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Purpose: To elucidate the structural-functional connectivity (SC-FC) coupling in white matter (WM) tracts in patients with major depressive disorder (MDD)., Methods: A total of 178 individuals diagnosed with MDD and 173 healthy controls (HCs) were recruited for this study. The Euclidean distance was calculated to assess SC-FC coupling. The primary analyses focused on investigating alterations in SC-FC coupling in WM tracts of individuals with MDD. Additionally, we explored the association between coupling and clinical symptoms. Secondary analyses examined differences among three subgroups of MDD: those with suicidal ideation (SI), those with a history of suicidal attempts (SA), and those non-suicidal (NS)., Results: The study revealed increased SC-FC coupling mainly in the middle cerebellar peduncle and bilateral corticospinal tract (P FDR  < 0.05) in patients with MDD compared with HCs. Additionally, right cerebral peduncle coupling strength exhibited a significant positive correlation with Hamilton Anxiety Scale scores (r = 0.269, P FDR  = 0.041), while right cingulum (hippocampus) coupling strength showed a significant negative correlation with Nurses' Global Assessment of Suicide Risk scores (r = -0.159, P FDR  = 0.036). An increase in left anterior limb of internal capsule (P Bonferroni  < 0.01) and left corticospinal tract (P Bonferroni  < 0.05) coupling has been observed in MDD with SI. Additionally, a decrease in right posterior limb of internal capsule coupling has been found in MDD with SA (P Bonferroni  < 0.05)., Conclusions: This study emphasizes the variations in SC-FC coupling in WM tracts in individuals with MDD and its subgroups, highlighting the crucial role of WM networks in the pathophysiology of MDD., Competing Interests: Declaration of competing interest All authors declare that they have no competing interests., (Copyright © 2025 Elsevier B.V. All rights reserved.)

Generalized anxiety disorder (GAD) is a common anxiety disorder characterized by excessive, uncontrollable worry and physical symptoms such as difficulty concentrating and sleep disturbances. Although functional magnetic resonance imaging (fMRI) studies have reported aberrant network-level activity related to cognition and emotion in GAD, its low temporal resolution restricts its ability to capture the rapid neural activity in mental processes. EEG microstate analysis offers millisecond-resolution for tracking the dynamic changes in brain electrical activity, thereby illuminating the neurophysiological mechanisms underlying the cognitive and emotional dysfunctions in GAD. This study collected 64-channel resting-state EEG data from 28 GAD patients and 28 healthy controls (HC), identifying five microstate classes (A-E) in both groups. Results showed that GAD patients exhibited significantly lower duration (p < 0.01), occurrence (p < 0.05), and coverage (p < 0.01) of microstate class D, potentially reflecting deficits in attention-related networks. Such alterations may contribute to the impairments in attention maintenance and cognitive control. Additionally, GAD patients displayed reduced transition probabilities in A → D, B → D, C → D, and E → D (all corrected p < 0.05), but increased in C → E (corrected p < 0.05) and E → C (corrected p < 0.01). These results highlight a significant reduction in the brain's ability to transition into microstate class D, alongside overactivity in switching between the default mode network and the salience network. Such neurophysiological changes may underlie cognitive control deficits, increased spontaneous rumination, and emotional regulation challenges observed in GAD. Together, these insights provide a new perspective for understanding the neurophysiological and pathological mechanisms underlying GAD., Competing Interests: Declaration of competing interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2025 Elsevier B.V. All rights reserved.)

Background: Seeking objective quantitative indicators is important for accurately recognizing major depressive disorder (MDD). Lempel-Ziv complexity (LZC), employed to characterize neurological disorders, faces limitations in tracking dynamic changes in EEG signals due to defects in the coarse-graining process, hindering its precision for MDD objective quantitative indicators., Methods: This work proposed Adaptive Permutation Lempel-Ziv Complexity (APLZC) and Adaptive Weighted Permutation Lempel-Ziv Complexity (AWPLZC) algorithms by refining the coarse-graining process and introducing weight factors to effectively improve the precision of LZC in characterizing EEGs and further distinguish MDD patients better. APLZC incorporated the ordinal pattern, while False Nearest Neighbor and Mutual Information algorithms were introduced to determine and adjust key parameters adaptively. Furthermore, we proposed AWPLZC by assigning different weights to each pattern based on APLZC. Thirty MDD patients and 30 healthy controls (HCs) were recruited and their 64-channel resting EEG signals were collected. The complexities of gamma oscillations were then separately computed using LZC, APLZC, and AWPLZC algorithms. Subsequently, a multi-channel adaptive K-nearest neighbor model was constructed for identifying MDD patients and HCs., Results: LZC, APLZC, and AWPLZC algorithms achieved accuracy rates of 78.29 %, 90.32 %, and 95.13 %, respectively. Sensitivities reached 67.96 %, 85.04 %, and 98.86 %, while specificities were 88.62 %, 95.35 %, and 89.92 %, respectively. Notably, AWPLZC achieved the best performance in accuracy and sensitivity, with a specificity limitation., Limitation: The sample size is relatively small., Conclusion: APLZC and AWPLZC algorithms, particularly AWPLZC, demonstrate superior effectiveness in differentiating MDD patients from HCs compared with LZC. These findings hold significant clinical implications for MDD diagnosis., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objective: Deviant γ auditory steady-state responses (γ-ASSRs) have been documented in some psychiatric disorders. Nevertheless, the role of γ-ASSR in drug-naïve first-episode major depressive disorder (FEMD) patients remains equivocal. This study aimed to examine whether γ-ASSRs are impaired in FEMD patients and predict depression severity., Methods: Cortical reactivity was assessed in a cohort of 28 FEMD patients relative to 30 healthy control (HC) subjects during an ASSR paradigm randomly presented at 40 and 60 Hz. Event-related spectral perturbation and inter-trial phase coherence (ITC) were calculated to quantify dynamic changes of the γ-ASSR. Receiver operating characteristic curve combined with binary logistic regression were then employed to summarize ASSR variables that maximally differentiated groups., Results: FEMD patients exhibited significantly inferior 40 Hz-ASSR-ITC in the right hemisphere versus HC subjects (p = 0.007), along with attenuated θ-ITC that reflected underlying impairments in θ responses during 60 Hz clicks (p < 0.05). Moreover, the 40 Hz-ASSR-ITC and θ-ITC in the right hemisphere can be used as a combinational marker to detect FEMD patients with 84.0 % sensitivity and 81.5 % specificity (area under the curve was 0.868, 95 % CI: 0.768-0.968). Pearson's correlations between the depression severity and ASSR variables were further conducted. The symptom severity of FEMD patients was negatively correlated with 60 Hz-ASSR-ITC in the midline and right hemisphere, possibly indicating that depression severity mediated high γ neural synchrony., Conclusions: Our findings provide critical insight into the pathological mechanism of FEMD, suggesting first that 40 Hz-ASSR-ITC and θ-ITC in right hemisphere constitute potential neurophysiological markers for early depression detection, and second, that high γ entrainment deficits may contribute to underlying symptom severity in FEMD patients., Competing Interests: Conflict of interest All authors declare that they have no conflicts of interest., (Copyright © 2023. Published by Elsevier B.V.)

Background: Increasing depression patients puts great pressure on clinical diagnosis. Audio-based diagnosis is a helpful auxiliary tool for early mass screening. However, current methods consider only speech perception features, ignoring patients' vocal tract changes, which may partly result in the poor recognition., Methods: This work proposes a novel machine speech chain model for depression recognition (MSCDR) that can capture text-independent depressive speech representation from the speaker's mouth to the listener's ear to improve recognition performance. In the proposed MSCDR, linear predictive coding (LPC) and Mel-frequency cepstral coefficients (MFCC) features are extracted to describe the processes of speech generation and of speech perception, respectively. Then, a one-dimensional convolutional neural network and a long short-term memory network sequentially capture intra- and inter-segment dynamic depressive features for classification., Results: We tested the MSCDR on two public datasets with different languages and paradigms, namely, the Distress Analysis Interview Corpus-Wizard of Oz and the Multi-modal Open Dataset for Mental-disorder Analysis. The accuracy of the MSCDR on the two datasets was 0.77 and 0.86, and the average F1 score was 0.75 and 0.86, which were better than the other existing methods. This improvement reveals the complementarity of speech production and perception features in carrying depressive information., Limitations: The sample size was relatively small, which may limit the application in clinical translation to some extent., Conclusion: This experiment proves the good generalization ability and superiority of the proposed MSCDR and suggests that the vocal tract changes in patients with depression deserve attention for audio-based depression diagnosis., Competing Interests: Conflict of interest No potential conflict of interest was reported by the authors., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Subclinical depression (SD) and major depressive disorder (MDD) can be considered as the early and late stages of depression, but the characteristics of intrinsic neural activity in different depressive stages are largely unknown., Methods: Twenty-six SD, 36 MDD subjects and 33 well-matched healthy controls (HCs) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Voxel-wise regional homogeneity (ReHo) was analyzed to explore the alterations of intrinsic neural activity, and machine learning classification based on ReHo features was performed to assess potential performance for diagnostic classification., Results: Common alterations of ReHo in both SD and MDD groups were found in the bilateral middle temporal gyrus and the left middle occipital gyrus. Opposite alterations in SD and MDD groups were found in the right superior cerebellum. Moreover, increased ReHo in the bilateral precuneus was only found in MDD, while increased ReHo in the right middle frontal gyrus and precentral gyrus were unique to SD. The distinct ReHo values correctly identified SD, MDD, and HC by linear support vector machine (SVM) with an accuracy of 77.89 %, which further verified the discrimination ability of altered ReHo in these brain regions., Limitation: The sample size is relatively small., Conclusion: Common and unique ReHo alterations provided insights into the development of brain impairments in depression, and helped to understand the pathophysiology of SD and MDD., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Objective: Gamma oscillations contribute to the pathogenesis mechanisms of major depressive disorder (MDD) have been proposed, but gamma activity is not well characterized. This study is the first attempt to investigate the altered gamma oscillations in first-episode MDD, particularly the beta-gamma coupling, and to determine the potential symptomatic relationship with the identified gamma dysregulation., Methods: Resting electroencephalography was recorded for 43 drug-naive first-episode MDD and 57 healthy control (HC) subjects. Integrated analysis of relative spectral power, weighted phase lag index, and phase-amplitude coupling (PAC) were utilized to reveal the alterations of gamma activities. Pearson's correlation was implemented to identify the relationship between altered gamma activities and the clinical depressive symptoms, which were categorized into four factors: anxiety somatization, retardation, cognitive disturbance, and sleep disturbance., Results: Compared with HC subjects, MDD patients showed not only significantly decreased gamma powers in the left temporal and the bilateral occipital regions but also weakened gamma connectivity between the left hemisphere and the right frontal region. Furthermore, attenuated beta-gamma PAC of MDD patients was observed in the left temporal regions. Importantly, the suppression of left occipital mid- and high gamma oscillations were negatively correlated with sleep disturbance, while the deficits in left temporal beta-mid-gamma PAC and beta-high gamma PAC showed negative correlations with cognitive disturbance., Limitations: Important limitations are the small sample size and the possible inclusion of bipolar depression in the MDD group., Conclusions: Our findings provide the first evidence that in first-episode MDD, aberrant gamma powers and beta-gamma coupling are associated with sleep and cognitive impairments, respectively, deepening our understanding of the physiological mechanisms underlying sleep and cognitive symptoms in first-episode MDD. Altered gamma oscillations emerge as promising biomarkers for diagnosing MDD., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: The diagnosis of subclinical depression (SD) currently relies exclusively on subjective clinical scores and structured interviews, which shares great similarities with major depression (MD) and increases the risk of misdiagnosis of SD and MD. This study aimed to develop a method of disease classification for SD and MD by resting-state functional features using radiomics strategy., Methods: Twenty-six SD, 36 MD subjects and 33 well-matched healthy controls (HC) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). A novel radiomics analysis was proposed to discriminate SD from MD. Multi-scale brain functional features were extracted to explore a comprehensive representation of functional characteristics. A two-level feature selection strategy and support vector machine (SVM) were employed for classification., Results: The overall classification accuracy among SD, MD and HC groups was 84.21%. Particularly, the model excellently distinguished SD from MD with 96.77% accuracy, 100% sensitivity, and 92.31% specificity. Moreover, features with high discriminative power to distinguish SD from MD showed a strong association with default mode network, frontoparietal network, affective network, and visual network regions., Limitation: The sample size was relatively small, which may limit the application in clinical translation to some extent., Conclusion: These findings demonstrated that a valid radiomics approach based on functional measures can discriminate SD from MD with a high classification performance, facilitating an objective and reliable diagnosis individually in clinical practice. Features with high discriminative power may provide insight into a profound understanding of the brain functional impairments and pathophysiology of SD and MD., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: This study aimed to examine the aberrant EEG oscillation in major depressive subjects with basal ganglia stroke with lesions in different hemispheres., Methods: Resting EEG of 16 electrodes in 58 stroke subjects, 26 of whom had poststroke depression (13 with left-hemisphere lesion and 13 with right) and 32 of whom did not (18 with left lesion and 14 with right), was recorded to obtain spectral power analysis for several frequency bands. Multiple analysis of variance and receiver operating characteristic (ROC) curves were used to identify differences between poststroke depression (PSD) and poststroke non-depression (PSND), treating the different lesion hemispheres separately. Moreover, Pearson linear correlation analysis was conducted to test the severity of depressive symptoms and EEG indices., Results: PSD with left-hemisphere lesion showed increased beta2 power in frontal and central areas, but PSD with right-hemisphere lesion showed increased theta and alpha power mainly in occipital and temporal regions. Additionally, for left-hemisphere lesions, beta2 power in central and right parietal regions provided high discrimination between PSD and PSND, and for right-hemisphere lesions, theta power was similarly discriminative in most regions, especially temporal regions. We also explored the association between symptoms of depression and the power of abnormal bands, but we found no such relationship., Limitations: The sample size was relatively small and included subjects with different lesions of the basal ganglia., Conclusions: The aberrant EEG oscillation in subjects with PSD differs between subjects with lesions of the left and right hemispheres, suggesting a complex association between depression and lesion location in stroke patients., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Background: Poststroke depression (PSD) is one of the most common emotional disorders affecting post-stroke patients. However, the neurophysiological mechanism remains elusive. This study was aimed to study the relationship between complexity of neural electrical activity and PSD., Methods: Resting state eye-closed electroencephalogram (EEG) signals of 16 electrodes were recorded in 21 ischemic poststroke depression (PSD) patients, 22 ischemic poststroke non-depression (PSND) patients and 15 healthy controls (CONT). Lempel-Ziv Complexity (LZC) was used to evaluate changes in EEG complexity in PSD patients. Statistical analysis was performed to explore difference among different groups and electrodes. Correlation between the severity of depression (HDRS) and EEG complexity was determined with pearson correlation coefficients. Receiver operating characteristic (ROC) and binary logistic regression analysis were conducted to estimate the discriminating ability of LZC for PSD in specificity, sensitivity and accuracy., Results: PSD patients showed lower neural complexity compared with PSND and CONT subjects in the whole brain regions. There was no significant difference among different brain regions, and no interactions between group and electrodes. None of the LZC significantly correlated with overall depression severity or differentiated symptom severity of 7 items in PSD patients, but in stroke patients, significant correlation was found between HDRS and LZC in the whole brain regions, especially in frontal and temporal. LZC parameters used for PSD recognition possessed more than 85% in specificity, sensitivity and accuracy, suggesting the feasibility of LZC to serve as screening indicators for PSD. Increased slow wave rhythms were found in PSD patients and clearly correlation was confirmed between neuronal complexity and spectral power of the four EEG rhythms., Limitations: Lesion location of stroke patients in the study distributed in different brain regions, and most of the PSD patients were mild or moderate in depressive severity., Conclusions: Compared with conventional spectral analysis, complexity of neural activity using LZC was more sensitive and stationary in the measurement of abnormal brain activity in PSD patients and may offer a potential approach to facilitate clinical screening of this disease., (Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.)