of the rsfMRI to predict the treatment response to ketamine in TRD patients.

Magnetoencephalography: physics, techniques, and applications in the basic and clinical neurosciences

Magnetoencephalography (MEG) is a technique used to measure the magnetic fields generated from neuronal activity in the brain. MEG has a high temporal resolution on the order of milliseconds and provides a more direct measure of brain activity when compared with hemodynamic-based neuroimaging methods such as magnetic resonance imaging and positron emission tomography. The current review focuses on basic features of MEG such as the instrumentation and the physics that are integral to the signals that can be measured, and the principles of source localization techniques, particularly the physics of beamforming and the techniques that are used to localize the signal of interest. In addition, we review several metrics that can be used to assess functional coupling in MEG and describe the advantages and disadvantages of each approach. Lastly, we discuss the current and future applications of MEG.

Prefrontal resting-state connectivity and antidepressant response: no associations in the ELECT-TDCS trial

Functional and structural MRI of prefrontal cortex (PFC) may provide putative biomarkers for predicting the treatment response to transcranial direct current stimulation (tDCS) in depression. A recent MRI study from ELECT-TDCS (Escitalopram versus Electrical Direct-Current Theror Depression Study) showed that depression improvement after tDCS was associated with gray matter volumes of PFC subregions. Based thereon, we investigated whether antidepressant effects of tDCS are similarly associated with baseline resting-state functional connectivity (rsFC). A subgroup of 51 patients underwent baseline rsFC-MRI. All patients of ELECT-TDCS were randomized to three treatment arms for 10 weeks (anodal-left, cathodal-right PFC tDCS plus placebo medication; escitalopram 10 mg/day for 3 weeks and 20 mg/day thereafter plus sham tDCS; and placebo medication plus sham tDCS). RsFC was calculated for various PFC regions and analyzed in relation to the individual antidepressant response. There was no significant association between baseline PFC connectivity of essential structural regions, nor any other PFC regions (after correction for multiple comparisons) and patients' individual antidepressant response. This study did not reveal an association between antidepressants effects of tDCS and baseline rsFC, unlike the gray matter volume findings. Thus, the antidepressant effects of tDCS may be differentially related to structural and functional MRI measurements.

Girls' brain structural connectivity in late adolescence relates to history of depression symptoms

BACKGROUND

Girls' depressive symptoms typically increase in adolescence, with individual differences in course and severity being key risk factors for impaired emotional functioning in young adulthood. Given the continued brain white matter (WM) maturation that occurs in adolescence, the present study tested whether structural connectivity patterns in late adolescence are associated with variation in the course of depression symptom severity throughout adolescence.

METHOD

Participants were girls (N = 115) enrolled in a multiyear prospective cohort study of risk for depression. Initial depression severity (intercept) at age 10 and change in severity (linear slope) across ages 10-19 were examined in relation to WM tractography collected at age 19. Network-based statistic analyses were used to identify clusters showing variation in structural connectivity in association with depressive symptom intercept, slope, and their interaction.

RESULTS

Higher initial depressive severity and steeper positive slope (separately) were associated with greater structural connectivity between temporal, subcortical socioaffective, and occipital regions. Intercept showed more connectivity associations than slope. The interaction effect indicated that higher initial symptom severity and a steeper negative slope (i.e., alleviating symptoms) were related to greater connectivity between cognitive control regions. Moderately severe symptoms that worsened over time were followed by greater connectivity between self-referential and cognitive regions (e.g., posterior cingulate and frontal gyrus).

CONCLUSIONS

Higher depressive symptom severity in early adolescence and increasing symptom severity over time may forecast structural connectivity differences in late adolescence, particularly in pathways involving cognitive and emotion-processing regions. Understanding how clinical course relates to neurobiological correlates may inform new treatment approaches to adolescent depression.

Modulation of functional network properties in major depressive disorder following electroconvulsive therapy (ECT): a resting-state EEG analysis

Electroconvulsive therapy (ECT) is a highly effective neuromodulatory intervention for treatment-resistant major depressive disorder (MDD). Presently, however, understanding of its neurophysiological effects remains incomplete. In the present study, we utilised resting-state electroencephalography (RS-EEG) to explore changes in functional connectivity, network topology, and spectral power elicited by an acute open-label course of ECT in a cohort of 23 patients with treatment-resistant MDD. RS-EEG was recorded prior to commencement of ECT and again within 48 h following each patient’s final treatment session. Our results show that ECT was able to enhance connectivity within lower (delta and theta) frequency bands across subnetworks largely confined to fronto-central channels, while, conversely, more widespread subnetworks of reduced connectivity emerged within faster (alpha and beta) bands following treatment. Graph-based topological analyses revealed changes in measures of functional segregation (clustering coefficient), integration (characteristic path length), and small-world architecture following ECT. Finally, post-treatment enhancement of delta and theta spectral power was observed, which showed a positive association with the number of ECT sessions received. Overall, our findings indicate that RS-EEG can provide a sensitive measure of dynamic neural activity following ECT and highlight network-based analyses as a promising avenue for furthering mechanistic understanding of the effects of convulsive therapies.

The Dynamics of Functional Brain Networks Associated With Depressive Symptoms in a Nonclinical Sample

Brain function depends on the flexible and dynamic coordination of functional subsystems within distributed neural networks operating on multiple scales. Recent progress has been made in the characterization of functional connectivity (FC) at the whole-brain scale from a dynamic, rather than static, perspective, but its validity for cognitive sciences remains under debate. Here, we analyzed brain activity recorded with functional Magnetic Resonance Imaging from 71 healthy participants evaluated for depressive symptoms after a relationship breakup based on the conventional Major Depression Inventory (MDI). We compared both static and dynamic FC patterns between participants reporting high and low depressive symptoms. Between-group differences in static FC were estimated using a standard pipeline for network-based statistic (NBS). Additionally, FC was analyzed from a dynamic perspective by characterizing the occupancy, lifetime, and transition profiles of recurrent FC patterns. Recurrent FC patterns were defined by clustering the BOLD phase-locking patterns obtained using leading eigenvector dynamics analysis (LEiDA). NBS analysis revealed a brain subsystem exhibiting significantly lower within-subsystem correlation values in more depressed participants (high MDI). This subsystem predominantly comprised connections between regions of the default mode network (i.e., precuneus) and regions outside this network. On the other hand, LEiDA results showed that high MDI participants engaged more in a state connecting regions of the default mode, memory retrieval, and frontoparietal network (p-FDR = 0.012); and less in a state connecting mostly the visual and dorsal attention systems (p-FDR = 0.004). Although both our analyses on static and dynamic FC implicate the role of the precuneus in depressive symptoms, only including the temporal evolution of BOLD FC helped to disentangle over time the distinct configurations in which this region plays a role. This finding further indicates that a holistic understanding of brain function can only be gleaned if the temporal dynamics of FC is included.

Parcel-guided rTMS for depression

Transcranial magnetic stimulation (TMS) is an approved intervention for treatment-resistant depression (TRD), but current targeting approaches are only partially successful. Our objectives were (1) to examine the feasibility of MRI-guided TMS in the clinical setting using a recently published surface-based, multimodal parcellation in patients with TRD who failed standard TMS (sdTMS); (2) to examine the neurobiological mechanisms and clinical outcomes underlying MRI-guided TMS compared to that of sdTMS. We used parcel-guided TMS (pgTMS) to target the left dorsolateral prefrontal cortex parcel 46. Resting-state functional connectivity (rsfc) was assessed between parcel 46 and predefined nodes within the default mode and visual networks, following both pgTMS and sdTMS. All patients (n = 10) who had previously failed sdTMS responded to pgTMS. Alterations in rsfc between frontal, default mode, and visual networks differed significantly over time between groups. Improvements in symptoms correlated with alterations in rsfc within each treatment group. The outcome of our study supports the feasibility of pgTMS within the clinical setting. Future prospective, double-blind studies of pgTMS vs. sdTMS appear warranted.

Transition and Dynamic Reconfiguration of Whole-Brain Network in Major Depressive Disorder

Major depressive disorder (MDD) has been characterized by abnormal brain activity and interactions across the whole-brain functional networks. However, the underlying alteration of brain dynamics remains unclear. Here, we aim to investigate in detail the temporal dynamics of brain activity for MDD, and to characterize the spatiotemporal specificity of whole-brain networks and transitions across them. We developed a hidden Markov model (HMM) analysis for resting-state functional magnetic resonance imaging (fMRI) from two independent cohorts with MDD. In particular, one cohort included 127 MDD patients and 117 gender- and age-matched healthy controls, and the other included 44 MDD patients and 33 controls. We identified brain states characterized by the engagement of distinct functional networks that recurred over time and assessed the dynamical configuration of whole-brain networks and the patterns of activation of states that characterized the MDD groups. Furthermore, we analyzed the community structure of transitions across states to investigate the specificity and abnormality of transitions for MDD. Based on our identification of 12 HMM states, we found that the temporal reconfiguration of states in MDD was associated with the high-order cognition network (DMN), subcortical network (SUB), and sensory and motor networks (SMN). Further, we found that the specific module of transitions was closely related to MDD, which were characterized by two HMM states with opposite activations in DMN, SMN, and subcortical areas. Notably, our results provide novel insights into the dynamical circuit configuration of whole-brain networks for MDD and suggest that brain dynamics should remain a prime target for further MDD research.

Maintenance ECT is associated with sustained improvement in depression symptoms without adverse cognitive effects in a retrospective cohort of 100 patients each receiving 50 or more ECT treatments

BACKGROUND

Electroconvulsive therapy (ECT) is the most effective acute treatment for depression, but relapse is common following discontinuation. One strategy for prolonging remission is the use of maintenance ECT, but the clinical evidence supporting its efficacy and safety are limited. We examined the effects of maintenance ECT on depression and cognition.

METHODS

Participants were from a retrospective cohort of 100 patients receiving ECT at a freestanding psychiatric hospital and who received at least 50 treatments during a single treatment series. QIDS, BASIS-24, and MoCA were assessed at baseline and every 10 treatments thereafter during the clinical course.

RESULTS

ECT was associated with a rapid decrease in depression symptoms and overall self-reported mental health status within the first 10 treatments, which was sustained throughout a median of 22.1 months of follow-up. There was no change in cognitive functioning as measured by the MoCA. Bilateral and brief pulse treatment parameters were more common by treatment 50 than at the first treatment. Most participants either continued in ECT at the end of the study period or discontinued due to sustained remission.

LIMITATIONS

retrospective observational study without control group who did not receive ECT.

CONCLUSIONS

In this ECT cohort with at least 50 treatments, improvement in depression was sustained on QIDS and BASIS-24 and adverse cognitive effects were not detected by serial MoCAs, supporting the utility of maintenance ECT in this cohort.

Predicting response to electroconvulsive therapy combined with antipsychotics in schizophrenia using multi-parametric magnetic resonance imaging

Electroconvulsive therapy (ECT) has been shown to be effective in schizophrenia, particularly when rapid symptom reduction is needed or in cases of resistance to drug treatment. However, there are no markers available to predict response to ECT. Here, we examine whether multi-parametric magnetic resonance imaging (MRI)-based radiomic features can predict response to ECT for individual patients. A total of 57 treatment-resistant schizophrenia patients, or schizophrenia patients with an acute episode or suicide attempts were randomly divided into primary (42 patients) and test (15 patients) cohorts. We collected T1-weighted structural MRI and diffusion MRI for 57 patients before receiving ECT and extracted 600 radiomic features for feature selection and prediction. To predict a continuous improvement in symptoms (ΔPANSS), the prediction process was performed with a support vector regression model based on a leave-one-out cross-validation framework in primary cohort and was tested in test cohort. The multi-parametric MRI-based radiomic model, including four structural MRI feature from left inferior frontal gyrus, right insula, left middle temporal gyrus and right superior temporal gyrus respectively and six diffusion MRI features from tracts connecting frontal or temporal gyrus possessed a low root mean square error of 15.183 in primary cohort and 14.980 in test cohort. The Pearson's correlation coefficients between predicted and actual values were 0.671 and 0.777 respectively. These results demonstrate that multi-parametric MRI-based radiomic features may predict response to ECT for individual patients. Such features could serve as prognostic neuroimaging biomarkers that provide a critical step toward individualized treatment response prediction in schizophrenia.

Preliminary prediction of individual response to electroconvulsive therapy using whole-brain functional magnetic resonance imaging data

•

The negative FC networks achieve predictive accuracy of 76.23% for ECT response.

•

The consensus FCs represent predominately frontal, temporal and subcortical regions.

•

FCs that changed significantly were concentrated in frontal and limbic networks.

•

Longitudinal change overlapped with two FCs compared with FC with predictive power.

Electroconvulsive therapy (ECT) works rapidly and has been widely used to treat depressive disorders (DEP). However, identifying biomarkers predictive of response to ECT remains a priority to individually tailor treatment and understand treatment mechanisms. This study used a connectome-based predictive modeling (CPM) approach in 122 patients with DEP to determine if pre-ECT whole-brain functional connectivity (FC) predicts depressive rating changes and remission status after ECT (47 of 122 total subjects or 38.5% of sample), and whether pre-ECT and longitudinal changes (pre/post-ECT) in regional brain network biomarkers are associated with treatment-related changes in depression ratings. Results show the networks with the best predictive performance of ECT response were negative (anti-correlated) FC networks, which predict the post-ECT depression severity (continuous measure) with a 76.23% accuracy for remission prediction. FC networks with the greatest predictive power were concentrated in the prefrontal and temporal cortices and subcortical nuclei, and include the inferior frontal (IFG), superior frontal (SFG), superior temporal (STG), inferior temporal gyri (ITG), basal ganglia (BG), and thalamus (Tha). Several of these brain regions were also identified as nodes in the FC networks that show significant change pre-/post-ECT, but these networks were not related to treatment response. This study design has limitations regarding the longitudinal design and the absence of a control group that limit the causal inference regarding mechanism of post-treatment status. Though predictive biomarkers remained below the threshold of those recommended for potential translation, the analysis methods and results demonstrate the promise and generalizability of biomarkers for advancing personalized treatment strategies.

Functional connectivity of the anterior cingulate cortex predicts treatment outcome for rTMS in treatment-resistant depression at 3-month follow-up

BACKGROUND AND OBJECTIVE

Repetitive transcranial magnetic stimulation (rTMS) is a first-line treatment for treatment-resistant depression (TRD). The mechanisms of action of rTMS are not fully understood, and no biomarkers are available to assist in clinical practice to predict response to rTMS. This study aimed to demonstrate that after-rTMS clinical improvement is associated with functional connectivity (FC) changes of the subgenual cingulate cortex (sgACC) and rostral anterior cingulate (rACC), and FC of sgACC and rACC might serve as potential predictors for treatment response.

METHODS

Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected within 1 week before rTMS initiation in 50 TRD patients to predict subsequent response to rTMS on the left dorsolateral prefrontal cortex (DLPFC). Follow-up rs-fMRI was obtained 12 weeks after completion of rTMS and neural correlates of rTMS in sgACC- and rACC-related FC patterns were compared to before rTMS data and with rs-fMRI from healthy participants.

RESULTS

Treatment response was associated with lower FC of sgACC to right DLPFC and higher FC of rACC to left lateral parietal cortex (IPL) measured at baseline. Using sgACC-DLPFC and rACC-IPL connectivity as features, responder-nonresponder classification accuracies of 84% and 76% (end-of-treatment), 88% and 81% (3-month follow-up), respectively were achieved. Longitudinal rs-fMRI data analyses revealed that the hyperconnectivity between sgACC and visual cortex was normalized to a level which was comparable to that of healthy participants.

CONCLUSIONS

Brain activity patterns in depression are predictive of treatment response to rTMS, and longitudinal change of brain activity in relevant brain circuits after rTMS is associated with treatment response in depression. Target engagement paradigms may offer opportunities to increase the efficacy of rTMS in TRD by optimal selection of patients for treatment.

TRIAL REGISTRATION

ClinicalTrials.gov Identifiers: NCT01887782 and NCT02800226.