Predictive Biomarkers of Treatment Response in Major Depressive Disorder DOI Open Access
Louise A. Stolz, Jordan N. Kohn,

Sydney E. Smith

et al.

Published: Oct. 25, 2023

Major depressive disorder (MDD) is a highly prevalent, debilitating with high rate of treatment resistance. One strategy to improve outcomes identify patient-specific, pre-intervention factors that can predict success. Neurophysiological measures such as electroencephalography (EEG), which the brain’s electrical activity from sensors on scalp, offer one promising approach for predicting response psychiatric illnesses, including MDD. In this study, secondary data analysis was conducted publicly available Two Decades-Brainclinics Research Archive Insights in Neurophysiology (TDBRAIN) database. Specifically, hierarchical regression modeling used baseline demographics, symptom severity, and resting-state EEG features 119 MDD patients receiving repetitive transcranial magnetic stimulation (rTMS). Across models, both age assessed by Beck’s Depression Inventory, were significant predictors rTMS response, older individuals more severe depression scores associated decreased odds positive response. contributed predictive power these models; however, improvements outcome predictability only trended towards statistical significance (p~0.07 multiple models). These findings provide confirmation previous demographic clinical predictors, while pointing metrics may information future studies.

Language: Английский

Characterizing Major Depressive Disorder (MDD) using alpha-band activity in resting-state electroencephalogram (EEG) combined with MATRICS Consensus Cognitive Battery (MCCB) DOI Creative Commons
Bin Wang, Meijia Li, Naem Haihambo

et al.

Journal of Affective Disorders, Journal Year: 2024, Volume and Issue: 355, P. 254 - 264

Published: March 30, 2024

The diagnosis of major depressive disorder (MDD) is commonly based on the subjective evaluation by experienced psychiatrists using clinical scales. Hence, it particularly important to find more objective biomarkers aid in and further treatment. Alpha-band activity (7-13 Hz) most prominent component resting electroencephalogram (EEG), which also thought be a potential biomarker. Recent studies have shown existence multiple sub-oscillations within alpha band, with distinct neural underpinnings. However, specific contribution these treatment MDD remains unclear. In this study, we recorded resting-state EEG from HC populations both open closed-eye state conditions. We assessed cognitive processing MATRICS Consensus Cognitive Battery (MCCB). found that group showed significantly higher power high range (10.5–11.5 lower low (7–8.5 compared group. Notably, negatively correlated working memory performance MCCB, whereas no such correlation was Furthermore, five established classification algorithms, discovered combining oscillations MCCB scores as features yielded highest accuracy or alone. Our results demonstrate frequency band When combined psychological scales, they may provide guidance relevant for MDD.

Language: Английский

Citations

19
Wearable physiological monitoring of physical exercise and mental health: A systematic review DOI Creative Commons
Feifei Chen, Lulu Zhao, Lanlan Pang

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

1
Increased Aperiodic Neural Activity During Sleep in Major Depressive Disorder DOI Creative Commons
Yevgenia Rosenblum, Leonore Bovy, Frederik D. Weber

et al.

Biological Psychiatry Global Open Science, Journal Year: 2022, Volume and Issue: 3(4), P. 1021 - 1029

Published: Oct. 25, 2022

In major depressive disorder (MDD), patients often express subjective sleep complaints, while polysomnographic studies report only subtle alterations of the electroencephalographic signal. We hypothesize that differentiating signal into its oscillatory and aperiodic components may bring new insights our understanding abnormalities in MDD. Specifically, we investigated neural activity during relationships with architecture, depression severity, responsivity to antidepressant treatment.Polysomnography was recorded 38 MDD (in unmedicated 7-day-medicated states) age-matched healthy control subjects (N= 76). The power component calculated using irregularly resampled auto-spectral analysis. Depression severity assessed Hamilton Rating Scale. replicated analysis 2 independently collected datasets medicated (N = 60 N 80, respectively).Unmedicated showed flatter slopes compared non-rapid eye movement (non-REM) stage (p .009). Medicated their earlier state values < .001) all stages .03). patients, non-REM were linked higher proportion N1, lower REM, delayed onset N3 shorter total time.Flatter reflect noisier due increased excitation-to-inhibition balance, representing a disease-relevant feature

Language: Английский

Citations

28
In-silico testing of new pharmacology for restoring inhibition and human cortical function in depression DOI Creative Commons
Alexandre Guet-McCreight, Homeira Moradi Chameh, Frank Mazza

et al.

Communications Biology, Journal Year: 2024, Volume and Issue: 7(1)

Published: Feb. 23, 2024

Abstract Reduced inhibition by somatostatin-expressing interneurons is associated with depression. Administration of positive allosteric modulators α5 subunit-containing GABA A receptor (α5-PAM) that selectively target this lost exhibit antidepressant and pro-cognitive effects in rodent models chronic stress. However, the functional α5-PAM on human brain vivo are unknown, currently cannot be assessed experimentally. We modeled tonic as measured neurons, tested silico detailed cortical microcircuits health found effectively recovered impaired processing quantified stimulus detection metrics, also power spectral density profile microcircuit EEG signals. performed an dose-response identified simulated biomarker candidates. Our results serve to de-risk facilitate translation provide biomarkers non-invasive signals for monitoring engagement drug efficacy.

Language: Английский

Citations

4
Characterizing PTSD Using Electrophysiology: Towards A Precision Medicine Approach DOI Creative Commons
Natasha Kovacevic, Amir H. Meghdadi, Chris Berka

et al.

Clinical EEG and Neuroscience, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 7, 2025

Objective. Resting-state EEG measures have shown potential in distinguishing individuals with PTSD from healthy controls. ERP components such as N2, P3, and late positive been consistently linked to cognitive abnormalities PTSD, especially tasks involving emotional or trauma-related stimuli. However, meta-analyses reported inconsistent findings. The understanding of biomarkers that can classify the varied symptoms remains limited. This study aimed develop a concise set electrophysiological biomarkers, using neutral tasks, could be applied across psychiatric conditions, identify associated anxiety depression dimensions PTSD. Approach. Continuous simultaneous recordings electrocardiogram (ECG) were obtained veterans (n = 29) controls 62) during computerized tasks. EEG, ERP, heart rate evaluated terms their ability discriminate between groups correlate psychological measures. Results. cohort exhibited faster alpha oscillations, reduced power, flatter power spectrum. Furthermore, stronger reduction was higher trait anxiety, while slope related more severe In visual memory sustained attention, demonstrated delayed exaggerated early components, along attenuated LPP amplitudes. three revealed distinct complementary signatures Significance. Multimodal individualized based on ERPs, ECG show promise objective tools for assessing mood disturbances within

Language: Английский

Citations

0
Spine loss in depression impairs dendritic signal integration in human cortical microcircuit models DOI Creative Commons

Heng Kang Yao,

Frank Mazza, Thomas D. Prévot

et al.

iScience, Journal Year: 2025, Volume and Issue: 28(5), P. 112136 - 112136

Published: March 3, 2025

Major depressive disorder (depression) is associated with altered dendritic structure and function of cortical pyramidal neurons, due to decreased inhibition from somatostatin (SST) interneurons loss spines synapses, as indicated in postmortem human studies. Dendrites mediate signal processing through synaptic integration nonlinear properties including backpropagating action potentials Na+ spikes that enhance the neuron's computational power. However, it currently unclear how depression-related changes impact integration. Here, we integrated neuronal data active spine depression into detailed models microcircuits. We show dampens response, worsening detection impairment than reduced SST interneuron alone. Furthermore, intrinsic abolished impaired recurrent microcircuit activity. Our study mechanistically links cellular

Language: Английский

Citations

0
A comprehensive investigation of intracortical and corticothalamic models of the alpha rhythm DOI Creative Commons
Sorenza P. Bastiaens, Davide Momi, John D. Griffiths

et al.

PLoS Computational Biology, Journal Year: 2025, Volume and Issue: 21(4), P. e1012926 - e1012926

Published: April 10, 2025

The electroencephalographic alpha rhythm is one of the most robustly observed and widely studied empirical phenomena in all neuroscience. However, despite its extensive implication a wide range cognitive processes clinical pathologies, mechanisms underlying generation neural circuits remain poorly understood. In this paper we offer renewed foundation for research on question, by undertaking systematic comparison synthesis prominent theoretical models rhythmogenesis published literature. We focus four models, each intensively multiple authors over past three decades: (i) Jansen-Rit, (ii) Moran-David-Friston, (iii) Robinson-Rennie-Wright, (iv) Liley-Wright. Several common elements are identified, such as use second-order differential equations sigmoidal potential-to-rate operators to represent population-level activity. Major differences seen other features wiring topologies conduction delays. Through series mathematical analyses numerical simulations, nevertheless demonstrate that selected can be meaningfully compared, associating parameters circuit motifs analogous biological significance. With established, conduct explorations rate constant synaptic connectivity parameter spaces, with aim identifying patterns key behaviours, role excitatory-inhibitory interactions oscillations. Finally, using linear stability analysis identify two qualitatively different alpha-generating dynamical regimes across models: noise-driven fluctuations self-sustained limit-cycle oscillations, emerging due an Andronov-Hopf bifurcation. comprehensive survey developed here can, suggest, used help guide future experimental work aimed at disambiguating these candidate theories rhythmogenesis.

Language: Английский

Citations

0
Magnetic seizure therapy and electroconvulsive therapy increase aperiodic activity DOI Creative Commons
Sydney Smith, Eena L. Kosik, Quirine van Engen

et al.

Translational Psychiatry, Journal Year: 2023, Volume and Issue: 13(1)

Published: Nov. 16, 2023

Major depressive disorder (MDD) is a leading cause of disability worldwide. One the most efficacious treatments for treatment-resistant MDD electroconvulsive therapy (ECT). Recently, magnetic seizure (MST) was developed as an alternative to ECT due its more favorable side effect profile. While these approaches have been very successful clinically, neural mechanisms underlying their therapeutic effects are unknown. For example, clinical "slowing" electroencephalogram beginning in postictal state and extending days weeks post-treatment has observed both treatment modalities. However, recent longitudinal study small cohort patients revealed that, rather than delta oscillations, slowing better explained by increases aperiodic activity, emerging EEG signal linked inhibition. Here we investigate role activity who received MST treatment. We find that significantly receiving either or MST. Although not directly related efficacy this dataset, increased greater amounts inhibition, which suggestive potential shared mechanism action across

Language: Английский

Citations

8
Predictive Biomarkers of Treatment Response in Major Depressive Disorder DOI Creative Commons
Louise A. Stolz, Jordan N. Kohn,

Sydney E. Smith

et al.

Brain Sciences, Journal Year: 2023, Volume and Issue: 13(11), P. 1570 - 1570

Published: Nov. 9, 2023

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

Language: Английский

Citations

7
Implications of Aperiodic and Periodic EEG Components in Classification of Major Depressive Disorder from Source and Electrode Perspectives DOI Creative Commons
Ahmad Zandbagleh, Saeid Sanei, Hamed Azami

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(18), P. 6103 - 6103

Published: Sept. 21, 2024

Electroencephalography (EEG) is useful for studying brain activity in major depressive disorder (MDD), particularly focusing on theta and alpha frequency bands via power spectral density (PSD). However, PSD-based analysis has often produced inconsistent results due to difficulties distinguishing between periodic aperiodic components of EEG signals. We analyzed data from 114 young adults, including 74 healthy controls (HCs) 40 MDD patients, assessing alongside conventional PSD at both source electrode levels. Machine learning algorithms classified versus HC based these features. Sensor-level showed stronger Hedge’s g effect sizes parietal frontal than source-level analysis. individuals exhibited reduced relative HC. Logistic regression-based classifications that slightly outperformed PSD, with the best achieved by combining features (AUC = 0.82). Strong negative correlations were found activities higher scores Beck Depression Inventory, anhedonia subscale. This study emphasizes superiority sensor-level over detecting MDD-related changes highlights value incorporating a more refined understanding disorders.

Language: Английский

Citations

2