Volume 2025, Issue 1 7390951

Research Article

Open Access

Neurophysiological Signatures of Major Depressive Disorder and Frontocentral Gamma Auditory Response Deficits

Xiaoya Liu,

Xiaoya Liu

orcid.org/0000-0002-4361-5166

Medical School of Tianjin University , Tianjin University , Tianjin , China , tju.edu.cn

Search for more papers by this author

Shuang Liu,

Corresponding Author

Shuang Liu

[email protected]

orcid.org/0000-0002-4372-8443

Medical School of Tianjin University , Tianjin University , Tianjin , China , tju.edu.cn

Search for more papers by this author

Fangyue Su,

Fangyue Su

orcid.org/0009-0007-8087-8487

College of Precision Instruments and Optoelectronics Engineering , Tianjin University , Tianjin , China , tju.edu.cn

Search for more papers by this author

Wenquan Zhang,

Wenquan Zhang

orcid.org/0000-0002-7757-3056

Medical School of Tianjin University , Tianjin University , Tianjin , China , tju.edu.cn

Search for more papers by this author

Yufeng Ke,

Yufeng Ke

orcid.org/0000-0002-8434-0322

Medical School of Tianjin University , Tianjin University , Tianjin , China , tju.edu.cn

Search for more papers by this author

Dong Ming,

Dong Ming

orcid.org/0000-0002-8192-2538

Medical School of Tianjin University , Tianjin University , Tianjin , China , tju.edu.cn

College of Precision Instruments and Optoelectronics Engineering , Tianjin University , Tianjin , China , tju.edu.cn

Search for more papers by this author

Xiaoya Liu,

Xiaoya Liu

orcid.org/0000-0002-4361-5166

Medical School of Tianjin University , Tianjin University , Tianjin , China , tju.edu.cn

Search for more papers by this author

Shuang Liu,

Corresponding Author

Shuang Liu

[email protected]

orcid.org/0000-0002-4372-8443

Medical School of Tianjin University , Tianjin University , Tianjin , China , tju.edu.cn

Search for more papers by this author

Fangyue Su,

Fangyue Su

orcid.org/0009-0007-8087-8487

College of Precision Instruments and Optoelectronics Engineering , Tianjin University , Tianjin , China , tju.edu.cn

Search for more papers by this author

Wenquan Zhang,

Wenquan Zhang

orcid.org/0000-0002-7757-3056

Medical School of Tianjin University , Tianjin University , Tianjin , China , tju.edu.cn

Search for more papers by this author

Yufeng Ke,

Yufeng Ke

orcid.org/0000-0002-8434-0322

Medical School of Tianjin University , Tianjin University , Tianjin , China , tju.edu.cn

Search for more papers by this author

Dong Ming,

Dong Ming

orcid.org/0000-0002-8192-2538

Medical School of Tianjin University , Tianjin University , Tianjin , China , tju.edu.cn

College of Precision Instruments and Optoelectronics Engineering , Tianjin University , Tianjin , China , tju.edu.cn

Search for more papers by this author

First published: 19 February 2025

https://doi.org/10.1155/da/7390951

Academic Editor: Drozdstoy Stoyanov

Share a link

Email
Wechat
Bluesky

Abstract

Background: Aberrant gamma oscillations in major depressive disorder (MDD) have attracted extensive attention, but evidence delineating such neural signatures is lacking. The auditory steady-state response (ASSR) elicited by periodic auditory stimuli is a robust probe of gamma oscillations. Here, we sought to characterize early transient auditory evoked responses (AEPs) and sustained gamma ASSRs in MDD, thereby identifying reliable neurophysiological signatures and providing preliminary interpretations of gamma auditory response deficits in MDD.

Methods: Electroencephalographic data were obtained from 40 first-episode drug-naïve patients with MDD and 41 demographically matched healthy controls (HCs) during a 40-Hz ASSR paradigm, encompassing two periodic stimuli—chirp and click stimuli. Source analysis of transient AEPs was performed to identify generators involved in early information processing dysfunction. In addition, spectrotemporal and spatial characteristics of 40-Hz ASSRs were analyzed using event-related spectral perturbation, inter-trial phase coherence, and functional connectivity index.

Results: Compared to HCs, patients showed a reduced P200 amplitude that was source-localized to the middle temporal gyrus, possibly reflecting an underlying impairment in the processes of early allocation or auditory information perception within the auditory pathways. Meanwhile, attenuated 40-Hz power and phase coherence, in conjunction with suppressed right frontotemporal and frontocentral connectivity, were observed in MDD, highlighting the multidimensional entrained gamma inhibition. Correlation analyses revealed that the decreased right frontocentral connectivity was strongly related to increased anxiety severity. Importantly, these abnormalities correlated with the patient’s symptoms were only found with the chirp stimulus, suggesting that the chirp stimulus has tremendous potential to reveal specific neurophysiological signatures of MDD.

Conclusions: Our data reveal impaired gamma auditory responses in first-episode drug-naïve patients with MDD and suggest that right frontocentral connectivity elicited by the chirp stimulus may represent a promising signature for predicting clinical symptoms.

1. Introduction

Major depressive disorder (MDD) is a serious, debilitating psychiatric disorder characterized by persistent anhedonia, feelings of worthlessness, and cognitive dysfunction [1]. It is a leading cause of disability worldwide and increases the risk of suicide [2]. However, the diagnosis of MDD remains entirely based on subjective symptomatology, with a certain degree of concealment. This may partly account for the high misdiagnosis rate of MDD [3, 4], necessitating the identification of neurobiological signatures to advance the mechanistic understanding of depressive psychopathology and ultimately yield important clinical implications.

Accumulating evidence over the past decades has suggested that gamma oscillations may be a predictor of MDD [5–7]. The role of gamma-aminobutyric acid-containing (GABAergic) inhibitory interneurons and excitatory pyramidal neurons in the generation of gamma oscillations has been extensively confirmed by neuroimaging data [8], and the abnormalities in GABAergic interneurons have been repeatedly reported in MDD [8–13]. In our previous electroencephalography (EEG) studies, we found a marked alteration of spontaneous gamma activity in first-episode drug-naïve patients with MDD, which was strongly correlated with depression severity [14, 15]. However, gamma oscillations are typically neglected due to their low amplitude properties and possible physiological artifacts [16, 17]. A common approach to enhance gamma activity is entrainment by exogenous rhythmic stimulation. The auditory steady-state response (ASSR), a fruitful probe of gamma synchronous activity, is a cortical oscillation entrained to both the frequency and phase of periodic auditory stimuli [18, 19]. In humans, the ASSR has been found to be remarkably pronounced at 40 Hz [20].

Although there have been some pathologic investigations of 40-Hz ASSRs in psychosis [21, 22], they provide little insight into MDD. To date, only two prospective studies have examined 40-Hz ASSRs in MDD [23, 24]. Most importantly, the evidence for 40-Hz ASSRs in MDD is derived from patients on pharmacological treatment. Pharmacological interventions have been shown to increase gamma power [25], thus explaining the absence of profound 40-Hz ASSR deficits in MDD, possibly due to auditory responses that cannot be disentangled from medication effects [23]. To rule out confounding medication effects, we examined 40-Hz ASSRs in a group of first-episode drug-naïve patients with MDD in the present study. In addition, previous studies of MDD have assessed 40-Hz ASSRs separately, either by power changes or phase abnormalities. However, changes in brain connectivity patterns of gamma auditory responses have not been investigated. Recent evidences suggest that the connections between brain regions support the latent mechanisms of cognitive function [26]. Therefore, mapping the connectivity patterns of 40-Hz ASSRs between spatially distributed brain regions may contribute to the elucidation of the pathophysiological mechanisms underlying auditory dysfunction in MDD.

Typically, auditory responses to repetitive auditory stimuli at 40 Hz can be decomposed not only into 40-Hz ASSRs but also into transient auditory evoked responses (AEPs; e.g., N100 and P200) [27–29]. Disturbances in AEPs elicited by the auditory oddball paradigm have been documented in MDD [30–32]. The characteristics of AEPs are known to represent a distinct pattern of cognitive mechanisms. Specifically, the N100 component reflects early information extraction during stimulus perception [33], localized in the primary auditory cortex and adjacent belt regions of the secondary auditory cortex [34, 35]. The P200 component reflects allocation processes [36], which may reflect overlapping activity in both temporal and frontal cortices [37]. Combined analysis of AEPs and 40-Hz ASSRs may provide a comprehensive understanding of neural circuit abnormalities in auditory information processing in MDD. Furthermore, enhancement of AEPs and 40-Hz ASSRs can be achieved by optimizing stimulus parameters—such as stimulus type [38, 39]. Chirp stimulus, which was developed to compensate for the cochlear traveling wave delay [40], is considered to be more sensitive in capturing gamma oscillations, mainly because of the higher signal-to-noise ratio [37, 41–44].

Here, we sought to characterize the transient AEPs and sustained 40-Hz ASSRs in first-episode drug-naïve patients with MDD, thereby identifying a neurophysiological signature and providing preliminary insight into the neural underpinnings of gamma auditory response deficits. We compared the N100 and P200 components in combination with source analysis to clarify the primary generators of early AEP changes. Moreover, event-related spectral perturbation (ERSP), inter-trial phase coherence (ITC), and weighted phase lag index were extracted to identify the spectrotemporal and spatial characteristics of 40-Hz ASSRs. Correlation analyses were performed to elucidate whether the altered EEG signatures could predict clinical symptom severity. In addition, based on our previous work [43], we further hypothesized that MDD-related differences in gamma auditory responses would be more pronounced with the chirp stimulus, ultimately providing a clinical avenue to explore the pathophysiological signatures of MDD.

2. Materials and Methods

2.1. Participants

First-episode drug-naïve patients with MDD (n = 40) and healthy controls (HCs, n = 41) were recruited from Tianjin Anding Hospital. All participants in both groups had normal or corrected-to-normal hearing and were matched for age, sex, educational level, handedness, and ethnicity (Table 1). Approval was obtained from the Ethics Committee of Tianjin Anding Hospital, and written informed consent was obtained from all participants.

Table 1. Demographic and clinical information.

Characteristics	HC	MDD	Statistics	p-Value
Sample size	41	40	—	—
Sex, male^a	18	12	χ²₍₁₎ = 1.678	0.252
Age, year^b	33.12 ± 7.47	31.53 ± 9.14	Z = −1.296	0.195
Education, years^b	14.27 ± 3.45	14.93 ± 2.65	Z = −0.672	0.501
Handedness, right^a	40	38	χ²₍₁₎ = 0.372	0.616
Ethnicity, Han/others^a	38/3	37/3	χ²₍₁₎ = 0.001	1.000
HAMD-17 scores^c	1.21 ± 1.34	23.35 ± 5.10	t₍₇₉₎ = 26.234	<0.001
HAMA scores^c	1.23 ± 1.27	22.10 ± 7.34	t₍₇₉₎ = 17.507	<0.001

Note: Results are expressed as mean ± standard deviation (SD).
^aChi-square test.
^bMann–Whitney U-test.
^cIndependent samples t-test.

The Structured Clinical Interview for the Diagnostic and Statistical Manual for Mental Disorders (DSM)-V [45] confirmed the diagnosis of MDD, and a score of ≥14 on the 17-item Hamilton Rating Scale for Depression (HAMD-17) [46] and a score of ≤10 on the Young Mania Rating Scale (YMRS) [47] confirmed an active depressive episode. MDD is often associated with higher levels of anxiety; therefore, the Hamilton Anxiety Scale (HAMA) [48] was used to assess anxiety levels (Table 1). Exclusion criteria were (i) previous or current organic brain disorders or neurological disorders; (ii) previous or current exposure to electroconvulsive therapy or psychotropic treatments; and (iii) history of alcohol and drug abuse. HCs were excluded if they or their first-degree relatives had a self-reported psychiatric disorder.

2.2. Stimuli and Paradigm

Two stimulus types (i.e., chirp and click) were applied in the ASSR paradigm. The chirp stimulus [49, 50] was generated according to the following formula:

(1)

where φ₀ determines the starting phase of the chirp signals; the phase φ(t) and amplitude factor A(t) were calculated as follows:

(2)

(3)

(4)

where f = 50 Hz, the constants c = 0.15, and α = −0.5. The click stimulus consisted of 1-ms bursts of white noise, similar to that used previously [21, 23, 43, 51]. Both stimuli were presented using MATLAB 2020a (The MathWorks. Inc., Natick, MA, USA) with the Psychtoolbox [52]. The typical session for the click or chirp stimulus is shown in Figure 1a, each consisting of 28 trials. The duration of each trial was 3000 ms with an inter-trial interval of 1500 ms [27]. Auditory stimuli were presented binaurally at 40 Hz with a sampling rate of 44,100 Hz; the sound pressure level was set at 45 dB. Participants were asked to minimize their movements, stay awake, keep their gaze on the screen directly in front of them, and not pay attention to the stimuli in the paradigm. Specifically, the order of the click and chirp sessions was randomized across participants.

Details are in the caption following the image — **Figure 1 (a)**
Open in figure viewer PowerPoint

The ASSR paradigm and brain regions showing stimulus-elicited 40-Hz responses. (a) The task flow and the waveforms of the two signals (chirp and click). (b) An example of whole-brain averaged 40-Hz narrowband filtered signals from MDD (black dashed line) and HC (red solid line) groups elicited by the chirp stimulus. Data show clear entrainment with stimulation frequency in both groups. (c) Source localization of 40-Hz entrainment responses. For the HC group (left), significant 40-Hz activity was localized in the cingulate gyrus during both stimuli. For the MDD group (right), significant 40-Hz activity was localized separately in the posterior cingulate during the chirp stimulus and in the subgyral during the click stimulus. Red voxels indicate areas of relative activation (500–2500 ms auditory stimulus duration vs. 500-ms baseline). ASSR, auditory steady-state response; HC, healthy control; MDD, major depressive disorder.

2.3. EEG Acquisition and Preprocessing

EEG was acquired using SynAmps RT amplifiers (NeuroScan Inc., Herndon, VA, USA) with 64 scalp electrodes arranged according to the international 10/20 system. All electrodes were referenced to the left mastoid electrode (“M1”) at a sampling rate of 1000 Hz, and the impedance of all electrodes was <10 kΩ.

EEG data were preprocessed using the EEGLAB toolbox [53] and custom MATLAB scripts. Imported data were rereferenced offline to the average bilateral mastoid electrodes ([“M1” + “M2”]/2) and low-pass filtered at 50 Hz. Subsequently, epochs were defined as nonoverlapping data segments from 1000 ms before stimulus onset to 3000 ms after stimulus onset and were discarded if the voltage at any electrode site exceeded 100 μV. Artifacts due to eye blinks, eye movements, and the heartbeat signal were removed using independent component analysis. The number of epochs after artifact removal from each group for each stimulus was as follows: HC (click: 26.59 ± 1.43, ≥23; chirp: 26.44 ± 1.60, ≥22) and MDD (click: 26.40 ± 0.93, ≥23; chirp: 26.08 ± 1.47, ≥21). A mixed model analysis of variance (ANOVA) with stimulus (chirp, click) as a within-subjects factor and group (MDD, HC) as a between-subjects factor, revealed no significant main effects (group: F_(1,79) = 0.975, p = 0.326; Stimulus: F_(1,79) = 3.233, p = 0.076) or interactions (F_(1,79) = 0.465, p = 0.497) for the remaining number of epochs. Figure 1b demonstrates the whole-brain averaged 40-Hz waveform elicited by the chirp stimulus, showing that clear stimulus frequency entrainment was evoked in both groups.

In addition, eight regions of interests (ROIs) were defined for inter-regional connectivity analyses, namely the left frontal (FL; FP1, AF3, F7, F5, F3, F1, FC5, FC3, and FC1), left temporal (TL; FT7, T7, and TP7), left central (CL; C5, C3, C1, CP5, CP3, and CP1), left parietal (PL; P7, P5, P3, P1, PO7, PO5, and PO3), right frontal (FR; FP2, AF4, F8, F6, F4, F2, FC6, FC4, and FC2), right temporal (TR; FT8, T8, and TP8), right central (CR; C6, C4, C2, CP6, CP4, and CP2), and right parietal (PR; P8, P6, P4, P2, PO8, PO6, and PO4) regions.

2.4. Event-Related Potential Analysis

Transient AEPs were assessed in all participants. In analogy to previous AEP studies [54, 55], we focused on central electrode sites (FCZ, CZ, and CPZ) to explore group differences. Epochs were low-pass filtered at 30 Hz and baseline corrected (from –300 to –100 ms). The N100 and P200 were extracted in the poststimulus windows of 70–140 ms and 140–260 ms, respectively [32].

2.5. Time–Frequency Analysis

ERSP and ITC were calculated to determine the changes in gamma oscillations, using a short-time Fourier transform with a Gauss window size of 1023 points. ERSP and ITC in the frequency range from 1 to 50 Hz were mapped to obtain a general idea of the frequency distribution. Then, 40-Hz entrainment responses (frequency range: 32–48 Hz) were extracted independently for each group within a time frame of 1000–2500 ms poststimulus. All data were expressed as the relative change from the baseline period (–300 to –100 ms), and ERSP values were averaged across epochs. Notably, one of the patients was excluded due to outliers (exclusion criterion: ERSP measures exceeding 4 SDs from the mean).

2.6. Connectivity Analysis

Inter-regional connectivity was measured using the weighted phase-lag-index (WPLI) [56], which is a measure of the synchronization between the signal pair over time and has been shown to be insensitive to almost-zero- and zero-lagging synchronization between signals [57]. Its mathematical formulation is as follows:

(5)

where S_xy,t indicates the complex cross-spectral density of the time series x(t) and y(t) at the time point t and || denotes the absolute value. The WPLI ranged from zero (no phase synchronization) to one (perfect phase synchronization). Here, WPLI of each pair of electrodes within the 38–42 Hz range was calculated for each epoch, then averaged across epochs, resulting in a connectivity matrix per participant. Further analysis was performed at a regional level of spatial resolution. The regional WPLI was based on the eight ROIs. For each ROI, the average connectivity of electrodes overlying the corresponding regions to all the electrodes belonging to the other ROIs was determined. In addition, the connectivity between any two ROIs was calculated as the average of all the electrode pairs between the two ROIs.

2.7. Source Estimation

Source-level estimation of AEPs and 40-Hz ASSRs was performed using standardized low-resolution brain electromagnetic tomography (sLORETA) [58]. A total of 6239 voxels at 5-mm spatial resolution were registered. To control for multiple comparisons, all statistical tests were performed using the implemented statistical nonparametric mapping (SnPM) tool, derived from 5000 randomizations. Significant difference t-values were presented in Talairach space.

For 40-Hz ASSRs, a separate paired t-test was performed for each group during each stimulus by contrasting the auditory stimulus periods (500–2500 ms after stimulus onset) with the baseline (−500 to 0 ms) for 40-Hz entrainment responses. In HC, the cingulate gyrus (Brodmann areas [BAs] 31) was activated (click: t = 11.520, XYZ = –10, –45, 35; chirp: t = 12.738, XYZ = –20, –45, 25). In MDD, the activating areas included the subgyral (click: t = 9.616, XYZ = –20, –50, 35; BA 31) and posterior cingulate (chirp: t = 12.393, XYZ = –5, –45, 25; BA 23). It can be speculated that the limbic lobe (BA 23 and BA 31; Figure 1c) plays a key role in the sustained auditory processing of continuous repetitive gamma-band stimuli.

For AEPs, paired and independent samples t-tests of N100 and P200 were performed (i) to compare stimulus-specific N100 sources between chirp and click stimuli and (ii) to compare P200 sources between MDD and HC, separately for the two stimuli. To control for multiple comparisons, all statistical tests used a nonparametric test, derived from 5000 randomizations.

2.8. Statistical Analyses

Mann–Whitney U-tests were performed for age and education between the two groups, as they did not meet the parametric assumptions of normal distribution by the Kolmogorov–Smirnov test. Chi-squared tests were performed for sex, handedness, and ethnicity. Independent samples t-tests were used to compare HAMD-17 and HAMA scores between the two groups.

For the primary analyses of 40-Hz ERSP and ITC, electrodes with main group differences were evaluated using the nonparametric permutation (n = 5000) t-test. Permutation tests were performed using the MATLAB function “mult_comp_perm_t2” [59]. The p-value was adjusted using the “tmax” method for multiple comparisons to control for family-wise error [59]. The averaged ERSP and ITC from the electrodes covering the significantly different entrainment activity between the two groups were further extracted for secondary analyses.

Secondary analyses evaluated HC vs. MDD for each dependent signature (AEPs, ERSP, ITC, and WPLI). Mixed model ANOVAs were performed for the within-subjects factor of Stimulus (chirp, click) and the between-subjects factor of Group (MDD, HC). Greenhouse–Geisser correction was applied for sphericity violations and Bonferroni corrections were applied for the post hoc pairwise comparisons. In addition, two-tailed Spearman’s correlation analysis was performed to explore the relationships between clinical symptoms and EEG signatures.

3. Results

3.1. Early Auditory Processing Deficits in MDD

The grand-averaged AEP waveforms elicited by the chirp and click stimuli were characterized by a negative N100 deflection in the range of 70–140 ms after stimulus onset, followed by a positive P200 deflection in the range of 140–260 ms (Figure 2a). A mixed model ANOVA revealed a significant main effect of Stimulus (F_{(1, 79)} = 6.523, p = 0.013; Figure 2b), as the chirp stimulus (mean ± standard error [M ± SE] = –0.616 ± 0.214) elicited a larger N100 compared to the click stimulus (M ± SE = 0.036 ± 0.198); however, the main effect of Group or the interaction effect of Group × Stimulus was not significant for the N100 component.

For the P200 component, a significant main effect of Group was observed (F_{(1, 79)} = 8.249, p = 0.005; Figure 2c), with MDD showing a reduced P200 amplitude (M ± SE = –0.037 ± 0.203) compared to HC (M ± SE = 0.781 ± 0.200). There were no Stimulus and Group × Stimulus interaction effects.

3.2. Source Reconstruction of AEP Components

Source localization analysis for the AEP components was performed only for the significant effects revealed by the scalp domain analyses, in order to find the generators responsible for the observed AEP changes. A voxel-by-voxel statistical analysis was performed by juxtaposing chirp vs. click conditions and MDD vs. HC groups, separately for N100 and P200. Tables 2 and 3 detail the spatial information of significant voxels for the N100 and P200 components. Figure 2b,c shows the LORETA X, Y, and Z slices around the maximum (t-score) condition and group differences, respectively.

Table 2. Source localization contrasts for the N100 component between chirp and click conditions, separately for HC and MDD groups.

Lobe	Best match (mm)	Region	Brodmann areas	t-Score	MNI (XYZ)
All participants
Temporal	0	Transverse temporal gyrus	41	3.92	−35, –35, 10
Temporal	7	Insula	13	3.73	−30, –30, 15

HC group
Temporal	0	Transverse temporal gyrus	41	3.51	−35, –35, 10
Temporal	7	Insula	13	3.25	−35, –40, 20

MDD group
Temporal	0	Insula	13	3.08	−40, –25, 20
Temporal	7	Insula	41	2.66	−45, –25, 15

Note: The critical t-scores for all participants, the HC group, and the MDD group are 3.57, 3.63, and 3.59, respectively, corresponding to p = 0.05.
Abbreviations: HC, healthy control; MDD, major depressive disorder.

Table 3. Source localization contrasts for the P200 component between MDD and HC groups, separately for chirp and click stimuli.

Lobe	Best match (mm)	Region	Brodmann areas	t-Score	MNI (XYZ)
All stimuli
Temporal	0	Middle temporal gyrus	39	−4.07	−40, –65, 20
Temporal	7	Middle temporal gyrus	39	−4.01	−40, –65, 20

Chirp stimulus
Temporal	0	Middle temporal gyrus	39	−4.02	−40, –65, 20
Temporal	7	Middle temporal gyrus	39	−3.93	−45, –75, 25
Click stimulus

Limbic lobe	0	Anterior cIngulate	33	−2.91	0, 20, 20
Limbic lobe	7	Anterior cIngulate	33	−2.90	5, 20, 20

Note: Critical t-scores for all stimuli, chirp stimulus, and click stimulus are 3.42, 3.45, and 3.33, respectively, corresponding to p = 0.05.
Abbreviations: HC, healthy control; MDD, major depressive disorder.

All participants showed significantly enhanced N100 activation for chirp compared to click conditions, with the responsible generators localized in the transverse temporal gyrus (TTG) (t = 3.920, p = 0.010; BA 41). A detailed within-group analysis revealed that the HC group showed a marginally significant enhancement of N100 activation for chirp compared to click conditions, localized in the TTG (t = 3.512, p = 0.075; BA 41). In the MDD group, patients showed increased but not significant N100 activation, with activity localized to the insula (t = 3.075, p = 0.302; BA 13).

Source localization contrasts of the P200 component between the MDD and HC groups revealed that MDD patients showed significantly reduced P200 activation in the middle temporal gyrus (MTG) (t = –4.072, p = 0.003; BA 39). As detailed in Table 3, the HC group showed a significant increase in P200 activation during the chirp stimulus (t = –4.022, p = 0.006; BA 39), whereas no significant voxels were observed during the click stimulus.

3.3. Spectrotemporal Characteristics of 40-Hz ASSRs in MDD

Compared to HC, MDD showed impaired gamma activity throughout the steady-state period (500–2500 ms poststimulus; Figure 3a,b). The significant group differences in 40-Hz entrainment power showed a more anterior distribution involving frontal, temporal, and central regions for both stimuli (p < 0.01; Figure 3c). Therefore, we examined the average ERSP in the anterior regions and a mixed model ANOVA revealed a significant main effect of Group (F_{(1, 78)} = 13.032, p = 0.001; Figure 3d), with a reduced 40-Hz power in MDD compared to HC for both stimuli. However, the interaction effect of Group × Stimulus and the main effect of Stimulus were not significant.

In addition, MDD presented a global attenuation in the 40-Hz ITC to the chirp stimulus, but a local attenuation to the click stimulus. Similarly, we examined the average ITC in the anterior regions, and a mixed model ANOVA revealed a significant main effect of Group (F_{(1, 78)} = 14.559, p = 0.0003) and a marginally significant interaction effect of Group × Stimulus (F_{(1, 78)} = 3.730, p = 0.057). The main effect of Stimulus was marginally significant (F_{(1, 78)} = 3.145, p = 0.080), suggesting that MDD showed a greater deficit in phase synchronization across trials during the chirp stimulus.

3.4. Connectivity Patterns of 40-Hz ASSRs in MDD

We observed that patients with MDD exhibited significantly reduced connectivity compared to HCs during the chirp stimulus (Figure 4a), including connections between FR and TR (p = 0.008), between FR and CR (p = 0.017), and between FL and TR (p = 0.048), suggesting that MDD is characterized by impaired right cortical communication. Notably, the attenuated connectivity was specific to the chirp stimulus, as no significant group difference was found for the click stimulus. Furthermore, connectivity between the FR and CR regions was significantly negatively correlated with HAMA scores in MDD patients during the chirp stimulus (Spearman’s r = –0.299, p = 0.041, Figure 4b). In addition, HAMA scores showed a marginally significant negative correlation with connectivity between the FR and TR regions (Spearman’s r = –0.259, p = 0.067).

4. Discussion

By combining AEPs and ASSRs, we present a promising stimulation—the chirp stimulus—for assessing aberrant gamma oscillations in first-episode drug-naïve patients with MDD. Two main findings emerged from the current study. First, patients with MDD showed substantial alterations in gamma auditory responses, characterized by a prominent reduction in P200 activity, suppressed 40-Hz entrainment power and ITC in the anterior cortex, and insufficient functional connectivity in the right hemisphere, in contrast to HCs. Second, attenuated right frontocentral connectivity was strongly related to the severity of depression and anxiety symptoms. Notably, these findings were stimulus driven, that is, the abnormalities associated with clinical symptoms of MDD were found with the chirp stimulus, but not with the click stimulus.

As expected, our results provided direct evidence that patients with MDD have shown a prominent reduction in P200 amplitude, consistent with previous research suggesting early auditory processing dysfunction in MDD [60, 61]. Boutros et al. [62, 63] proposed a link between the P200 component and resource allocation in auditory processing. Subsequently, Ross and Fujioka [64] further elucidated that the P200 component signifies the successful completion of the process whereby the neural representation of an auditory object is established and can be subconsciously processed. The reduced P200 amplitude observed in MDD patients may indicate an effect on the formation and perception of auditory objects during auditory processing. In particular, the P200 component is considered to be an indirect indicator of central serotonin function [65]. Given that serotonergic dysfunction in the central nervous system is thought to be one of the major pathophysiological factors in MDD and that serotonin plays a mediatory role in the generation of the ASSR [25], this physiological basis underpins the investigation of the association between P200 and ASSR indices. It is worth noting that some studies have found increased P200 amplitudes in MDD [66, 67] possibly due to variations in stimulation modality or intensity. Whereas previous research has used high-intensity stimuli (70- and 80-dB SPL) [68], our study used low-intensity stimuli, providing a plausible explanation for the divergent findings. Specifically, we found that reduced P200 activity was localized in the MTG during the chirp stimulus. A meta-analysis supports this perspective, showing a reduction in MTG gray matter in first-episode MDD [69, 70]. Clearly, our results have shed additional light on the abnormal P200 patterns in first-episode MDD. Recent evidence from Parker et al. [71, 72] suggests that earlier reductions in evoked responses (e.g., P200) serve as a marker of severity in severe mental illness. However, the relationship between P200 activity and depressive symptoms remains to be elucidated. Future studies that expand the sample size to investigate the relationship between the P200 component and clinical symptoms of MDD during click and chirp stimuli, particularly with an emphasis on source-level analysis, will provide a promising avenue for research into the neurobiological basis of the aberrant auditory processing patterns observed in patients with MDD.

In our previous research, we implemented an ASSR paradigm using click stimulus that alternated between 40 and 60 Hz and found a reduction in right hemisphere 40-Hz ITC in patients with MDD [73]. Capitalizing on these findings, the current study focused on the 40-Hz stimuli and incorporated chirp signals to conduct an in-depth investigation, with the aim of uncovering the aberrant auditory response patterns in MDD. Our comparison of 40-Hz entrainment power in the auditory stimulus period and baseline revealed gamma activation in the limbic lobe. The limbic system receives either direct or indirect neural input from the auditory system [74]. Dysfunction of the limbic system may underlie depressive mood disorders [75]. In addition, our results showed that the 40-Hz entrainment abnormalities involve reduced ERSP and ITC in frontal, temporal, and central brain regions. The attenuated power and phase consistency of neural responses in MDD during a 40-Hz ASSR paradigm, possibly reflecting an inability to generate or maintain gamma activity. Specifically, with respect to the phase consistency of 40-Hz ASSRs, the marginally significant interaction effect of Group × Stimulus, together with the main effect of Stimulus, indicated that MDD exhibited a more severe deficit in phase synchronization across trials, particularly during the presentation of chirp stimuli. Furthermore, most previous studies have used relatively short (500–1000 ms) trains of 1-ms clicks [23], and analyzed 500-ms stimulus durations, whereas the ASSR requires at least 240 ms to develop [76]. Combined with longer stimulus durations (3000 ms), our stimulus presentation may have driven the abnormal cortical neural responses in MDD. Future studies with different stimulus conditions are needed to better understand these discrepancies.

Functional connectivity is emerging as a critical neurophysiological endophenotype of MDD, highlighting its importance in research. A recent resting-state EEG study showed that MDD patients have weaker functional connectivity between occipital and parietal regions of the right hemisphere in the 4–30 Hz range compared to the HC group [77]. While studies of gamma functional connectivity have generally not reported significant differences between MDD and HC groups. In their review of the literature, Miljevic et al. [78] suggested that gamma activity may be more pronounced during task-related processes, which may partially explain the variability in findings. In our study, we first observed that MDD disrupted the “gamma-modulated” functional interaction between frontal cortex and right temporal cortex during the chirp stimulus. Given that the right auditory cortex plays a central role in frequency discrimination [79], disturbances in MDD may reflect the absence of appropriate task-related neural facilitation. These findings are consistent with those of Zweerings et al. [80], who found disrupted frontotemporal connections in this disorder during an auditory mismatch task. In addition, EEG-derived functional connectivity is thought to be strongly influenced by white matter myelinated corticocortical axons [81]. Studies have shown that in the absence of white matter, functional connectivity is significantly reduced for high-frequency bands compared to low-frequency bands [81, 82]. Therefore, it is imperative for future research to investigate the interplay between white matter structure and gamma functional connectivity in MDD, and in particular to elucidate how white matter provides structural support for the coupling of gamma activity. In addition, functional connectivity between the right frontal and central cortex was significantly associated with patients’ HAMA scores. Roh et al. [14] reported that frontocentral gamma oscillations are associated with symptoms of inattention, which in turn appear to be related to subjective symptom severity. Future studies using the inattention subscale of the Adult ADHD Scale or an integrative approach in conjunction with other methods are needed to clarify the role of gamma synchrony and attention deficits in MDD.

It should be noted that when evaluating 40-Hz ASSRs, the effect in the gamma band should be interpreted with caution due to the risk of contamination of the EEG signal by, for example, muscle artifacts. For example, Bidelman et al. [83] pointed out that the use of a mastoid reference electrode would promote extraneous recording of postauricular muscle (PAM) artifacts. The PAM is part of a vestigial startle reflex, responsible for retracting the pinna to protect hearing, and can produce large bilateral contractions (>100 µV) [84]. In the present study, PAM muscle artifacts were minimized by using low-intensity stimuli, averaging references, and discarding epochs with voltages greater than 100 µV. The observed 40-Hz ASSRs were widespread and generally present in the central regions of the recording electrodes, where contamination by, for example, muscle artifacts is expected to be low. Therefore, the findings within the 40-Hz ASSRs appear to be robust, but it is still emphasized that any effects observed in high-frequency oscillations should be interpreted with caution.

Finally, our findings represent a novel application of chirp stimuli to the analysis of auditory responses in MDD. Artieda et al. [85] proposed that chirp-modulated tones provide a more global view of brain activity. In the present study, we found larger N100 activation with the chirp stimulus compared to the click stimulus, which was source-localized to the transverse temporal gyrus (TTG) or insula. The TTG, as an important structure of the auditory cortex, is closely related to sound frequency. Several fMRI studies have suggested that the medial half of the TTG contains a tonotopic gradient, with more medial aspects processing high-frequency sound information and more lateral aspects processing low-frequency sound information [86, 87], which is consistent with the formation principle of chirp signals [40]. The TTG plays an important role in the primary auditory center responsible for the perception and processing of external auditory information [88]. In addition, the N100 is considered to be an index of early information registration of stimuli (i.e., attention) [32], suggesting that the chirp stimulus may elicit greater early attention in the primary auditory cortex compared to the click stimulus.

Interestingly, more pronounced abnormalities of the 40-Hz entrainment responses were also observed during the chirp stimulus in this study. Galambos, Makeig, and Talmachoff [41] suggested that the 40-Hz ASSR is essentially related to the N100 component, and several reports [89] have found empirical evidence to support this, while others have not [90]. More recently, a growing body of evidence suggests that the N100 component and ASSRs may not represent the same underlying neural substrate but are likely to share some overlap [91]. Considering the basic findings of existing research, we speculate that the N100 component may exert some influence on ASSRs in MDD, even in the absence of detectable abnormalities. To investigate the contribution of the N100 to the 40-Hz entrainment variables (ERSP, ITC, and WPLI), Spearman’s correlations were performed in this study. During the chirp stimulus, the average N100 amplitude was significantly negatively correlated with both 40-Hz ERSP (Spearman’s r = –0.217, p = 0.027) and ITC (Spearman’s r = –0.195, p = 0.042) in the FCZ, CZ, and CPZ electrodes (Figure 5a). Meanwhile, the N100 amplitude was significantly negatively correlated with the 40-Hz WPLI between FL and TR (Spearman’s r = –0.205, p = 0.034; Figure 5b). However, no significant correlations were found during the click stimulus (ERSP: p = 0.206; ITC: p = 0.203; WPLI: all p > 0.2). The prominent N100 component, which is a negative deflection, indicates impaired early auditory processing when it appears with an increased amplitude, as this corresponds to a reduced absolute value. Thus, we speculate that the ability of the cerebral cortex to generate and transmit gamma oscillations during the chirp stimulus may depend on its ability to generate the N100 and integrate early auditory information. Moreover, these findings may be due to the superior signal-to-noise ratio enjoyed by transient AEP and ASSR measures. Future research is needed to elucidate the physiological basis of the 40-Hz ASSR during chirp stimulation, and then to characterize the relationship between ASSR and AEP deficits in patients with MDD.

5. Conclusions

Our data provide initial evidence for deficits in AEPs and 40-Hz ASSRs in first-episode drug-naïve patients with MDD using a chirp stimulus. Patients with MDD showed a prominent reduction in P200 activity, suppressed 40-Hz entrainment power and ITC in the frontocentral cortex, and insufficient functional connectivity in the right hemisphere compared to HCs. Importantly, attenuated right frontocentral connectivity was strongly related to anxiety severity in MDD. Notably, these findings were stimulus driven, that is, the abnormalities associated with clinical symptoms of MDD were found only with the chirp stimulus but not with the click stimulus. Our findings highlight that symptom-related right frontocentral connectivity elicited by the chirp stimulus may be a promising signature for predicting depression severity and support the potential usefulness of the chirp stimulus as a novel approach to reveal the specific neurophysiological signatures of MDD.

Conflicts of Interest

The authors declare no conflicts of interest.

Funding

This work was funded by the National Key Research and Development Program of China (grant no. 2023YFF1203700) and the National Natural Science Foundation of China (grant nos. 81630051 and 81925020).

Acknowledgments

We would like to express our sincere gratitude to all the participants for their voluntary participation. We also thank the doctors of Tianjin Anding Hospital for their assistance. At the same time, we thank the research colleagues for their help in this research.

Open Research

Data Availability Statement

Data are available from the first or corresponding author upon reasonable request. The data are not publicly available as they contain private information about the study participants.

References

1 Otte C., Gold S. M., and Penninx B. W., et al.Major Depressive Disorder, Nature Reviews Disease Primers. (2016) 2, no. 1, 1–65, https://doi.org/10.1038/nrdp.2016.65, 2-s2.0-85006094874.
10.1038/nrdp.2016.65
Web of Science® Google Scholar
2 Ferrari A. J., Charlson F. J., and Norman R. E., et al.Burden of Depressive Disorders by Country, Sex, Age, and Year: Findings From the Global Burden of Disease Study 2010, PLoS Medicine. (2013) 10, no. 11, e1001547–e1001559, https://doi.org/10.1371/journal.pmed.1001547, 2-s2.0-84889076134.
10.1371/journal.pmed.1001547
PubMed Web of Science® Google Scholar
3 Mitchell A. J., Vaze A., and Rao S., Clinical Diagnosis of Depression in Primary Care: A Meta-Analysis, The Lancet. (2009) 374, no. 9690, 609–619, https://doi.org/10.1016/S0140-6736(09)60879-5, 2-s2.0-68849088976.
10.1016/S0140-6736(09)60879-5
PubMed Web of Science® Google Scholar
4 Floyd B. J., Problems in Accurate Medical Diagnosis of Depression in Female Patients, Social Science & Medicine. (1997) 44, no. 3, 403–412, https://doi.org/10.1016/S0277-9536(96)00159-1, 2-s2.0-0031018098.
10.1016/S0277-9536(96)00159-1
CAS PubMed Web of Science® Google Scholar
5 Fitzgerald P. J. and Watson B. O., Gamma Oscillations as a Biomarker for Major Depression: An Emerging Topic, Translational Psychiatry. (2018) 8, https://doi.org/10.1038/s41398-018-0239-y, 2-s2.0-85052787262, 177.
10.1038/s41398-018-0239-y
PubMed Web of Science® Google Scholar
6 Gilbert J. R. and ZarateC. A.Jr., Electrophysiological Biomarkers of Antidepressant Response to Ketamine in Treatment-Resistant Depression: Gamma Power and Long-Term Potentiation, Pharmacology Biochemistry and Behavior. (2020) 189, https://doi.org/10.1016/j.pbb.2020.172856, 172856.
10.1016/j.pbb.2020.172856
CAS PubMed Web of Science® Google Scholar
7 Dai Z., Zhang S., and Wang H., et al.Hampered Gamma Oscillations Induced by Sad Emotion Underlying Suicide Attempt in Major Depressive Disorder, Psychiatry and Clinical Neurosciences. (2023) 77, no. 1, 20–29, https://doi.org/10.1111/pcn.13487.
10.1111/pcn.13487
PubMed Web of Science® Google Scholar
8 Sohal V. S., Zhang F., Yizhar O., and Deisseroth K., Parvalbumin Neurons and Gamma Rhythms Enhance Cortical Circuit Performance, Nature. (2009) 459, no. 7247, 698–702, https://doi.org/10.1038/nature07991, 2-s2.0-66649110345.
10.1038/nature07991
CAS PubMed Web of Science® Google Scholar
9 Levinson A. J., Fitzgerald P. B., Favalli G., Blumberger D. M., Daigle M., and Daskalakis Z. J., Evidence of Cortical Inhibitory Deficits in Major Depressive Disorder, Biological Psychiatry. (2010) 67, no. 5, 458–464, https://doi.org/10.1016/j.biopsych.2009.09.025, 2-s2.0-75849152966.
10.1016/j.biopsych.2009.09.025
CAS PubMed Web of Science® Google Scholar
10 Bhagwagar Z., Wylezinska M., and Jezzard P., et al.Low GABA Concentrations in Occipital Cortex and Anterior Cingulate Cortex in Medication-Free, Recovered Depressed Patients, The International Journal of Neuropsychopharmacology. (2008) 11, no. 2, 255–260, https://doi.org/10.1017/S1461145707007924, 2-s2.0-38949102995.
10.1017/S1461145707007924
CAS PubMed Web of Science® Google Scholar
11 Lener M. S., Niciu M. J., and Ballard E. D., et al.Glutamate and Gamma-Aminobutyric Acid Systems in the Pathophysiology of Major Depression and Antidepressant Response to Ketamine, Biological Psychiatry. (2017) 81, no. 10, 886–897, https://doi.org/10.1016/j.biopsych.2016.05.005, 2-s2.0-84978796197.
10.1016/j.biopsych.2016.05.005
CAS PubMed Web of Science® Google Scholar
12 Chen S., Chen F., and Amin N., et al.Defects of Parvalbumin-Positive Interneurons in the Ventral Dentate Gyrus Region Are Implicated Depression-Like Behavior in Mice, Brain, Behavior, and Immunity. (2022) 99, 27–42, https://doi.org/10.1016/j.bbi.2021.09.013.
10.1016/j.bbi.2021.09.013
CAS PubMed Web of Science® Google Scholar
13 Sullivan E. M., Timi P., Hong L. E., and O’Donnell P., Effects of NMDA and GABA-A Receptor Antagonism on Auditory Steady-State Synchronization in Awake Behaving Rats, International Journal of Neuropsychopharmacology. (2015) 18, no. 7, https://doi.org/10.1093/ijnp/pyu118, 2-s2.0-84939168328, pyu118.
10.1093/ijnp/pyu118
PubMed Web of Science® Google Scholar
14 Roh S.-C., Park E.-J., Shim M., and Lee S.-H., EEG Beta and Low Gamma Power Correlates With Inattention in Patients With Major Depressive Disorder, Journal of Affective Disorders. (2016) 204, 124–130, https://doi.org/10.1016/j.jad.2016.06.033, 2-s2.0-84975781025.
10.1016/j.jad.2016.06.033
PubMed Web of Science® Google Scholar
15 Liu X., Liu S., and Li M., et al.Altered Gamma Oscillations and Beta-Gamma Coupling in Drug-Naive First-Episode Major Depressive Disorder: Association With Sleep and Cognitive Disturbance, Journal of Affective Disorders. (2022) 316, 99–108, https://doi.org/10.1016/j.jad.2022.08.022.
10.1016/j.jad.2022.08.022
PubMed Web of Science® Google Scholar
16 Wu W., Zhang Y., and Jiang J., et al.An Electroencephalographic Signature Predicts Antidepressant Response in Major Depression, Nature Biotechnology. (2020) 38, no. 4, 439–447, https://doi.org/10.1038/s41587-019-0397-3.
10.1038/s41587-019-0397-3
CAS PubMed Web of Science® Google Scholar
17 Synigal S. R., Teoh E. S., and Lalor E. C., Including Measures of High Gamma Power Can Improve the Decoding of Natural Speech From EEG, Frontiers in Human Neuroscience. (2020) 14, 1–12, https://doi.org/10.3389/fnhum.2020.00130.
10.3389/fnhum.2020.00130
PubMed Web of Science® Google Scholar
18 Pastor M. A., Artieda J., Arbizu J., Marti-Climent J. M., Peñuelas I., and Masdeu J. C., Activation of Human Cerebral and Cerebellar Cortex by Auditory Stimulation at 40 Hz, The Journal of Neuroscience. (2002) 22, no. 23, 10501–10506, https://doi.org/10.1523/JNEUROSCI.22-23-10501.2002.
10.1523/JNEUROSCI.22-23-10501.2002
CAS PubMed Web of Science® Google Scholar
19 Grent T., Gajwani R., and Gross J., et al.40-Hz Auditory Steady-State Responses Characterize Circuit Dysfunctions and Predict Clinical Outcomes in Clinical High-Risk for Psychosis Participants: A Magnetoencephalography Study, Biological Psychiatry. (2021) 90, no. 6, 419–429, https://doi.org/10.1016/j.biopsych.2021.03.018.
10.1016/j.biopsych.2021.03.018
PubMed Web of Science® Google Scholar
20 Picton T. W., John M. S., Dimitrijevic A., and Purcell D., Human Auditory Steady-State Responses: Respuestas Auditivas de Estado Estable en Humanos, International Journal of Audiology. (2003) 42, no. 4, 177–219, https://doi.org/10.3109/14992020309101316, 2-s2.0-0141988343.
10.3109/14992020309101316
PubMed Web of Science® Google Scholar
21 Spencer K. M., Salisbury D. F., Shenton M. E., and McCarley R. W., γ-Band Auditory Steady-State Responses Are Impaired in First Episode Psychosis, Biological Psychiatry. (2008) 64, no. 5, 369–375, https://doi.org/10.1016/j.biopsych.2008.02.021, 2-s2.0-48749097275.
10.1016/j.biopsych.2008.02.021
PubMed Web of Science® Google Scholar
22 Sugiyama S., Ohi K., and Kuramitsu A., et al.The Auditory Steady-State Response: Electrophysiological Index for Sensory Processing Dysfunction in Psychiatric Disorders, Frontiers in Psychiatry. (2021) 12, https://doi.org/10.3389/fpsyt.2021.644541, 644541.
10.3389/fpsyt.2021.644541
PubMed Web of Science® Google Scholar
23 Isomura S., Onitsuka T., and Tsuchimoto R., et al.Differentiation Between Major Depressive Disorder and Bipolar Disorder by Auditory Steady-State Responses, Journal of Affective Disorders. (2016) 190, 800–806, https://doi.org/10.1016/j.jad.2015.11.034, 2-s2.0-84948427512.
10.1016/j.jad.2015.11.034
PubMed Web of Science® Google Scholar
24 Chen J., Gong Q., and Wu F., Deficits in the 30-Hz Auditory Steady-State Response in Patients With Major Depressive Disorder, NeuroReport. (2016) 27, no. 15, 1147–1152, https://doi.org/10.1097/WNR.0000000000000671, 2-s2.0-84983735452.
10.1097/WNR.0000000000000671
PubMed Web of Science® Google Scholar
25 Sivarao D. V., Chen P., and Senapati A., et al.40 Hz Auditory Steady-State Response Is a Pharmacodynamic Biomarker for Cortical NMDA Receptors, Neuropsychopharmacology. (2016) 41, no. 9, 2232–2240, https://doi.org/10.1038/npp.2016.17, 2-s2.0-84959078392.
10.1038/npp.2016.17
CAS PubMed Web of Science® Google Scholar
26 de Schotten M. T. and Forkel S. J., The Emergent Properties of the Connected Brain, Science. (2022) 378, no. 6619, 505–510, https://doi.org/10.1126/science.abq2591.
10.1126/science.abq2591
PubMed Web of Science® Google Scholar
27 Hamm J. P., Gilmore C. S., Picchetti N. A. M., Sponheim S. R., and Clementz B. A., Abnormalities of Neuronal Oscillations and Temporal Integration to Low- and High-Frequency Auditory Stimulation in Schizophrenia, Biological Psychiatry. (2011) 69, no. 10, 989–996, https://doi.org/10.1016/j.biopsych.2010.11.021, 2-s2.0-79955484981.
10.1016/j.biopsych.2010.11.021
PubMed Web of Science® Google Scholar
28 Hamm J. P., Gilmore C. S., and Clementz B. A., Augmented Gamma Band Auditory Steady-State Responses: Support for NMDA Hypofunction in Schizophrenia, Schizophrenia Research. (2012) 138, no. 1, 1–7, https://doi.org/10.1016/j.schres.2012.04.003, 2-s2.0-84861528000.
10.1016/j.schres.2012.04.003
PubMed Web of Science® Google Scholar
29 Tada M., Nagai T., and Kirihara K., et al.Differential Alterations of Auditory Gamma Oscillatory Responses Between Pre-Onset High-Risk Individuals and First-Episode Schizophrenia, Cerebral Cortex. (2016) 26, no. 3, 1027–1035, https://doi.org/10.1093/cercor/bhu278, 2-s2.0-84961262969.
10.1093/cercor/bhu278
PubMed Web of Science® Google Scholar
30 Feldmann L., Piechaczek C. E., and Pehl V., et al.State or Trait? Auditory Event-Related Potentials in Adolescents With Current and Remitted Major Depression, Neuropsychologia. (2018) 113, 95–103, https://doi.org/10.1016/j.neuropsychologia.2018.03.035, 2-s2.0-85045210245.
10.1016/j.neuropsychologia.2018.03.035
PubMed Google Scholar
31 Greimel E., Trinkl M., Bartling J., Bakos S., Grossheinrich N., and Schulte-Körne G., Auditory Selective Attention in Adolescents With Major Depression: An Event-Related Potential Study, Journal of Affective Disorders. (2015) 172, 445–452, https://doi.org/10.1016/j.jad.2014.10.022, 2-s2.0-84910089479.
10.1016/j.jad.2014.10.022
CAS PubMed Web of Science® Google Scholar
32 Giannopoulos A. E., Zioga I., and Papageorgiou P. C., et al.Early Auditory-Evoked Potentials in Body Dysmorphic Disorder: An ERP/sLORETA Study, Psychiatry Research. (2021) 299, https://doi.org/10.1016/j.psychres.2021.113865, 113865.
10.1016/j.psychres.2021.113865
PubMed Web of Science® Google Scholar
33 Bruder G. E., Kayser J., and Tenke C. E., Event-Related Brain Potentials in Depression: Clinical, Cognitive and Neurophysiologic Implications, The Oxford Handbook of Event-Related Potential Components, 2012, Oxford Academic, 563–592.
Google Scholar
34 Wang D., Mo F., and Zhang Y., et al.Auditory Evoked Potentials in Patients With Major Depressive Disorder Measured by Emotiv System, Bio-Medical Materials and Engineering. (2015) 26, no. s1, S917–S923, https://doi.org/10.3233/BME-151385, 2-s2.0-84979094114.
10.3233/BME-151385
PubMed Web of Science® Google Scholar
35 Allison T., Recording and Interpreting Event-Related Potentials, Cognitive Psychophysiology: Event-Related Potentials and the Study of Cognition, 1984, Routledge, 1–36.
Google Scholar
36 Shen C. L., Chou T. L., and Lai W. S., et al.P50, N100, and P200 Auditory Sensory Gating Deficits in Schizophrenia Patients, Frontiers in Psychiatry. (2020) 11, https://doi.org/10.3389/fpsyt.2020.00868, 868.
10.3389/fpsyt.2020.00868
PubMed Web of Science® Google Scholar
37 Poulsen C., Picton T. W., and Paus T., Age-Related Changes in Transient and Oscillatory Brain Responses to Auditory Stimulation in Healthy Adults 19-45 Years Old, Cerebral Cortex. (2007) 17, no. 6, 1454–1467, https://doi.org/10.1093/cercor/bhl056, 2-s2.0-34347210709.
10.1093/cercor/bhl056
PubMed Web of Science® Google Scholar
38 Griskova-Bulanova I., Dapsys K., and Melynyte S., et al.40Hz Auditory Steady-State Response in Schizophrenia: Sensitivity to Stimulation Type (Clicks Versus Flutter Amplitude-Modulated Tones), Neuroscience Letters. (2018) 662, 152–157, https://doi.org/10.1016/j.neulet.2017.10.025, 2-s2.0-85031497760.
10.1016/j.neulet.2017.10.025
CAS PubMed Web of Science® Google Scholar
39 Thuné H., Recasens M., and Uhlhaas P. J., The 40-Hz Auditory Steady-State Response in Patients With Schizophrenia: A Meta-Analysis, JAMA Psychiatry. (2016) 73, no. 11, 1145–1153, https://doi.org/10.1001/jamapsychiatry.2016.2619, 2-s2.0-84997724692.
10.1001/jamapsychiatry.2016.2619
PubMed Web of Science® Google Scholar
40 Dau T., Wegner O., Mellert V., and Kollmeier B., Auditory Brainstem Responses With Optimized Chirp Signals Compensating Basilar-Membrane Dispersion, The Journal of the Acoustical Society of America. (2000) 107, no. 3, 1530–1540, https://doi.org/10.1121/1.428438, 2-s2.0-0034009534.
10.1121/1.428438
CAS PubMed Web of Science® Google Scholar
41 Galambos R., Makeig S., and Talmachoff P. J., A 40-Hz Auditory Potential Recorded From the Human Scalp, Proceedings of the National Academy of Sciences. (1981) 78, no. 4, 2643–2647, https://doi.org/10.1073/pnas.78.4.2643.
10.1073/pnas.78.4.2643
CAS PubMed Web of Science® Google Scholar
42 Elberling C., Don M., Cebulla M., and Stürzebecher E., Auditory Steady-State Responses to Chirp Stimuli Based on Cochlear Traveling Wave Delay, The Journal of the Acoustical Society of America. (2007) 122, no. 5, 2772–2785, https://doi.org/10.1121/1.2783985, 2-s2.0-38049056787.
10.1121/1.2783985
PubMed Web of Science® Google Scholar
43 Liu X., Liu S., and Guo D., et al.Enhanced Auditory Steady-State Response Using an Optimized Chirp Stimulus-Evoked Paradigm, Sensors. (2019) 19, no. 3, 748–762, https://doi.org/10.3390/s19030748, 2-s2.0-85061584962.
10.3390/s19030748
Web of Science® Google Scholar
44 Gransier R., Hofmann M., van Wieringen A., and Wouters J., Stimulus-Evoked Phase-Locked Activity Along the Human Auditory Pathway Strongly Varies Across Individuals, Scientific Reports. (2021) 11, https://doi.org/10.1038/s41598-020-80229-w, 143.
10.1038/s41598-020-80229-w
CAS PubMed Web of Science® Google Scholar
45 Tolentino J. C. and Schmidt S. L., DSM-5 Criteria and Depression Severity: Implications for Clinical Practice, Frontiers in Psychiatry. (2018) 9, https://doi.org/10.3389/fpsyt.2018.00450, 2-s2.0-85055292600, 450.
10.3389/fpsyt.2018.00450
PubMed Web of Science® Google Scholar
46 Hamilton M., A Rating Scale for Depression, Journal of Neurology, Neurosurgery & Psychiatry. (1960) 23, no. 1, 56–62, https://doi.org/10.1136/jnnp.23.1.56, 2-s2.0-72949151592.
10.1136/jnnp.23.1.56
CAS PubMed Web of Science® Google Scholar
47 Young R. C., Biggs J. T., Ziegler V. E., and Meyer D. A., Young Mania Rating Scale, Handbook of Psychiatric Measures, 2000, American Psychiatric Association, Washington, 540–542.
Google Scholar
48 Gjerris A., Bech P., and Bøjholm S., et al.The Hamilton Anxiety Scale: Evaluation of Homogeneity and Inter-Observer Reliability in Patients With Depressive Disorders, Journal of Affective Disorders. (1983) 5, no. 2, 163–170, https://doi.org/10.1016/0165-0327(83)90009-5, 2-s2.0-0020534333.
10.1016/0165-0327(83)90009-5
CAS PubMed Web of Science® Google Scholar
49 Keefe D. H., Feeney M. P., Hunter L. L., and Fitzpatrick D. F., Comparisons of Transient Evoked Otoacoustic Emissions Using Chirp and Click Stimuli, The Journal of the Acoustical Society of America. (2016) 140, no. 3, 1949–1973, https://doi.org/10.1121/1.4962532, 2-s2.0-84988822453.
10.1121/1.4962532
PubMed Web of Science® Google Scholar
50 Goodman S. S., Withnell R. H., De Boer E., Lilly D. J., and Nuttall A. L., Cochlear Delays Measured With Amplitude-Modulated Tone-Burst-Evoked OAEs, Hearing Research. (2004) 188, no. 1-2, 57–69, https://doi.org/10.1016/S0378-5955(03)00375-7, 2-s2.0-0842305216.
10.1016/S0378-5955(03)00375-7
PubMed Web of Science® Google Scholar
51 Chen S., Liu X., and Huang Z., et al.Spatiotemporal Connectivity Maps Abnormal Communication Pathways in Major Depressive Disorder Underlying Gamma Oscillations, Cerebral Cortex. (2023) 33, no. 15, 9313–9324, https://doi.org/10.1093/cercor/bhad204.
10.1093/cercor/bhad204
PubMed Web of Science® Google Scholar
52 Brainard D. H. and Vision S., The Psychophysics Toolbox, Spatial Vision. (1997) 10, no. 4, 433–436, https://doi.org/10.1163/156856897X00357, 2-s2.0-0030612822.
10.1163/156856897X00357
CAS PubMed Web of Science® Google Scholar
53 Delorme A. and Makeig S., EEGLAB: An Open Source Toolbox for Analysis of Single-Trial EEG Dynamics Including Independent Component Analysis, Journal of Neuroscience Methods. (2004) 134, no. 1, 9–21, https://doi.org/10.1016/j.jneumeth.2003.10.009, 2-s2.0-1242283941.
10.1016/j.jneumeth.2003.10.009
PubMed Web of Science® Google Scholar
54 Schwartze M., Farrugia N., and Kotz S. A., Dissociation of Formal and Temporal Predictability in Early Auditory Evoked Potentials, Neuropsychologia. (2013) 51, no. 2, 320–325, https://doi.org/10.1016/j.neuropsychologia.2012.09.037, 2-s2.0-84872324200.
10.1016/j.neuropsychologia.2012.09.037
PubMed Web of Science® Google Scholar
55 Quant S., Adkin A. L., Staines W. R., and McIlroy W. E., Cortical Activation Following a Balance Disturbance, Experimental Brain Research. (2004) 155, no. 3, 393–400, https://doi.org/10.1007/s00221-003-1744-6, 2-s2.0-1642401667.
10.1007/s00221-003-1744-6
CAS PubMed Web of Science® Google Scholar
56 Vinck M., Oostenveld R., van Wingerden M., Battaglia F., and Pennartz C. M. A., An Improved Index of Phase-Synchronization for Electrophysiological Data in the Presence of Volume-Conduction, Noise and Sample-Size Bias, NeuroImage. (2011) 55, no. 4, 1548–1565, https://doi.org/10.1016/j.neuroimage.2011.01.055, 2-s2.0-79952707728.
10.1016/j.neuroimage.2011.01.055
PubMed Web of Science® Google Scholar
57 Peraza L. R., Asghar A. U. R., Green G., and Halliday D. M., Volume Conduction Effects in Brain Network Inference From Electroencephalographic Recordings Using Phase Lag Index, Journal of Neuroscience Methods. (2012) 207, no. 2, 189–199, https://doi.org/10.1016/j.jneumeth.2012.04.007, 2-s2.0-84860817562.
10.1016/j.jneumeth.2012.04.007
PubMed Web of Science® Google Scholar
58 Pascual-Marqui R. D., Standardized Low-Resolution Brain Electromagnetic Tomography (sLORETA): Technical Details, Methods and Findings in Experimental and Clinical Pharmacology. (2002) 24, no. Suppl D, 5–12.
PubMed Web of Science® Google Scholar
59 Blair R. C. and Karniski W., An Alternative Method for Significance Testing of Waveform Difference Potentials, Psychophysiology. (1993) 30, no. 5, 518–524, https://doi.org/10.1111/j.1469-8986.1993.tb02075.x, 2-s2.0-0027237538.
10.1111/j.1469-8986.1993.tb02075.x
CAS PubMed Web of Science® Google Scholar
60 Roth W. T., Pfefferbaum A., Kelly A. F., Berger P. A., and Kopell B. S., Auditory Event-Related Potentials in Schizophrenia and Depression, Psychiatry Research. (1981) 4, no. 2, 199–212, https://doi.org/10.1016/0165-1781(81)90023-8, 2-s2.0-0019475711.
10.1016/0165-1781(81)90023-8
CAS PubMed Web of Science® Google Scholar
61 Kähkönen S., Yamashita H., Rytsälä H., Suominen K., Ahveninen J., and Isometsä E., Dysfunction in Early Auditory Processing in Major Depressive Disorder Revealed by Combined MEG and EEG, Journal of Psychiatry & Neuroscience: JPN. (2007) 32, no. 5, 316–322.
PubMed Web of Science® Google Scholar
62 Boutros N. N., Korzyukov O., Jansen B., Feingold A., and Bell M., Sensory Gating Deficits During the Mid-Latency Phase of Information Processing in Medicated Schizophrenia Patients, Psychiatry Research. (2004) 126, no. 3, 203–215, https://doi.org/10.1016/j.psychres.2004.01.007, 2-s2.0-2442505773.
10.1016/j.psychres.2004.01.007
PubMed Web of Science® Google Scholar
63 Chien Y.-L., Hsieh M. H., and Gau S. S.-F., P50-N100-P200 Sensory Gating Deficits in Adolescents and Young Adults With Autism Spectrum Disorders, Progress in Neuro-Psychopharmacology and Biological Psychiatry. (2019) 95, 109683–109689, https://doi.org/10.1016/j.pnpbp.2019.109683, 2-s2.0-85068266163.
10.1016/j.pnpbp.2019.109683
PubMed Web of Science® Google Scholar
64 Ross B. and Fujioka T., 40-Hz Oscillations Underlying Perceptual Binding in Young and Older Adults, Psychophysiology. (2016) 53, no. 7, 974–990, https://doi.org/10.1111/psyp.12654, 2-s2.0-85027954664.
10.1111/psyp.12654
PubMed Web of Science® Google Scholar
65 Kangas E. S., Vuoriainen E., Lindeman S., and Astikainen P., Auditory Event-Related Potentials in Separating Patients With Depressive Disorders and Non-Depressed Controls: A Narrative Review, International Journal of Psychophysiology. (2022) 179, 119–142, https://doi.org/10.1016/j.ijpsycho.2022.07.003.
10.1016/j.ijpsycho.2022.07.003
PubMed Web of Science® Google Scholar
66 Vandoolaeghe E., van Hunsel F., Nuyten D., and Maes M., Auditory Event Related Potentials in Major Depression: Prolonged P300 Latency and Increased P200 Amplitude, Journal of Affective Disorders. (1998) 48, no. 2-3, 105–113, https://doi.org/10.1016/S0165-0327(97)00165-1, 2-s2.0-0031782492.
10.1016/S0165-0327(97)00165-1
CAS PubMed Web of Science® Google Scholar
67 Klumpp H. and Shankman S. A., Using Event-Related Potentials and Startle to Evaluate Time Course in Anxiety and Depression, Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. (2018) 3, no. 1, 10–18, https://doi.org/10.1016/j.bpsc.2017.09.004, 2-s2.0-85032261244.
10.1016/j.bpsc.2017.09.004
PubMed Web of Science® Google Scholar
68 Medvedeva A. D., Portnova G. V., and Sysoeva O. V., Lower Loss Rate of Serotonergically Modulated Neuronal Accumulator for Time in Patients With Major Depressive Disorder, Journal of Psychiatric Research. (2023) 165, 345–351, https://doi.org/10.1016/j.jpsychires.2023.08.005.
10.1016/j.jpsychires.2023.08.005
PubMed Web of Science® Google Scholar
69 Zhang H., Li L., and Wu M., et al.Brain Gray Matter Alterations in First Episodes of Depression: A Meta-Analysis of Whole-Brain Studies, Neuroscience & Biobehavioral Reviews. (2016) 60, 43–50, https://doi.org/10.1016/j.neubiorev.2015.10.011, 2-s2.0-84949752473.
10.1016/j.neubiorev.2015.10.011
PubMed Web of Science® Google Scholar
70 Kemp A. H., Hopkinson P. J., and Hermens D. F., et al.Fronto-Temporal Alterations Within the First 200-Ms During an Attentional Task Distinguish Major Depression, Non-Clinical Participants With Depressed Mood and Healthy Controls: A Potential Biomarker?, Human Brain Mapping. (2009) 30, no. 2, 602–614, https://doi.org/10.1002/hbm.20528, 2-s2.0-59949099371.
10.1002/hbm.20528
PubMed Web of Science® Google Scholar
71 Parker D. A., Hamm J. P., and McDowell J. E., et al.Auditory Steady-State EEG Response Across the Schizo-Bipolar Spectrum, Schizophrenia Research. (2019) 209, 218–226, https://doi.org/10.1016/j.schres.2019.04.014, 2-s2.0-85065229547.
10.1016/j.schres.2019.04.014
PubMed Web of Science® Google Scholar
72 Hu B., Rao J., and Li X., et al.Emotion Regulating Attentional Control Abnormalities In Major Depressive Disorder: An Event-Related Potential Study, Scientific Reports. (2017) 7, no. 1, 13530–13551, https://doi.org/10.1038/s41598-017-13626-3, 2-s2.0-85031902612.
10.1038/s41598-017-13626-3
PubMed Web of Science® Google Scholar
73 Liu S., Liu X., and Chen S., et al.Neurophysiological Markers of Depression Detection and Severity Prediction in First-Episode Major Depressive Disorder, Journal of Affective Disorders. (2023) 331, 8–16, https://doi.org/10.1016/j.jad.2023.03.038.
10.1016/j.jad.2023.03.038
CAS PubMed Web of Science® Google Scholar
74 Kraus K. S. and Canlon B., Neuronal Connectivity and Interactions Between the Auditory and Limbic Systems. Effects of Noise and Tinnitus, Hearing Research. (2012) 288, no. 1-2, 34–46, https://doi.org/10.1016/j.heares.2012.02.009, 2-s2.0-84861850256.
10.1016/j.heares.2012.02.009
PubMed Web of Science® Google Scholar
75 Sabatini U., Pozzilli C., and Pantano P., et al.Involvement of the Limbic System in Multiple Sclerosis Patients With Depressive Disorders, Biological Psychiatry. (1996) 39, no. 11, 970–975, https://doi.org/10.1016/0006-3223(95)00291-X, 2-s2.0-0029979163.
10.1016/0006-3223(95)00291-X
CAS PubMed Web of Science® Google Scholar
76 Roß B., Picton T. W., and Pantev C., Temporal Integration in the Human Auditory Cortex as Represented by the Development of the Steady-State Magnetic Field, Hearing Research. (2002) 165, no. 1-2, 68–84, https://doi.org/10.1016/S0378-5955(02)00285-X, 2-s2.0-0036108920.
10.1016/S0378-5955(02)00285-X
PubMed Web of Science® Google Scholar
77 Mitiureva D., Sysoeva O., Proshina E., Portnova G., Khayrullina G., and Martynova O., Comparative Analysis of Resting-State EEG Functional Connectivity in Depression and Obsessive-Compulsive Disorder, Psychiatry Research: Neuroimaging. (2024) 342, https://doi.org/10.1016/j.pscychresns.2024.111828, 111828.
10.1016/j.pscychresns.2024.111828
PubMed Web of Science® Google Scholar
78 Miljevic A., Bailey N. W., Murphy O. W., Perera M. P. N., and Fitzgerald P. B., Alterations in EEG Functional Connectivity in Individuals With Depression: A Systematic Review, Journal of Affective Disorders. (2023) 328, 287–302, https://doi.org/10.1016/j.jad.2023.01.126.
10.1016/j.jad.2023.01.126
PubMed Web of Science® Google Scholar
79 Scarr E. and Dean B., Muscarinic Receptors: Do They Have a Role in the Pathology and Treatment of Schizophrenia?, Journal of Neurochemistry. (2008) 107, no. 5, 1188–1195, https://doi.org/10.1111/j.1471-4159.2008.05711.x, 2-s2.0-55849094786.
10.1111/j.1471-4159.2008.05711.x
CAS PubMed Web of Science® Google Scholar
80 Zweerings J., Zvyagintsev M., and Turetsky B. I., et al.Fronto-Parietal and Temporal Brain Dysfunction in Depression: A fMRI Investigation of Auditory Mismatch Processing, Human Brain Mapping. (2019) 40, no. 12, 3657–3668, https://doi.org/10.1002/hbm.24623, 2-s2.0-85065713187.
10.1002/hbm.24623
PubMed Web of Science® Google Scholar
81 Nunez P. L. and Srinivasan R., Neocortical Dynamics due to Axon Propagation Delays in Cortico-Cortical Fibers: EEG Traveling and Standing Waves With Implications for Top-Down Influences on Local Networks and White Matter Disease, Brain Research. (2014) 1542, 138–166, https://doi.org/10.1016/j.brainres.2013.10.036, 2-s2.0-84890801322.
10.1016/j.brainres.2013.10.036
CAS PubMed Web of Science® Google Scholar
82 Nunez P. L., Srinivasan R., and Fields R. D., EEG Functional Connectivity, Axon Delays and White Matter Disease, Clinical Neurophysiology. (2015) 126, no. 1, 110–120, https://doi.org/10.1016/j.clinph.2014.04.003, 2-s2.0-84917670976.
10.1016/j.clinph.2014.04.003
PubMed Web of Science® Google Scholar
83 Bidelman G. M., Sisson A., Rizzi R., MacLean J., and Baer K., Myogenic Artifacts Masquerade as Neuroplasticity in the Auditory Frequency-Following Response, Frontiers in Neuroscience. (2024) 18, https://doi.org/10.3389/fnins.2024.1422903, 1422903.
10.3389/fnins.2024.1422903
PubMed Web of Science® Google Scholar
84 Hackley S. A., Evidence for a Vestigial Pinna-Orienting System in Humans, Psychophysiology. (2015) 52, no. 10, 1263–1270, https://doi.org/10.1111/psyp.12501, 2-s2.0-84940790137.
10.1111/psyp.12501
PubMed Web of Science® Google Scholar
85 Artieda J., Valencia M., Alegre M., Olaziregi O., Urrestarazu E., and Iriarte J., Potentials Evoked by Chirp-Modulated Tones: A New Technique to Evaluate Oscillatory Activity in the Auditory Pathway, Clinical Neurophysiology. (2004) 115, no. 3, 699–709, https://doi.org/10.1016/j.clinph.2003.10.021, 2-s2.0-1242292269.
10.1016/j.clinph.2003.10.021
CAS PubMed Web of Science® Google Scholar
86 Skipper J. I. and Small S. L., fMRI Studies of Language, Encyclopedia of Language & Linguistics, 2006, Elsevier Ltd., 496–511.
10.1016/B0-08-044854-2/02399-3
Google Scholar
87 Bilecen D., Seifritz E., Scheffler K., Henning J., and Schulte A.-C., Amplitopicity of the Human Auditory Cortex: An fMRI Study, NeuroImage. (2002) 17, no. 2, 710–718, https://doi.org/10.1006/nimg.2002.1133.
10.1006/nimg.2002.1133
PubMed Web of Science® Google Scholar
88 Hou Z., Kong Y., He X., Yin Y., Zhang Y., and Yuan Y., Increased Temporal Variability of Striatum Region Facilitating the Early Antidepressant Response in Patients With Major Depressive Disorder, Progress in Neuro-Psychopharmacology and Biological Psychiatry. (2018) 85, 39–45, https://doi.org/10.1016/j.pnpbp.2018.03.026, 2-s2.0-85045477596.
10.1016/j.pnpbp.2018.03.026
CAS PubMed Web of Science® Google Scholar
89 Bohórquez J. and Özdamar Ö., Generation of the 40-Hz Auditory Steady-State Response (ASSR) Explained Using Convolution, Clinical Neurophysiology. (2008) 119, no. 11, 2598–2607, https://doi.org/10.1016/j.clinph.2008.08.002, 2-s2.0-54149109128.
10.1016/j.clinph.2008.08.002
PubMed Web of Science® Google Scholar
90 Zhang L., Peng W., Zhang Z., Hu L., and Mouraux A., Distinct Features of Auditory Steady-State Responses as Compared to Transient Event-Related Potentials, PLoS ONE. (2013) 8, no. 7, e69164–e69174, https://doi.org/10.1371/journal.pone.0069164, 2-s2.0-84879902426.
10.1371/journal.pone.0069164
CAS PubMed Web of Science® Google Scholar
91 Sivarao D. V., The 40-Hz Auditory Steady-State Response: A Selective Biomarker for Cortical NMDA Function, Annals of the New York Academy of Sciences. (2015) 1344, no. 1, 27–36, https://doi.org/10.1111/nyas.12739, 2-s2.0-84928242064.
10.1111/nyas.12739
PubMed Web of Science® Google Scholar

All articles

Neurophysiological Signatures of Major Depressive Disorder and Frontocentral Gamma Auditory Response Deficits

Abstract

1. Introduction

2. Materials and Methods

2.1. Participants

2.2. Stimuli and Paradigm

2.3. EEG Acquisition and Preprocessing

2.4. Event-Related Potential Analysis

2.5. Time–Frequency Analysis

2.6. Connectivity Analysis

2.7. Source Estimation

2.8. Statistical Analyses

3. Results

3.1. Early Auditory Processing Deficits in MDD

3.2. Source Reconstruction of AEP Components

3.3. Spectrotemporal Characteristics of 40-Hz ASSRs in MDD

3.4. Connectivity Patterns of 40-Hz ASSRs in MDD

4. Discussion

5. Conclusions

Conflicts of Interest

Funding

Acknowledgments

Open Research

Data Availability Statement

References

Figures

References

Information

About Wiley Online Library

Help & Support

Opportunities

Connect with Wiley