
==== Front
Neuroimage Clin
Neuroimage Clin
NeuroImage : Clinical
2213-1582
Elsevier

S2213-1582(24)00105-0
10.1016/j.nicl.2024.103666
103666
Regular Article
Aberrant high-beta band functional connectivity during reward processing in melancholic major depressive disorder: An MEG study
Zhang Qiaoyang ab1
Du Yishan a1
Bao Ciqing a1
Hua Lingling a
Yan Rui a
Dai Zhongpeng c
Xia Yi a
Zou Haowen a
He Chen a
Sun Hao a
Lu Qing luq@seu.edu.cn
cd⁎
Yao Zhijian zjyao@njmu.edu.cn
ae⁎⁎
a Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
b Department of Psychology, the Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou 213000, China
c School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China
d Child Development and Learning Science, Key Laboratory of Ministry of Education, Southeast University, Nanjing 210096, China
e Nanjing Brain Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, Nanjing, 210093, China.
⁎ Corresponding author at: School of Biological Sciences and Medical Engineering, Southeast University, No. 2 Sipailou, Nanjing 210096, China. luq@seu.edu.cn
⁎⁎ Corresponding author at: Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, No. 264 Guangzhou Road, Nanjing 210029, China. zjyao@njmu.edu.cn
1 Qiaoyang Zhang, Yishan Du and Ciqing Bao contributed equally to this work.

31 8 2024
2024
31 8 2024
43 10366610 5 2024
18 8 2024
30 8 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Highlights

• Melancholic MDD shows reduced high-beta connectivity in limbic and visual regions.

• Post-happy faces stimuli.

• Abnormal connectivity strength is negatively linked to melancholic symptoms.

• High-beta connectivity disruption may serve as a biomarker for melancholic MDD.

Objective

To identify the spatial–temporal pattern variation of whole-brain functional connectivity (FC) during reward processing in melancholic major depressive disorder (MDD) patients, and to determine the clinical correlates of connectomic differences.

Methods

61 MDD patients and 32 healthy controls were enrolled into the study. During magnetoencephalography (MEG) scanning, all participants completed the facial emotion recognition task. The MDD patients were further divided into two groups: melancholic (n = 31) and non-melancholic (n = 30), based on the Mini International Neuropsychiatric Interview (M.I.N.I.) assessment. Melancholic symptoms were examined by using the 6-item melancholia subscale from the Hamilton Depression Rating Scale (HAM-D6). The whole-brain orthogonalized power envelope connections in the high-beta band (20–35 Hz) were constructed in each period after the happy emotional stimuli (0–200 ms, 100–300 ms, 200–400 ms, 300–500 ms, and 400–600 ms). Then, the network-based statistic (NBS) was used to determine the specific abnormal connection patterns in melancholic MDD patients.

Results

The NBS identified a sub-network difference at the mid-late period (300–500 ms) in response to happy faces among the three groups (corrected P = 0.035). Then, the post hoc and correlation analyses found five FCs were decreased in melancholic MDD patients and were related to HAM-D6 score, including FCs of left fusiform gyrus-right orbital inferior frontal gyrus (r = −0.52, P < 0.001), left fusiform gyrus-left amygdala (r = −0.26, P = 0.049), left posterior cingulate gyrus-right precuneus (r = −0.32, P = 0.025), left precuneus-right precuneus (r = −0.27, P = 0.049), and left precuneus-left inferior occipital gyrus (r = −0.32, P = 0.025).

Conclusion

In response to happy faces, melancholic MDD patients demonstrated a disrupted functional connective pattern (20–35 Hz, 300–500 ms), which involved brain regions in visual information processing and the limbic system. The aberrant functional connective pattern in reward processing might be a biomarker of melancholic MDD.

Keywords

Melancholic depression
Magnetoencephalography
Network-based statistic
Reward processing
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pmc1 Introduction

Among the global burden of mental health-related diseases, depression ranks first, affecting approximately 350 million people worldwide (Patel et al., 2016). Patients’ response rate to antidepressant treatments varies from 30 % to 50 % because of the highly heterogeneous nature of depression (Rush et al., 2006). According to the Statistical Manual of Mental Disorders, 4th Edition (DSM-IV), depressive disorders with accompanying features of melancholia are a distinct subset of depression (APA, 1994). Compared to other forms of depression, melancholic depression is characterized by more severe symptoms (Peselow et al., 1992). Thus, understanding the neurobiological mechanisms underlying melancholic depression could be helpful to detect the heterogeneity of depression and facilitate the development of novel diagnostic and therapeutic strategies.

Melancholic depression is characterized by impaired mood reactivity to positive events (Rush and Weissenburger, 1994). Multiple studies have found that reward dysfunction may be more pronounced in patients with melancholic depression. Weinberg and colleagues measured the Event-related potentials (ERP) during a gambling task and found blunted reward response (RewP) in remitted depression patients with melancholic features (Weinberg and Shankman, 2017). Additionally, research combining electroencephalography (EEG) with functional magnetic resonance imaging (fMRI) showed reduced activation of the ventral striatum and decreased feedback negativity (FN) amplitude in depressed patients during reward tasks, with this effect mainly driven by the melancholic phenotype (Foti et al., 2014). Besides, happy facial expressions are positive social cues that could also elicit rewards (Meier et al., 2016, Syal et al., 2015). A previous study reported abnormalities in the reward-relevant emotional processing of happy facial expressions in individuals with melancholic depression (Day et al., 2015). However, the corresponding electrophysiological mechanisms remain to be thoroughly investigated.

It is crucial to examine the neural activity of individuals with melancholic depression in response to positive emotional stimuli, particularly from the perspective of electrophysiological activities that provide rich information on temporal, spectral, and spatial dimensions. High-beta band activity may contribute to the reward process through its role in rapid and transient dopamine release patterns (Andreou et al., 2017). This involvement is crucial for processing and responding to rewarding stimuli. Early intra-recording studies have shown that ventral tegmental area (VTA) neurons projecting to prefrontal cortex preferentially fire between 20 and 30 Hz (Lammel et al., 2008). More recent studies have revealed an increase in high-beta activity (20–35 Hz) following unexpected or significant positive reward outcomes (Marco-Pallarés et al., 2015). C. Andreou’s study (Andreou et al., 2017) used single-trial EEG-fMRI coupling during a two-choice gambling task. It found that high-beta oscillations were linked to activations in subcortical reward networks, including the ventral striatum, hippocampus, anterior lateral temporal cortex, and posterior cingulate cortex, in response to positive feedback. Consistently, a previous study by Mas-Herrero et al. (2015) discovered that high-beta frequency oscillations may mediate the synchronization of brain regions engaged in learning from positive feedback. These finding underscores the significance of high-beta activity in processing rewarding outcomes.

In the temporal dimension of reward feedback processing, the interval from 0 to 600 ms post-feedback is particularly significant. Within this period, the interval from 200 to 300 ms, associated with the feedback-related negativity (FRN) in event-related potential (ERP) studies, is commonly linked to the bottom-up detection of feedback value (Zhang et al., 2022). Concurrently, the interval from 300 to 600 ms, associated with the feedback P3 (fb-P3), reflects the top-down evaluation of feedback information (Zhang et al., 2021). Notably, in another EEG study we conducted using a social incentive delay task (SID), we found that melancholic individuals exhibited diminished responsiveness to social rewards, as evidenced by reduced amplitudes in the 200–600 ms interval (Zhang et al., 2024).

Nevertheless, it is important to acknowledge that EEG has relatively limited spatial resolution. Consequently, our ability to localize the brain regions associated with reward feedback processing in these patients remains constrained. Magnetoencephalography (MEG), a high-resolution technique with high temporal and spatial precision enables the acquisition of neuroelectric signals across different scales within complex cerebral regions (Magnetoencephalography, 1990, Baillet, 2017, Hari and Salmelin, 2012). In the present study, the MEG scanning task of facial emotion recognition was performed by melancholic, non-melancholic MDD patients and healthy controls (HCs). We investigated the whole-brain functional connectivity (FC) in the high-beta band during the 0–600 ms after the onset of happy facial expression stimuli. Our objective was to investigate the alteration of FC during reward processing in melancholic MDD patients. Finally, the correlations of abnormal FC and melancholic symptoms were explored, which might contribute to revealing clinical significance.

2 Materials and methods

2.1 Ethics statement

The present study obtained approval from the Local Medical Ethics Committee at the Affiliated Brain Hospital of Nanjing Medical University, and it adhered to the ethical guidelines outlined in the World Medical Association Declaration of Helsinki. All participants provided written informed consent prior to their participation.

2.2 Participants

In the present study, 32 HCs and 61 MDD inpatients from the Affiliated Brain Hospital of Nanjing Medical University were recruited from April 2021 to September 2023. MDD was diagnosed according to the DSM-5 criteria by two trained psychiatrists. The inclusion criteria for patients were: (1) aged 18–55 years; (2) right-handed; (3) the overall score of the 17-item Hamilton Depression Rating Scale (HAMD-17) ≥ 17; (4) no comorbidity with other DSM-5 axis-1 disorder; (5) had not taken any medication in the past four weeks. The exclusion criteria were: (1) a substance use disorder other than nicotine; (2) with a history of repetitive transcranial stimulation or electroconvulsive therapy within the past six months; (3) with any physical severe illnesses; (4) being pregnant or breastfeeding.

32 HCs were recruited from the community. Inclusion criteria for the HC group were as follows: Aged 18–55 years, right-handed, without any severe physical disorder, and no family history of depression. Exclusion criteria for the HC group were similar to those applied to the MDD patients

The presence of melancholic features was assessed using the melancholic feature module of the Mini International Neuropsychiatric Interview (M.I.N.I), based on the DSM-IV criteria for melancholia (Sheehan et al., 1998, Oliva et al., 2023). In addition, to measure the severity of melancholic symptoms, the 6-item subscale (HAM-D6) of the Hamilton Depression Rating Scale (HAMD) was used (Caldieraro et al., 2015, Bech et al., 1975). The HAM-D6 is designed to capture the symptoms of melancholic or endogenous MDD and has good biological validity. The 6 items from the HAMD include: depressed mood, work and interests, somatic symptoms, psychic anxiety, guilt feelings, and psychomotor retardation.

2.3 Stimuli and task

A series of facial expressions selected from the Chinese Facial Expression Video System were randomly shown to subjects (Du et al., 2007). The clips included 40 neutral facial videos, 40 happy facial videos, and 40 fixation cross images. Each clip was displayed for 3 s, followed by a blank screen with a randomly assigned inter-stimulus interval (ISI) of 0.5, 1, or 1.5 s. Participants should press a button when a happy face was presented.

2.4 MEG and MRI acquisition

The MEG data were captured using a 275-channel MEG system (CTF Omega 2000, Canada) while the subjects were positioned supine in a magnetically shielded room. The task was displayed on a screen at a specific distance in front of the subjects. Magnetic fields were recorded at 1200 Hz. Three head localization coils tracked the subjects' head position concerning the MEG sensors.

The structural 3D T1 images were collected via the Siemens Verio 3 T MRI system. The specific scanning parameters were as follows: a repetition time (TR) of 1900 ms, an echo time (TE) of 2.48 ms, a flip angle (FA) of 9°, a slice thickness of 1 mm, a total of 176 slices, an acquisition voxel size of 1 × 1 × 1 mm3, a matrix size of 256 × 256, and a field of view (FOV) of 250 × 250 mm2.

2.5 MEG preprocessing and source reconstruction

Initially, the data was down-sampled to 600 Hz. Then, a band-stop filter (49–51 Hz) was used to eliminate 50 Hz power line interference. Subsequently, it was band-pass filtered with a cut-off frequency of 1–100 Hz and epoched (−0.5 s to 1 s) to remove stray trials and channels. There was no significant difference in the removed channels (Melancholic: 2.32 ± 0.60; Non-melancholic: 2.27 ± 0.87; HC: 2.34 ± 0.75; F(2, 90) = 0.088; P = 0.916) and trials (Melancholic: 6.84 ± 1.53; Non-melancholic: 6.93 ± 2.08; HC: 6.47 ± 1.72; F(2, 90) = 0.590; P = 0.556) between the three groups. Furthermore, an independent component analysis (ICA) was conducted to remove artifacts related to eye blinks, movements, cardiac activity, and muscle activity through visual inspection. No more than 5 ICA components were removed from each group. According to the purpose of our study, only trials with happy emotional stimuli were used for further analysis.

Subsequently, subjects’ 3D T1-weighted MRI images were utilized to form a volume conduction model of the head. With the application of linearly constrained minimum variance (LCMV) beamforming, the preprocessed MEG data was remapped onto a grid within the Montreal Neurological Institute space (grid step = 6 mm). Spatial filters were generated based on the lead fields corresponding to each grid point in conjunction with the covariance matrix of the signal data. The spatial orientations of each epoch were adjusted accordingly to maximize variance. Multiplying the spatial filters with the sensor-level time series across the entire frequency range allowed for the determination of source activity.

2.6 Functional connectivity analysis

MEG power spectra were estimated for 90 cerebrum regions of interest (ROIs) in the automated anatomical labeling (AAL) atlas with a Fast Fourier transform. Orthogonalized power envelope correlation (Hipp et al., 2012, Toll et al., 2020, Siems et al., 2016), a method that could reduce the effects of pseudo-correlation induced by volume conduction, was conducted between all 90 ROIs under the 20–35 Hz. The (90 × (90 − 1)/2 = 4005) connective matrix of each subject was Fisher r-to-z transformed to be normally distributed and allow further analysis. Only the neuronal activity within 600 ms after stimulus onset was analyzed due to the button press effect. We selected the 200 ms preceding the stimulation as the baseline to address the bias observed in the 600 ms following the stimuli. The window length of the time series was 200 ms with a 100 ms overlapping. We performed the entire analysis above using the FieldTrip toolbox based on MATLAB (Oostenveld et al., 2011).

We used the network-based statistics (NBS) toolbox (Zalesky et al., 2010) to identify significant statistical networks among groups across separate time periods (0–200 ms, 100–300 ms, 200–400 ms, 300–500 ms, and 400–600 ms). Within this framework, an independent F test was conducted on each pair of regional functional connectivity. Topological clusters were determined based on F values that exceeded the primary threshold (P = 0.001) to control Type I error. We then performed 5000 randomizations for each group to establish a reference distribution of maximum cluster connection values. Cluster connection values exceeding the 95th percentile of this distribution was considered statistically significant (P < 0.05).

2.7 Statistical analysis

The statistical analysis was performed with the statistical package R 4.2.2. Non-normal distributed variables were expressed as median (Q1-Q3) and analyzed using the Kruskal-Wallis H test or Mann-Whitney U test. Normally distributed variables were expressed as mean ± SD and examined by independent-sample t-test. Categorical variables were expressed as n (%) and examined using the chi-square test. The significance level was defined as P < 0.05 (two-sided).

Furthermore, for the post hoc analyses (e.g., melancholic vs. non-melancholic), an independent two-sample t-test and false discovery rate (FDR) correction were applied to the FC values of the sub-network identified by NBS (Tao et al., 2023). The comparison between melancholic and non-melancholic MDD patients was additionally adjusted for the depression severity (HAMD-17) to exclude the potential confounding effect. Finally, we used Spearman correlation analyses and FDR correction to explore the relationships between aberrant FCs and melancholic symptoms. The entire analysis steps are described in Fig. 1.Fig. 1 The flow chart of analysis steps. NBS: network-based statistic; HAMD: Hamilton depression rating scale; FC: Functional connectivity.

3 Results

3.1 Demographics

As shown in Table 1, there was no difference in demographics between HCs and two MDD groups, including age, gender, education, and marital status. In addition, melancholic and non-melancholic subjects did not differ in age of onset. Nevertheless, melancholic subjects did score higher on HAMD-17 and, as expected, higher melancholic symptoms (HAM-D6) than non-melancholic subjects.Table 1 Socio-demographics and clinical characteristics of subjects.

Variable	Melancholic	Non-melancholic	Healthy controls	P	
N	31	30	32		
Age, (years)	27.00 (20.00, 36.00)	28.00 (21.25, 37.00)	30.00 (24.00, 34.00)	0.523 a	
Gender, n (%)				0.699 b	
 Male	13 (41.9)	12 (40.0)	16 (50.0)		
 Female	18 (58.1)	18 (60.0)	16 (50.0)		
Education, (years)	15.00 (12.00, 16.00)	14.50 (11.25, 15.75)	15.00 (12.75, 16.00)	0.328 a	
Marital status, n (%)				0.550 b	
 Not married	16 (51.6)	16 (53.3)	13 (40.6)		
 Married	15 (48.4)	14 (46.7)	19 (59.4)		
Age of onset, (years)	23.00 (19.00, 23.50)	22.00 (20.00, 23.75)		0.884 c	
HAMD-17 score	26.42 ± 5.33	19.70 ± 6.48		<0.001d	
HAM-D6 score	14.00 (12.00, 15.00)	10.00 (8.00, 12.00)		<0.001c	
Notes: data presented are mean ± SD, median (Q1-Q3), or n (%).

Bold indicated that the results were statistically significant.

Abbreviations: HAMD = Hamilton depression rating scale; HAM-D6 = 6-item melancholia subscale from HAMD.

a Comparison was tested by the Kruskal-Wallis H test.

b Comparison was tested by χ2-test.

c Comparison was tested by Mann-Whitney U test.

d Comparison was tested by independent-sample t test.

3.2 Different functional connective network in the three groups identified by NBS

NBS was applied to examine the significant connective network difference under 20–35 Hz among the three groups at each time period. We found a significant network difference at the mid-late period (300–500 ms) (threshold F = 6.8, corrected P = 0.035), whereas other periods showed no differences. Fig. 2 shows the significant network, with melancholic patients exhibiting decreased connectivity than non-melancholic MDD patients and HCs. This network involves 12 regions and 11 connections, mainly of the prefrontal, parietal, temporal, and occipital lobes, and the limbic system. Specifically, it comprised the posterior cingulate gyrus (PCG), precuneus (PCUN), left opercular inferior frontal gyrus (IFGoperc.L), right orbital inferior frontal gyrus (ORBinf.R), left amygdala (AMYG.L), left calcarine fissure (CAL.L), right superior occipital gyrus (SOG.R), right middle occipital gyrus (MOG.R), left inferior occipital gyrus (IOG.L), left fusiform gyrus (FFG.L).Fig. 2 The functional connective network differences identified by NBS (20–35 Hz, 300–500 ms). NBS: network-based statistic; FC: Functional connectivity. **P < 0.01, ***P < 0.001.

3.3 Aberrant functional connection in melancholic depression after post-hoc analyses

In the post hoc analyses, we compared the FC values of the sub-network defined by NBS. Fig. 3 shows the results of melancholic vs. non-melancholic MDD patients and melancholic vs. HCs (20–35 Hz, 300–500 ms). After correcting for multiple testing, nine significant edges existed between melancholic and non-melancholic groups (all FDR-corrected P < 0.05) (Fig. 3A). Meanwhile, the melancholic group and healthy controls differed in eight functional connections (all FDR-corrected P < 0.01) (Fig. 3B). Taken together, there were six overlapped aberrant edges found both in the comparison of melancholic vs. non-melancholic and melancholic vs. HCs.Fig. 3 The significant connections (20–35 Hz, 300–500 ms) after post hoc analyses and FDR correction. (A) Melancholic vs. Non-melancholic. (B) Melancholic vs. Healthy controls. The brain map shows the distribution of connections, with the node size corresponding to the number of connections. The blue lines are significant connections both in (A) and (B).

The disrupted six functional connections in melancholic group included the FCs of left fusiform gyrus-left amygdala (FFG.L-AMYG.L), left fusiform gyrus-right orbital inferior frontal gyrus (FFG.L-ORBinf.R), left amygdala-right posterior cingulate gyrus (AMYG.L-PCG.R), left posterior cingulate gyrus-right precuneus (PCG.L-PCUN.R), left precuneus-right precuneus (PCUN.L-PCUN.R), left precuneus-left inferior occipital gyrus (PCUN.L-IOG.L). Notably, these specific variants remained significant when HAMD-17 entered as a covariate.

3.4 Clinical correlations

The associations between decreased FCs and melancholic symptoms in all MDD patients were provided in Fig. 4 (20–35 Hz, 300–500 ms). We found significant negative correlations of melancholic symptoms with FCs of FFG.L-ORBinf.R (r = −0.52, PFDR < 0.001), PCG.L-PCUN.R (r = −0.32, PFDR = 0.025), FFG.L-AMYG.L (r = −0.26, PFDR = 0.049), PCUN.L-IOG.L (r = −0.32, PFDR = 0.025), PCUN.L-PCUN.R (r = −0.27, PFDR = 0.049), However, no significant correlation was found between melancholic symptoms and the AMYG.L-PCG.R functional connectivity.Fig. 4 (A–F) Correlations between aberrant FC values (20–35 Hz, 300–500 ms) and HAM-D6 scores in all MDD subjects.

4 Discussion

In the present study, we explored the high-beta band FC patterns after happy facial expression stimuli among groups of melancholic, non-melancholic major MDD patients and HCs. The main results were: (1) in 20–35 Hz and at 300–500 ms, a sub-network with reduced internal connectivity to happy faces was identified in the melancholic MDD patients, mainly of the visual information processing-related cortex and limbic system; (2) the strength of abnormal connections was negatively associated with melancholic symptoms, especially the connectivity of left fusiform gyrus to right orbital inferior frontal gyrus.

Our study found that melancholic MDD patients have a deficit reward processing than non-melancholic individuals, which was consistent with previous investigations (Weinberg and Shankman, 2017, Foti et al., 2014, Lin et al., 2016). The stimulation of happy facial expression is often associated with social rewards (Aldridge-Waddon et al., 2020). A voxel-based meta-analysis of neuroimaging studies reported that in response to social rewards, a robust network of brain regions comprising the ventromedial frontal and orbitofrontal cortices, the anterior cingulate cortex, the amygdala, the hippocampus, and the occipital cortex (Martins et al., 2021). Since our analysis primarily focused on neuronal activity within 600 ms after stimulus onset, we identified a sub-network that included key brain structures involved in reward abnormalities in MDD patients, such as the orbitofrontal cortices and amygdala (Ng et al., 2019), as well as several regions related to visual information processing, including the fusiform gyrus, occipital cortex, and precuneus. Besides, we also identified the abnormal connectivity of the posterior cingulate gyrus (PCG), which was consistent with another EEG-fMRI study, showing that high-beta oscillatory response to gain involved the PCG (Andreou et al., 2017). As a region belongs to the default mode network, PCG has been consistently implicated in reward processing (Liu et al., 2011, Silverman et al., 2015).

Despite the critical role of the striatum in the blunted reward response among MDD patients, we did not observe any abnormal FC involved the striatum (Pizzagalli, 2022, Pan et al., 2022). It might be related to the distinct brain regions recruited in reward anticipation and reward outcome (Chen et al., 2022, Gu et al., 2019). A neuroimaging meta-analysis of the monetary incentive delay task reported that the striatum was mainly implicated during reward anticipation. In contrast, the orbitofrontal prefrontal regions were recruited only during the reward outcome (Oldham et al., 2018). Future studies could employ the incentive delay task to profoundly investigate the spatial–temporal connectivity patterns among melancholic depression both in anticipation and feedback.

We captured the abnormal connectivity during reward processing within the high-beta band, consistent with previous findings. The high-beta activity is related to the movement preparation and the communication within the reward circuit (Savoie et al., 2019). The engagement of high-beta activity in positive feedback may be associated with the dopamine system (Andreou et al., 2017). Midbrain dopamine neurons display two distinct signaling patterns in vivo: a pacemaker-like firing at low frequencies (<10 Hz), and transient bursts of high-frequency activity (15–30 Hz). The low-frequency activity is believed to regulate the continuous baseline levels of dopamine, while the high-frequency bursts are responsible for generating rapid and transient dopamine responses. Recently, investigators also observed that beta oscillations in monkey striatum could encode reward prediction error (RPE) signals (Basanisi et al., 2023).

The strength of this study was the utilization of MEG with high temporal and spatial resolution to investigate the specific frequency-band spatial–temporal connectivity patterns in response to happy faces. This approach allows for a comprehensive analysis of the reward dysfunction in MDD patients from three analytical dimensions: time, space, and frequency. However, this research still has several limitations. Firstly, the study was cross-sectional, so causality between the abnormal high-beta band FC pattern during reward processing and melancholia could not be established. Secondly, we did not thoroughly assess the complete history of antidepressant use over the lifetime. Thus, we cannot disregard the potential influence of antidepressant exposure on our findings. Thirdly, although facial emotion stimuli have been well established for studying reward-related functions in MDD, the employment of emotional faces task in this study limited the differentiation of reward anticipation and outcome (Day et al., 2015, Zhang et al., 2013). In the future, utilizing the social incentive delay task may be worthwhile to thoroughly explore the impaired reward processing in melancholic MDD patients (Zhang et al., 2022, Seitz et al., 2023). Fourthly, the study lacks behavioral data (e.g., reaction times, accuracy), limiting neural interpretation. Future research should include these measures to better correlate neural responses with behavioral outcomes, enhancing our understanding of reward processing.

5 Conclusion

In conclusion, the present study found that melancholic MDD patients exhibited a disrupted functional connective pattern in the high-beta band between brain regions involved in visual information processing and the limbic system after happy facial stimuli. Notably, this specific impairment was independent of the impacts of the overall depression severity, indicating that this dysfunction is not simply because melancholic MDD is a more severe form. Therefore, the deviations in high-beta band FC within the 300–500 ms window may represent a critical neurophysiological marker of abnormal reward processing in melancholic MDD. Investigating this phenomenon could not only enhance our understanding of the pathophysiology of melancholic MDD but also offer potential targets for future neuromodulation therapies.

Funding

The 10.13039/501100001809 National Natural Science Foundation of China (82271568 , 82151315 , 82101573 , 82301718 ); the Jiangsu Medical Innovation Center for Mental Illness (CXZX202226 ); the 10.13039/501100013058 Jiangsu Provincial Key Research and Development Program (BE2019675 ); the Key Project of Science and Technology Innovation for Social Development in Suzhou (2022SS04 ); the Jiangsu Provincial Natural Science Youth Fund (BK20230154 ); the Science and Technology Development Program of Nanjing Medical University (NMUB20220190 ); the Young Talent Development Plan of Changzhou Health Commission (CZQM2023012 ); the Science and Technology Development Project of Changzhou Health Commission (QN202369 ).

CRediT authorship contribution statement

Qiaoyang Zhang: Writing – review & editing, Writing – original draft, Investigation. Yishan Du: Investigation. Ciqing Bao: Writing – original draft, Investigation. Lingling Hua: Methodology. Rui Yan: Methodology, Funding acquisition. Zhongpeng Dai: Methodology. Yi Xia: Data curation. Haowen Zou: Investigation. Chen He: Investigation. Hao Sun: Investigation. Qing Lu: Validation, Supervision. Zhijian Yao: Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

Data will be made available on request.
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