Uncovering neural dynamics of inhibitory control in depression - a Machine Learning Analysis of EEG/ERP microstates

Aug 2, 2026

Major Depressive Disorder (MDD) is linked to impairments in executive functions, especially inhibitory control. This thesis evaluates whether EEG/ERP microstate parameters derived during a visuomotor Go/NoGo task can act as reliable, interpretable biomarkers to separate MDD patients from healthy controls. Forty participants completed simultaneous EEG and fMRI acquisition across excitatory and inhibitory conditions, focusing on N2 and P3 ERP components. Six microstate prototypes were identified, and features were used to train supervised machine learning classifiers under nested cross-validation. SHAP-based interpretability showed key contributions from MST-related GFP and specific transition probabilities to MDD prediction, with the MLP achieving the best performance.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages67

Reading Time2 h 49 min

AccessFree to download

Abstract

Study objective and task

Participants and data processing

Microstate extraction and modeling

ERP findings

Classification and interpretability

Uncovering neural dynamics of inhibitory control in depression: a Machine Learning Analysis of EEG/ERP microstates Tesi di Laurea Magistrale in Biomedical Engineering-Ingegneria Biomedica Author: Micol Malosso Student ID: 10617863 Advisor: Prof. Eleonora Maggioni Co-advisors: Elena Bondi, Gianluca De Franceschi, Paolo Brambilla Academic Year: 2024-25 i Abstract Major Depressive Disorder (MDD) is associated with significant impairments in executive functions, like inhibitory control. Despite growing interest in electroencephalography (EEG) for identifying with excellent temporal resolution the neural biomarkers of psychiatric conditions, few studies have exploited the innovative EEG microstates’framework to explore the functional brain dynamics during active cognitive tasks in MDD. This thesis investigates whether EEG microstate parameters derived from eventrelated potentials (ERPs) during a visuomotor Go/NoGo task can serve as reliable and interpretable biomarkers for distinguishing MDD patients from healthy controls. Forty participants (20 MDD patients, 20 healthy controls (HC)) performed a Go/NoGotask during the simultaneous acquisition of EEG and functional magnetic resonance imaging (fMRI) . The task included two conditions using the right hand. In the excitatory condition, participants pressed a button whenever a square appeared, while in the inhibitory condition, they responded only to green squares and withheld responses to red ones. EEG preprocessing included artifact correction for MR-related and physiological noise, followed by ERP extraction that focused on N2 and P3 components. Statistical comparisons of ERPswere combined with EEG/ERP microstates and machine learning analyses. Grand-average ERPs were used to identify six microstate prototypes, which were then backfitted to each subject’s data to extract microstate metrics (e.g., global field power (GFP), duration, transition probabilities) . These features were compiled into a structured dataset used to train multiple machine learning classifiers under a nested cross-validation framework. Among all the machine learning models available, seven supervised classifiers were chosen to evaluate their ability in distinguishing MDD patients from HC, based on EEG/ERP microstates features. To enhance interpretability, SHAP (SHapley Additive exPlanations) values were computed, allowing detailed insight into the contribution of each feature to model predictions. ii | Abstract ERP analyses revealed significant group differences in frontal N2 and P3 components. In HC, the N2 and P3 showed stronger amplitudes and shorter latencies in the NoGo condition (non-target stimuli in inhibitory blocks) compared to Go/NoGo (target stimuli in inhibitory blocks), suggesting more efficient engagement of inhibitory mechanisms. In contrast, MDD patients exhibited reduced or absent N2 amplitude modulation and delayed P3 latencies, suggesting impaired conflict monitoring and slower cognitive processing. Microstate analysis identified six topographies from grand-average ERPs. HC displayed well-organized and diverse microstate sequences, especially during Go/NoGo trials, reflecting flexible neural transitions. In contrast, MDD participants showed more repetitive, less variable patterns, and reduced activation of key microstates such as MST2 (linked to conflict detection) and MST3 (linked to attentional control) . Additionally, MST6 appeared exclusively in MDD and was not aligned with task-relevant ERP components, suggesting disease-specific neural correlates of task response. Among the classifiers tested, the MLP achieved the highest performance (accuracy = 0.748; AUC (Area under the curve) = 0.791) . SHAP analysis revealed that the most influential features were the GFP of MST1 and MST2 and the transition probability from MST5 to MST6 . Remarkably, lower GFP values in MST1 and MST2, particularly during NoGo trials, were strongly associated with MDD predictions, su

FAQs

本研究使用了什么认知任务来评估抑制控制？

受试者完成右手的视觉运动Go/NoGo任务：出现方形时进行按键反应（激活条件），仅对绿色方形反应并在红色方形时抑制反应（抑制条件）。

研究如何从EEG/ERP中提取并分析微状态？

通过聚焦N2与P3成分的ERP提取，使用ERP的时域信息识别出6个微状态原型，并将其反向拟合到每个受试者数据中得到如全局场功率（GFP）、持续时间与转移概率等指标。

机器学习模型如何用于区分MDD与健康对照？

将微状态相关特征构建为结构化数据集，在嵌套交叉验证框架下训练多种监督分类器，并用SHAP计算特征贡献以提升可解释性。结果显示MLP性能最高，且较低的MST1与MST2的GFP（尤其在NoGo试次）与MDD预测强相关。

Aug 2, 2026

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