Changes in Functional Connectivity Predict Outcome of Repetitive Transcranial Magnetic Stimulation Treatment of Major Depressive Disorder

Resting-state EEG connectivity recorded before and after rTMS treatment in patients with treatment-resistant depression

Repetitive transcranial magnetic stimulation (rTMS) has shown efficacy and tolerability in Major Depressive Disorder (MDD). However, the underlying mechanisms of its antidepressant effects remain unclear. This open-label study investigated electroencephalography (EEG) functional connectivity markers associated with response and the antidepressant effects of rTMS. Resting-state EEG data were collected from 28 participants with MDD before and after a four-week rTMS course. Source-space functional connectivity between 38 cortical regions was compared using an orthogonalised amplitude approach. Depressive symptoms significantly improved following rTMS, with 43 % of participants classified as responders. While the study's functional connectivity findings did not withstand multiple comparison corrections, exploratory analyses suggest an association between theta band connectivity and rTMS treatment mechanisms. Fronto-parietal theta connectivity increased after treatment but did not correlate with antidepressant response. Notably, low baseline theta connectivity was associated with greater response. However, due to the exploratory nature and small sample size, further replication is needed. The findings provide preliminary evidence that EEG functional connectivity, particularly within the theta band, may reflect the mechanisms by which rTMS exerts its therapeutic effects.

Cortical glutamate, Glx, and total N-acetylaspartate: potential biomarkers of repetitive transcranial magnetic stimulation treatment response and outcomes in major depression

Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for individuals with major depressive disorder (MDD) who have not improved with standard therapies. However, only 30-45% of patients respond to rTMS. Predicting response to rTMS will benefit both patients and providers in terms of prescribing and targeting treatment for maximum efficacy and directing resources, as individuals with lower likelihood of response could be redirected to more suitable treatment alternatives. In this exploratory study, our goal was to use proton magnetic resonance spectroscopy to examine how glutamate (Glu), Glx, and total N-acetylaspartate (tNAA) predict post-rTMS changes in overall MDD severity and symptoms, and treatment response. Metabolites were measured in a right dorsal anterior cingulate cortex voxel prior to a standard course of 10 Hz rTMS to the left DLPFC in 25 individuals with MDD. MDD severity and symptoms were evaluated via the Inventory of Depression Symptomatology Self-Report (IDS-SR). rTMS response was defined as ≥50% change in full-scale IDS-SR scores post treatment. Percent change in IDS-SR symptom domains were evaluated using principal component analysis and established subscales. Generalized linear and logistic regression models were used to evaluate the relationship between baseline Glu, Glx, and tNAA and outcomes while controlling for age and sex. Participants with baseline Glu and Glx levels in the lower range had greater percent change in full scale IDS-SR scores post-treatment (p < 0.001), as did tNAA (p = 0.007). Low glutamatergic metabolite levels also predicted greater percent change in mood/cognition symptoms (p ≤ 0.001). Low-range Glu, Glx, and tNAA were associated with greater improvement on the immuno-metabolic subscale (p ≤ 0.003). Baseline Glu predicted rTMS responder status (p = 0.025) and had an area under the receiving operating characteristic curve of 0.81 (p = 0.009), demonstrating excellent discriminative ability. Baseline Glu, Glx, and tNAA significantly predicted MDD improvement after rTMS; preliminary evidence also demonstrates metabolite association with symptom subdomain improvement post-rTMS. This work provides feasibility for a personalized medicine approach to rTMS treatment selection, with individuals with Glu levels in the lower range potentially being the best candidates.

Prediction of antidepressant responses to non-invasive brain stimulation using frontal electroencephalogram signals: Cross-dataset comparisons and validation

BACKGROUND

10-Hz repetitive transcranial magnetic stimulation(rTMS) and intermittent theta-burst stimulation(iTBS) over left prefrontal cortex are FDA-approved, effective options for treatment-resistant depression (TRD). Optimal prediction models for iTBS and rTMS remain elusive. Therefore, our primary objective was to compare prediction accuracy between classification by frontal theta activity alone and machine learning(ML) models by linear and non-linear frontal signals. The second objective was to study an optimal ML model for predicting responses to rTMS and iTBS.

METHODS

Two rTMS and iTBS datasets (n = 163) were used: one randomized controlled trial dataset (RCTD; n = 96) and one outpatient dataset (OPD; n = 67). Frontal theta and non-linear EEG features that reflect trend, stability, and complexity were extracted. Pretreatment frontal EEG and ML algorithms, including classical support vector machine(SVM), random forest(RF), XGBoost, and CatBoost, were analyzed. Responses were defined as ≥50 % depression improvement after treatment. Response rates between those with and without pretreatment prediction in another independent outpatient cohort (n = 208) were compared.

RESULTS

Prediction accuracy using combined EEG features by SVM was better than frontal theta by logistic regression. The accuracy for OPD patients significantly dropped using the RCTD-trained SVM model. Modern ML models, especially RF (rTMS = 83.3 %, iTBS = 88.9 %, p-value(ACC > NIR) < 0.05 for iTBS), performed significantly above chance and had higher accuracy than SVM using both selected features (p < 0.05, FDR corrected for multiple comparisons) or all EEG features. Response rates among those receiving prediction before treatment were significantly higher than those without prediction (p = 0.035).

CONCLUSION

The first study combining linear and non-linear EEG features could accurately predict responses to left PFC iTBS. The bootstraps-based ML model (i.e., RF) had the best predictive accuracy for rTMS and iTBS.

Rumination symptoms in treatment-resistant major depressive disorder, and outcomes of repetitive Transcranial Magnetic Stimulation (rTMS) treatment

Rumination is a maladaptive style of regulating thoughts and emotions. It is a common symptom of Major Depressive Disorder (MDD), and more severe rumination is associated with poorer medication and psychotherapy treatment outcomes, particularly among women. It is unclear to what extent rumination may influence the outcomes of, or be responsive to, repetitive Transcranial Magnetic Stimulation (rTMS) treatment of MDD. We retrospectively examined data collected during rTMS treatment of 155 patients (age 42.52 ± 14.22, 79 female) with moderately severe treatment-resistant MDD. The severity of rumination and depression was assessed before and during a course of 30 sessions of measurement-based rTMS treatment using the Ruminative Responses Scale (RSS) and the Patient Health Questionnaire (PHQ-9), respectively. Relationships among baseline levels of rumination, depression, and treatment outcome were assessed using a series of repeated measures linear mixed effects models. Both depression and rumination symptoms significantly improved after treatment, but improvement in depression was not a significant mediator of rumination improvement. Higher baseline rumination (but not depression severity) was associated with poorer depression outcomes independently of depression severity. Female gender was a significant predictor of worse outcomes for all RRS subscales. Both depressive and ruminative symptoms in MDD improved following rTMS treatment. These improvements were correlated, but improvement in rumination was not fully explained by reduction in depressive symptoms. These findings suggest that while improvement in rumination and depression severity during rTMS treatment are correlated, they are partly independent processes. Future studies should examine whether rumination symptoms should be specifically targeted with different rTMS treatment parameters.

Prediction of response to repetitive transcranial magnetic stimulation for major depressive disorder using hybrid Convolutional recurrent neural networks and raw Electroencephalogram Signal

Major Depressive Disorder (MDD) is a high prevalence disease that needs an effective and timely treatment to prevent its progress and additional costs. Repetitive Transcranial Magnetic Stimulation (rTMS) is an effective treatment option for MDD patients which uses strong magnetic pulses to stimulate specific regions of the brain. However, some patients do not respond to this treatment which causes the waste of multiple weeks as treatment time and clinical resources. Therefore developing an effective way for the prediction of response to the rTMS treatment of depression is necessary. In this work, we proposed a hybrid model created by pre-trained Convolutional Neural Networks (CNN) models and Bidirectional Long Short-Term Memory (BLSTM) cells to predict response to rTMS treatment from raw EEG signal. Three pre-trained CNN models named VGG16, InceptionResNetV2, and EffecientNetB0 were utilized as Transfer Learning (TL) models to construct hybrid TL-BLSTM models. Then an ensemble of these models was created using weighted majority voting which the weights were optimized by Differential Evolution (DE) optimization algorithm. Evaluation of these models shows the superior performance of the ensemble model by the accuracy of 98.51%, sensitivity of 98.64%, specificity of 98.36%, F1-score of 98.6%, and AUC of 98.5%. Therefore, the ensemble of the proposed hybrid convolutional recurrent networks can efficiently predict the treatment outcome of rTMS using raw EEG data.

A convolutional recurrent neural network with attention for response prediction to repetitive transcranial magnetic stimulation in major depressive disorder

Prediction of response to Repetitive Transcranial Magnetic Stimulation (rTMS) can build a very effective treatment platform that helps Major Depressive Disorder (MDD) patients to receive timely treatment. We proposed a deep learning model powered up by state-of-the-art methods to classify responders (R) and non-responders (NR) to rTMS treatment. Pre-treatment Electro-Encephalogram (EEG) signal of public TDBRAIN dataset and 46 proprietary MDD subjects were utilized to create time-frequency representations using Continuous Wavelet Transform (CWT) to be fed into the two powerful pre-trained Convolutional Neural Networks (CNN) named VGG16 and EfficientNetB0. Equipping these Transfer Learning (TL) models with Bidirectional Long Short-Term Memory (BLSTM) and attention mechanism for the extraction of most discriminative spatiotemporal features from input images, can lead to superior performance in the prediction of rTMS treatment outcome. Five brain regions named Frontal, Central, Parietal, Temporal, and occipital were assessed and the highest evaluated performance in 46 proprietary MDD subjects was acquired for the Frontal region using the TL-LSTM-Attention model based on EfficientNetB0 with accuracy, sensitivity, specificity, and Area Under the Curve (AUC) of 97.1%, 97.3%, 97.0%, and 0.96 respectively. Additionally, to test the generalizability of the proposed models, these TL-BLSTM-Attention models were evaluated on a public dataset called TDBRAIN and the highest accuracy of 82.3%, the sensitivity of 80.2%, the specificity of 81.9% and the AUC of 0.83 were obtained. Therefore, advanced deep learning methods using a time-frequency representation of EEG signals from the frontal brain region and the convolutional recurrent neural networks equipped with the attention mechanism can construct an accurate platform for the prediction of response to the rTMS treatment.

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.

An effective brain connectivity technique to predict repetitive transcranial magnetic stimulation outcome for major depressive disorder patients using EEG signals

One of the most effective treatments for drug-resistant Major depressive disorder (MDD) patients is repetitive transcranial magnetic stimulation (rTMS). To improve treatment efficacy and reduce health care costs, it is necessary to predict the treatment response. In this study, we intend to predict the rTMS treatment response in MDD patients from electroencephalogram (EEG) signals before starting the treatment using machine learning approaches. Effective brain connectivity of 19-channel EEG data of MDD patients was calculated by the direct directed transfer function (dDTF) method. Then, using three feature selection methods, the best features were selected and patients were classified as responders or non-responders to rTMS treatment by using the support vector machine (SVM). Results on the 34 MDD patients indicated that the Fp2 region in the delta and theta frequency bands has a significant difference between the two groups and can be used as a significant brain biomarker to assess the rTMS treatment response. Also, the highest accuracy (89.6%) using the SVM classifier for the best features of the dDTF method based on the area under the receiver operating characteristic curve (AUC-ROC) criteria was obtained by combining the delta and theta frequency bands. Consequently, the proposed method can accurately detect the rTMS treatment response in MDD patients before starting treatment on the EEG signal to avoid financial and time costs to patients and medical centers.

Functional Activity in the Effect of Transcranial Magnetic Stimulation Therapy for Patients with Depression: A Meta-Analysis

Depression is a long-lasting mental disorder that affects more than 264 million people worldwide. Transcranial magnetic stimulation (TMS) can be a safe and effective choice for the treatment of depression. Functional neuroimaging provides unique insights into the neuropsychiatric effects of antidepressant TMS. In this meta-analysis, we aimed to assess the functional activity of brain regions caused by TMS for depression. A literature search was conducted from inception to 5 January 2022. Studies were then selected according to predetermined inclusion and exclusion criteria. Activation likelihood estimation was applied to analyze functional activation. Five articles were ultimately included after selection. The main analysis results indicated that TMS treatment for depression can alter the activity in the right precentral gyrus, right posterior cingulate, left inferior frontal gyrus and left middle frontal gyrus. In resting-state studies, increased activation was shown in the right precentral gyrus, right posterior cingulate, left inferior frontal gyrus and left superior frontal gyrus associated with TMS treatment. In task-related studies, clusters in the right middle frontal gyrus, left sub-gyrus, left middle frontal gyrus and left posterior cingulate were hyperactivated post-treatment. Our study offers an overview of brain activity changes in patients with depression after TMS treatment.

Attention-Based Convolutional Recurrent Deep Neural Networks for the Prediction of Response to Repetitive Transcranial Magnetic Stimulation for Major Depressive Disorder

Repetitive Transcranial Magnetic Stimulation (rTMS) is proposed as an effective treatment for major depressive disorder (MDD). However, because of the suboptimal treatment outcome of rTMS, the prediction of response to this technique is a crucial task. We developed a deep learning (DL) model to classify responders (R) and non-responders (NR). With this aim, we assessed the pre-treatment EEG signal of 34 MDD patients and extracted effective connectivity (EC) among all electrodes in four frequency bands of EEG signal. Two-dimensional EC maps are put together to create a rich connectivity image and a sequence of these images is fed to the DL model. Then, the DL framework was constructed based on transfer learning (TL) models which are pre-trained convolutional neural networks (CNN) named VGG16, Xception, and EfficientNetB0. Then, long short-term memory (LSTM) cells are equipped with an attention mechanism added on top of TL models to fully exploit the spatiotemporal information of EEG signal. Using leave-one subject out cross validation (LOSO CV), Xception-BLSTM-Attention acquired the highest performance with 98.86% of accuracy and 97.73% of specificity. Fusion of these models as an ensemble model based on optimized majority voting gained 99.32% accuracy and 98.34% of specificity. Therefore, the ensemble of TL-LSTM-Attention models can predict accurately the treatment outcome.

Repetitive transcranial magnetic stimulation treatment of major depressive disorder and comorbid chronic pain: response rates and neurophysiologic biomarkers

BACKGROUND

Major depressive disorder (MDD) and chronic pain are highly comorbid, and pain symptoms are associated with a poorer response to antidepressant medication treatment. It is unclear whether comorbid pain also is associated with a poorer response to treatment with repetitive transcranial magnetic stimulation (rTMS).

METHODS

162 MDD subjects received 30 sessions of 10 Hz rTMS treatment administered to the left dorsolateral prefrontal cortex (DLPFC) with depression and pain symptoms measured before and after treatment. For a subset of 96 patients, a resting-state electroencephalogram (EEG) was recorded at baseline. Clinical outcome was compared between subjects with and without comorbid pain, and the relationships among outcome, pain severity, individual peak alpha frequency (PAF), and PAF phase-coherence in the EEG were examined.

RESULTS

64.8% of all subjects reported pain, and both depressive and pain symptoms were significantly reduced after rTMS treatment, irrespective of age or gender. Patients with severe pain were 27% less likely to respond to MDD treatment than pain-free individuals. PAF was positively associated with pain severity. PAF phase-coherence in the somatosensory and default mode networks was significantly lower for MDD subjects with pain who failed to respond to MDD treatment.

CONCLUSIONS

Pain symptoms improved after rTMS to left DLPFC in MDD irrespective of age or gender, although the presence of chronic pain symptoms reduced the likelihood of treatment response. Individual PAF and baseline phase-coherence in the sensorimotor and midline regions may represent predictors of rTMS treatment outcome in comorbid pain and MDD.

The Role of Alpha Oscillations among the Main Neuropsychiatric Disorders in the Adult and Developing Human Brain: Evidence from the Last 10 Years of Research

Alpha oscillations (7-13 Hz) are the dominant rhythm in both the resting and active brain. Accordingly, translational research has provided evidence for the involvement of aberrant alpha activity in the onset of symptomatological features underlying syndromes such as autism, schizophrenia, major depression, and Attention Deficit and Hyperactivity Disorder (ADHD). However, findings on the matter are difficult to reconcile due to the variety of paradigms, analyses, and clinical phenotypes at play, not to mention recent technical and methodological advances in this domain. Herein, we seek to address this issue by reviewing the literature gathered on this topic over the last ten years. For each neuropsychiatric disorder, a dedicated section will be provided, containing a concise account of the current models proposing characteristic alterations of alpha rhythms as a core mechanism to trigger the associated symptomatology, as well as a summary of the most relevant studies and scientific contributions issued throughout the last decade. We conclude with some advice and recommendations that might improve future inquiries within this field.

A systematic data-driven approach to analyze sensor-level EEG connectivity: Identifying robust phase-synchronized network components using surface Laplacian with spectral-spatial PCA

Although conventional averaging across predefined frequency bands reduces the complexity of EEG functional connectivity (FC), it obscures the identification of resting-state brain networks (RSN) and impedes accurate estimation of FC reliability. Extending prior work, we combined scalp current source density (CSD; spherical spline surface Laplacian) and spectral-spatial PCA to identify FC components. Phase-based FC was estimated via debiased-weighted phase-locking index from CSD-transformed resting EEGs (71 sensors, 8 min, eyes open/closed, 35 healthy adults, 1-week retest). Spectral PCA extracted six robust alpha and theta components (86.6% variance). Subsequent spatial PCA for each spectral component revealed seven robust regionally focused (posterior, central, and frontal) and long-range (posterior-anterior) alpha components (peaks at 8, 10, and 13 Hz) and a midfrontal theta (6 Hz) component, accounting for 37.0% of FC variance. These spatial FC components were consistent with well-known networks (e.g., default mode, visual, and sensorimotor), and four were sensitive to eyes open/closed conditions. Most FC components had good-to-excellent internal consistency (odd/even epochs, eyes open/closed) and test-retest reliability (ICCs ≥ .8). Moreover, the FC component structure was generally present in subsamples (session × odd/even epoch, or smaller subgroups [n = 7-10]), as indicated by high similarity of component loadings across PCA solutions. Apart from systematically reducing FC dimensionality, our approach avoids arbitrary thresholds and allows quantification of meaningful and reliable network components that may prove to be of high relevance for basic and clinical research applications.

Predicting treatment response using EEG in major depressive disorder: A machine-learning meta-analysis

Selecting a course of treatment in psychiatry remains a trial-and-error process, and this long-standing clinical challenge has prompted an increased focus on predictive models of treatment response using machine learning techniques. Electroencephalography (EEG) represents a cost-effective and scalable potential measure to predict treatment response to major depressive disorder. We performed separate meta-analyses to determine the ability of models to distinguish between responders and non-responders using EEG across treatments, as well as a performed subgroup analysis of response to transcranial magnetic stimulation (rTMS), and antidepressants (Registration Number: CRD42021257477) in Major Depressive Disorder by searching PubMed, Scopus, and Web of Science for articles published between January 1960 and February 2022. We included 15 studies that predicted treatment responses among patients with major depressive disorder using machine-learning techniques. Within a random-effects model with a restricted maximum likelihood estimator comprising 758 patients, the pooled accuracy across studies was 83.93% (95% CI: 78.90-89.29), with an Area-Under-the-Curve (AUC) of 0.850 (95% CI: 0.747-0.890), and partial AUC of 0.779. The average sensitivity and specificity across models were 77.96% (95% CI: 60.05-88.70), and 84.60% (95% CI: 67.89-92.39), respectively. In a subgroup analysis, greater performance was observed in predicting response to rTMS (Pooled accuracy: 85.70% (95% CI: 77.45-94.83), Area-Under-the-Curve (AUC): 0.928, partial AUC: 0.844), relative to antidepressants (Pooled accuracy: 81.41% (95% CI: 77.45-94.83, AUC: 0.895, pAUC: 0.821). Furthermore, across all meta-analyses, the specificity (true negatives) of EEG models was greater than the sensitivity (true positives), suggesting that EEG models thus far better identify non-responders than responders to treatment in MDD. Studies varied widely in important features across models, although relevant features included absolute and relative power in frontal and temporal electrodes, measures of connectivity, and asymmetry across hemispheres. Predictive models of treatment response using EEG hold promise in major depressive disorder, although there is a need for prospective model validation in independent datasets, and a greater emphasis on replicating physiological markers. Crucially, standardization in cut-off values and clinical scales for defining clinical response and non-response will aid in the reproducibility of findings and the clinical utility of predictive models. Furthermore, several models thus far have used data from open-label trials with small sample sizes and evaluated performance in the absence of training and testing sets, which increases the risk of statistical overfitting. Large consortium studies are required to establish predictive signatures of treatment response using EEG, and better elucidate the replicability of specific markers. Additionally, it is speculated that greater performance was observed in rTMS models, since EEG is assessing neural networks more likely to be directly targeted by rTMS, comprising electrical activity primarily near the surface of the cortex. Prospectively, there is a need for models that examine the comparative effectiveness of multiple treatments across the same patients. However, this will require a thoughtful consideration towards cumulative treatment effects, and whether washout periods between treatments should be utilised. Regardless, longitudinal cross-over trials comparing multiple treatments across the same group of patients will be an important prerequisite step to both facilitate precision psychiatry and identify generalizable physiological predictors of response between and across treatment options.

Cold Cognition as Predictor of Treatment Response to rTMS; A Retrospective Study on Patients With Unipolar and Bipolar Depression

Background

Cognitive impairments are prevalent in patients with unipolar and bipolar depressive disorder (UDD and BDD, respectively). Considering the fact assessing cognitive functions is increasingly feasible for clinicians and researchers, targeting these problems in treatment and using them at baseline as predictors of response to treatment can be very informative.

Method

In a naturalistic, retrospective study, data from 120 patients (Mean age: 33.58) with UDD (n = 56) and BDD (n = 64) were analyzed. Patients received 20 sessions of bilateral rTMS (10 Hz over LDLPFC and 1 HZ over RDLPFC) and were assessed regarding their depressive symptoms, sustained attention, working memory, and executive functions, using the Beck Depression Inventory (BDI-II) and Neuropsychological Test Automated Battery Cambridge, at baseline and after the end of rTMS treatment course. Generalized estimating equations (GEE) and logistic regression were used as the main statistical methods to test the hypotheses.

Results

Fifty-three percentage of all patients (n = 64) responded to treatment. In particular, 53.1% of UDD patients (n = 34) and 46.9% of BDD patients (n = 30) responded to treatment. Bilateral rTMS improved all cognitive functions (attention, working memory, and executive function) except for visual memory and resulted in more modulations in the working memory of UDD compared to BDD patients. More improvements in working memory were observed in responded patients and visual memory, age, and sex were determined as treatment response predictors. Working memory, visual memory, and age were identified as treatment response predictors in BDD and UDD patients, respectively.

Conclusion

Bilateral rTMS improved cold cognition and depressive symptoms in UDD and BDD patients, possibly by altering cognitive control mechanisms (top-down), and processing negative emotional bias.

Predicting the Effects of Repetitive Transcranial Magnetic Stimulation on Cognitive Functions in Patients With Alzheimer's Disease by Automated EEG Analysis

Alzheimer's disease (AD) is a progressive, neurodegenerative brain disorder that generally affects the elderly. Today, after the limited benefit of the pharmacological treatment strategies, numerous noninvasive brain stimulation techniques have been developed. Transcranial magnetic stimulation (TMS), based on electromagnetic stimulation, is one of the most widely used methods. The main problem in the use of TMS is the existence of large individual variability in the results. This causes a waste of money, time, and more importantly, a burden for delicate patients. Hence, it is a necessity to form an efficient and personalized TMS application protocol. In this paper, we performed a machine-learning analysis to see whether it is possible to predict the responses of patients with AD to TMS by analyzing their electroencephalography (EEG) signals. For that purpose, we analyzed both the EEG signals collected before and after the TMS application (EEG1 and EEG2, respectively). Through correlating EEG1 and repetitive transcranial magnetic stimulation (rTMS) outcomes, we tried to see whether it is possible to predict patients' responses before the treatment application. On the other hand, by EEG2 analysis, we investigated TMS impacts on EEG, more importantly if this impact is correlated with patients' response to the treatment. We used the support vector machine (SVM) classifier due to its multiple advantages for the current task with feature selection processes by stepwise linear discriminant analysis (SWLDA) and SVM. However, to justify our numerical analysis framework, we examined and compared the performances of different feature selection and classification techniques. Since we have a limited sample number, we used the leave-one-out method for the validation with the Monte Carlo technique to eliminate bias by a small sample size. In the conclusion, we observed that the correlation between rTMS outcomes and EEG2 is stronger than EEG1, since we observed, respectively, 93 and 79% of accuracies during our data analysis. Besides the informative features of EEG2 are focused on theta band, it indicates that TMS is characterizing the theta band signals in patients with AD in direct relation to patients' response to rTMS. This shows that it is more possible to determine patients' benefit from the TMS at the early stages of the treatment, which would increase the efficiency of rTMS applications in patients with Alzheimer's disease.

Repetitive Transcranial Magnetic Stimulation-Associated Changes in Neocortical Metabolites in Major Depression: A Systematic Review

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We reviewed 12 studies that measured metabolites pre and post rTMS in MDD.

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Frontal lobe Glu, Gln, NAA, and GABA increased after rTMS.

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Increases in metabolites were often associated with MDD symptom improvement.

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We propose novel intracellular mechanisms by which metabolites are altered by rTMS.

Introduction

Repetitive Transcranial magnetic stimulation (rTMS) is an FDA approved treatment for major depressive disorder (MDD). However, neural mechanisms contributing to rTMS effects on depressive symptoms, cognition, and behavior are unclear. Proton magnetic resonance spectroscopy (MRS), a noninvasive neuroimaging technique measuring concentrations of biochemical compounds within the brain in vivo, may provide mechanistic insights.

Methods

This systematic review summarized published MRS findings from rTMS treatment trials to address potential neurometabolic mechanisms of its antidepressant action. Using PubMed, Google Scholar, Web of Science, and JSTOR, we identified twelve empirical studies that evaluated changes in MRS metabolites in a within-subjects, pre- vs. post-rTMS treatment design in patients with MDD.

Results

rTMS protocols ranged from four days to eight weeks duration, were applied at high frequency to the left dorsolateral prefrontal cortex (DLPFC) in most studies, and were conducted in patients aged 13-to-70. Most studies utilized MRS point resolved spectroscopy acquisitions at 3 Tesla in the bilateral anterior cingulate cortex and DLPFC. Symptom improvements were correlated with rTMS-related increases in the concentration of glutamatergic compounds (glutamate, Glu, and glutamine, Gln), GABA, and N-acetylated compounds (NAA), with some results trend-level.

Conclusions

This is the first in-depth systematic review of metabolic effects of rTMS in individuals with MDD. The extant literature suggests rTMS stimulation does not produce changes in neurometabolites independent of clinical response; increases in frontal lobe glutamatergic compounds, N-acetylated compounds and GABA following high frequency left DLPFC rTMS therapy were generally associated with clinical improvement. Glu, Gln, GABA, and NAA may mediate rTMS treatment effects on MDD symptomatology through intracellular mechanisms.

Laterality of prefrontal hemodynamic response measured by functional near-infrared spectroscopy before and after repetitive transcranial magnetic stimulation: A potential biomarker of clinical outcome

The factors associated with the clinical outcomes of repetitive transcranial magnetic stimulation (rTMS) in patients with major depressive disorder (MDD) remain largely unexplored. Therefore, this study aimed to examine whether rTMS can change the functional laterality of the prefrontal hemodynamic response and whether baseline functional laterality can predict the clinical outcomes of rTMS using functional near-infrared spectroscopy (fNIRS). We included 19 patients with MDD who were treated with high-frequency rTMS. The verbal fluency task was used as the activation task. We calculated the laterality index (LI) based on the task-related oxygenation response in the frontal region. First, the LI was compared before and after rTMS treatment. Second, the reduction in the Montgomery-Åsberg Depression Rating Scale (MADRS) score was compared between the rightward dominance group (pre-LI < 0) and the leftward dominance group (pre-LI ≥ 0). The findings showed a significant change in the LI after rTMS treatment. The rightward dominance group had a significantly greater reduction in MADRS score than the leftward dominance group. Subsequently, the laterality of the task-related hemodynamic response of the prefrontal region shifted leftward following left high-frequency rTMS treatment. Thus, the pre-LI calculated using fNIRS data is a possible predictor of rTMS outcomes in patients with MDD.

Effectiveness of Repetitive Transcranial Magnetic Stimulation in the Treatment of Bipolar Disorder in Comparison to the Treatment of Unipolar Depression in a Naturalistic Setting

Repetitive transcranial magnetic stimulation (rTMS) is effective in the treatment of depression. However, for the subset of patients with bipolar disorder, less data is available and overall strength of evidence is weaker than for its use in unipolar depression. A cohort of 505 patients (of which 46 had a diagnosis of bipolar disorder) with depression who were treated with rTMS were analyzed retrospectively with regards to their response to several weeks of treatment. Hamilton Depression Rating Scale (HDRS) was assessed as main outcome. Unipolar and bipolar patients with depression did not differ significantly in baseline demographic variables or severity of depression. Both groups did not differ significantly in their response to treatment as indicated by absolute and relative changes in the HDRS and response and remission rates. On HDRS subitem-analysis, bipolar patients showed superior amelioration of the symptom “paranoid symptoms” in a statistically significant manner. In conclusion, depressed patients with a diagnosis of bipolar disorder benefit from rTMS in a similar fashion as patients with unipolar depression in a naturalistic setting. rTMS might be more effective in reducing paranoia in bipolar than in unipolar patients.

Intersubject correlation analysis reveals the plasticity of cerebral functional connectivity in the long‐term use of social media

Owing to the limitations of cross‐sectional studies, it is unclear whether social media induce brain changes, or if individuals with certain biological traits are more likely to use social media. Functional connectivity (FC) can reflect cerebral functional plasticity, and if social media can influence cerebral FC, then the FC of light social media users should be more similar to that of heavy users after they “heavily” used social media for a long period. We combined longitudinal study design and intersubject correlation (ISC) analysis to investigate this similarity. Thirty‐five heavy and 21 light social media users underwent cognitive tests and functional MRIs. The 21 light social media users underwent another functional MRI scan after completing an additional four‐week social media task. We conducted the ISC at the group, individual, and brain‐region levels to investigate the similarity of FC and locate the brain regions most affected by social media. The FC of light social media users was more similar to that of heavy social media users after they completed the four‐week social media task. Then, social media had an impact on half of the brain, involving almost all brain networks. Finally, cerebral FC that mostly affected by social media was associated with selective attention. We concluded that the impact of social media use on cerebral functional connectivity changes is revealed by ISC method and longitudinal design, which may provide guidance for clinical practice. The methods used in the current research could also be applied to similar domains.

Large-scale EEG neural network changes in response to therapeutic TMS

BACKGROUND

Transcranial magnetic stimulation (TMS) is an effective therapy for patients with treatment-resistant depression. TMS likely induces functional connectivity changes in aberrant circuits implicated in depression. Electroencephalography (EEG) "microstates" are topographies hypothesized to represent large-scale resting networks. Canonical microstates have recently been proposed as markers for major depressive disorder (MDD), but it is not known if or how they change following TMS.

METHODS

Resting EEG was obtained from 49 MDD patients at baseline and following six weeks of daily TMS. Polarity-insensitive modified k-means clustering was used to segment EEGs into constituent microstates. Microstates were localized via sLORETA. Repeated-measures mixed models tested for within-subject differences over time and t-tests compared microstate features between TMS responder and non-responder groups.

RESULTS

Six microstates (MS-1 - MS-6) were identified from all available EEG data. Clinical response to TMS was associated with increases in features of MS-2, along with decreased metrics of MS-3. Nonresponders showed no significant changes in any microstate. Change in occurrence and coverage of both MS-2 (increased) and MS-3 (decreased) correlated with symptom change magnitude over the course of TMS treatment.

CONCLUSIONS

We identified EEG microstates associated with clinical improvement following a course of TMS therapy. Results suggest selective modulation of resting networks observable by EEG, which is inexpensive and easily acquired in the clinic setting.

Predicting treatment selections for individuals with major depressive disorder according to functional connectivity subgroups

BACKGROUND

Major depressive disorder (MDD) is a highly prevalent and disabling disease. Currently, patients' treatment choices depend on their clinical symptoms observed by clinicians, which are subjective. Rich evidence suggests that different functional networks' dysfunctions correspond to different intervention preferences. Here, we aimed to develop a prediction model based on data-driven subgroups to provide treatment recommendations.

METHODS

All 630 participants enrolled from four sites underwent functional magnetic resonances imaging at baseline. In the discovery dataset (n=228), we firstly identified MDD subgroups by the hierarchical clustering method using the canonical variates of resting-state functional connectivity (FC) through canonical correlation analyses. The demographic, symptom improvement and FC were compared among subgroups. The preference intervention for each subgroup was also determined. Next, we predicted the individual treatment strategy. Specifically, a patient was assigned into predefined subgroups based on FC similarities and then his/her treatment strategy was determined by the subgroups' preferred interventions.

RESULTS

Three subgroups with specific treatment recommendations were emerged including: (1) a selective serotonin reuptake inhibitors-oriented subgroup with early improvements in working and activities. (2) a stimulation-oriented subgroup with more alleviation in suicide. (3) a selective serotonin noradrenaline reuptake inhibitors-oriented subgroup with more alleviation in hypochondriasis. Through cross-dataset testing respectively conducted on three testing datasets, results showed an overall accuracy of 72.83%.

CONCLUSIONS

Our works revealed the correspondences between subgroups and their treatment preferences and predicted individual treatment strategy based on such correspondences. Our model has the potential to support psychiatrists in early clinical decision making for better treatment outcomes.

Electroencephalographic connectivity predicts clinical response to repetitive transcranial magnetic stimulation in patients with insomnia disorder

BACKGROUND

Accumulating evidence suggests that low frequency repetitive transcranial magnetic stimulation (rTMS), which generally decreases cortical excitability and remodels plastic connectivity, improves sleep quality in patients with insomnia disorder. However, the effects of rTMS vary substantially across individuals and treatment is sometimes unsatisfactory, calling for biomarkers for predicting clinical outcomes.

OBJECTIVE

This study aimed to investigate whether functional connectivity of the target network in electroencephalography is associated with the clinical response to low frequency rTMS in patients with insomnia disorder.

METHODS

Twenty-five patients with insomnia disorder were subjected to 10 sessions of treatment with 1 Hz rTMS over the right dorsolateral prefrontal cortex. Resting-state electroencephalography was collected before rTMS. Pittsburgh Sleep Quality Index, Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale, and Mini-Mental State Exam were performed before and after rTMS treatment, with a follow-up after one month. Electroencephalographic connectivity was measured by the power envelope connectivity at the source level. Partial least squares regression identified models of connectivity that maximally accounted for the rTMS response.

RESULTS

Scores of Pittsburgh Sleep Quality Index, Hamilton Depression Rating Scale, and Hamilton Anxiety Rating Scale were decreased after rTMS and one-month later. Baseline weaker connectivity of a network in the beta and alpha bands between a brain region approximating the stimulated right dorsolateral prefrontal cortex and areas located in the frontal, insular, and limbic cortices was associated with a greater change in Pittsburgh Sleep Quality Index and Hamilton Depression Rating Scale following rTMS.

CONCLUSIONS

Low frequency rTMS could improve sleep quality and depressive moods in patients with insomnia disorder. Moreover, electroencephalographic functional connectivity would potentially be a robust biomarker for predicting the therapeutic effects.

Understanding complex functional wiring patterns in major depressive disorder through brain functional connectome

Brain function relies on efficient communications between distinct brain systems. The pathology of major depressive disorder (MDD) damages functional brain networks, resulting in cognitive impairment. Here, we reviewed the associations between brain functional connectome changes and MDD pathogenesis. We also highlighted the utility of brain functional connectome for differentiating MDD from other similar psychiatric disorders, predicting recurrence and suicide attempts in MDD, and evaluating treatment responses. Converging evidence has now linked aberrant brain functional network organization in MDD to the dysregulation of neurotransmitter signaling and neuroplasticity, providing insights into the neurobiological mechanisms of the disease and antidepressant efficacy. Widespread connectome dysfunctions in MDD patients include multiple, large-scale brain networks as well as local disturbances in brain circuits associated with negative and positive valence systems and cognitive functions. Although the clinical utility of the brain functional connectome remains to be realized, recent findings provide further promise that research in this area may lead to improved diagnosis, treatments, and clinical outcomes of MDD.

Precise Modulation Strategies for Transcranial Magnetic Stimulation: Advances and Future Directions

Transcranial magnetic stimulation (TMS) is a popular modulatory technique for the noninvasive diagnosis and therapy of neurological and psychiatric diseases. Unfortunately, current modulation strategies are only modestly effective. The literature provides strong evidence that the modulatory effects of TMS vary depending on device components and stimulation protocols. These differential effects are important when designing precise modulatory strategies for clinical or research applications. Developments in TMS have been accompanied by advances in combining TMS with neuroimaging techniques, including electroencephalography, functional near-infrared spectroscopy, functional magnetic resonance imaging, and positron emission tomography. Such studies appear particularly promising as they may not only allow us to probe affected brain areas during TMS but also seem to predict underlying research directions that may enable us to precisely target and remodel impaired cortices or circuits. However, few precise modulation strategies are available, and the long-term safety and efficacy of these strategies need to be confirmed. Here, we review the literature on possible technologies for precise modulation to highlight progress along with limitations with the goal of suggesting future directions for this field.

Novel method for identification of individualized resonant frequencies for treatment of Major Depressive Disorder (MDD) using repetitive Transcranial Magnetic Stimulation (rTMS): A proof-of-concept study

BACKGROUND

Repetitive Transcranial Magnetic Stimulation (rTMS) is an effective treatment for Major Depressive Disorder (MDD), but therapeutic benefit is highly variable. Clinical improvement is related to changes in brain circuits, which have preferred resonant frequencies (RFs) and vary across individuals.

OBJECTIVE

We developed a novel rTMS-electroencephalography (rTMS-EEG) interrogation paradigm to identify RFs using the association of power/connectivity measures with symptom severity and treatment outcome.

METHODS

35 subjects underwent rTMS interrogation at 71 frequencies ranging from 3 to 17 Hz administered to left dorsolateral prefrontal cortex (DLPFC). rTMS-EEG was used to assess resonance in oscillatory power/connectivity changes (phase coherence [PC], envelope correlation [EC], and spectral correlation coefficient [SCC]) after each frequency. Multiple regression was used to detect relationships between 10 Hz resonance and baseline symptoms as well as clinical improvement after 10 sessions of 10 Hz rTMS treatment.

RESULTS

Baseline symptom severity was significantly associated with SCC resonance in left sensorimotor (SM; p < 0.0004), PC resonance in fronto-parietal (p = 0.001), and EC resonance in centro-posterior channels (p = 0.002). Subjects significantly improved with 10 sessions of rTMS treatment. Only decreased SCC SM resonance was significantly associated with clinical improvement (r = 0.35, p = 0.04). Subjects for whom 10 Hz SM SCC was highly ranked as an RF among all stimulation frequencies had better outcomes from 10 Hz treatment.

CONCLUSIONS

Resonance of 10 Hz stimulation measured using SCC correlated with both symptom severity and improvement with 10 Hz rTMS treatment. Research should determine whether this interrogation paradigm can identify individualized rTMS treatment frequencies.

Subthreshold stimulation intensity is associated with greater clinical efficacy of intermittent theta-burst stimulation priming for Major Depressive Disorder

BACKGROUND

Intermittent theta-burst stimulation priming (iTBS-P) can improve clinical outcome of patients with Major Depressive Disorder (MDD) who do not show early benefit from 10 Hz stimulation of left dorsolateral prefrontal cortex (DLPFC), also known as high-frequency left-sided (HFL) stimulation. The intensity and pulse number for iTBS-P needed to induce clinical benefit have not been systematically examined.

OBJECTIVE

To study the effect of intensity and pulse number on the clinical efficacy of iTBS-P.

METHODS

We conducted a retrospective review of 71 participants who received at least five sessions of HFL with limited clinical benefit and received iTBS-P augmentation for between 5 and 25 sessions. Intensity of iTBS-P priming stimuli ranged from 75 to 120% of motor threshold (MT) and pulse number ranged from 600 to 1800. Associations among intensity, pulse number, and clinical outcome were analyzed using a mixed methods linear model with change in IDS-SR as the primary outcome variable, priming stimulation intensity (subthreshold or suprathreshold), pulse number (<1200 or >1200 pulses), and gender as fixed factors, and number of iTBS-P treatments and age as continuous covariates.

RESULTS

Subjects who received subthreshold intensity iTBS-P experienced greater reduction in depressive symptoms than those who received suprathreshold iTBS-P (p = 0.011) with no effect of pulse number after controlling for stimulus intensity.

CONCLUSIONS

Subthreshold intensity iTBS-P was associated with greater clinical improvement than suprathreshold stimulation. This finding is consistent with iTBS-P acting through homeostatic plasticity mechanisms.

An investigation of working memory deficits in depression using the n-back task: A systematic review and meta-analysis

BACKGROUND

Depression is associated with cognitive deficits across multiple domains, including working memory. The n-back task, a convenient psychometric tool capable of computerised delivery and concurrent use with neuroimaging, can provide enhanced insight into working memory dysfunction in depression. This meta-analysis sought to investigate the n-back task under varying cognitive load conditions (i.e. different levels of 'n') to clarify the pattern of working memory deficits in depression.

METHODS

We conducted a systematic review and meta-analysis of studies involving unipolar depressed participants and matched controls utilising the n-back task. Meta-analyses were performed for accuracy and response times at four levels of cognitive load (0-, 1-, 2-, and 3-back).

RESULTS

31 studies (total 1,666 participants) met inclusion criteria and were included for quantitative analyses. Depressed individuals had significantly reduced accuracy compared to controls for 1-, 2-, and 3-back tasks, but not the attentional 0-back task. Likewise, response latencies were prolonged for all task levels (0-, 1-, 2-, and 3-back). Additional meta-regression analyses indicated that participant age and clinical status (i.e. inpatient/outpatient) may exacerbate working memory deficits associated with depression.

LIMITATIONS

Our results indicate high levels of heterogeneity between studies, particularly for response times.

CONCLUSIONS

Accuracy impairments were worse at higher levels of n, with the largest effect size obtained on the 2-back task, suggesting deficits to higher executive functions. Response times were consistently prolonged at all cognitive loads in agreement with a pattern of generalised psychomotor retardation.

An update on the clinical use of repetitive transcranial magnetic stimulation in the treatment of depression

BACKGROUND

Repetitive transcranial magnetic stimulation (rTMS) is an increasingly used treatment for patients with depression. The use of rTMS in depression is supported by over 20 years of clinical trials. There has been a significant increase in knowledge around the use of rTMS in recent years.

OBJECTIVE

The aim of this paper was to review the use of rTMS in depression to provide an update for rTMS practitioners and clinicians interested in the clinical use of this treatment.

METHODS

A targeted review of the literature around the use of rTMS treatment of depression with a specific focus on studies published in the last 3 years.

RESULTS

High-frequency rTMS applied to the left dorsolateral prefrontal cortex is an effective treatment for acute episodes of major depressive disorder. There are several additional methods of rTMS delivery that are supported by clinical trials and meta-analyses but no substantive evidence that any one approach is any more effective than any other. rTMS is effective in unipolar depression and most likely bipolar depression. rTMS courses may be repeated in the management of depressive relapse but there is less evidence for the use of rTMS in the maintenance phase.

CONCLUSIONS

The science around the use of rTMS is rapidly evolving and there is a considerable need for practitioners to remain abreast of the current state of this literature and its implications for clinical practice. rTMS is an effective antidepressant treatment but its optimal use should be continually informed by knowledge of the state of the art.

Effects of HF-rTMS over the left and right DLPFC on proactive and reactive cognitive control

Previous research supports the distinction between proactive and reactive control. Although the dorsolateral prefrontal cortex (DLPFC) has been consistently related to these processes, lateralization of proactive and reactive control is still under debate. We manipulated brain activity to investigate the role of the left and right DLPFC in proactive and reactive cognitive control. Using a single-blind, sham-controlled crossover within-subjects design, 25 young healthy females performed the 'AX' Continuous Performance Task after receiving sham versus active High-Frequency repetitive Transcranial Magnetic Stimulation (HF-rTMS) to increase left and right DLPFC activity. RTs and pupillometry were used to assess patterns of proactive and reactive cognitive control and task-related resource allocation respectively. We observed that, compared to sham, HF-rTMS over the left DLPFC increased proactive control. After right DLPFC HF-rTMS, participants showed slower RTs on AX trials, suggesting more reactive control. However, this latter result was not supported by RTs on BX trials (i.e. the trial that specifically assess reactive control). Pupil measures showed a sustained increase in resource allocation after both active left and right HF-rTMS. Our results with RT data provide evidence on the role of the left DLPFC in proactive control and suggest that the right DLPFC is implicated in reactive control.

Resting-state global EEG connectivity predicts depression and anxiety severity

There is a recent interest in finding neurophysiological biomarkers which will facilitate the diagnosis and understanding of the neural basis of different psychiatric disorders. In this paper, we evaluated the resting-state global EEG connectivity as a potential biomarker for depressive and anxiety symptoms. For this, we evaluated a population of 119 subjects, including 75 healthy subjects and 44 patients with major depressive disorder. We calculated the global connectivity (spectral coherence) in a setup of 60 EEG channels, for six different spectral bands: theta, alpha1, alpha2, beta1, beta2, and gamma. These global connectivity scores were used to train a Support Vector Regressor to predict symptoms measured by the Beck Depression Inventory (BDI) and the Spielberger Trait Anxiety Inventory (TAI). Experiments showed a significant prediction of both symptoms, with a mean absolute error (MAE) of 8.07±6.98 and 11.52±8.7 points, respectively. Among the most discriminating features, the global connectivity in the alpha2 band (10.0-12.0Hz) presented significantly positive Spearman's correlation with the depressive (rho = 0.32, p_FDR <0.01), and the anxiety symptoms (rho = 0.26, p_FDR<0.01).Clinical relevance-This study demonstrates that EEG global connectivity can be used to predict depression and anxiety symptoms measured by widely used questionnaires.

Transcranial Direct Current Stimulation Over Prefrontal Areas Improves Psychomotor Inhibition State in Patients With Traumatic Brain Injury: A Pilot Study

Objectives

Many post-traumatic patients with minimally conscious state are complicated by psychomotor inhibition state (PIS), which impedes further rehabilitation. The treatment of PIS is not satisfactory. This pilot study aimed to investigate effects of anodal transcranial direct current stimulation (A-tDCS) on PIS in post-traumatic patients and examine the altered cortical activation after tDCS using non-linear electroencephalogram (EEG).

Methods

The study included 10 patients with post-traumatic PIS. An A–B design was used. The patients received 4 weeks of sham tDCS during Phase A, and they received A-tDCS over the prefrontal area and left dorsolateral prefrontal cortex (DLPFC) for 4 weeks (40 sessions) during Phase B. Conventional treatments were administered throughout both phases. JFK Coma Recovery Scale-Revised (CRS-R), apathy evaluation scale (AES), and the EEG non-linear indices of approximate entropy (ApEn) and cross approximate entropy (C-ApEn) were measured before Phase A, before Phase B, and after Phase B.

Results

After A-tDCS treatment, CRS-R and AES were improved significantly. ApEn and C-ApEn results showed that the local cortical connection of bilateral sensorimotor areas with their peripheral areas could be activated by affected painful stimuli, while bilateral cerebral hemispheres could be activated by the unaffected painful-stimuli condition. Linear regression analysis revealed that the affected sensorimotor cortex excitability and unaffected local and distant cortical networks connecting the sensorimotor area to the prefrontal area play a major role in AES improvement.

Conclusion

A-tDCS over the prefrontal area and left DLPFC improves PIS. The recovery might be related to increased excitability in local and distant cortical networks connecting the sensorimotor area to the prefrontal area. Thus, tDCS may be an alternative treatment for post-traumatic PIS.

The Effect of Ketamine on Electrophysiological Connectivity in Major Depressive Disorder

Major depressive disorder (MDD) is highly prevalent and frequently disabling. Only about 30% of patients respond to a first-line antidepressant treatment, and around 30% of patients are classified as "treatment-resistant" after failing to respond to multiple adequate trials. While most antidepressants target monoaminergic targets, ketamine is an N-methyl-D-aspartate (NMDA) antagonist that has shown rapid antidepressant effects when delivered intravenously or intranasally. While there is evidence that ketamine exerts its effects via enhanced α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) throughput, its mechanism for relieving depressive symptoms is largely unknown. This study acquired resting-state magnetoencephalography (MEG) recordings after both ketamine and placebo infusions and investigated functional connectivity using a multilayer amplitude-amplitude correlation technique spanning the canonical frequency bands. Twenty-four healthy volunteers (HVs) and 27 unmedicated participants with MDD took part in a double-blind, placebo-controlled, crossover trial of 0.5 mg/kg IV ketamine. Order of infusion was randomized, and participants crossed over to receive the second infusion after two weeks. The results indicated widespread ketamine-induced reductions in connectivity in the alpha and beta bands that did not correlate with magnitude of antidepressant response. In contrast, the magnitude of ketamine's antidepressant effects in MDD participants was associated with cross-frequency connectivity for delta-alpha and delta-gamma bands, with HVs and ketamine non-responders showing connectivity decreases post-ketamine and ketamine responders demonstrating small increases in connectivity. These results may indicate functional subtypes of MDD and also suggest that neural responses to ketamine are fundamentally different between responders and non-responders.

Identification of Clinical Features and Biomarkers that may inform a Personalized Approach to rTMS for Depression

Repetitive transcranial magnetic stimulation (rTMS), an established treatment for treatment-resistant depression, may hold promise as a personalized medicine approach for the treatment of major depressive disorder (MDD). Clinical research has begun to identify patient-specific factors that could be used to guide rTMS treatment decisions or individualized treatment approaches. This literature review describes a range of patient factors which have been evaluated as potential biomarkers of rTMS treatment response, including patient- and illness-related characteristics, genetic factors, and biomarkers derived from neuroimaging and EEG. We highlight the need for validation data for imaging and electrophysiological biomarkers associated with rTMS as well as prospective evaluation of clinical predictors. Finally, we consider implications for future efforts to move toward a personalized medicine approach in the treatment of depression with rTMS.