Effect of Electroconvulsive Therapy on Striatal Morphometry in Major Depressive Disorder

The effects of psychotherapy for anhedonia on subcortical brain volumes measured with ultra-high field MRI

BACKGROUND

Anhedonia is a transdiagnostic symptom often resistant to treatment. The identification of biomarkers sensitive to anhedonia treatment will aid in the evaluation of novel anhedonia interventions.

METHODS

This is an exploratory analysis of changes in subcortical brain volumes accompanying psychotherapy in a transdiagnostic anhedonic sample using ultra-high field (7-Tesla) MRI. Outpatients with clinically impairing anhedonia (n = 116) received Behavioral Activation Treatment for Anhedonia, a novel psychotherapy, or Mindfulness-Based Cognitive Therapy (ClinicalTrials.gov Identifiers NCT02874534 and NCT04036136). Subcortical brain volumes were estimated via the MultisegPipeline, and regions of interest were the amygdala, caudate nucleus, hippocampus, pallidum, putamen, and thalamus. Bivariate mixed effects models estimated pre-treatment relations between anhedonia severity and subcortical brain volumes, change over time in subcortical brain volumes, and associations between changes in subcortical brain volumes and changes in anhedonia symptoms.

RESULTS

As reported previously (Cernasov et al., 2023), both forms of psychotherapy resulted in equivalent and significant reductions in anhedonia symptoms. Pre-treatment anhedonia severity and subcortical brain volumes were not related. No changes in subcortical brain volumes were observed over the course of treatment. Additionally, no relations were observed between changes in subcortical brain volumes and changes in anhedonia severity over the course of treatment.

LIMITATIONS

This trial included a modest sample size and did not have a waitlist-control condition or a non-anhedonic comparison group.

CONCLUSIONS

In this exploratory analysis, psychotherapy for anhedonia was not accompanied by changes in subcortical brain volumes, suggesting that subcortical brain volumes may not be a candidate biomarker sensitive to response to psychotherapy.

Chemoarchitectural signatures of subcortical shape alterations in generalized epilepsy

Genetic generalized epilepsies (GGE) exhibit widespread morphometric alterations in the subcortical structures. Subcortical structures are essential for understanding GGE pathophysiology, but their fine-grained morphological diversity has yet to be comprehensively investigated. Furthermore, the relationships between macroscale morphological disturbances and microscale molecular chemoarchitectures are unclear. High-resolution structural images were acquired from patients with GGE (n = 97) and sex- and age-matched healthy controls (HCs, n = 184). Individual measurements of surface shape features (thickness and surface area) of seven bilateral subcortical structures were quantified. The patients and HCs were then compared vertex-wise, and shape anomalies were co-located with brain neurotransmitter profiles. We found widespread morphological alterations in GGE and prominent disruptions in the thalamus, putamen, and hippocampus. Shape area dilations were observed in the bilateral ventral, medial, and right dorsal thalamus, as well as the bilateral lateral putamen. We found that the shape area deviation pattern was spatially correlated with the norepinephrine transporter and nicotinic acetylcholine (Ach) receptor (α4β2) profiles, but a distinct association was seen in the muscarinic Ach receptor (M1). The findings provided a comprehensive picture of subcortical morphological disruptions in GGE, and further characterized the associated molecular mechanisms. This information may increase our understanding of the pathophysiology of GGE.

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.

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.

Mechanism of Action of ECT in Depression

Electroconvulsive therapy (ECT) remains the most potent antidepressant treatment available for patients with major depressive disorder (MDD). ECT is highly effective, achieving a response rate of 70-80% and a remission rate of 50-60% even in treatment-resistant patients. The underlying mechanisms of ECT are not fully understood, although several hypotheses have been proposed, including the monoamine hypothesis, anticonvulsive hypothesis, neuroplastic effects, and immunomodulatory properties. In this paper, we provide an overview of magnetic resonance imaging evidence that addresses the neuroplastic changes that occur after ECT at the human systems level and elaborate further on ECTs potent immunomodulatory properties. Despite a growing body of evidence that suggests ECT may normalize many of the structural and functional changes in the brain associated with severe depression, there is a lack of convergence between neurobiological changes and the robust clinical effects observed in depression. This may be due to sample sizes used in ECT studies being generally small and differences in data processing and analysis pipelines. Collaborations that acquire large datasets, such as the GEMRIC consortium, can help translate ECT's clinical efficacy into a better understanding of its mechanisms of action.

Non-linear processing and reinforcement learning to predict rTMS treatment response in depression

BACKGROUND

Forecasting the efficacy of repetitive transcranial magnetic stimulation (rTMS) therapy can lead to substantial time and cost savings by preventing futile treatments. To achieve this objective, we've formulated a machine learning approach aimed at categorizing patients with major depressive disorder (MDD) into two groups: individuals who respond (R) positively to rTMS treatment and those who do not respond (NR).

METHODS

Preceding the commencement of treatment, we obtained resting-state EEG data from 106 patients diagnosed with MDD, employing 32 electrodes for data collection. These patients then underwent a 7-week course of rTMS therapy, and 54 of them exhibited positive responses to the treatment. Employing Independent Component Analysis (ICA) on the EEG data, we successfully pinpointed relevant brain sources that could potentially serve as markers of neural activity within the dorsolateral prefrontal cortex (DLPFC). These identified sources were further scrutinized to estimate the sources of activity within the sensor domain. Then, we integrated supplementary physiological data and implemented specific criteria to yield more realistic estimations when compared to conventional EEG analysis. In the end, we selected components corresponding to the DLPFC region within the sensor domain. Features were derived from the time-series data of these relevant independent components. To identify the most significant features, we used Reinforcement Learning (RL). In categorizing patients into two groups - R and NR to rTMS treatment - we utilized three distinct classification algorithms including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Multilayer Perceptron (MLP). We assessed the performance of these classifiers through a ten-fold cross-validation method. Additionally, we conducted a statistical test to evaluate the discriminative capacity of these features between responders and non-responders, opening the door for further exploration in this field.

RESULTS

We identified EEG features that can anticipate the response to rTMS treatment. The most robust discriminators included EEG beta power, the sum of bispectrum diagonal elements in the delta and beta frequency bands. When these features were combined into a single vector, the classification of responders and non-responders achieved impressive performance, with an accuracy of 95.28 %, specificity at 94.23 %, sensitivity reaching 96.29 %, and precision standing at 94.54 %, all achieved using SVM.

CONCLUSIONS

The results of this study suggest that the proposed approach, utilizing power, non-linear, and bispectral features extracted from relevant independent component time-series, has the capability to forecast the treatment outcome of rTMS for MDD patients based solely on a single pre-treatment EEG recording session. The achieved findings demonstrate the superior performance of our method compared to previous techniques.

Reviewing the neurobiology of electroconvulsive therapy on a micro- meso- and macro-level

BACKGROUND

Electroconvulsive therapy (ECT) remains the one of the most effective of biological antidepressant interventions. However, the exact neurobiological mechanisms underlying the efficacy of ECT remain unclear. A gap in the literature is the lack of multimodal research that attempts to integrate findings at different biological levels of analysis METHODS: We searched the PubMed database for relevant studies. We review biological studies of ECT in depression on a micro- (molecular), meso- (structural) and macro- (network) level.

RESULTS

ECT impacts both peripheral and central inflammatory processes, triggers neuroplastic mechanisms and modulates large scale neural network connectivity.

CONCLUSIONS

Integrating this vast body of existing evidence, we are tempted to speculate that ECT may have neuroplastic effects resulting in the modulation of connectivity between and among specific large-scale networks that are altered in depression. These effects could be mediated by the immunomodulatory properties of the treatment. A better understanding of the complex interactions between the micro-, meso- and macro- level might further specify the mechanisms of action of ECT.

Looking through the lens of stigma: Understanding and anticipating concerns about the responsible development and use of psychiatric electroceutical interventions (PEIs)

Psychiatric electroceutical interventions (PEIs) show promise for treating depression, but few studies have examined stakeholders' views on them. Using interview data and survey data that analyzed the views of psychiatrists, patients, caregivers, and the general public, a conceptual map was created to represent stakeholders' views on four PEIs: electroconvulsive therapy (ECT), repetitive transcranial magnetic stimulation (TMS), deep brain stimulation (DBS), and adaptive brain implants (ABIs). Stigma emerged as a key theme connecting diverse views, revealing that it is a significant factor in the acceptance and usage of PEIs. Stigma not only discourages seeking mental health services for depression but also inhibits the acceptance of PEIs. Addressing the pervasive and complex effects of stigma highlights the need to change societal attitudes toward mental illnesses and their treatments and to provide support to patients who may benefit from these interventions. The map also demonstrates the value of conceptual mapping for anticipating and mitigating ethical considerations in the development and use of PEIs.

Ventral pallidal glutamatergic neurons regulate wakefulness and emotion through separated projections

Insomnia is often comorbid with depression, but the underlying neuronal circuit mechanism remains elusive. Recently, we reported that GABAergic ventral pallidum (VP) neurons control wakefulness associated with motivation. However, whether and how other subtypes of VP neurons regulate arousal and emotion are largely unknown. Here, we report glutamatergic VP (VPVglut2) neurons control wakefulness and depressive-like behaviors. Physiologically, the calcium activity of VPVglut2 neurons was increased during both NREM sleep-to-wake transitions and depressive/anxiety-like behaviors in mice. Functionally, activation of VPVglut2 neurons was sufficient to increase wakefulness and induce anxiety/depressive-like behaviors, whereas inhibition attenuated both. Dissection of the circuit revealed that separated projections of VPVglut2 neurons to the lateral hypothalamus and lateral habenula promote arousal and depressive-like behaviors, respectively. Our results demonstrate a subtype of VP neurons is responsible for wakefulness and emotion through separated projections, and may provide new lines for the intervention of insomnia and depression in patients.

Brain volumetric correlates of electroconvulsive therapy versus transcranial magnetic stimulation for treatment-resistant depression

BACKGROUND

Electroconvulsive therapy (ECT) and repetitive transcranial magnetic stimulation (rTMS) are effective neuromodulation therapies for treatment-resistant depression (TRD). While ECT is generally considered the most effective antidepressant, rTMS is less invasive, better tolerated and leads to more durable therapeutic benefits. Both interventions are established device antidepressants, but it remains unknown if they share a common mechanism of action. Here we aimed to compare the brain volumetric changes in patients with TRD after right unilateral (RUL) ECT versus left dorsolateral prefrontal cortex (lDLPFC) rTMS.

METHODS

We assessed 32 patients with TRD before the first treatment session and after treatment completion using structural magnetic resonance imaging. Fifteen patients were treated with RUL ECT and seventeen patients received lDLPFC rTMS.

RESULTS

Patients receiving RUL ECT, in comparison with patients treated with lDLPFC rTMS, showed a greater volumetric increase in the right striatum, pallidum, medial temporal lobe, anterior insular cortex, anterior midbrain, and subgenual anterior cingulate cortex. However, ECT- or rTMS-induced brain volumetric changes were not associated with the clinical improvement.

LIMITATIONS

We evaluated a modest sample size with concurrent pharmacological treatment and without neuromodulation therapies randomization.

CONCLUSIONS

Our findings suggest that despite comparable clinical outcomes, only RUL ECT is associated with structural change, while rTMS is not. We hypothesize that structural neuroplasticity and/or neuroinflammation may explain the larger structural changes observed after ECT, whereas neurophysiological plasticity may underlie the rTMS effects. More broadly, our results support the notion that there are multiple therapeutic strategies to move patients from depression to euthymia.

Brain morphometric features predict medication response in youth with bipolar disorder: a prospective randomized clinical trial

BACKGROUND

Identification of treatment-specific predictors of drug therapies for bipolar disorder (BD) is important because only about half of individuals respond to any specific medication. However, medication response in pediatric BD is variable and not well predicted by clinical characteristics.

METHODS

A total of 121 youth with early course BD (acute manic/mixed episode) were prospectively recruited and randomized to 6 weeks of double-blind treatment with quetiapine (n = 71) or lithium (n = 50). Participants completed structural magnetic resonance imaging (MRI) at baseline before treatment and 1 week after treatment initiation, and brain morphometric features were extracted for each individual based on MRI scans. Positive antimanic treatment response at week 6 was defined as an over 50% reduction of Young Mania Rating Scale scores from baseline. Two-stage deep learning prediction model was established to distinguish responders and non-responders based on different feature sets.

RESULTS

Pre-treatment morphometry and morphometric changes occurring during the first week can both independently predict treatment outcome of quetiapine and lithium with balanced accuracy over 75% (all p < 0.05). Combining brain morphometry at baseline and week 1 allows prediction with the highest balanced accuracy (quetiapine: 83.2% and lithium: 83.5%). Predictions in the quetiapine and lithium group were found to be driven by different morphometric patterns.

CONCLUSIONS

These findings demonstrate that pre-treatment morphometric measures and acute brain morphometric changes can serve as medication response predictors in pediatric BD. Brain morphometric features may provide promising biomarkers for developing biologically-informed treatment outcome prediction and patient stratification tools for BD treatment development.

Machine learning approaches and non-linear processing of extracted components in frontal region to predict rTMS treatment response in major depressive disorder

Predicting the therapeutic result of repetitive transcranial magnetic stimulation (rTMS) treatment could save time and costs as ineffective treatment can be avoided. To this end, we presented a machine-learning-based strategy for classifying patients with major depression disorder (MDD) into responders (R) and nonresponders (NR) to rTMS treatment. Resting state EEG data were recorded using 32 electrodes from 88 MDD patients before treatment. Then, patients underwent 7 weeks of rTMS, and 46 of them responded to treatment. By applying Independent Component Analysis (ICA) on EEG, we identified the relevant brain sources as possible indicators of neural activity in the dorsolateral prefrontal cortex (DLPFC). This was served through estimating the generators of activity in the sensor domain. Subsequently, we added physiological information and placed certain terms and conditions to offer a far more realistic estimation than the classic EEG. Ultimately, those components mapped in accordance with the region of the DLPFC in the sensor domain were chosen. Features extracted from the relevant ICs time series included permutation entropy (PE), fractal dimension (FD), Lempel-Ziv Complexity (LZC), power spectral density, correlation dimension (CD), features based on bispectrum, frontal and prefrontal cordance, and a combination of them. The most relevant features were selected by a Genetic Algorithm (GA). For classifying two groups of R and NR, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Multilayer Perceptron (MLP) were applied to predict rTMS treatment response. To evaluate the performance of classifiers, a 10-fold cross-validation method was employed. A statistical test was used to assess the capability of features in differentiating R and NR for further research. EEG characteristics that can predict rTMS treatment response were discovered. The strongest discriminative indicators were EEG beta power, the sum of bispectrum diagonal elements in delta and beta bands, and CD. The Combined feature vector classified R and NR with a high performance of 94.31% accuracy, 92.85% specificity, 95.65% sensitivity, and 92.85% precision using SVM. This result indicates that our proposed method with power and nonlinear and bispectral features from relevant ICs time-series can predict the treatment outcome of rTMS for MDD patients only by one session pretreatment EEG recording. The obtained results show that the proposed method outperforms previous methods.

Dynorphin promotes stress-induced depressive behaviors by inhibiting ventral pallidal neurons in rats

AIM

Endogenous dynorphin signaling via kappa opioid receptors (KORs) plays a key role in producing the depressive and aversive consequences of stress. We investigated the behavioral effects of the dynorphin/KOR system in the ventral pallidum (VP) and studied the underlying mechanisms.

METHODS

To investigate the effects of dynorphin on the VP, we conducted behavioral experiments after microinjection of drugs or shRNA and brain-slice electrophysiological recordings. Histological tracing and molecular biological experiments were used to identify the distribution of KORs and the possible sources of dynorphin projections to the VP.

RESULTS

An elevated dynorphin concentration and increased KOR activity were observed in the VP after acute stress. Infusion of dynorphin-A into the VP produced depressive-like phenotypes including anhedonia and despair and anxiety behaviors, but did not alter locomotor behavior. Mechanistically, dynorphin had an inhibitory effect on VP neurons-reducing their firing rate and inhibiting excitatory transmission-through direct activation of KORs and modulation of downstream G-protein-gated inwardly rectifying potassium (GIRK) channels and high-voltage gated calcium channels (VGCCs). Tracing revealed direct innervation of VP neurons by dynorphin-positive projections; potential sources of these dynorphinergic projections include the nucleus accumbens, amygdala, and hypothalamus. Blockade of dynorphin/KOR signaling in the VP by drugs or viral knock-down of KORs significantly reduced despair behavior in rats.

CONCLUSIONS

Endogenous dynorphinergic modulation of the VP plays a critical role in mediating depressive reactions to stress.

Investigation of Neurofunctional Changes Over the Course of Electroconvulsive Therapy

BACKGROUND

Electroconvulsive therapy (ECT) is an effective treatment for patients suffering from depression. Yet the exact neurobiological mechanisms underlying the efficacy of ECT and indicators of who might respond best to it remain to be elucidated. Identifying neural markers that can inform about an individual's response to ECT would enable more optimal treatment strategies and increase clinical efficacy.

METHODS

Twenty-one acutely depressed inpatients completed an emotional working memory task during functional magnetic resonance imaging before and after receiving treatment with ECT. Neural activity was assessed in 5 key regions associated with the pathophysiology of depression: bilateral dorsolateral prefrontal cortex and pregenual, subgenual, and dorsal anterior cingulate cortex. Associations between brain activation and clinical improvement, as reflected by Montgomery-Åsberg Depression Rating Scale scores, were computed using linear regression models, t tests, and Pearson correlational analyses.

RESULTS

Significant neurobiological prognostic markers or changes in neural activity from pre- to post ECT did not emerge.

CONCLUSIONS

We could not confirm normalization effects and did not find significant neural markers related to treatment response. These results demonstrate that the search for reliable and clinically useful biomarkers for ECT treatment remains in its initial stages and still faces challenges.

Clinical MRI morphological analysis of functional seizures compared to seizure-naïve and psychiatric controls

PURPOSE

Functional seizures (FS), also known as psychogenic nonepileptic seizures (PNES), are physical manifestations of acute or chronic psychological distress. Functional and structural neuroimaging have identified objective signs of this disorder. We evaluated whether magnetic resonance imaging (MRI) morphometry differed between patients with FS and clinically relevant comparison populations.

METHODS

Quality-screened clinical-grade MRIs were acquired from 666 patients from 2006 to 2020. Morphometric features were quantified with FreeSurfer v6. Mixed-effects linear regression compared the volume, thickness, and surface area within 201 regions-of-interest for 90 patients with FS, compared to seizure-naïve patients with depression (n = 243), anxiety (n = 68), and obsessive-compulsive disorder (OCD, n = 41), respectively, and to other seizure-naïve controls with similar quality MRIs, accounting for the influence of multiple confounds including depression and anxiety based on chart review. These comparison populations were obtained through review of clinical records plus research studies obtained on similar scanners.

RESULTS

After Bonferroni-Holm correction, patients with FS compared with seizure-naïve controls exhibited thinner bilateral superior temporal cortex (left 0.053 mm, p = 0.014; right 0.071 mm, p = 0.00006), thicker left lateral occipital cortex (0.052 mm, p = 0.0035), and greater left cerebellar white-matter volume (1085 mm³, p = 0.0065). These findings were not accounted for by lower MRI quality in patients with FS.

CONCLUSIONS

These results reinforce prior indications of structural neuroimaging correlates of FS and, in particular, distinguish brain morphology in FS from that in depression, anxiety, and OCD. Future work may entail comparisons with other psychiatric disorders including bipolar and schizophrenia, as well as exploration of brain structural heterogeneity within FS.

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.

Current progress in neuroimaging research for the treatment of major depression with electroconvulsive therapy

Electroconvulsive therapy (ECT) uses a certain amount of electric current to pass through the head of the patient, causing convulsions throughout the body, to relieve the symptoms of the disease and achieve the purpose of treatment. ECT can effectively improve the clinical symptoms of patients with major depression, but its therapeutic mechanism is still unclear. With the rapid development of neuroimaging technology, it is necessary to explore the neurobiological mechanism of major depression from the aspects of brain structure, brain function and brain metabolism, and to find that ECT can improve the brain function, metabolism and even brain structure of patients to a certain extent. Currently, an increasing number of neuroimaging studies adopt various neuroimaging techniques including functional magnetic resonance imaging (MRI), positron emission tomography, magnetic resonance spectroscopy, structural MRI, and diffusion tensor imaging to reveal the neural effects of ECT. This article reviews the recent progress in neuroimaging research on ECT for major depression. The results suggest that the neurobiological mechanism of ECT may be to modulate the functional activity and connectivity or neural structural plasticity in specific brain regions to the normal level, to achieve the therapeutic effect.

Shaping plasticity with non-invasive brain stimulation in the treatment of psychiatric disorders: Present and future

The final chapter of this book addresses plasticity in the setting of treating psychiatric disorders. This chapter largely focuses on the treatment of depression and reviews the established antidepressant brain stimulation treatments, focusing on plasticity and maladaptive plasticity. Depression is a unique neuropsychiatric disease in that the brain goes from a healthy state into a pathologic state, and then, with appropriate treatment, can return to health often without permanent sequelae. Depression thus differs fundamentally from neurodegenerative brain diseases like Parkinson's disease or stroke. Some have theorized that depression involves a lack of flexibility or a lack of plasticity. The proven brain stimulation methods for treating depression cause plastic changes and include acute and maintenance electroconvulsive therapy (ECT), acute and maintenance transcranial magnetic stimulation (TMS), and chronically implanted cervical vagus nerve stimulation (VNS). These treatments vary widely in their speed of onset and durability. This variability in onset speed and durability raises interesting, and so far, largely unanswered questions about the underlying neurobiological mechanisms and forms of plasticity being invoked. The chapter also covers exciting recent work with vagus nerve stimulation (VNS) that is delivered paired with behaviors to cause learning and memory and plasticity changes. Taken together these current and future brain stimulation treatments for psychiatric disorders are especially promising. They are unlocking how to shape the brain in diseases to restore balance and health, with an increasing understanding of how to effectively and precisely induce therapeutic neuroplastic changes in the brain.

Increased plasma levels of IL-6 are associated with striatal structural atrophy in major depressive disorder patients with anhedonia

BACKGROUND

Anhedonia, as the core endophenotype of major depressive disorder (MDD), is closely related to poor prognosis, but the mechanism of this feature remains to be understood. The aim of this study was to investigate the inflammatory factors and brain structural alterations in MDD patients with anhedonia and evaluate the relationship between these factors.

METHODS

We assessed the plasma levels of interleukin-1 beta (IL-1β), interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-α) in MDD patients with anhedonia (n = 22), MDD patients without anhedonia (n = 20), and age- and sex-matched healthy controls (HCs, n = 20) by enzyme-linked immunosorbent assay kits. All participants underwent high-resolution brain magnetic resonance imaging (MRI) scans, and voxel-based morphometry (VBM) was used to evaluate their gray matter volume (GMV). We compared inflammatory factors and GMV among the three groups and explored their relationships in MDD patients with anhedonia.

RESULTS

Compared with those of HCs, plasma levels of IL-1β were increased in patients with MDD independent of anhedonia features, while plasma levels of IL-6 were elevated in MDD patients with anhedonia only. Meanwhile, MDD patients with anhedonia exhibited reduced GMV in the left striatal structures compared to MDD patients without anhedonia and HCs. Moreover, a significant association was observed between increased plasma levels of IL-6 and decreased GMV of the left putamen in MDD patients with anhedonia.

CONCLUSIONS

The present research outcomes suggest that anhedonia is associated with increased plasma levels of IL-6 and decreased GMV in the left striatal structures. In addition, this study demonstrates that GMV loss in the left putamen is related to increased plasma levels of IL-6 in MDD with anhedonia, which provides further insights into the possible mechanisms of anhedonia.

Short and long-term effects of single and multiple sessions of electroconvulsive therapy on brain gray matter volumes

BACKGROUND

Electroconvulsive therapy (ECT) has been shown to induce broadly distributed cortical and subcortical volume increases, more prominently in the amygdala and the hippocampus. Structural changes after one ECT session and in the long-term have been understudied.

OBJECTIVE

The aim of this study was to describe short-term and long-term volume changes induced in cortical and subcortical regions by ECT.

METHODS

Structural brain data were acquired from depressed patients before and 2 h after their first ECT session, 7-14 days after the end of the ECT series and at 6 months follow up (N = 34). Healthy, age and gender matched volunteers were scanned according to the same schedule (N = 18) and patients affected by atrial fibrillation were scanned 1-2 h before and after undergoing electrical cardioversion (N = 16). Images were parcelled using FreeSurfer and estimates of cortical gray matter volume and subcortical volume changes were obtained using Quarc.

RESULTS

Volume increase was observable in most of gray matter regions after 2 h from the first ECT session, with significant results in brain stem, bilateral hippocampi, right putamen and left thalamus, temporal and occipital regions in the right hemisphere. At the end of treatment series, widespread significant volume changes were observed. After six months, the right amygdala volume was still significantly increased. No significant changes were observed in the comparison groups.

CONCLUSIONS

Volume increases in gray matter areas can be detected 2 h after a single ECT session. Further studies are warranted to explore the underlying molecular mechanisms.

Accounting for symptom heterogeneity can improve neuroimaging models of antidepressant response after electroconvulsive therapy

Depression symptom heterogeneity limits the identifiability of treatment‐response biomarkers. Whether improvement along dimensions of depressive symptoms relates to separable neural networks remains poorly understood. We build on work describing three latent symptom dimensions within the 17‐item Hamilton Depression Rating Scale (HDRS) and use data‐driven methods to relate multivariate patterns of patient clinical, demographic, and brain structural changes over electroconvulsive therapy (ECT) to dimensional changes in depressive symptoms. We included 110 ECT patients from Global ECT‐MRI Research Collaboration (GEMRIC) sites who underwent structural MRI and HDRS assessments before and after treatment. Cross validated random forest regression models predicted change along symptom dimensions. HDRS symptoms clustered into dimensions of somatic disturbances (SoD), core mood and anhedonia (CMA), and insomnia. The coefficient of determination between predicted and actual changes were 22%, 39%, and 39% (all p < .01) for SoD, CMA, and insomnia, respectively. CMA and insomnia change were predicted more accurately than HDRS‐6 and HDRS‐17 changes (p < .05). Pretreatment symptoms, body‐mass index, and age were important predictors. Important imaging predictors included the right transverse temporal gyrus and left frontal pole for the SoD dimension; right transverse temporal gyrus and right rostral middle frontal gyrus for the CMA dimension; and right superior parietal lobule and left accumbens for the insomnia dimension. Our findings support that recovery along depressive symptom dimensions is predicted more accurately than HDRS total scores and are related to unique and overlapping patterns of clinical and demographic data and volumetric changes in brain regions related to depression and near ECT electrodes.

Interleukin-8 and lower severity of depression in females, but not males, with treatment-resistant depression

INTRODUCTION

In cross-sectional studies of depressed patients, relationships between depression and levels of IL-8 are inconsistent, and have not been examined in relation to sex. Given identified sex differences in longitudinal data, it is important to evaluate sex-specific cross-sectional relationships between IL-8 and depressive symptoms, which may explain some inconsistency in the extant literature. It is further unknown whether IL-8 levels may relate to specific symptom profiles among depressed patients, with or without regard to sex.

METHODS

Among 108 patients with treatment resistant depression (50 females), we evaluated cross-sectional relationships between IL-8 and depression severity, as measured by the Hamilton Depression Rating Scale [HAM-D] Score, and examined sex-specific relationships, as well as relationships with depressive symptom profiles. Other inflammatory markers (IL-6, IL-10, TNF-α, CRP) were also explored in relation to HAM-D.

RESULTS

Higher IL-8 was associated with lower total HAM-D score (standardized β = -0.19, p = 0.049). Sex-specific effects were identified (IL-8 x sex interaction: p = 0.03), in which higher IL-8 related to lower HAM-D score in females (standardized β = -0.41, p = 0.004, effect size (sr2) = 0.17), but not males (standardized β = 0.02, p = 0.91). Among a subset of 94 patients (41 females) who had individual HAM-D items available, we evaluated relationships between IL-8 and HAM-D factor subscores. Across sexes, higher IL-8 was associated with lower anxiety/hypochondriasis subscores (standardized β = -0.31, p = 0.002; sex interaction: p = 0.99). Sex differences were identified for relationships between IL-8 and two other HAM-D factor subscores.

CONCLUSIONS

IL-8 may be related to anxiety symptoms across sexes, but may have a sex-specific relationship with other depressive symptoms. Further evaluation of sex-specific relationships between IL-8, depression symptom profiles, treatment response, and potential neurobiological correlates, may inform mechanisms of depression pathophysiology and aid in development of precision medicine strategies.

Hippocampal subregions and networks linked with antidepressant response to electroconvulsive therapy

Electroconvulsive therapy (ECT) has been repeatedly linked to hippocampal plasticity. However, it remains unclear what role hippocampal plasticity plays in the antidepressant response to ECT. This magnetic resonance imaging (MRI) study tracks changes in separate hippocampal subregions and hippocampal networks in patients with depression (n = 44, 23 female) to determine their relationship, if any, with improvement after ECT. Voxelwise analyses were restricted to the hippocampus, amygdala, and parahippocampal cortex, and applied separately for responders and nonresponders to ECT. In analyses of arterial spin-labeled (ASL) MRI, nonresponders exhibited increased cerebral blood flow (CBF) in bilateral anterior hippocampus, while responders showed CBF increases in right middle and left posterior hippocampus. In analyses of gray matter volume (GMV) using T1-weighted MRI, GMV increased throughout bilateral hippocampus and surrounding tissue in nonresponders, while responders showed increased GMV in right anterior hippocampus only. Using CBF loci as seed regions, BOLD-fMRI data from healthy controls (n = 36, 19 female) identified spatially separable neurofunctional networks comprised of different brain regions. In graph theory analyses of these networks, functional connectivity within a hippocampus-thalamus-striatum network decreased only in responders after two treatments and after index. In sum, our results suggest that the location of ECT-related plasticity within the hippocampus may differ according to antidepressant outcome, and that larger amounts of hippocampal plasticity may not be conducive to positive antidepressant response. More focused targeting of hippocampal subregions and/or circuits may be a way to improve ECT outcome.

Elevated body weight modulates subcortical volume change and associated clinical response following electroconvulsive therapy

Background

Obesity is a frequent somatic comorbidity of major depression, and it has been associated with worse clinical outcomes and brain structural abnormalities. Converging evidence suggests that electroconvulsive therapy (ECT) induces both clinical improvements and increased subcortical grey matter volume in patients with depression. However, it remains unknown whether increased body weight modulates the clinical response and structural neuroplasticity that occur with ECT.

Methods

To address this question, we conducted a longitudinal investigation of structural MRI data from the Global ECT-MRI Research Collaboration (GEMRIC) in 223 patients who were experiencing a major depressive episode (10 scanning sites). Structural MRI data were acquired before and after ECT, and we assessed change in subcortical grey matter volume using FreeSurfer and Quarc.

Results

Higher body mass index (BMI) was associated with a significantly lower increase in subcortical grey matter volume following ECT. We observed significant negative associations between BMI and change in subcortical grey matter volume, with pronounced effects in the thalamus and putamen, where obese participants showed increases in grey matter volume that were 43.3% and 49.6%, respectively, of the increases found in participants with normal weight. As well, BMI significantly moderated the association between subcortical grey matter volume change and clinical response to ECT. We observed no significant association between BMI and clinical response to ECT.

Limitations

Because only baseline BMI values were available, we were unable to study BMI changes during ECT and their potential association with clinical and grey matter volume change.

Conclusion

Future studies should take into account the relevance of body weight as a modulator of structural neuroplasticity during ECT treatment and aim to further explore the functional relevance of this novel finding.

Movement, mood and cognition: Preliminary insights into the therapeutic effects of electroconvulsive therapy for depression through a resting-state connectivity analysis

BACKGROUND

Electroconvulsive therapy (ECT) is a highly effective treatment for depression but how it achieves its clinical effects remains unclear.

METHODS

We set out to study the brain's response to ECT from a large-scale brain-network perspective. Using a voxelwise analysis, we looked at resting-state functional connectivity before and after a course of ECT at the whole-brain and the between- and within-network levels in 17 patients with a depressive episode. Using a group-independent component analysis approach, we focused on four networks known to be affected in depression: the salience network (SN), the default mode network (DMN), the cognitive executive network (CEN), and a subcortical network (SCN). Our clinical measures included mood, cognition, and psychomotor symptoms.

RESULTS

We found ECT to have increased the connectivity of the left CEN with the left angular gyrus and left middle frontal gyrus as well as its within-network connectivity. Both the right CEN and the SCN showed increased connectivity with the precuneus and the anterior DMN with the left amygdala. Finally, improvement of psychomotor retardation was positively correlated with an increase of within-posterior DMN connectivity.

LIMITATIONS

The limitations of our study include its small sample size and the lack of a control dataset to confirm our findings.

CONCLUSION

Our voxelwise data demonstrate that ECT induces a significant increase of connectivity across the whole brain and at the within-network level. Furthermore, we provide the first evidence on the association between an increase of within-posterior DMN connectivity and an improvement of psychomotor retardation, a core symptom of depression.

Meta-analysis of brain structural changes after electroconvulsive therapy in depression

BACKGROUND

Increases in the volume of the amygdala and hippocampus after electroconvulsive therapy (ECT) are among the most robust effects known to the brain-imaging field. Recent advances in the segmentation of substructures of these regions allow for novel insights on the relationship between brain structure and clinical outcomes of ECT.

OBJECTIVE

We aimed to provide a comprehensive synthesis of evidence available on changes in brain structure after ECT, including recently published data on hippocampal subfields.

METHODS

A meta-analysis of published studies was carried out using random-effects models of standardized mean change of regional brain volumes measured with longitudinal magnetic resonance imaging of depressive patients before and after a series of ECT.

RESULTS

Data from 21 studies (543 depressed patients) were analysed, including 6 studies (118 patients) on hippocampal subfields. Meta-analyses could be carried out for seven brain regions for which data from at least three published studies was available. We observed increases in left and right hippocampi, amygdalae, cornua ammonis (CA) 1, CA 2/3, dentate gyri (DG) and subicula with standardized mean change scores ranging between 0.34 and 1.15. The model did not reveal significant volume increases in the caudate. Meta-regression indicated a negative relationship between the reported increases in the DG and relative symptom improvement (-0.27 (SE: 0.09) per 10%).

CONCLUSIONS

ECT is accompanied by significant volume increases in the bilateral hippocampus and amygdala that are not associated with treatment outcome. Among hippocampal subfields, the most robust volume increases after ECT were measured in the dentate gyrus. The indicated negative correlation of this effect with antidepressant efficacy warrants replication in data of individual patients.

Psychiatric Neural Networks and Precision Therapeutics by Machine Learning

Learning and environmental adaptation increase the likelihood of survival and improve the quality of life. However, it is often difficult to judge optimal behaviors in real life due to highly complex social dynamics and environment. Consequentially, many different brain regions and neuronal circuits are involved in decision-making. Many neurobiological studies on decision-making show that behaviors are chosen through coordination among multiple neural network systems, each implementing a distinct set of computational algorithms. Although these processes are commonly abnormal in neurological and psychiatric disorders, the underlying causes remain incompletely elucidated. Machine learning approaches with multidimensional data sets have the potential to not only pathologically redefine mental illnesses but also better improve therapeutic outcomes than DSM/ICD diagnoses. Furthermore, measurable endophenotypes could allow for early disease detection, prognosis, and optimal treatment regime for individuals. In this review, decision-making in real life and psychiatric disorders and the applications of machine learning in brain imaging studies on psychiatric disorders are summarized, and considerations for the future clinical translation are outlined. This review also aims to introduce clinicians, scientists, and engineers to the opportunities and challenges in bringing artificial intelligence into psychiatric practice.

A longitudinal study of the association between basal ganglia volumes and psychomotor symptoms in subjects with late life depression undergoing ECT

Psychomotor dysfunction (PMD) is a core element and key contributor to disability in late life depression (LLD), which responds well to electroconvulsive therapy (ECT). The neurobiology of PMD and its response to ECT are not well understood. We hypothesized that PMD in LLD is associated with lower striatal volume, and that striatal volume increase following ECT explains PMD improvement. We analyzed data from a two-center prospective cohort study of 110 LLD subjects (>55 years) receiving ECT. Brain MRI and assessment of mood, cognition, and PMD was performed 1 week before, 1 week after, and 6 months after ECT. Volumetry of the caudate nucleus, putamen, globus pallidus, and nucleus accumbens was derived from automatically segmented brain MRIs using Freesurfer®. Linear multiple regression analyses were used to study associations between basal ganglia volume and PMD. Brain MRI was available for 66 patients 1 week post ECT and in 22 patients also six months post ECT. Baseline PMD was associated with a smaller left caudate nucleus. One week after ECT, PMD improved and volume increases were detected bilaterally in the caudate nucleus and putamen, and in the right nucleus accumbens. Improved PMD after ECT did not relate to the significant volume increases in these structures, but was predicted by a nonsignificant volume change in the right globus pallidus. No volume differences were detected 6 months after ECT, compared to baseline. Although PMD is related to lower striatal volume in LLD, ECT-induced increase of striatal volume does not explain PMD improvement.

Cognitive behavioral therapy for patients with mild to moderate depression: Treatment effects and neural mechanisms

In this study, we combined clinical assessment and magnetic resonance imaging (MRI) techniques to investigate the brain mechanisms in mild to moderate depression (MMD) patients following cognitive behavioral therapy (CBT). Data were collected from 30 MMD patients and 18 healthy controls, and we divided patients into two treatment periods (4 weeks, 8 weeks). Clinical assessment indicated that depression characteristics, as quantified by Hamilton Depression Rating Scale (HAMD), were significantly higher in MMD patients than in healthy controls. At the baseline, MRI data revealed abnormalities in the hippocampus and nucleus accumbens (NAc) of patients with MMD, e.g., smaller gray matter volumes of the hippocampus and nucleus accumbens (NAc), as well as weaker functional connectivity between NAc and the posterior cingulate cortex/precuneus. Moreover, the hippocampus and NAc volumes were negatively correlated with the HAMD scores in MMD patients. After CBT intervention, the HAMD scores decreased, and the structural and functional characteristics of NAc in MMD patients obtained at 8-week were improved; e.g., no significant differences in NAc volume or NAc-based functional connectivity between the two groups. Taken together, our results provided evidence suggesting that CBT is an effective treatment for MMD patients. Alterations of gray matter volume and resting-state functional connectivity after 8 weeks of CBT indicated a potential modulation mechanism in brain structural modifications and functional connectivity plasticity within the NAc in MMD patients.

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.

Improvement of psychomotor retardation after electroconvulsive therapy is related to decreased IL-6 levels

BACKGROUND

Prior studies suggest that IL-6 may be involved in the pathophysiology of psychomotor symptoms in depression. Electroconvulsive therapy (ECT), as yet the most effective biological treatment of severe depression, is known to improve psychomotor functioning, while recent studies have shown a decrease in the elevated IL-6 levels of depressed patients following ECT.

OBJECTIVES

This study investigates whether the improvement in psychomotor functions in patients with depression after ECT is related to changes in IL-6 levels.

METHODS

Peripheral IL-6 was quantified and the severity of psychomotor agitation and retardation determined using the CORE assessment of psychomotor symptoms in 62 patients with a (unipolar or bipolar) depressive episode within one week before and within one week after their course of ECT.

RESULTS

IL-6 levels had decreased significantly following ECT and both psychomotor retardation and agitation had improved. The decrease in IL-6 levels was related to the improvement of psychomotor retardation, with post-hoc analysis revealing that higher baseline IL-6 levels positively correlated with higher psychomotor retardation scores.

CONCLUSION

With this study, we provide the first evidence that the improvement of psychomotor retardation after ECT for depression is related to the immunomodulatory properties of the treatment, most specifically the decrease in IL-6 levels.

The Application of a Machine Learning-Based Brain Magnetic Resonance Imaging Approach in Major Depression

Major depressive disorder (MDD) shows a high prevalence and is associated with increased disability. While traditional studies aimed to investigate global characteristic neurobiological substrates of MDD, machine learning-based approaches focus on individual people rather than a group. Therefore, machine learning has been increasingly conducted and applied to clinical practice. Several previous neuroimaging studies used machine learning for stratifying MDD patients from healthy controls as well as in differentially diagnosing MDD apart from other psychiatric disorders. Also, machine learning has been used to predict treatment response using magnetic resonance imaging (MRI) results. Despite the recent accomplishments of machine learning-based MRI studies, small sample sizes and the heterogeneity of the depression group limit the generalizability of a machine learning-based predictive model. Future neuroimaging studies should integrate various materials such as genetic, peripheral, and clinical phenotypes for more accurate predictability of diagnosis and treatment response.

Inflammation and depression treatment response to electroconvulsive therapy: Sex-specific role of interleukin-8

Females suffer from depression at twice the rate of males and have differential neural and emotional responses to inflammation. However, sex-specific evaluation of relationships between inflammation and response to depression treatments are lacking. Some data suggest that interleukin(IL)-8 predicts treatment response to antidepressants and has a relationship with depressive symptom severity. This study examines whether IL-8 predicts treatment response to electroconvulsive therapy (ECT), and whether there are sex specific effects. In 40 depressed patients (22 female), plasma levels of IL-8, as well as other markers of inflammation including IL-6, IL-10, tumor necrosis factor (TNF)-α, and C-reactive protein (CRP) were obtained prior to administration of ECT and after completion of the index treatment series. Depression treatment response was defined as ≥ 50% reduction in Hamilton Depression Rating Scale (HAM-D) Score. Baseline levels of IL-8 differed by responder status, depending on sex (group × sex interaction: β = -0.571, p = 0.04), with female responders having lower levels of IL-8 at baseline as compared to female non-responders [t(20) = 2.37, p = 0.03]. Further, IL-8 levels from baseline to end of treatment differed by responder status, depending on sex (group × sex × time interaction: [F(1,36) = 9.48, p = 0.004]), and change in IL-8 from baseline to end of treatment was negatively correlated with percentage change in HAM-D score in females (β = -0.458, p = 0.03), but not in males (β = 0.315, p = 0.20). Other inflammatory markers did not differ in relation to responder status and sex. Further evaluation of sex differences in the relationship between IL-8, depression, and treatment response, across disparate treatment modalities, may inform mechanisms of response and aid in development of personalized medicine strategies.

Predicting Individual Remission After Electroconvulsive Therapy Based on Structural Magnetic Resonance Imaging: A Machine Learning Approach

OBJECTIVE

To identify important clinical or imaging features predictive of an individual's response to electroconvulsive therapy (ECT) by utilizing a machine learning approach.

METHODS

Twenty-seven depressed patients who received ECT were recruited. Clinical demographics and pretreatment structural magnetic resonance imaging (MRI) data were used as candidate features to build models to predict remission and post-ECT Hamilton Depression Rating Scale scores. Support vector machine and support vector regression with elastic-net regularization were used to build models using (i) only clinical features, (ii) only MRI features, and (iii) both clinical and MRI features. Consistently selected features across all individuals were identified through leave-one-out cross-validation.

RESULTS

Compared with models that include only clinical variables, the models including MRI data improved the prediction of ECT remission: the prediction accuracy improved from 70% to 93%. Features selected consistently across all individuals included volumes in the gyrus rectus, the right anterior lateral temporal lobe, the cuneus, and the third ventricle, as well as 2 clinical features: psychotic features and family history of mood disorder.

CONCLUSIONS

Pretreatment structural MRI data improved the individual predictive accuracy of ECT remission, and only a small subset of features was important for prediction.

Does electroconvulsive therapy cause brain damage: An update

Electroconvulsive therapy (ECT) is an effective modality of treatment for a variety of psychiatric disorders. However, it has always been accused of being a coercive, unethical, and dangerous modality of treatment. The dangerousness of ECT has been mainly attributed to its claimed ability to cause brain damage. This narrative review aims to provide an update of the evidence with regard to whether the practice of ECT is associated with damage to the brain. An accepted definition of brain damage remains elusive. There are also ethical and technical problems in designing studies that look at this question specifically. Thus, even though there are newer technological tools and innovations, any review attempting to answer this question would have to take recourse to indirect methods. These include structural, functional, and metabolic neuroimaging; body fluid biochemical marker studies; and follow-up studies of cognitive impairment and incidence of dementia in people who have received ECT among others. The review of literature and present evidence suggests that ECT has a demonstrable impact on the structure and function of the brain. However, there is a lack of evidence at present to suggest that ECT causes brain damage.

Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions

OBJECTIVES

The existence of anatomofunctional brain abnormalities in bipolar disorder (BD) is now well established by magnetic resonance imaging (MRI) studies. To create diagnostic and prognostic tools, as well as identifying biologically valid subtypes of BD, research has recently turned towards the use of machine learning (ML) techniques. We assessed both supervised ML and unsupervised ML studies in BD to evaluate their robustness, reproducibility and the potential need for improvement.

METHOD

We systematically searched for studies using ML algorithms based on MRI data of patients with BD until February 2019.

RESULT

We identified 47 studies, 45 using supervised ML techniques and 2 including unsupervised ML analyses. Among supervised studies, 43 focused on diagnostic classification. The reported accuracies for classification of BD ranged between (a) 57% and 100%, for BD vs healthy controls; (b) 49.5% and 93.1% for BD vs patients with major depressive disorder; and (c) 50% and 96.2% for BD vs patients with schizophrenia. Reported accuracies for discriminating subjects genetically at risk for BD (either from control or from patients with BD) ranged between 64.3% and 88.93%.

CONCLUSIONS

Although there are strong methodological limitations in previous studies and an important need for replication in large multicentric samples, the conclusions of our review bring hope of future computer-aided diagnosis of BD and pave the way for other applications, such as treatment response prediction. To reinforce the reliability of future results we provide methodological suggestions for good practice in conducting and reporting MRI-based ML studies in BD.

Depressive Symptom Dimensions in Treatment-Resistant Major Depression and Their Modulation With Electroconvulsive Therapy

OBJECTIVE

Symptom heterogeneity in major depressive disorder obscures diagnostic and treatment-responsive biomarker identification. Whether symptom constellations are differentially changed by electroconvulsive therapy (ECT) remains unknown. We investigate the clustering of depressive symptoms over the ECT index and whether ECT differentially influences symptom clusters.

METHODS

The 17-item Hamilton Depression Rating Scale (HDRS-17) was collected from 111 patients with current depressive episode before and after ECT from 4 independent participating sites of the Global ECT-MRI Research Collaboration. Exploratory factor analysis of HDRS-17 items pre- and post-ECT treatment identified depressive symptom dimensions before and after ECT. A 2-way analysis of covariance was used to determine whether baseline symptom clusters were differentially changed by ECT between treatment remitters (defined as patients with posttreatment HDRS-17 total score ≤8) and nonremitters while controlling for pulse width, titration method, concurrent antidepressant treatment, use of benzodiazepine, and demographic variables.

RESULTS

A 3-factor solution grouped pretreatment HDRS-17 items into core mood/anhedonia, somatic, and insomnia dimensions. A 2-factor solution best described the symptoms at posttreatment despite poorer separation of items. Among remitters, core mood/anhedonia symptoms were significantly more reduced than somatic and insomnia dimensions. No differences in symptom dimension trajectories were observed among nonremitting patients.

CONCLUSIONS

Electroconvulsive therapy targets the underlying source of depressive symptomatology and may confer differential degrees of improvement in certain core depressive symptoms. Our findings of differential trajectories of symptom clusters over the ECT index might help related predictive biomarker studies to refine their approaches by identifying predictors of change along each latent symptom dimension.

Predicting responses to electroconvulsive therapy in schizophrenia patients undergoing antipsychotic treatment: Baseline functional connectivity among regions with strong electric field distributions

This study explored imaging predictors of electroconvulsive therapy (ECT) outcome in schizophrenia patients based on pre-treatment functional connectivity (FC) within regions with strong ECT electric fields distribution. Forty-seven patients received standard antipsychotic drugs combined with ECT as well as two brain imaging sessions. Regions of interest (ROI) with strong electric field distribution were determined by ECT simulation. Using baseline functional connectivity between ROIs, a model was constructed to predict the percentage reduction of Positive and Negative Syndrome Scale (PANSS) scores. The strong electric fields were distributed in the orbital prefrontal lobe, medial temporal lobe, and other parts of the temporal lobe. Ten functional connectivity features within the electric field distribution areas showed a predictive ability for ECT outcome. The correlation coefficient between the predictive and real values of cross-validation was 0.7165. Among the predictive features, ECT induced a significant decrease in functional connectivity between the right amygdala and the left hippocampus. These results suggest that pretreatment functional connectivity patterns in brain regions with strong electric field distributions during ECT could be potential predictors of the efficacy of ECT augmentation in schizophrenia. These findings may help to improve individualized clinical treatment in the future.

Striatal and white matter volumes in chronic ketamine users with or without recent regular stimulant use

BACKGROUND

Previous studies found enlarged striatum and white matter in those with stimulants use disorders. Whether primarily ketamine users (Primarily-K) and ketamine users who co-used stimulants and other substances (K+PolyS) have abnormal brain volumes is unknown. This study aims to evaluate possible brain structural abnormalities, cognitive function and depressive symptoms, between Primarily-K and K+PolyS users.

METHODS

Striatal and white matter volumes were automatically segmented in 39 Primarily-K users, 41 K+PolyS users and 46 non-drug users (ND). Cognitive performance in 7 neurocognitive domains and depressive symptoms were also evaluated.

RESULTS

Ketamine users had larger caudates than ND-controls (Right: 1-way-ANCOVA-p=0.035; K+PolyS vs. ND, p=0.030; Linear trend for K+PolyS>Primarily-K>ND, p=0.011; Left: 1-way-ANCOVA-p=0.047, Primarily-K vs. ND p=0.051) and larger total white matter (1-way ANCOVA-p=0.009, Poly+K vs. Primarily-K, p=0.05; Poly+K vs. ND p=0.011; Linear trend for K+PolyS>Primarily-K >ND, p=0.004). Across all ketamine users, they performed poorer on Arithmetic, learning and memory tasks, and were more depressed than Non-users (p<0.001 to p=0.001). Greater lifetime ketamine usage correlated with more depressive symptoms (r=0.27, p=0.008). Larger white matter correlated with better learning across all participants (r=0.21, p=0.019), while larger right caudate correlated with lower depression scores in ketamine users (r=-0.28, p=0.013).

CONCLUSION

Ketamine users had larger caudates and total white matter than ND-controls. The even larger white matter in K+PolyS users suggests additive effects from co-use of ketamine and stimulants. However, across the ketamine users, since greater volumes were associated with better learning and less depressive symptom, the enlarged caudates and white matter might represent a compensatory response.

Electroconvulsive therapy treatment responsive multimodal brain networks

Electroconvulsive therapy is regarded as the most effective antidepressant treatment for severe and treatment-resistant depressive episodes. Despite the efficacy of electroconvulsive therapy, the neurobiological underpinnings and mechanisms underlying electroconvulsive therapy induced antidepressant effects remain unclear. The objective of this investigation was to identify electroconvulsive therapy treatment responsive multimodal biomarkers with the 17-item Hamilton Depression Rating Scale guided brain structure-function fusion in 118 patients with depressive episodes and 60 healthy controls. Results show that reduced fractional amplitude of low frequency fluctuations in the prefrontal cortex, insula and hippocampus, linked with increased gray matter volume in anterior cingulate, medial temporal cortex, insula, thalamus, caudate and hippocampus represent electroconvulsive therapy responsive covarying functional and structural brain networks. In addition, relative to nonresponders, responder-specific electroconvulsive therapy related brain networks occur in frontal-limbic network and are associated with successful therapeutic outcomes. Finally, electroconvulsive therapy responsive brain networks were unrelated to verbal declarative memory. Using a data-driven, supervised-learning method, we demonstrated that electroconvulsive therapy produces a remodeling of brain functional and structural covariance that was unique to antidepressant symptom response, but not linked to memory impairment.

The basal ganglia: A central hub for the psychomotor effects of electroconvulsive therapy

BACKGROUND

Electroconvulsive therapy (ECT) is the most effective biological treatment for depression. Aside the well-known therapeutic effect on mood symptoms, it has also a unique positive impact on psychomotor agitation and retardation, which are core symptoms of depression. The neurobiology behind these effects, however, remains unclear. The basal ganglia are proposed to be important regions in the pathogenesis of psychomotor symptoms in depression. Since ECT can trigger neuroplasticity in these subcortical nuclei, we speculate that ECT-induced volumetric changes of the basal ganglia will positively influence psychomotor symptoms.

METHODS

Psychomotor symptoms were analyzed in 17 patients with severe depression before and after an acute ECT course using a CORE assessment of the retardation, agitation, and non-interaction domains. The volumes of the caudate, putamen, pallidum, and accumbens regions were determined using magnetic resonance imaging one week before and after ECT.

RESULTS

Psychomotor functions had improved significantly after ECT and significant volume increases were found for the accumbens region, the putamen, and pallidum. The volume increase of the nucleus accumbens correlated with an improvement of psychomotor retardation, while the volume increase of the pallidum correlated negatively with an improvement of the agitation subscore.

CONCLUSION

Our findings support the notion of an association between the impact of ECT on depression-related psychomotor symptoms and volume increases of the accumbens region and pallidum, pointing to the importance of the basal ganglia in the therapeutic effect of ECT on psychomotor functioning.

Brain Changes Induced by Electroconvulsive Therapy Are Broadly Distributed

BACKGROUND

Electroconvulsive therapy (ECT) is associated with volumetric enlargements of corticolimbic brain regions. However, the pattern of whole-brain structural alterations following ECT remains unresolved. Here, we examined the longitudinal effects of ECT on global and local variations in gray matter, white matter, and ventricle volumes in patients with major depressive disorder as well as predictors of ECT-related clinical response.

METHODS

Longitudinal magnetic resonance imaging and clinical data from the Global ECT-MRI Research Collaboration (GEMRIC) were used to investigate changes in white matter, gray matter, and ventricle volumes before and after ECT in 328 patients experiencing a major depressive episode. In addition, 95 nondepressed control subjects were scanned twice. We performed a mega-analysis of single subject data from 14 independent GEMRIC sites.

RESULTS

Volumetric increases occurred in 79 of 84 gray matter regions of interest. In total, the cortical volume increased by mean ± SD of 1.04 ± 1.03% (Cohen's d = 1.01, p < .001) and the subcortical gray matter volume increased by 1.47 ± 1.05% (d = 1.40, p < .001) in patients. The subcortical gray matter increase was negatively associated with total ventricle volume (Spearman's rank correlation ρ = -.44, p < .001), while total white matter volume remained unchanged (d = -0.05, p = .41). The changes were modulated by number of ECTs and mode of electrode placements. However, the gray matter volumetric enlargements were not associated with clinical outcome.

CONCLUSIONS

The findings suggest that ECT induces gray matter volumetric increases that are broadly distributed. However, gross volumetric increases of specific anatomically defined regions may not serve as feasible biomarkers of clinical response.

Outward subcortical curvature associated with sub-clinical depression symptoms in adolescents

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We related subcortical morphology to subthreshold depression (StD) in adolescents.

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StD had mostly positively associations (outward shape associated with higher StD).

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StD associated with outward hippocampal and amygdala morphology in females (N = 160).

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And outward hippocampal, thalamic, and basal ganglia morphology in males (N = 96).

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Pro-inflammatory cytokines did not mediate these relationships.

Objective

Subclinical or subthreshold depressive symptoms (StD) are frequent in adolescence and are related to suicidality and onset of depression in adulthood, however, their neurobiology is poorly understood. We examined the relationship between StD and subcortical grey matter structures in unmedicated adolescents with no history of axis I diagnosis.

Methods

277 youths from Chicago aged 14 years participated, undergoing a structural MRI scan and completing the Revised Children's Anxiety and Depression Scale (RCADS). Blood samples provided a composite of five pro-inflammatory cytokines. Regions of interest (ROI) for vertex-based surface analysis were the left and right amygdala, hippocampus, thalamus, caudate, nucleus accumbens, pallidum and putamen. Covariates were age, pubertal status, socioeconomic disadvantage and intracranial volume. Males and females were analysed separately.

Results

StD had positive associations (outward shape) with subcortical morphology in the right amygdala and left hippocampus in females, and the bilateral putamen and the left caudate, hippocampus and thalamus in males. However, we also found negative associations with StD (inward contractions) in the hippocampus in females and the caudate in males. Pro-inflammatory cytokines did not mediate the relationship between StD and outward morphology or volume.

Conclusion

This is one of the first studies to examine subcortical morphology of basal ganglia and thalamic regions related to StD in adolescents, and the first study to report mostly positive associations between StD, volume and outward morphology in youths. These findings could reflect intact neurogenesis or resilience to depression, however longitudinal research is needed to further understand the neurobiology of StD in adolescents.

Brain structural effects of treatments for depression and biomarkers of response: a systematic review of neuroimaging studies

Antidepressive pharmacotherapy (AD), electroconvulsive therapy (ECT) and cognitive behavioural therapy (CBT) are effective treatments for major depressive disorder. With our review, we aim to provide a systematic overview of neuroimaging studies that investigate the effects of AD, ECT and CBT on brain grey matter volume (GMV) and biomarkers associated with response. After a systematic database research on PubMed, we included 50 studies using magnetic resonance imaging and investigating (1) changes in GMV, (2) pre-treatment GMV biomarkers associated with response, or (3) the accuracy of predictions of response to AD, ECT or CBT based on baseline GMV data. The strongest evidence for brain structural changes was found for ECT, showing volume increases within the temporal lobe and subcortical structures - such as the hippocampus-amygdala complex, anterior cingulate cortex (ACC) and striatum. For AD, the evidence is heterogeneous as only 4 of 11 studies reported significant changes in GMV. The results are not sufficient in order to draw conclusions about the structural brain effects of CBT. The findings show consistently that higher pre-treatment ACC volume is associated with response to AD, ECT and CBT. An association of higher pre-treatment hippocampal volume and response has only been reported for AD. Machine learning approaches based on pre-treatment whole brain patterns reach accuracies of 64-90% for predictions of AD or ECT response on the individual patient level. The findings underline the potential of brain biomarkers for the implementation in clinical practice as an additive feature within the process of treatment selection.

Common increased hippocampal volume but specific changes in functional connectivity in schizophrenia patients in remission and non-remission following electroconvulsive therapy: A preliminary study

Electroconvulsive therapy (ECT) is considered a treatment option in patients with drug-resistant schizophrenia (SZ). However, approximately one-third of patients do not benefit from ECT in the clinic. Thus, it is critical to investigate differences between ECT responders and non-responders. Accumulated evidence has indicated that one region of ECT action is the hippocampus, which also plays an important role in SZ pathophysiology. To date, no studies have investigated differences in ECT effects in the hippocampus between treatment responders and non-responders. This study recruited twenty-one SZ patients treated for four weeks with ECT (MSZ, n = 21) and twenty-one SZ patients who received pharmaceutical therapy (DSZ, n = 21). The MSZ group was further categorized into responders (MSR, n = 10) or non-responders (MNR, n = 11) based on treatment outcomes by the criterion of a 50% reduction in the Positive and Negative Syndrome Scale total scores. Using structural and resting-state functional MRI, we measured the hippocampal volume and functional connectivity (FC) in all SZ patients (before and after treatment) and 23 healthy controls. In contrast to pharmaceutical therapy, ECT induced bilateral hippocampal volume increases in the MSZ. Both the MSR and MNR exhibited hippocampal expansion after ECT, whereas a lower baseline volume in one of hippocampal subfield (hippocampus-amygdala transition area) was found in the MNR. After ECT, increased FC between the hippocampus and brain networks associated with cognitive function was only observed in the MSR. The mechanism of action of ECT in schizophrenia is complex. A combination of baseline impairment level, ECT-introduced morphological changes and post-ECT FC increases in the hippocampus may jointly contribute to the post-ECT symptom improvements in patients with SZ.

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

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

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

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

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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.

Association between hippocampal volume change and change in memory following electroconvulsive therapy in late-life depression

OBJECTIVE

Electroconvulsive therapy (ECT)-induced hippocampal volume change (HVC) has been repeatedly described in recent years. The similar time course of HVC and ECT-related cognitive effects suggest a relation, that is to date, understudied. This study investigates whether HVC following ECT predicts the change in memory performance six months after the end of the ECT treatment.

METHODS

Hippocampal volume (HV) was measured via high-resolution 3D T1-weighted images in 88 patients with late-life depression, within 1 week before and after ECT. Memory performance was assessed before and six months after ECT. Multiple linear regression was used to examine whether change in memory performance could be predicted based on ECT-induced changes in HV.

RESULTS

Larger right absolute HVC predicts less pronounced improvement on the VAT (visual memory) in the whole sample. For the 8-Word Test (verbal memory), Category Fluency Test (semantic memory), and MMSE, the effect is only present in patients who switched from right unilateral to bitemporal stimulation after six ECT sessions. Absolute HVC in the left hemisphere was not significantly related to cognitive change.

CONCLUSION

A larger absolute change in right HV during ECT is associated with less improvement in memory performance up to 6 months post-ECT.

Machine learning and big data analytics in bipolar disorder: A position paper from the International Society for Bipolar Disorders Big Data Task Force

OBJECTIVES

The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD.

METHOD

A task force was convened to examine and integrate findings from the scientific literature related to machine learning and big data based studies to clarify terminology and to describe challenges and potential applications in the field of BD. We also systematically searched PubMed, Embase, and Web of Science for articles published up to January 2019 that used machine learning in BD.

RESULTS

The results suggested that big data analytics has the potential to provide risk calculators to aid in treatment decisions and predict clinical prognosis, including suicidality, for individual patients. This approach can advance diagnosis by enabling discovery of more relevant data-driven phenotypes, as well as by predicting transition to the disorder in high-risk unaffected subjects. We also discuss the most frequent challenges that big data analytics applications can face, such as heterogeneity, lack of external validation and replication of some studies, cost and non-stationary distribution of the data, and lack of appropriate funding.

CONCLUSION

Machine learning-based studies, including atheoretical data-driven big data approaches, provide an opportunity to more accurately detect those who are at risk, parse-relevant phenotypes as well as inform treatment selection and prognosis. However, several methodological challenges need to be addressed in order to translate research findings to clinical settings.

[Neuronal effects and biomarkers of antidepressant treatments : Current review from the perspective of neuroimaging]

Depression is one of the most frequent and disabling mental disorders worldwide and is accompanied by a severe impairment in the quality of life. There are numerous imaging studies showing differences in the volume of gray and white brain matter and function between patients suffering from depression and healthy controls. Neuroimaging studies show that pharmacotherapy and electroconvulsive therapy are accompanied by an increase of hippocampal gray matter volume while as a result of psychotherapy activity changes in the anterior cingulate cortex (ACC) have repeatedly been reported. By the identification of neuroanatomical markers, baseline volumes of the ACC have also been shown to be associated with therapy response to all treatments. The identification of such neuronal biomarkers in combination with machine learning techniques provide a promising step towards a neurobiologically based application for the prediction of treatment response.

Prediction of rTMS treatment response in major depressive disorder using machine learning techniques and nonlinear features of EEG signal

BACKGROUND

Prediction of therapeutic outcome of repetitive transcranial magnetic stimulation (rTMS) treatment is an important purpose that eliminates financial and psychological consequences of applying inefficient therapy. To achieve this goal we proposed a method based on machine learning to classify responders (R) and non- responders (NR) to rTMS treatment for major depression disorder (MDD) patients.

METHODS

19 electrodes resting state EEG was recorded from 46 MDD patients before treatment. Then patients underwent 7 weeks of rTMS, and 23 of them responded to treatment. Features extracted from EEG include Lempel-Ziv complexity (LZC), Katz fractal dimension (KFD), correlation dimension (CD), the power spectral density, features based on bispectrum, frontal and prefrontal cordance and combination of them. The most relevant features were selected by the minimal-redundancy-maximal-relevance (mRMR) feature selection algorithm. For classifying two groups of R and NR, k-nearest neighbors (KNN) were applied. The performance of the proposed method was evaluated by leave-1-out cross-validation. For further study, the capability of features in differentiating R and NR was investigated by a statistical test.

RESULTS

Effective EEG features for prediction of rTMS treatment response were found. EEG beta power, the sum of bispectrum diagonal elements in delta and beta bands and CD were the most discriminative features. Power of beta classified R and NR with the high performance of 91.3% accuracy, 91.3% specificity, and 91.3% sensitivity.

LIMITATIONS

Lack of large sample size restricted our method for using in clinical applications.

CONCLUSION

This considerable high accuracy indicates that our proposed method with power and some of the nonlinear and bispectral features can lead to promising results in predicting treatment outcome of rTMS for MDD patients only by one session pretreatment EEG recording.