Evaluating Robustness of Brain Stimulation Biomarkers for Depression: A Systematic Review of Magnetic Resonance Imaging and Electroencephalography Studies

Klooster, Debby; Voetterl, Helena; Baeken, Chris; Arns, Martijn

doi:10.1016/j.biopsych.2023.09.009

Cited by 15 publications

(8 citation statements)

References 68 publications

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“…These structural changes may be associated with certain symptoms of depression, such as depressed mood, cognitive dysfunction, and difficulty concentrating. 19 Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) have shown 20 that PFC activity may be reduced in depressed patients, particularly in the face of negative stimuli or stressful events. Positron emission tomography showed that depressed patients had lower PFC gray matter volume and cerebral blood flow values, while cerebral blood flow in this region was closely related to depressive symptoms.…”

Section: Abnormal Neural Circuitsmentioning

confidence: 99%

Treatment of Depression with Acupuncture Based on Pathophysiological Mechanism

Sun,

Cao,

Xin

et al. 2024

IJGM

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Depression is a prevalent mental disorder and has a profound impact on an individual’s psychological and physical well-being. It is characterized by a persistently depressed mood, loss of interest, energy loss, and cognitive dysfunction. In recent years, more and more people have changed to mental diseases, such as depression, anxiety, mania and so on. In the incidence of depression, covering all ages, but still mainly young and middle-aged women. Traditional treatments for depression mainly rely on medication and psychotherapy, but these methods are not effective for all patients and are often accompanied by certain side effects. Therefore, finding safe and effective alternative or adjuvant treatments has become a priority. Here we highlight the research progress of acupuncture in the treatment of depression and to explore the mechanism of acupuncture in the treatment of depression. Acupuncture treatment of depression is an ancient and effective method, the mechanism involves multiple biological pathways, for example, by regulating neurotransmitter levels, regulating the neuroendocrine axis, improving neuroplasticity, anti-inflammatory and other effects, improving emotional state and play an antidepressant role. To provide evidence to support the widespread use of acupuncture in clinical practice. We hope to provide new treatment ideas and methods for patients with depression, and even reduce the incidence of depression.

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Section: Abnormal Neural Circuitsmentioning

confidence: 99%

Treatment of Depression with Acupuncture Based on Pathophysiological Mechanism

Sun,

Cao,

Xin

et al. 2024

IJGM

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“…The FOOOF algorithm extracts both aperiodic (exponent and offset) and periodic components of the power spectra (center of frequency, bandwidth, power above the aperiodic exponent slope, and total band power). These parameters were extracted for the delta (1-4 Hz), theta (4-7 Hz), alpha (7-12 Hz), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (30-45 Hz) bands. EEG data were converted from time-series data into frequency representations using Welch's method.…”

Section: Eeg Analysismentioning

confidence: 99%

“…While several groups have reported that higher rostral anterior cingulate cortex theta activity at baseline leads to greater improvement in depressive symptoms after both neuromodulatory and pharmacological interventions [24,25], other groups report that theta power is elevated in more severe depression, corresponding to increased treatment resistance [26,27]. However, a recent review of fMRI and EEG biomarkers of depression in response to brain stimulation therapy by Klooster et al [28] ultimately found that taskbased frontal-midline theta activity and individual alpha frequency were the most robust EEG biomarkers. As such, there remains a high degree of uncertainty surrounding the viability of using EEG biomarkers to predict MDD treatment outcomes.…”

Section: Introductionmentioning

confidence: 99%

Predictive Biomarkers of Treatment Response in Major Depressive Disorder

Stolz,

Kohn,

Smith

et al. 2023

Brain Sciences

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Major depressive disorder (MDD) is a highly prevalent, debilitating disorder with a high rate of treatment resistance. One strategy to improve treatment outcomes is to identify patient-specific, pre-intervention factors that can predict treatment success. Neurophysiological measures such as electroencephalography (EEG), which measures the brain’s electrical activity from sensors on the scalp, offer one promising approach for predicting treatment response for psychiatric illnesses, including MDD. In this study, a secondary data analysis was conducted on the publicly available Two Decades Brainclinics Research Archive for Insights in Neurophysiology (TDBRAIN) database. Logistic regression modeling was used to predict treatment response, defined as at least a 50% improvement on the Beck’s Depression Inventory, in 119 MDD patients receiving repetitive transcranial magnetic stimulation (rTMS). The results show that both age and baseline symptom severity were significant predictors of rTMS treatment response, with older individuals and more severe depression scores associated with decreased odds of a positive treatment response. EEG measures contributed predictive power to these models; however, these improvements in outcome predictability only trended towards statistical significance. These findings provide confirmation of previous demographic and clinical predictors, while pointing to EEG metrics that may provide predictive information in future studies.

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“…Further, the opaque nature of the deep-learning approach means it is often difficult to interpret which aspects of the data lead to accurate predictions, leaving researchers and clinicians unable to understand how the algorithm discerns responders and non-responders (Squarcina et al, 2021;Van Der Donckt et al, 2022). Given these limitations, a recent review of the literature identified only four potentially robust biomarkers for the prediction of response to non-invasive brain stimulation (two fMRI biomarkers and two EEG biomarkers) (Klooster et al, 2023). However, given the heterogeneity across individuals with depression, and the likely subtle relationship between any baseline marker and treatment response, it may be that single biomarkers will be unable to obtain the predictive accuracy required for clinical implementation.…”

Section: Introductionmentioning

confidence: 99%

Prediction of response to transcranial magnetic stimulation treatment for depression using electroencephalography and statistical learning methods, including an out-of-sample validation

Bailey,

Fulcher,

Arns

et al. 2023

Preprint

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Background: Repetitive transcranial magnetic stimulation (rTMS) has shown efficacy for treating depression, but not for all patients. Accurate treatment response prediction could lower treatment burden. Research suggests machine learning trained with electroencephalographic (EEG) data may predict response, but only a limited range of measures have been tested. Objectives: We used >7000 time-series features to comprehensively test whether rTMS treatment response could be predicted in a discovery dataset and an independent dataset. Methods: Baseline EEG from 188 patients with depression treated with rTMS (125 responders) were decomposed into the top five principal components (PCs). The hctsa toolbox was used to extract 7304 time-series features from each participant and PC. A classification algorithm was trained to predict responders from the feature matrix separately for each PC. The classifier was applied to an independent dataset (N = 58) to test generalizability on an unseen sample. Results: Within the discovery dataset, the third PC (which showed a posterior-maximum and prominent alpha power) showed above-chance classification accuracy (68%, pFDR = 0.005, normalised positive predictive value = 114%). Other PCs did not outperform chance. The model generalized to the independent dataset with above-chance balanced accuracy (60%, p = 0.046, normalised positive predictive value = 114%). Analysis of feature-clusters suggested responders showed more high frequency power relative to total power, and a more negative skew in the distribution of their time-series values. Conclusion: The dynamical properties of PC3 predicted treatment response with moderate accuracy, which generalized to an independent dataset. Results suggest treatment stratification from pre-treatment EEG may be possible, potentially enabling better outcomes than "one-size-fits-all" treatment approaches.

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Evaluating Robustness of Brain Stimulation Biomarkers for Depression: A Systematic Review of Magnetic Resonance Imaging and Electroencephalography Studies

Cited by 15 publications

References 68 publications

Treatment of Depression with Acupuncture Based on Pathophysiological Mechanism

Treatment of Depression with Acupuncture Based on Pathophysiological Mechanism

Predictive Biomarkers of Treatment Response in Major Depressive Disorder

Prediction of response to transcranial magnetic stimulation treatment for depression using electroencephalography and statistical learning methods, including an out-of-sample validation

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