Background: Depression has become a major health burden worldwide, and effective detection depression is a great public-health challenge. This Electroencephalography (EEG)-based research is to explore the effective biomarkers for depression recognition. Methods: Resting state EEG data was collected from 24 major depressive patients (MDD) and 29 normal controls using 128 channel HydroCel Geodesic Sensor Net (HCGSN). To better identify depression, we extracted different types of EEG features including linear features, nonlinear features and functional connectivity features phase lagging index (PLI) to comprehensively analyze the EEG signals in patients with MDD. And using different feature selection methods and classifiers to evaluate the optimal feature sets. Results: Functional connectivity feature PLI is superior to the linear features and nonlinear features. And when combining all the types of features to classify MDD patients, we can obtain the highest classification accuracy 82.31% using ReliefF feature selection method and logistic regression (LR) classifier. Analyzing the distribution of optimal feature set, it was found that intrahemispheric connection edges of PLI were much more than the interhemispheric connection edges, and the intrahemispheric connection edges had a significant differences between two groups. Conclusion: Functional connectivity feature PLI plays an important role in depression recognition. Especially, intrahemispheric connection edges of PLI might be an effective biomarker to identify depression. And statistic results suggested that MDD patients might exist functional dysfunction in left hemisphere.
University of Belgrade（贝尔格莱德大学）；Amsterdam Health and Technology Institute（阿姆斯特丹健康与技术研究所）；HealthInc；Universidad Complutense（康普顿斯大学）
In this paper, we aimed at reviewing present literature on employing nonlinear analysis in combination with machine learning methods, in depression detection or prediction task. We are focusing on an affordable data-driven approach, applicable for everyday clinical practice, and in particular, those based on electroencephalographic (EEG) recordings. Among those studies utilizing EEG, we are discussing a group of applications used for detecting the depression based on the resting state EEG (detection studies) and interventional studies (using stimulus in their protocols or aiming to predict the…
In universal environment, a patient-friendly inexpensive method is needed to realize the early diagnosis of depression, which is believed to be an effective way to reduce the mortality of depression. The purpose of this study is only to collect EEG signal from three electrodes Fp1, Fpz and Fp2, then the linear and nonlinear features of EEG used to classify depression patients and healthy controls. The EEG recordings were carried out on a group of 18 medication-free depressive patients and 25 gender and age matched controls. In this paper, the selected features include three linear (maximum, me…
Discovering robust biomarkers of psychiatric disorders from resting-state functional MRI via graph neural networks: A systematic review
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Department of Computer Science and Engineering, Indian Institute of Technology（印度理工学院计算机科学与工程系）
Contactless gait-derived features as candidate digital biomarkers of depression severity in adolescents
School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）；Engineering Research Center of Intelligent Unmanned System, Ministry of Education, China（教育部智能无人系统工程研究中心）；Department of Psychology, Shandong Provincial Hospital Affiliated to Shandong First Medical University（山东第一医科大学附属山东省医院心理科）；Department of Psychiatry, The Chinese University of Hong Kong（香港大学精神科学系）
Task-evoked eye movements as candidate digital biomarkers of depression severity in adolescents and young adults
School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）；Engineering Research Center of Intelligent Unmanned System, Ministry of Education, China（教育部智能无人系统工程研究中心）；Department of Psychology, Shandong Provincial Hospital Affiliated to Shandong First Medical University（山东第一医科大学附属山东省医院心理学系）；Department of Psychiatry, The Chinese University of Hong Kong（香港大学精神病学系）
University of Rochester（罗切斯特大学）；Ludwig Maximilian University（路德维希·马克西米利安大学）
It has been shown in the literature that marijuana use is associated with changes in brain network connectivity. We propose large-scale Extended Granger Causality (lsXGC) and investigate whether it can capture such changes using resting-state fMRI. This method combines dimension reduction with source time-series augmentation and uses predictive time-series modeling for estimating directed causal relationships among fMRI time-series. It is a multivariate approach, since it is capable of identifying the interdependence of time-series in the presence of all other time-series of the underlying dyn…
University of Rochester（罗切斯特大学）；Ludwig Maximilian University（路德维希-马克西米利安大学）
The literature manifests that schizophrenia is associated with alterations in brain network connectivity. We investigate whether large-scale Extended Granger Causality (lsXGC) can capture such alterations using resting-state fMRI data. Our method utilizes dimension reduction combined with the augmentation of source time-series in a predictive time-series model for estimating directed causal relationships among fMRI time-series. The lsXGC is a multivariate approach since it identifies the relationship of the underlying dynamic system in the presence of all other time-series. Here lsXGC serves a…
University of Rajshahi（拉杰沙希大学）；Hajee Mohammad Danesh Science and Technology University（哈吉·穆罕默德·达内什科技大学）；Deakin University（迪肯大学）；Charles Sturt University（查尔斯·斯特特大学）；COMSATS University Islamabad（伊斯兰堡COMSATS大学）
Historically, the analysis of stimulus-dependent time-frequency patterns has been the cornerstone of most electroencephalography (EEG) studies. The abnormal oscillations in high-frequency waves associated with psychotic disorders during sensory and cognitive tasks have been studied many times. However, any significant dissimilarity in the resting-state low-frequency bands is yet to be established. Spectral analysis of the alpha and delta band waves shows the effectiveness of stimulus-independent EEG in identifying the abnormal activity patterns of pathological brains. A generalized model incor…
Multiscale Neuroimaging Features for the Identification of Medication Class and Non-Responders in Mood Disorder Treatment
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)（三机构神经影像与数据科学转化研究中心（TReNDS））；Georgia State University（佐治亚州立大学）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）；Lawson Health Research Institute（劳森健康研究所）；London Health Sciences Centre（伦敦健康科学中心）；FEMAP；Schulich School of Medicine and Dentistry（舒立克医学与牙科学院）；Western University（韦仕敦大学）
Parkinson's disease (PD) is a common and complex neurodegenerative disorder with 5 stages in the Hoehn and Yahr scaling. Given the heterogeneity of PD, it is challenging to classify early stages 1 and 2 and detect brain function alterations. Functional magnetic resonance imaging (fMRI) is a promising tool in revealing functional connectivity (FC) differences and developing biomarkers in PD. Some machine learning approaches like support vector machine and logistic regression have been successfully applied in the early diagnosis of PD using fMRI data, which outperform classifiers based on manual…
Multi-feature concatenation and multi-classifier stacking: an interpretable and generalizable machine learning method for MDD discrimination with rsfMRI
College of Computer and Information Science, Southwest University（西南大学计算机与信息科学学院）；Key Laboratory of Cognition and Personality(SWU), Ministry of Education（教育部认知与人格重点实验室（西南大学））；School of Psychology, Southwest University (SWU)（西南大学心理学部）；Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University（北京师范大学基础教育质量评价协同创新中心西南大学分中心）
TReND: Transformer derived features and Regularized NMF for neonatal functional network Delineation
University of Pennsylvania（宾夕法尼亚大学）；Children’s Hospital of Philadelphia（费城儿童医院）；Peking University（北京大学）；Beijing Children’s Hospital（北京儿童医院）；Beijing Normal University（北京师范大学）
