Examining relations between alpha power as well as anterior cingulate cortex-localized theta activity and response to single or dual antidepressant pharmacotherapies

Jaworska, Natalia; Blondeau, Claude; Tessier, Pierre; Norris, Sandhaya; Fusee, Wendy; Blier, Pierre; Knott, Verner

doi:10.1177/0269881114523862

Cited by 18 publications

(27 citation statements)

References 49 publications

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“…On the other hand, increased MSE at coarse temporal scales appears to reflect the positive association between treatment response and SPD at lower frequencies (alpha/theta band). Elevated alpha power/amplitude at baseline has been quite consistently observed in eventual responders (vs. non-responders) to various antidepressant pharmacotherapies ( Knott et al, 1996 , Tenke et al, 2011 , Jaworska et al, 2014 ). One interpretation is that this may reflect a hypo-aroused state that benefits from the arousing electrocortical effects of some antidepressants.…”

Section: Discussionmentioning

confidence: 99%

Pre-treatment EEG signal variability is associated with treatment success in depression

Jaworska

Wang

Smith

et al. 2018

NeuroImage: Clinical

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BackgroundPrevious work suggests that major depressive disorder (MDD) is associated with disturbances in global connectivity among brain regions, as well as local connectivity within regions. However, the relative importance of these global versus local changes for successful antidepressant treatment is unknown. We used multiscale entropy (MSE), a measure of brain signal variability, to examine how the propensity for local (fine scale MSE) versus global (coarse scale MSE) neural processing measured prior to antidepressant treatment is related to subsequent treatment response.MethodsWe collected resting-state EEG activity during eyes-open and closed conditions from unmedicated individuals with MDD prior to antidepressant pharmacotherapy (N = 36) as well as from non-depressed controls (N = 36). Treatment response was assessed after 12 weeks of treatment using the Montgomery-Åsberg Depression Rating Scale (MADRS), at which time participants with MDD were characterized as either responders (≥ 50% MADRS decrease) or non-responders. MSE was calculated from baseline EEG, and compared between controls, future treatment responders and non-responders. Putative interactions with the well-documented age effect on signal variability (increased reliance on local neural communication with increasing age, indexed by greater finer-scale variability) were assessed.ResultsOnly in responders, we found that reduced MSE at fine temporal scales (especially fronto-centrally) and increased MSE diffusely at coarser temporal scales was related to the magnitude of the antidepressant response. In controls and MDD non-responders, but not MDD responders, there was an increase in MSE with age at fine temporal scales and a decrease in MSE with age at coarse temporal scales.ConclusionOur results suggest that an increased propensity toward global processing, indexed by greater MSE at coarser timescales, at baseline appears to facilitate eventual antidepressant treatment response.

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Section: Discussionmentioning

confidence: 99%

Pre-treatment EEG signal variability is associated with treatment success in depression

Jaworska

Wang

Smith

et al. 2018

NeuroImage: Clinical

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“…In total, 51 adults (18–60 years) with a primary diagnosis of MDD, and enrolled in a clinical trial assessing antidepressant pharmacotherapies [details below; (48)], participated in this EEG study. As previously outlined (49), psychiatrists ascertained the diagnosis with the Structured Clinical Interview for DSM (Diagnostic & Statistical Manual of Mental Disorders) IV-TR Diagnoses, Axis I, Patient Version [SCID-I/P; (50)]. Symptom severity was evaluated using the MADRS (46), with scores ≥22 at enrollment.…”

Section: Methodsmentioning

confidence: 99%

“…As described elsewhere (49), prior to each EEG session, participants abstained for >3 h from caffeine and/or nicotine, as well as from alcohol/drugs (excluding prescribed drugs) as of midnight. Using an average scalp reference, AF z ground, and a sampling rate of 500 Hz, EEG recordings were obtained from 32 sites using the 10–20 system (see Supplementary Figure 1) by way of Ag/AgCl electrodes embedded in a cap (EasyCap, Inning A. Ammersee, Germany).…”

Section: Methodsmentioning

confidence: 99%

Leveraging Machine Learning Approaches for Predicting Antidepressant Treatment Response Using Electroencephalography (EEG) and Clinical Data

et al. 2019

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Background: Individuals with major depressive disorder (MDD) vary in their response to antidepressants. However, identifying objective biomarkers, prior to or early in the course of treatment that can predict antidepressant efficacy, remains a challenge.Methods: Individuals with MDD participated in a 12-week antidepressant pharmacotherapy trial. Electroencephalographic (EEG) data was collected before and 1 week post-treatment initiation in 51 patients. Response status at week 12 was established with the Montgomery-Asberg Depression Scale (MADRS), with a ≥50% decrease characterizing responders (N = 27/24 responders/non-responders). We used a machine learning (ML)-approach for predicting response status. We focused on Random Forests, though other ML methods were compared. First, we used a tree-based estimator to select a relatively small number of significant features from: (a) demographic/clinical data (age, sex, individual item/total MADRS scores at baseline, week 1, change scores); (b) scalp-level EEG power; (c) source-localized current density (via exact low-resolution electromagnetic tomography [eLORETA] software). Second, we applied kernel principal component analysis to reduce and map important features. Third, a set of ML models were constructed to classify response outcome based on mapped features. For each dataset, predictive features were extracted, followed by a model of all predictive features, and finally by a model of the most predictive features.Results: Fifty eLORETA features were predictive of response (across bands, both time-points); alpha1/theta eLORETA features showed the highest predictive value. Eighty-eight scalp EEG features were predictive of response (across bands, both time-points), with theta/alpha2 being most predictive. Clinical/demographic data consisted of 31 features, with the most important being week 1 “concentration difficulty” scores. When all features were included into one model, its predictive utility was high (88% accuracy). When the most important features were extracted in the final model, 12 predictive features emerged (78% accuracy), including baseline scalp-EEG frontopolar theta, parietal alpha2 and frontopolar alpha1.Conclusions: These findings suggest that ML models of pre- and early treatment-emergent EEG profiles and clinical features can serve as tools for predicting antidepressant response. While this must be replicated using large independent samples, it lays the groundwork for research on personalized, “biomarker”-based treatment approaches.

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“…Three studies by Jaworska et al [14][15][16] have used EEG measures to predict treatment response in depressed patients randomized to receive escitalopram, bupropion, or their combination (n=51-53). In the first study, the loudness dependence of auditory evoked potentials (LDAEP) was assessed at baseline [14].…”

Section: Pretreatment Predictorsmentioning

confidence: 99%

“…In both these studies, general predictors of outcome were identified which were not specific to the medication received despite the differences in neurochemical actions between escitalopram (an SSRI) and buproprion (a noradrenaline and dopamine reuptake inhibitor). The third study assessed resting frontal and posterior alpha 2 power and asymmetry as well as ACC localized theta [16]. Treatment response (across arms) was weakly predicted by frontal baseline alpha 2 power, with treatment responders exhibiting greater baseline alpha 2 .…”

Section: Pretreatment Predictorsmentioning

confidence: 99%

Neural Predictors of Treatment Response in Depression

Harmer

2014

Curr Behav Neurosci Rep

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Depression is a leading cause of disability and treatment response is highly variable between patients. The current review focuses on recent findings from studies exploring neural predictors of treatment response to conventional antidepressant drugs, psychological treatment, and novel candidate interventions. Structural markers of response have revealed that decreased volume of key substrates such as the hippocampus and orbitofrontal cortex are predictive of poor response to antidepressant drug treatment, and diffusion tensor imaging offers potentially relevant information for future studies. Pretreatment measures of rostral anterior cingulate activity (from functional magnetic resonance imaging-and electroencephalogram-based approaches) have also been highlighted as a biomarker for antidepressant drug response. The relative utility of measurements taken at baseline versus early change for refining treatment in individual patients is considered. The response to novel treatments such as scopolamine, ketamine, and deep brain stimulation also offer promise for understanding targets of treatment and predictors for tailoring treatment. There is considerable overlap between neural biomarkers for different treatments although positron emission tomography metabolism in the anterior insula may offer a differential predictor of response to pharmacotherapy versus psychotherapy in depression. These insights provide important information about the potential targets and mechanisms underlying the prediction of treatment effects in depression. Future work needs to explore these different predictors between treatment arms in prospectively defined studies to evaluate whether these markers may have utility in the clinic.

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Examining relations between alpha power as well as anterior cingulate cortex-localized theta activity and response to single or dual antidepressant pharmacotherapies

Cited by 18 publications

References 49 publications

Pre-treatment EEG signal variability is associated with treatment success in depression

Pre-treatment EEG signal variability is associated with treatment success in depression

Leveraging Machine Learning Approaches for Predicting Antidepressant Treatment Response Using Electroencephalography (EEG) and Clinical Data

Neural Predictors of Treatment Response in Depression

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