Temporal dynamic brain-heart interaction alterations associated with suicidal ideation in major depressive disorder

Temporal dynamic brain-heart interaction alterations associated with suicidal ideation in major depressive disorder | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Temporal dynamic brain-heart interaction alterations associated with suicidal ideation in major depressive disorder Zhaobo Li, Yuanyuan Huang, Jing Zhou, Fengchun Wu, Kai Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8839132/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Previous studies have indicated abnormal brain activation or heart rate variability in patients with major depressive disorder (MDD). Suicidal ideation, as one of the main concerns of MDD, is a serious public health problem. However, the interactions between brain and heart of MDD patients with and without suicidal ideation remain largely unknown. Methods In this study, resting-state EEG and ECG data were simultaneously collected from 42 healthy controls (HCs), 69 MDD patients with suicidal ideation (MDDSI), and 39 MDD patients without suicidal ideation (MDDNSI). We proposed a novel methodology for analyzing temporal dynamic brain-heart interactions (TD-BHI). Then, we calculated the correlations between abnormalities of TD-BHI and cognitive performance, as well as sleep quality. Results We found that the TD-BHI values in the MDDSI group significantly increased in the parietal lobes and decreased in the right parietal lobes, compared with those in the HCs group. Additionally, the attention / vigilance score of the MATRICS consensus cognitive battery (MCCB) and the Pittsburgh sleep quality index scores were significantly correlated with the TD-BHI values in the MDDSI group. Conclusion Distinct abnormalities of TD-BHI may mediate suicidal ideation in MDD patients and can serve as a reliable biomarker for the diagnosis of MDDSI. Major depressive disorder Brain-heart interactions Suicidal ideation EEG ECG Figures Figure 1 Figure 2 Figure 3 1. Introduction Major depressive disorder (MDD) is a very common psychiatric condition that affects more than 350 million people around the world [ 1 ]. As the same time, suicide has also become a global problem, and approximately one million people commit suicide per year [ 2 ]. Depression and hopelessness are the most commonly cited risk factors for suicide ideation, attempt and death [ 3 ]. Previous studies have demonstrated that the memory of suicidal thoughts in MDD patients can become strengthened, thus lowering the threshold for suicide and potentially leading to suicide attempts [ 4 ]. Thus, early detection and timely intervention are crucial for MDD patients with suicidal ideation (MDDSI). However, the diagnosis of MDDSI rely on scales and questionnaires that needs the objective physiologic measures to improve the ability to identify suicidal ideation. Several studies have used electroencephalography (EEG) to investigate band power and brain network alternations in depression subjects with suicidal ideation [ 5 ]. In the alpha frequencies, major depressive disorder patients with suicidal ideation have more activity compare to the ones with low suicidal ideation [ 6 ]. Furthermore, high gamma band may also serve as an identified marker for depressive patients with suicidality [ 7 ]. Brain-heart interactions govern the homeostatic regulation of interoception through anatomical and functional connections, which have been confirmed to play a pivotal role in cardiovascular diseases (CVD) [ 8 , 9 ], emotional states [ 10 , 11 ], behavioral controls [ 12 ], and neurological disorders [ 13 , 14 ]. Depression is recognized as a disorder that involves the interaction of the brain, heart and blood [ 15 ]. Furthermore, a meta-analysis revealed that abnormalities in brain structure and function are highly correlated with suicidal ideation [ 16 ]. There was found that an increase in suicidal ideation among patients diagnosed with heart disease [ 17 ]. Brain and heart changes might be not only the cause but also the consequence of a depressive disorder or suicidal ideation [ 18 ]. Brain-heart-interactions may be not only related to the depression, but also connected with the suicidal ideation. The mechanisms of how these interactions influence suicide or depression are still unclear. Moreover, no study has revealed the differences between healthy controls and MDD patients with or without suicidal ideation through simultaneous detection of central and autonomic nervous activity. Accumulating evidences have shown that MDD patients experience impairments in various cognitive domains, such as working memory, processing speed, executive function, and attention [ 19 – 21 ]. Some studies have pointed out that cognitive dysfunctions are more frequently detected in MDD patients with suicidal ideation [ 22 , 23 ]. Additionally, there is an urgent need in this field to establish an indicator for identifying suicidal ideation. Bidirectional information exchange between the brain and heart has been extensively studied and recognized as a complex and dynamic mechanism rather than a static input [ 24 ]. The temporal dynamic brain-heart interactions (TD-BHI) method proposed in this paper can more clearly describe the normal interaction patterns between the brain and heart. By controlling for other confounding variables, we attempt to explain the relationship between the TD-BHI and depression, as well as the presence or absence of suicidal ideation. We hypothesize that MDD patients with suicidal ideation (MDDSI) would have different TD-BHI compared to MDD patients without suicidal ideation (MDDNSI) and healthy individuals. In this study, MDD patients were classified into MDDSI and MDDNSI groups according to the presence or absence of suicidal ideation. The EEG and ECG data were acquired simultaneously for all subjects and analyzed to calculate TD-BHI values. Furthermore, the correlations between TD-BHI and MDD patients with or without suicidal ideation were analyzed. Last, the relationships between TD-BHI and the total score of suicidal ideation were calculated. 2. Methods 2.1. Participants One hundred and eight patients diagnosed with FeMDD and forty-two healthy controls were included in this study. All participants comprehended the study procedure and signed informed consent forms. The entire study process was reviewed and approved by the Ethics Committee of the Affiliated Brain Hospital of Guangzhou Medical University (Number AF/SC-02/02.1) and conducted in accordance with the most recent version of the Declaration of Helsinki (2013). The diagnosis of MDD was made by a trained clinician who conducted a structured clinical interview with all participants according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). The inclusion criteria for all patients were as follows: (1) aged between 16 and 40 years, (2) had a first-episode of MDD for less than 2 years, and (3) did not take any medication regularly or use medication for more than 2 weeks. HCs matched for age and education level were recruited from the general population through advertising. HCs with a history of psychiatric disorders, such as depressive disorder, bipolar disorder, or substance abuse/dependence, were excluded. Those with current or past significant medical and neurological illnesses were also excluded. The Bech Scale for Suicide Ideation-Chinese Version (BSI-CV) was used to assess the severity of patients’ specific attitudes, behaviors, and plans related to suicide within the past week [ 25 ]. The total BSI-CV score ranges from 0 to 38, with higher scores reflecting a more severe risk of suicide. Item 4 and Item 5 (Have you had any active or passive thoughts of suicide in the past seven days?) assess the presence of active or passive suicidal thoughts. In this study, participants who select a rating of 1 or 2 for either statement are identified as having suicidal ideation [ 26 – 28 ]. The FeMDD patients were divided into two groups based on the presence or absence of suicidal ideation. The group with suicidal ideation was referred to as MDDSI (n = 69), while the group without suicidal ideation was referred to as MDDNSI (n = 39). Healthy control group participants were confirmed by a trained clinician to have had no suicidal ideation in the past seven days. 2.2. Clinical and Cognitive Evaluation The severity of depressive symptoms was assessed using the Hamilton Depression Rating Scale (HAMD-17) [ 29 ]. Additionally, the score on HAMD-17 without including the suicide item (Item 3) was calculated. Sleep quality was assessed using the Pittsburgh sleep quality index (PSQI), a self-assessment scale consisting of 7 dimensions, with each dimension scored on a scale of 0–3, related to sleep quality. A higher total score indicates poorer sleep quality [ 30 ]. Cognitive performance of all participants was evaluated using the MCCB [ 31 ]. Five dimensions of the MCCB were selected based on previous studies [ 32 , 33 ]: the speed of processing domain (SOP), AV, working memory (WM), verbal learning (VRB) and visual learning (VIS). 2.3. EEG and ECG Data Acquisition The 32-channel EEG and a single-lead ECG (Neuracle, Inc.) connected through a synchronous control box were used to record physiological data from all participants. Resting-state physiological data were collected for 10 minutes and sampled at a rate of 1000 Hz. All participants were instructed to sit comfortably in a chair, close their eyes, stay awake, and try to clear their minds during the data acquisition procedure. 2.4. EEG and ECG Data Preprocessing EEG data was preprocessed using the Python-MNE toolbox [ 34 ]. The preprocessing procedures included 1) bandpass filtering the data to 0.5–70 Hz and applying a notch filter at 50 Hz to remove power line noise, 2) baseline correction and reference of the data using REST [ 35 ], 3) resampling of the data to a sampling rate of 500 Hz to reduce the data length, 4) applying independent component analysis to remove other artifacts, and 5) removing abnormally fluctuating time-period signals. ECG data was preprocessed using the MNE and NeuroKit2 toolboxes in Python. The preprocessing procedures included the following steps: 1) bandpass filtering of the data from 0.02 to 40 Hz, 2) baseline correction using the wavelet function, and 3) resampling of the data to a sampling rate of 500 Hz for alignment with the EEG data. 2.5. Calculation of the TD-BHI The power spectral density (PSD) of EEG signals represents the energy distribution of the signals across the entire frequency spectrum and can be used to characterize the level of brain activation. As shown in Fig. 1 , the dynamic PSD was calculated using a short-time Fourier transform (STFT) with a Hanning taper over a sliding time window of 2 seconds with a 50% overlap. The time-series data were then integrated across five frequency bands (delta: 1–4 Hz, theta: 4–8 Hz, alpha:8–13 Hz, beta: 13–30 Hz, gamma: 30–70 Hz). The ECG signal was analyzed using a QRS complex detection algorithm to extract the RR intervals, which represent the HRV. Subsequently, cubic spline interpolation was used to obtain the equidistant time series of the RR intervals. Finally, the PSD of the RR intervals was calculated using the same method as that used for the EEG data, and the low-frequency PSD (0.04–0.15 Hz) and high-frequency PSD (0.15–0.4 Hz) were extracted from the data. TD-BHI demonstrated the relationship between brain activation and heartbeat by calculating the Pearson’s correlation coefficient (PCC) between the dynamic PSD of EEG and ECG in various frequency bands. Specifically, there were 320 groups of TD-BHI that represented the relationships between the brain and heart in various brain regions and frequency bands. In consideration of the resting state test, the time-series PSD of the EEG or ECG for each individual was segmented into 20-second epochs to increase the sample size of the data. Subsequently, the mean TD-BHI value was calculated for each individual, and these results were aggregated to create new data representing the TD-BHI for the entire group. 2.6. Statistical Analysis The analysis separated the three groups into three distinct components as follows: (1) First, one-way analysis of variance (ANOVA) and chi-square tests were used to assess group differences in demographic characteristics (e.g., gender, age, level of education) among the three groups. The general linear model and ANOVA were used to compare clinical characteristics (e.g., SOP, AV, WM, VRB, VIS of the MCCB) among the three groups, with demographic factors serving as covariates. Bonferroni correction was applied for group-level multiple comparisons at the group level. Finally, the two-sample t -tests were used to examine differences in clinical characteristics between MDDNSI and MDDSI (e.g., HAMD-17, HAMD-17 without suicide, PSQI), while also including gender, age, and level of education as covariates. The aforementioned methods were carried out using SPSS 24.0 (IBM Corporation). (2) Initially, the Kruskal-Wallis test was used to compare TD-BHI values among the three groups, as the data consisted of continuous variables with a nonnormal distribution. Next, significant differences in the TD-BHI values were extracted to determine group-level differences using the Mann-Whitney U test. (3) A partial correlation analysis was performed to assess the relationship between the significant differences in the TD-BHI values from the previous step and cognitive performance (e.g., five dimensions of the MCCB) in the three groups. The correlation between significant differences in TD-BHI values and clinical characteristics (e.g., HAMD-17, HAMD-17 without suicide, PSQI) was also evaluated in two groups of MDD patients. 3. Results 3.1. Demographic and Clinical Characteristics Table 1 shows the demographic and clinical characteristics of all subjects. In particular, demographically, the MDDNSI, MDDSI and HCs groups did not differ significantly in terms of gender ( = 5.03, p = 0.081, df = 2), but they differed significantly in age (F = 7.43, p = 0.001, df = 2) and level of education (F = 7.10, p = 0.001, df = 2). These differences should be considered covariates to eliminate potential confounding effects. Furthermore, the scores of HAMD-17 ( t = 3.10, p = 0.081), HAMD-17 without Suicide ( t = 0.61, p = 0.442), and PSQI ( t = 0.01, p = 0.961) did not differ significantly between the MDDNSI group and the MDDSI group after controlling for the confounding effects of gender, age, and level of education. Additionally, we observed significant differences in the four dimensions of the MCCB among the three groups, with gender, age, and level of education as covariates (Table 2). Specifically, MCCB-SOP (F = 14.48, p < 0.001, df = 2), MCCB-AV (F = 5.68, p = 0.004, df = 2), MCCB-WM (F = 5.5, p = 0.005, df = 2), and MCCB-VRB (F = 8.04, p < 0.001, df = 2) were significantly different among the three groups. Moreover, post hoc analysis was used to identify significant differences in the MCCB-SOP, MCCB-AV, and MCCB-VRB scores between the MDD group and HCs group. There were no differences between the MDDNSI group and the MDDSI group in the three dimensions of the MCCB. Interestingly, a significant difference was observed only between the MDDSI group and the HCs group in MCCB-WM ( t = 3.32, p = 0.003). 3.2. Intergroup differences in the TD-BHI values First, the significant differences in the TD-BHI between the three groups are illustrated in Fig. 2. Most of the significant relationships we observed included all-ECG (LF-ECG and HF-ECG) data with theta-EEG, alpha-EEG and beta-EEG data. In other words, the areas with significantly different values were distributed in the right superior frontal gyrus, the temporal poles, and the parietal lobes. Then, the significantly different TD-BHI values were extracted to observe the intergroup differences (Fig. 3). Compared with HCs, the MDDNSI group exhibited a decrease in TD-BHI values in the left parietal lobe (all-ECG with alpha-EEG and beta-EEG) and in the right superior frontal gyrus (all-ECG with beta-EEG). In addition to the above differences, the MDDSI group also had decreases in the temporal lobes (all-ECG with beta-EEG) and the right parietal lobe (all-ECG with beta-EEG), as well as increases in the TD-BHI values in the parietal lobes (all-ECG with theta-EEG) and the right temporal lobe (all-ECG with theta-EEG). However, there were no differences between the MDDNSI and MDDSI group. 3.3. Correlations between TD-BHI and clinical characteristics Demographic characteristics (gender, age, and level of education) were included as covariates in the correlation procedure. As indicated in Table 3, the correlation analysis of the MDDNSI group revealed that the VRB of the MCCB was negatively correlated with TD-BHI in the left parietal lobe (all-ECG with theta-EEG) ( r = -0.48, p = 0.002) and was positively correlated with PSQI ( r = 0.45/0.46, p = 0.014). In the MDDSI group, we observed a negative correlation between the TD-BHI values and the VRB score of MCCB ( r = -0.26/-0.25, p = 0.044/0.046) in the left temporal lobe (HF-ECG with beta-EEG) as well as the VIS score of the MCCB ( r = -0.28, p = 0.02/0.023) in the left temporal lobe (all-ECG with beta-EEG) and PSQI ( r = -0.22, p = 0.01/0.011) in the right parietal lobe (all-ECG with beta-EEG). Furthermore, we found a positive correlation between the TD-BHI values and the AV score of MCCB ( r = 0.17, p = 0.033) in the middle parietal lobe (LF-ECG with theta-EEG). However, no significant relationships were found between the TD-BHI values and the scores of the other dimensions in the MCCB, HAMD-17, or HAMD-17 without suicide. 4. Discussion To our knowledge, this is the first study to demonstrate significant correlations between TD-BHI and cognitive performance, as well as sleep quality, in MDDSI. The main findings of this study were as follows: (1) the MDDSI group exhibited an increased TD-BHI in the theta band in the parietal lobe and a decreased TD-BHI in the beta band in the right parietal lobe; (2) the AV score of MCCB was significantly positively correlated with the TD-BHI values in the MDDSI group; and (3) the PSQI score was significantly positively and negatively correlated with the TD-BHI values in the MDDNSI and MDDSI groups, respectively. First, the significant differences in the TD-BHI values among the three groups indicate that brain-heart interactions could serve as the important potential biomarkers to predict suicidal ideation in FeMDD patients. Although the MDDSI group did not show significant differences in TD-BHI values compared with the MDDNSI group, they had more abnormalities in TD-BHI values in the MDDSI group. This finding suggested more pronounced disruptions in brain-heart interactions in MDDSI group. The present study revealed a decrease in TD-BHI in the alpha and beta bands of the parietal lobes, as well as the right frontal lobe, in FeMDD patients. These findings are consistent with previous studies indicating reduced activity in the prefrontal cortex (PFC), specifically the ventromedial PFC and dorsolateral PFC, and decreased high-frequency HRV [ 36 – 39 ]. Another study also reported a lower global field power in several regions of MDD patients, including the frontal cortices, parietal regions, and temporal regions, by evaluating heartbeat-evoked potentials [ 40 ]. The interoceptive neural circuit is responsible for coordinating communication between the central nervous system (CNS) and the autonomic nervous system (ANS), playing a vital role in regulating emotional experiences [ 41 ]. As William James famously stated, ‘emotion is the feeling of bodily changes’ [ 42 ]. A decrease in TD-BHI inhibited effective emotional processing, further emphasizing the importance of disrupted BHI in affecting emotional experiences. Several studies have examined the correlations between interoceptive awareness and suicidal ideation, and the results have consistently demonstrated a negative correlation between them [ 43 , 44 ]. Based on these findings, our results suggested that reduced interoceptive awareness, as indicated by the decreased TD-BHI values in the MDDSI group, may contribute to the development of suicidal ideation in FeMDD patients. Interestingly, we observed significant increases in the TD-BHI of the theta band in the middle and right parietal lobes in the MDDSI group, compared with those in the HCs, but these differences were not found in the MDDNSI group. Previous studies have indicated that emotion elicitation, which involves an increased level of attention, is related to the interactions between EEG oscillations in the theta band and vagal modulations in high-frequency power [ 45 ]. Furthermore, previous studies have shown that emotion-related stimuli, particularly those involving highly aggression-related content in films, can provoke the most significant increases in EEG responses within the theta band [ 46 , 47 ]. The TD-BHI of the theta band could be specifically related to impulsive emotions or behaviors, which may mediate the development of suicidal ideation. Additionally, heightened levels of arousal resulting from persistent emotional stimulation were found to be correlated with increased BHI over the parietal electrodes [ 48 ]. Abnormalities in parietal networks were consistent findings in MDD patients [ 49 ]. A recent study on treatment-resistant depression with suicidal ideation has shown that theta burst stimulation is an effective approach for reducing suicidal ideation and anxiety symptoms [ 50 ]. These findings indicate that TD-BHI in the theta band of the parietal lobe is closely correlated with emotional processing sensitivity [ 51 ], which is correlated with suicidal ideation in MDD patients [ 52 ]. When compared with the HCs, MDD patients demonstrated significantly lower scores of the MCCB (e.g., SOP, AV, WM, VRB), which is consistent with the findings of a previous study [ 53 ]. It was confirmed that the functional impairment observed in MDD patients was mediated by dysfunction within the frontal cortex and the middle temporal lobe [ 54 ]. In this study, we observed significant correlations between the TD-BHI and VRB as well as the VIS of the MCCB in both the MDDSI and MDDNSI groups. There were similar significant differences in verbal learning performance among patients with non-suicidal self-injury [ 55 ]. In fact, VRB deficits are symptoms of memory loss in MDD patients. The exciting thing is that TD-BHI can describe certain cognitive states of MDD patients, which is consistent with the findings of previous studies. Furthermore, we found a positive correlation between only the TD-BHI values and the AV (attention / vigilance) score of the MCCB in the MDDSI group. AV is related to impulsive behavior, and impulsiveness has long been recognized as a key factor in suicide [ 56 ]. On the other hand, the infralimbic cortex plays a crucial role in cognitive flexibility and is also involved in the presence of suicidal ideation [ 57 ]. Cognitive impairments were associated with suicidal ideation in psychosis patients [ 58 ]. Poorer cognitive function was consistently associated with an increased risk of suicidal ideation [ 59 , 60 ]. In summary, cognitive impairments were correlated with the emergence of suicidal ideation, and TD-BHI could specifically characterize suicidal ideation in FeMDD patients. Circadian rhythms are modulated by bidirectional BHI, and disruptions in this interaction could contribute to sleep disturbances, ultimately leading to the development of depressive disorders [ 61 ]. It is important to note that sleep plays a critical role in mental disorders [ 62 ]. As in previous studies, our study also revealed lower sleep quality in FeMDD patients [ 63 , 64 ]. However, we found that the correlation between the TD-BHI values and the PSQI score was opposite in the MDDSI and MDDNSI groups. These intriguing findings suggest contrasting or contradictory correlations between BHI values and sleep quality in the MDD subgroups. In particular, previous studies have indicated that the correlation between sleep quality and suicidal ideation may have relatively independent effects in comorbid mental disorders [ 62 , 65 ]. Based on our findings, we hypothesized that there may be distinct neural mechanisms underlying sleep disturbances in MDD patients with or without SI. A recent meta-analysis supported this hypothesis, indicating that total sleep time was significantly lower in the MDDSI group than in MDDNSI group [ 66 ]. These findings suggest that differences in sleep quality could serve as biomarkers for identifying MDD subgroups with or without SI. Emotion is a complex mental phenomenon that encompasses various intricate processes. In addition, the TD-BHI has the ability to capture adaptive and flexible modulation of both central and autonomic nerves by the brainstem. In our study, we found that TD-BHI could effectively distinguish significant differences between MDD patients and HCs. Furthermore, we observed a significant correlation between the TD-BHI values and clinical symptoms (e.g., cognitive impairments and sleep disturbances) associated with suicidal ideation. Moreover, the BHI has been identified as a potential marker for evaluating the effects of antidepressant medications [ 67 ]. Taking into account these findings, it is plausible to consider TD-BHI as a robust diagnostic biomarker that could distinguish between MDD subgroups with or without SI. Limitations Although our results are significant and provide insight into the underlying mechanisms of brain-heart interactions in MDDSI, it is important to acknowledge several limitations within the study. First, this study was limited by an imbalance in sample size and the sex ratio among the three groups. Although no significant difference in gender ( \(\:{\chi\:}^{2}\) = 5.03, p = 0.081, df = 2) was found, it is important to consider that previous studies have indicated the possible influence of gender on brain networks and clinical symptoms in MDD patients [ 68 , 69 ]. Second, we observed significant differences in age and level of education among the three groups. These differences may introduce bias, which can only be addressed by controlling for age and level of education as covariates. Third, we did not explore the specific mechanisms through which TD-BHI affected suicidal ideation in MDD patients. Future study should investigate the underlying neural mechanisms through how brain-heart interactions contribute to suicidal ideation in MDD patients. Furthermore, this approach has the potential to lead to the development of more effective early intervention strategies for individuals at risk. 5. Conclusion In conclusion, our results indicated that there are more pronounced differences in the TD-BHI values in the MDDSI group compared with those in the MDDNSI as well as those in the HCs groups. These results suggested that the diminished function of interoceptive neural circuits may contribute to impaired emotion regulation, which, in turn, may be associated with the development of suicidal ideation in MDD patients. Additionally, we observed significant correlations between TD-BHI values and clinical symptoms, including cognitive performance and sleep quality. These results highlighted the potential correlations between TD-BHI and MDDSI, as these symptoms have been strongly linked to suicidal ideation. Our findings underscored the crucial role of TD-BHI in the development of suicidal ideation in MDD patients, suggesting its potential as a reliable biomarker for auxiliary diagnosis, particularly in those with or without suicidal ideation. This study may provide valuable information on the potential utility of TD-BHI as a diagnostic biomarker and emphasizes the importance of further exploration in the field of brain-heart interactions to enhance our understanding and improve clinical interventions for MDDSI. Abbreviations BHI Brain Heart Interaction TD-BHI Temporal Dynamic Brain Heart Interaction MDD Major Depressive Disorder FeMDD First-episode Drug-naïve Major Depressive Disorder SI Suicidal Ideation EEG Electroencephalogram ECG Electrocardiogram MCCB MATRICS Consensus Cognitive Battery CVD Cardiovascular diseases DSM-5 Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition BSI-CV Bech Scale for Suicide Ideation-Chinese Version HAMD-17 Hamilton Depression Rating Scale PSQI Pittsburgh Sleep Quality Index Declarations Ethics approval and consent to participate Prior to enrollment, all participants demonstrated a comprehensive understanding of the study procedures and provided written informed consent. All participants were aged 16 years or older. For participants aged 16 years, written informed consent was obtained from both the participants themselves and their parents or legal guardians. For participants older than 16 years, parental or legal guardian consent was not required. The study protocol was reviewed and approved by the Institutional Review Board of the Affiliated Brain Hospital of Guangzhou Medical University (ethical approval number: AF/SC-02/02.1) and was conducted in accordance with the ethical standards of the 2013 revision of the Declaration of Helsinki. Consent for publication Not applicable. Competing Interests The authors declare no conflicts of interest in conducting this study or preparing the manuscript. Funding This work was supported by the National Key Research and Development Program of China (2023YFC2414500, 2023YFC2414504), announcement and Leading Science and Technical Foundation of Guangzhou Civil Affairs (GCAAL2022001 to G.Z.), Guangzhou Planned Project of Science and Technology (2023B04J0106)); the National Natural Science Foundation of China (82271953, 82301688), the Key Research and Development Program of Guangdong (2023B0303020001, 2023B0303010003), the Natural Science Foundation of Guangdong Province (2024A1515013058), the Guangdong Key Laboratory of Battery Safety at Guangzhou Institute of Energy Testing (2019B121203008-KJ-2024-040/ KJ-2024-041), the Science and Technology Program of Guangzhou (2025A03J3357), clinical Collaboration Project on Integrated Traditional Chinese and Western Medicine for Major and Difficult Diseases (Bipolar Disorder, ZDYN-2024-A-121), the Research capacity improvement project of Guangzhou Medical University (2024SRP200), and Guangzhou Key Clinical Specialty (Clinical Medical Research Institute). Author Contribution Zhaobo Li and Jing Zhou completed the data analysis and wrote the main manuscript; Yuanyuan Huang acquired the data and critically revised the manuscript; Fengchun Wu and Kai Wu revised the manuscript. All authors read and approved the final manuscript. Acknowledgments We wish to express our gratitude to all the volunteers from the Affiliated Brain Hospital, Guangzhou Medical University, and South China University of Technology. Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. References Woelfer M, Kasties V, Kahlfuss S, Walter M. The Role of Depressive Subtypes within the Neuroinflammation Hypothesis of Major Depressive Disorder. Neuroscience. 2019;403:93–110. https://doi.org/10.1016/j.neuroscience.2018.03.034 . Zhou X, Lin Z, Liu J, Xiang M, Deng X, Zou Z. The relationship between event-related potential components and suicide risk in major depressive disorder. 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MDDNSI (n = 39) MDDSI (n = 69) HCs (n = 42) F/ t / ( p ) Gender (Female/male) 25/14 43/26 18/24 5.03 (0.081) Age Year 25.79 (4.61) 22.91 (4.95) 22.36 (2.86) 7.43 ( 0.001 ) Level of education Year 15.13 (2.67) 14.12 (2.42) 15.90 (2.37) 7.10 ( 0.001 ) HAMD-17 20.79 (5.32) 22.87 (4.63) - 3.10 (0.081) HAMD-17 without Suicide 20.05 (5.36) 20.94 (4.25) - 0.61 (0.442) HAMD-14 BSI 1.53 (7.62) 11.61 (7.74) PSQI 12.38 (3.60) 12.22 (3.42) - 0.01 (0.961) Abbreviations: MDDNSI, first-episode major depressive disorder patient without suicidal ideation; MDDSI, first-episode major depressive disorder patient with suicidal ideation; HCs, healthy controls; HAMD-17, Hamilton Depression Rating Scale; PSQI, pittsburgh sleep quality index. Table 2. Cognitive performance of participants. MCCB mean (S.D.) MDDNSI (①, n = 39) MDDSI (②, n = 69) HCs (③, n = 42) F ( p ) Post-Hoc t ( p ) SOP 34.92 (11.74) 32.29 (10.32) 46.21 (11.3) 14.48 ( ① 3.50 ( 0.002 ) ③>② 5.36 ( ② 1.34 (0.547) AV 36.14 (11.81) 34.65 (9.83) 42.14 (8.32) 5.68 ( 0.004 ) ③>① 2.74 ( 0.021 ) ③>② 3.18 ( 0.005 ) ①>② 0.02 (1) WM 39.82 (13.79) 38.72 (11.37) 48.48 (11.33) 5.55 ( 0.005 ) ③>① 2.22 (0.085) ③>② 3.32 ( 0.003 ) ①>② 0.78 (1) VRB 34.13 (9.33) 33.29 (10.73) 42.88 (8.24) 8.04 ( ① 2.47 ( 0.044 ) ③>② 4.01 ( ② 1.18 (0.72) VIS 40.49 (8.27) 40.9 (8.50) 46.05 (7.21) 2.89 (0.059) ③>① 2.03 (0.133) ③>② 2.24 (0.08) ①<② 0.10 (1) Abbreviations: MCCB, MATRICS consensus cognitive battery; MDDNSI, first-episode major depressive disorder patient without suicidal ideation; MDDSI, first-episode major depressive disorder patient with suicidal ideation; HCs, healthy controls; SOP, speed of processing domain; AV, attention/vigilance; WM, working memory; VRB, verbal learning; VIS, visual learning. Tabel 3. Partial correlation analysis between TD-BHI and clinical characteristics. Groups Clinical characteristics TD-BHI r ( p ) MDDNSI MCCB-VRB low-θ-Pz -0.48 ( 0.002 ) high-θ-Pz -0.48 ( 0.002 ) high- -CP5 -0.34 ( 0.028 ) PSQI low-θ-CP6 0.45 ( 0.014 ) high-θ-CP6 0.46 ( 0.014 ) MDDSI MCCB-AV low-θ-PZ 0.17 ( 0.033 ) MCCB-VRB high-β-C4 -0.26 ( 0.044 ) high-β-CP2 -0.25 ( 0.046 ) MCCB-VIS low-β-CP2 -0.28 ( 0.023 ) high-β-CP2 -0.28 ( 0.020 ) PSQI low-β-P3 -0.22 ( 0.011 ) high-β-P3 -0.22 ( 0.010 ) Abbreviations: TD-BHI, temporal dynamic brain-heart interactions; MDDNSI, first-episode major depressive disorder patient without suicidal ideation; MDDSI, first-episode major depressive disorder patient with suicidal ideation; MCCB, MATRICS consensus cognitive battery; VRB, verbal learning; AV, attention/vigilance; VIS, visual learning; PSQI, pittsburgh sleep quality index; low, low-frequency PSD of ECG; high, high-frequency PSD of ECG; means the PSD of various bands in EEG; P3, Pz, T7, CP5, P4 means the position of electrode; low-β-P3 means interaction between low-frequency PSD of ECG and the PSD of β in EEG on the scalp position of P3. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8839132","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":598958121,"identity":"25b78090-ab90-42eb-952b-19af1bc34c88","order_by":0,"name":"Zhaobo Li","email":"","orcid":"","institution":"South China University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhaobo","middleName":"","lastName":"Li","suffix":""},{"id":598958122,"identity":"2f82d812-b157-4d34-8c72-996f2fcbaa77","order_by":1,"name":"Yuanyuan Huang","email":"","orcid":"","institution":"The Affiliated Brain Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Huang","suffix":""},{"id":598958123,"identity":"bdefcadb-f101-47ae-89d2-d72013e5bee5","order_by":2,"name":"Jing Zhou","email":"","orcid":"","institution":"South China University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Zhou","suffix":""},{"id":598958124,"identity":"f4e54a13-8112-4ea7-bc19-7d7e5914a631","order_by":3,"name":"Fengchun Wu","email":"","orcid":"","institution":"The Affiliated Brain Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fengchun","middleName":"","lastName":"Wu","suffix":""},{"id":598958125,"identity":"0ad55b94-8d0c-43af-9a2d-8dabc7d8d5c7","order_by":4,"name":"Kai Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYBACCQbGBgaGCiiPh3gtZ4AsNuK1AAFjGylaJNsPt0nzzquTN7jfwPjgbRuDvDkhLdI8iUAt2w4bbjjGwGw4t43BcGcDAS1yDIltt3m3HUgwOMbAJs3bxpBgcICQFv6HQC1z6kBa2H8TpUVaAmRLAzPYFmaitEjOeNj+c86xw4YzjyU2S845J2G4gZAWifPpjw3e1NTJ8x0+fPDDmzIbeYK2IAFQnELiaRSMglEwCkYBpQAAPh88DwmoQBsAAAAASUVORK5CYII=","orcid":"","institution":"South China University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Kai","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2026-02-10 09:38:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8839132/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8839132/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104171027,"identity":"43818d39-1e9e-4bd6-a8ca-2e74a889c5a7","added_by":"auto","created_at":"2026-03-08 14:51:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":259294,"visible":true,"origin":"","legend":"\u003cp\u003eThe calculation process of the TD-BHI.\u003c/p\u003e\n\u003cp\u003eDynamic EEG power spectral density (PSD) was estimated using short-time Fourier transform (2s Hanning window, 50% overlap) and integrated into five frequency bands (delta, theta, alpha, beta, and gamma). ECG-derived RR intervals were interpolated to an equidistant time series, and their PSD was computed to extract low-frequency and high-frequency components. Temporal dynamic brain-heart interactions (TD-BHI) was quantified by Pearson’s correlation coefficients (PCC) between dynamic EEG and ECG PSDs across corresponding frequency bands.\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-8839132/v1/6dca2abf067557da0c736782.png"},{"id":104171026,"identity":"74134da5-fde4-46a3-a6e7-c9ab1a4b1b07","added_by":"auto","created_at":"2026-03-08 14:51:55","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":90097,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of brain regions with significant TD-BHI differences among MDDSI, MDDNSI, and HCs.\u003c/p\u003e\n\u003cp\u003eAbbreviations: LF, low-frequency PSD of ECG; HF, high-frequency PSD of ECG; θ, α, β means the rhythm PSD of EEG; PSD, power spectral density; TD-BHI, temporal dynamic brain-heart interactions; MDDSI, first-episode major depressive disorder patient (MDD) with suicidal ideation; MDDNSI, MDD without suicidal ideation; HCs, healthy controls.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8839132/v1/f07422d73854429241105c27.jpeg"},{"id":104403490,"identity":"dec32e4f-b46c-40a4-a71c-b7c71af1a6b1","added_by":"auto","created_at":"2026-03-11 12:18:25","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":172038,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of brain regions with significant TD-BHI differences between HCs and MDDNSI and between HCs and MDDSI.\u003c/p\u003e\n\u003cp\u003eAbbreviations: LF, low-frequency PSD of ECG; HF, high-frequency PSD of ECG; θ, α, β means the rhythm PSD of EEG; PSD, power spectral density; TD-BHI, temporal dynamic brain-heart interactions; MDDSI, first-episode major depressive disorder patient (MDD) with suicidal ideation; MDDNSI, MDD without suicidal ideation; HCs, healthy controls.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8839132/v1/67c51de228c68c69a56dec76.jpeg"},{"id":104408594,"identity":"2928da8e-69d2-4df3-aee2-14694d591340","added_by":"auto","created_at":"2026-03-11 12:42:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1281270,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8839132/v1/afc06a99-2fa3-48fe-8368-f2bcbfc8546b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Temporal dynamic brain-heart interaction alterations associated with suicidal ideation in major depressive disorder","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMajor depressive disorder (MDD) is a very common psychiatric condition that affects more than 350\u0026nbsp;million people around the world [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As the same time, suicide has also become a global problem, and approximately one million people commit suicide per year [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Depression and hopelessness are the most commonly cited risk factors for suicide ideation, attempt and death [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Previous studies have demonstrated that the memory of suicidal thoughts in MDD patients can become strengthened, thus lowering the threshold for suicide and potentially leading to suicide attempts [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Thus, early detection and timely intervention are crucial for MDD patients with suicidal ideation (MDDSI). However, the diagnosis of MDDSI rely on scales and questionnaires that needs the objective physiologic measures to improve the ability to identify suicidal ideation.\u003c/p\u003e \u003cp\u003eSeveral studies have used electroencephalography (EEG) to investigate band power and brain network alternations in depression subjects with suicidal ideation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In the alpha frequencies, major depressive disorder patients with suicidal ideation have more activity compare to the ones with low suicidal ideation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, high gamma band may also serve as an identified marker for depressive patients with suicidality [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Brain-heart interactions govern the homeostatic regulation of interoception through anatomical and functional connections, which have been confirmed to play a pivotal role in cardiovascular diseases (CVD) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], emotional states [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], behavioral controls [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and neurological disorders [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Depression is recognized as a disorder that involves the interaction of the brain, heart and blood [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Furthermore, a meta-analysis revealed that abnormalities in brain structure and function are highly correlated with suicidal ideation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. There was found that an increase in suicidal ideation among patients diagnosed with heart disease [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Brain and heart changes might be not only the cause but also the consequence of a depressive disorder or suicidal ideation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Brain-heart-interactions may be not only related to the depression, but also connected with the suicidal ideation. The mechanisms of how these interactions influence suicide or depression are still unclear. Moreover, no study has revealed the differences between healthy controls and MDD patients with or without suicidal ideation through simultaneous detection of central and autonomic nervous activity.\u003c/p\u003e \u003cp\u003eAccumulating evidences have shown that MDD patients experience impairments in various cognitive domains, such as working memory, processing speed, executive function, and attention [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Some studies have pointed out that cognitive dysfunctions are more frequently detected in MDD patients with suicidal ideation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Additionally, there is an urgent need in this field to establish an indicator for identifying suicidal ideation.\u003c/p\u003e \u003cp\u003eBidirectional information exchange between the brain and heart has been extensively studied and recognized as a complex and dynamic mechanism rather than a static input [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The temporal dynamic brain-heart interactions (TD-BHI) method proposed in this paper can more clearly describe the normal interaction patterns between the brain and heart. By controlling for other confounding variables, we attempt to explain the relationship between the TD-BHI and depression, as well as the presence or absence of suicidal ideation.\u003c/p\u003e \u003cp\u003eWe hypothesize that MDD patients with suicidal ideation (MDDSI) would have different TD-BHI compared to MDD patients without suicidal ideation (MDDNSI) and healthy individuals. In this study, MDD patients were classified into MDDSI and MDDNSI groups according to the presence or absence of suicidal ideation. The EEG and ECG data were acquired simultaneously for all subjects and analyzed to calculate TD-BHI values. Furthermore, the correlations between TD-BHI and MDD patients with or without suicidal ideation were analyzed. Last, the relationships between TD-BHI and the total score of suicidal ideation were calculated.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Participants\u003c/h2\u003e \u003cp\u003eOne hundred and eight patients diagnosed with FeMDD and forty-two healthy controls were included in this study. All participants comprehended the study procedure and signed informed consent forms. The entire study process was reviewed and approved by the Ethics Committee of the Affiliated Brain Hospital of Guangzhou Medical University (Number AF/SC-02/02.1) and conducted in accordance with the most recent version of the Declaration of Helsinki (2013). The diagnosis of MDD was made by a trained clinician who conducted a structured clinical interview with all participants according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5).\u003c/p\u003e \u003cp\u003eThe inclusion criteria for all patients were as follows: (1) aged between 16 and 40 years, (2) had a first-episode of MDD for less than 2 years, and (3) did not take any medication regularly or use medication for more than 2 weeks. HCs matched for age and education level were recruited from the general population through advertising. HCs with a history of psychiatric disorders, such as depressive disorder, bipolar disorder, or substance abuse/dependence, were excluded. Those with current or past significant medical and neurological illnesses were also excluded.\u003c/p\u003e \u003cp\u003eThe Bech Scale for Suicide Ideation-Chinese Version (BSI-CV) was used to assess the severity of patients\u0026rsquo; specific attitudes, behaviors, and plans related to suicide within the past week [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The total BSI-CV score ranges from 0 to 38, with higher scores reflecting a more severe risk of suicide. Item 4 and Item 5 (Have you had any active or passive thoughts of suicide in the past seven days?) assess the presence of active or passive suicidal thoughts. In this study, participants who select a rating of 1 or 2 for either statement are identified as having suicidal ideation [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The FeMDD patients were divided into two groups based on the presence or absence of suicidal ideation. The group with suicidal ideation was referred to as MDDSI (n\u0026thinsp;=\u0026thinsp;69), while the group without suicidal ideation was referred to as MDDNSI (n\u0026thinsp;=\u0026thinsp;39). Healthy control group participants were confirmed by a trained clinician to have had no suicidal ideation in the past seven days.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Clinical and Cognitive Evaluation\u003c/h2\u003e \u003cp\u003eThe severity of depressive symptoms was assessed using the Hamilton Depression Rating Scale (HAMD-17) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, the score on HAMD-17 without including the suicide item (Item 3) was calculated. Sleep quality was assessed using the Pittsburgh sleep quality index (PSQI), a self-assessment scale consisting of 7 dimensions, with each dimension scored on a scale of 0\u0026ndash;3, related to sleep quality. A higher total score indicates poorer sleep quality [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Cognitive performance of all participants was evaluated using the MCCB [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Five dimensions of the MCCB were selected based on previous studies [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]: the speed of processing domain (SOP), AV, working memory (WM), verbal learning (VRB) and visual learning (VIS).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. EEG and ECG Data Acquisition\u003c/h2\u003e \u003cp\u003eThe 32-channel EEG and a single-lead ECG (Neuracle, Inc.) connected through a synchronous control box were used to record physiological data from all participants. Resting-state physiological data were collected for 10 minutes and sampled at a rate of 1000 Hz. All participants were instructed to sit comfortably in a chair, close their eyes, stay awake, and try to clear their minds during the data acquisition procedure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. EEG and ECG Data Preprocessing\u003c/h2\u003e \u003cp\u003eEEG data was preprocessed using the Python-MNE toolbox [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The preprocessing procedures included 1) bandpass filtering the data to 0.5\u0026ndash;70 Hz and applying a notch filter at 50 Hz to remove power line noise, 2) baseline correction and reference of the data using REST [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], 3) resampling of the data to a sampling rate of 500 Hz to reduce the data length, 4) applying independent component analysis to remove other artifacts, and 5) removing abnormally fluctuating time-period signals.\u003c/p\u003e \u003cp\u003eECG data was preprocessed using the MNE and NeuroKit2 toolboxes in Python. The preprocessing procedures included the following steps: 1) bandpass filtering of the data from 0.02 to 40 Hz, 2) baseline correction using the wavelet function, and 3) resampling of the data to a sampling rate of 500 Hz for alignment with the EEG data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Calculation of the TD-BHI\u003c/h2\u003e \u003cp\u003eThe power spectral density (PSD) of EEG signals represents the energy distribution of the signals across the entire frequency spectrum and can be used to characterize the level of brain activation. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the dynamic PSD was calculated using a short-time Fourier transform (STFT) with a Hanning taper over a sliding time window of 2 seconds with a 50% overlap. The time-series data were then integrated across five frequency bands (delta: 1\u0026ndash;4 Hz, theta: 4\u0026ndash;8 Hz, alpha:8\u0026ndash;13 Hz, beta: 13\u0026ndash;30 Hz, gamma: 30\u0026ndash;70 Hz).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe ECG signal was analyzed using a QRS complex detection algorithm to extract the RR intervals, which represent the HRV. Subsequently, cubic spline interpolation was used to obtain the equidistant time series of the RR intervals. Finally, the PSD of the RR intervals was calculated using the same method as that used for the EEG data, and the low-frequency PSD (0.04\u0026ndash;0.15 Hz) and high-frequency PSD (0.15\u0026ndash;0.4 Hz) were extracted from the data.\u003c/p\u003e \u003cp\u003eTD-BHI demonstrated the relationship between brain activation and heartbeat by calculating the Pearson\u0026rsquo;s correlation coefficient (PCC) between the dynamic PSD of EEG and ECG in various frequency bands. Specifically, there were 320 groups of TD-BHI that represented the relationships between the brain and heart in various brain regions and frequency bands. In consideration of the resting state test, the time-series PSD of the EEG or ECG for each individual was segmented into 20-second epochs to increase the sample size of the data. Subsequently, the mean TD-BHI value was calculated for each individual, and these results were aggregated to create new data representing the TD-BHI for the entire group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe analysis separated the three groups into three distinct components as follows:\u003c/p\u003e \u003cp\u003e(1) First, one-way analysis of variance (ANOVA) and chi-square tests were used to assess group differences in demographic characteristics (e.g., gender, age, level of education) among the three groups. The general linear model and ANOVA were used to compare clinical characteristics (e.g., SOP, AV, WM, VRB, VIS of the MCCB) among the three groups, with demographic factors serving as covariates. Bonferroni correction was applied for group-level multiple comparisons at the group level. Finally, the two-sample \u003cem\u003et\u003c/em\u003e-tests were used to examine differences in clinical characteristics between MDDNSI and MDDSI (e.g., HAMD-17, HAMD-17 without suicide, PSQI), while also including gender, age, and level of education as covariates. The aforementioned methods were carried out using SPSS 24.0 (IBM Corporation).\u003c/p\u003e \u003cp\u003e(2) Initially, the Kruskal-Wallis test was used to compare TD-BHI values among the three groups, as the data consisted of continuous variables with a nonnormal distribution. Next, significant differences in the TD-BHI values were extracted to determine group-level differences using the Mann-Whitney U test.\u003c/p\u003e \u003cp\u003e(3) A partial correlation analysis was performed to assess the relationship between the significant differences in the TD-BHI values from the previous step and cognitive performance (e.g., five dimensions of the MCCB) in the three groups. The correlation between significant differences in TD-BHI values and clinical characteristics (e.g., HAMD-17, HAMD-17 without suicide, PSQI) was also evaluated in two groups of MDD patients.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1. Demographic and Clinical Characteristics\u003c/p\u003e\n\u003cp\u003eTable 1 shows the demographic and clinical characteristics of all subjects. In particular, demographically, the MDDNSI, MDDSI and HCs groups did not differ significantly in terms of gender (\u003cimg width=\"17\" height=\"29\" src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1772571102.gif\" alt=\"image\"\u003e = 5.03,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e = 0.081, df = 2), but they differed significantly in age (F = 7.43, \u003cem\u003ep\u003c/em\u003e = 0.001, df = 2) and level of education (F = 7.10, \u003cem\u003ep\u003c/em\u003e = 0.001, df = 2). These differences should be considered covariates to eliminate potential confounding effects. Furthermore, the scores of HAMD-17 (\u003cem\u003et\u003c/em\u003e = 3.10,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e = 0.081), HAMD-17 without Suicide (\u003cem\u003et\u003c/em\u003e = 0.61,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e = 0.442), and PSQI (\u003cem\u003et\u003c/em\u003e = 0.01,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e = 0.961) did not differ significantly between the MDDNSI group and the MDDSI group after controlling for the confounding effects of gender, age, and level of education.\u003c/p\u003e\n\u003cp\u003eAdditionally, we observed significant differences in the four dimensions of the MCCB among the three groups, with gender, age, and level of education as covariates (Table 2). Specifically, MCCB-SOP (F = 14.48, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, df = 2), MCCB-AV (F = 5.68, \u003cem\u003ep\u003c/em\u003e = 0.004, df = 2), MCCB-WM (F = 5.5, \u003cem\u003ep\u003c/em\u003e = 0.005, df = 2), and MCCB-VRB (F = 8.04, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, df = 2) were significantly different among the three groups. Moreover, post hoc analysis was used to identify significant differences in the MCCB-SOP, MCCB-AV, and MCCB-VRB scores between the MDD group and HCs group. There were no differences between the MDDNSI group and the MDDSI group in the three dimensions of the MCCB. Interestingly, a significant difference was observed only between the MDDSI group and the HCs group in MCCB-WM (\u003cem\u003et\u003c/em\u003e = 3.32,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e = 0.003).\u003c/p\u003e\n\u003cp\u003e3.2. Intergroup differences in the TD-BHI values\u003c/p\u003e\n\u003cp\u003eFirst, the significant differences in the TD-BHI between the three groups are illustrated in Fig. 2. Most of the significant relationships we observed included all-ECG (LF-ECG and HF-ECG) data with theta-EEG, alpha-EEG and beta-EEG data. In other words, the areas with significantly different values were distributed in the right superior frontal gyrus, the temporal poles, and the parietal lobes. Then, the significantly different TD-BHI values were extracted to observe the intergroup differences (Fig. 3). Compared with HCs, the MDDNSI group exhibited a decrease in TD-BHI values in the left parietal lobe (all-ECG with alpha-EEG and beta-EEG) and in the right superior frontal gyrus (all-ECG with beta-EEG). In addition to the above differences, the MDDSI group also had decreases in the temporal lobes (all-ECG with beta-EEG) and the right parietal lobe (all-ECG with beta-EEG), as well as increases in the TD-BHI values in the parietal lobes (all-ECG with theta-EEG) and the right temporal lobe (all-ECG with theta-EEG). However, there were no differences between the MDDNSI and MDDSI group.\u003c/p\u003e\n\u003cp\u003e3.3. Correlations between TD-BHI and clinical characteristics\u003c/p\u003e\n\u003cp\u003eDemographic characteristics (gender, age, and level of education) were included as covariates in the correlation procedure. As indicated in Table 3, the correlation analysis of the MDDNSI group revealed that the VRB of the MCCB was negatively correlated with TD-BHI in the left parietal lobe (all-ECG with theta-EEG) (\u003cem\u003er\u003c/em\u003e = -0.48, \u003cem\u003ep\u003c/em\u003e = 0.002) and was positively correlated with PSQI (\u003cem\u003er\u003c/em\u003e = 0.45/0.46, \u003cem\u003ep\u003c/em\u003e = 0.014). In the MDDSI group, we observed a negative correlation between the TD-BHI values and the VRB score of MCCB (\u003cem\u003er\u003c/em\u003e = -0.26/-0.25, \u003cem\u003ep\u003c/em\u003e = 0.044/0.046) in the left temporal lobe (HF-ECG with beta-EEG) as well as the VIS score of the MCCB (\u003cem\u003er\u003c/em\u003e = -0.28, \u003cem\u003ep\u003c/em\u003e = 0.02/0.023) in the left temporal lobe (all-ECG with beta-EEG) and PSQI (\u003cem\u003er\u003c/em\u003e = -0.22, \u003cem\u003ep\u003c/em\u003e = 0.01/0.011) in the right parietal lobe (all-ECG with beta-EEG). Furthermore, we found a positive correlation between the TD-BHI values and the AV score of MCCB (\u003cem\u003er\u003c/em\u003e = 0.17, \u003cem\u003ep\u003c/em\u003e = 0.033) in the middle parietal lobe (LF-ECG with theta-EEG). However, no significant relationships were found between the TD-BHI values and the scores of the other dimensions in the MCCB, HAMD-17, or HAMD-17 without suicide.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTo our knowledge, this is the first study to demonstrate significant correlations between TD-BHI and cognitive performance, as well as sleep quality, in MDDSI. The main findings of this study were as follows: (1) the MDDSI group exhibited an increased TD-BHI in the theta band in the parietal lobe and a decreased TD-BHI in the beta band in the right parietal lobe; (2) the AV score of MCCB was significantly positively correlated with the TD-BHI values in the MDDSI group; and (3) the PSQI score was significantly positively and negatively correlated with the TD-BHI values in the MDDNSI and MDDSI groups, respectively.\u003c/p\u003e \u003cp\u003eFirst, the significant differences in the TD-BHI values among the three groups indicate that brain-heart interactions could serve as the important potential biomarkers to predict suicidal ideation in FeMDD patients. Although the MDDSI group did not show significant differences in TD-BHI values compared with the MDDNSI group, they had more abnormalities in TD-BHI values in the MDDSI group. This finding suggested more pronounced disruptions in brain-heart interactions in MDDSI group. The present study revealed a decrease in TD-BHI in the alpha and beta bands of the parietal lobes, as well as the right frontal lobe, in FeMDD patients. These findings are consistent with previous studies indicating reduced activity in the prefrontal cortex (PFC), specifically the ventromedial PFC and dorsolateral PFC, and decreased high-frequency HRV [\u003cspan additionalcitationids=\"CR37 CR38\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Another study also reported a lower global field power in several regions of MDD patients, including the frontal cortices, parietal regions, and temporal regions, by evaluating heartbeat-evoked potentials [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The interoceptive neural circuit is responsible for coordinating communication between the central nervous system (CNS) and the autonomic nervous system (ANS), playing a vital role in regulating emotional experiences [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. As William James famously stated, \u0026lsquo;emotion is the feeling of bodily changes\u0026rsquo; [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. A decrease in TD-BHI inhibited effective emotional processing, further emphasizing the importance of disrupted BHI in affecting emotional experiences. Several studies have examined the correlations between interoceptive awareness and suicidal ideation, and the results have consistently demonstrated a negative correlation between them [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Based on these findings, our results suggested that reduced interoceptive awareness, as indicated by the decreased TD-BHI values in the MDDSI group, may contribute to the development of suicidal ideation in FeMDD patients. Interestingly, we observed significant increases in the TD-BHI of the theta band in the middle and right parietal lobes in the MDDSI group, compared with those in the HCs, but these differences were not found in the MDDNSI group. Previous studies have indicated that emotion elicitation, which involves an increased level of attention, is related to the interactions between EEG oscillations in the theta band and vagal modulations in high-frequency power [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Furthermore, previous studies have shown that emotion-related stimuli, particularly those involving highly aggression-related content in films, can provoke the most significant increases in EEG responses within the theta band [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The TD-BHI of the theta band could be specifically related to impulsive emotions or behaviors, which may mediate the development of suicidal ideation. Additionally, heightened levels of arousal resulting from persistent emotional stimulation were found to be correlated with increased BHI over the parietal electrodes [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Abnormalities in parietal networks were consistent findings in MDD patients [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. A recent study on treatment-resistant depression with suicidal ideation has shown that theta burst stimulation is an effective approach for reducing suicidal ideation and anxiety symptoms [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. These findings indicate that TD-BHI in the theta band of the parietal lobe is closely correlated with emotional processing sensitivity [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], which is correlated with suicidal ideation in MDD patients [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhen compared with the HCs, MDD patients demonstrated significantly lower scores of the MCCB (e.g., SOP, AV, WM, VRB), which is consistent with the findings of a previous study [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. It was confirmed that the functional impairment observed in MDD patients was mediated by dysfunction within the frontal cortex and the middle temporal lobe [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In this study, we observed significant correlations between the TD-BHI and VRB as well as the VIS of the MCCB in both the MDDSI and MDDNSI groups. There were similar significant differences in verbal learning performance among patients with non-suicidal self-injury [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. In fact, VRB deficits are symptoms of memory loss in MDD patients. The exciting thing is that TD-BHI can describe certain cognitive states of MDD patients, which is consistent with the findings of previous studies. Furthermore, we found a positive correlation between only the TD-BHI values and the AV (attention / vigilance) score of the MCCB in the MDDSI group. AV is related to impulsive behavior, and impulsiveness has long been recognized as a key factor in suicide [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. On the other hand, the infralimbic cortex plays a crucial role in cognitive flexibility and is also involved in the presence of suicidal ideation [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Cognitive impairments were associated with suicidal ideation in psychosis patients [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Poorer cognitive function was consistently associated with an increased risk of suicidal ideation [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. In summary, cognitive impairments were correlated with the emergence of suicidal ideation, and TD-BHI could specifically characterize suicidal ideation in FeMDD patients.\u003c/p\u003e \u003cp\u003eCircadian rhythms are modulated by bidirectional BHI, and disruptions in this interaction could contribute to sleep disturbances, ultimately leading to the development of depressive disorders [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. It is important to note that sleep plays a critical role in mental disorders [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. As in previous studies, our study also revealed lower sleep quality in FeMDD patients [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. However, we found that the correlation between the TD-BHI values and the PSQI score was opposite in the MDDSI and MDDNSI groups. These intriguing findings suggest contrasting or contradictory correlations between BHI values and sleep quality in the MDD subgroups. In particular, previous studies have indicated that the correlation between sleep quality and suicidal ideation may have relatively independent effects in comorbid mental disorders [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Based on our findings, we hypothesized that there may be distinct neural mechanisms underlying sleep disturbances in MDD patients with or without SI. A recent meta-analysis supported this hypothesis, indicating that total sleep time was significantly lower in the MDDSI group than in MDDNSI group [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. These findings suggest that differences in sleep quality could serve as biomarkers for identifying MDD subgroups with or without SI.\u003c/p\u003e \u003cp\u003eEmotion is a complex mental phenomenon that encompasses various intricate processes. In addition, the TD-BHI has the ability to capture adaptive and flexible modulation of both central and autonomic nerves by the brainstem. In our study, we found that TD-BHI could effectively distinguish significant differences between MDD patients and HCs. Furthermore, we observed a significant correlation between the TD-BHI values and clinical symptoms (e.g., cognitive impairments and sleep disturbances) associated with suicidal ideation. Moreover, the BHI has been identified as a potential marker for evaluating the effects of antidepressant medications [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Taking into account these findings, it is plausible to consider TD-BHI as a robust diagnostic biomarker that could distinguish between MDD subgroups with or without SI.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimitations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAlthough our results are significant and provide insight into the underlying mechanisms of brain-heart interactions in MDDSI, it is important to acknowledge several limitations within the study. First, this study was limited by an imbalance in sample size and the sex ratio among the three groups. Although no significant difference in gender ( \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\)\u003c/span\u003e\u003c/span\u003e = 5.03, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.081, df\u0026thinsp;=\u0026thinsp;2) was found, it is important to consider that previous studies have indicated the possible influence of gender on brain networks and clinical symptoms in MDD patients [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Second, we observed significant differences in age and level of education among the three groups. These differences may introduce bias, which can only be addressed by controlling for age and level of education as covariates. Third, we did not explore the specific mechanisms through which TD-BHI affected suicidal ideation in MDD patients. Future study should investigate the underlying neural mechanisms through how brain-heart interactions contribute to suicidal ideation in MDD patients. Furthermore, this approach has the potential to lead to the development of more effective early intervention strategies for individuals at risk.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, our results indicated that there are more pronounced differences in the TD-BHI values in the MDDSI group compared with those in the MDDNSI as well as those in the HCs groups. These results suggested that the diminished function of interoceptive neural circuits may contribute to impaired emotion regulation, which, in turn, may be associated with the development of suicidal ideation in MDD patients. Additionally, we observed significant correlations between TD-BHI values and clinical symptoms, including cognitive performance and sleep quality. These results highlighted the potential correlations between TD-BHI and MDDSI, as these symptoms have been strongly linked to suicidal ideation. Our findings underscored the crucial role of TD-BHI in the development of suicidal ideation in MDD patients, suggesting its potential as a reliable biomarker for auxiliary diagnosis, particularly in those with or without suicidal ideation. This study may provide valuable information on the potential utility of TD-BHI as a diagnostic biomarker and emphasizes the importance of further exploration in the field of brain-heart interactions to enhance our understanding and improve clinical interventions for MDDSI.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBHI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBrain Heart Interaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTD-BHI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTemporal Dynamic Brain Heart Interaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMDD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMajor Depressive Disorder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFeMDD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFirst-episode Drug-na\u0026iuml;ve Major Depressive Disorder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSuicidal Ideation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEEG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eElectroencephalogram\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eECG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eElectrocardiogram\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMCCB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMATRICS Consensus Cognitive Battery\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCVD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardiovascular diseases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDSM-5\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiagnostic and Statistical Manual of Mental Disorders, Fifth Edition\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBSI-CV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBech Scale for Suicide Ideation-Chinese Version\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHAMD-17\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHamilton Depression Rating Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSQI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePittsburgh Sleep Quality Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003e Prior to enrollment, all participants demonstrated a comprehensive understanding of the study procedures and provided written informed consent. All participants were aged 16 years or older. For participants aged 16 years, written informed consent was obtained from both the participants themselves and their parents or legal guardians. For participants older than 16 years, parental or legal guardian consent was not required. The study protocol was reviewed and approved by the Institutional Review Board of the Affiliated Brain Hospital of Guangzhou Medical University (ethical approval number: AF/SC-02/02.1) and was conducted in accordance with the ethical standards of the 2013 revision of the Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest in conducting this study or preparing the manuscript.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Key Research and Development Program of China (2023YFC2414500, 2023YFC2414504), announcement and Leading Science and Technical Foundation of Guangzhou Civil Affairs (GCAAL2022001 to G.Z.), Guangzhou Planned Project of Science and Technology (2023B04J0106)); the National Natural Science Foundation of China (82271953, 82301688), the Key Research and Development Program of Guangdong (2023B0303020001, 2023B0303010003), the Natural Science Foundation of Guangdong Province (2024A1515013058), the Guangdong Key Laboratory of Battery Safety at Guangzhou Institute of Energy Testing (2019B121203008-KJ-2024-040/ KJ-2024-041), the Science and Technology Program of Guangzhou (2025A03J3357), clinical Collaboration Project on Integrated Traditional Chinese and Western Medicine for Major and Difficult Diseases (Bipolar Disorder, ZDYN-2024-A-121), the Research capacity improvement project of Guangzhou Medical University (2024SRP200), and Guangzhou Key Clinical Specialty (Clinical Medical Research Institute).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZhaobo Li and Jing Zhou completed the data analysis and wrote the main manuscript; Yuanyuan Huang acquired the data and critically revised the manuscript; Fengchun Wu and Kai Wu revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eWe wish to express our gratitude to all the volunteers from the Affiliated Brain Hospital, Guangzhou Medical University, and South China University of Technology.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWoelfer M, Kasties V, Kahlfuss S, Walter M. The Role of Depressive Subtypes within the Neuroinflammation Hypothesis of Major Depressive Disorder. 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Demographic and clinical features of participants.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003cp\u003emean(S.D.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eMDDNSI\u003c/p\u003e\n \u003cp\u003e(n = 39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMDDSI\u003c/p\u003e\n \u003cp\u003e(n = 69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eHCs\u003c/p\u003e\n \u003cp\u003e(n = 42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eF/\u003cem\u003et\u003c/em\u003e/\u003cimg width=\"14\" height=\"29\" src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1772571167.gif\" alt=\"image\"\u003e (\u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003cp\u003e(Female/male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e25/14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e43/26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e18/24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.03 (0.081)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e25.79 (4.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e22.91 (4.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e22.36 (2.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e7.43 (\u003cstrong\u003e0.001\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eLevel of education\u003c/p\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e15.13 (2.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e14.12 (2.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e15.90 (2.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e7.10 (\u003cstrong\u003e0.001\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eHAMD-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e20.79 (5.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e22.87 (4.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e3.10 (0.081)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eHAMD-17 without Suicide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e20.05 (5.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e20.94 (4.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.61 (0.442)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eHAMD-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eBSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.53 (7.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e11.61 (7.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003ePSQI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e12.38 (3.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e12.22 (3.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.01 (0.961)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: MDDNSI, first-episode major depressive disorder patient without suicidal ideation; MDDSI, first-episode major depressive disorder patient with suicidal ideation; HCs, healthy controls; HAMD-17, Hamilton Depression Rating Scale; PSQI, pittsburgh sleep quality index.\u003c/p\u003e\n\u003cp\u003eTable 2. Cognitive performance of participants.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"585\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eMCCB\u003c/p\u003e\n \u003cp\u003emean (S.D.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eMDDNSI\u003c/p\u003e\n \u003cp\u003e(①, n = 39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eMDDSI\u003c/p\u003e\n \u003cp\u003e(②, n = 69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eHCs\u003c/p\u003e\n \u003cp\u003e(③, n = 42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eF (\u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003ePost-Hoc\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cem\u003et\u0026nbsp;\u003c/em\u003e(\u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e34.92\u003c/p\u003e\n \u003cp\u003e(11.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e32.29\u003c/p\u003e\n \u003cp\u003e(10.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e46.21\u003c/p\u003e\n \u003cp\u003e(11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e14.48\u003c/p\u003e\n \u003cp\u003e(\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e③\u0026gt;①\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e3.50 (\u003cstrong\u003e0.002\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e③\u0026gt;②\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e5.36 (\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e①\u0026gt;②\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1.34 (0.547)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e36.14\u003c/p\u003e\n \u003cp\u003e(11.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e34.65\u003c/p\u003e\n \u003cp\u003e(9.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e42.14\u003c/p\u003e\n \u003cp\u003e(8.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e5.68\u003c/p\u003e\n \u003cp\u003e(\u003cstrong\u003e0.004\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e③\u0026gt;①\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e2.74 (\u003cstrong\u003e0.021\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e③\u0026gt;②\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e3.18 (\u003cstrong\u003e0.005\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e①\u0026gt;②\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.02 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eWM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e39.82\u003c/p\u003e\n \u003cp\u003e(13.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e38.72\u003c/p\u003e\n \u003cp\u003e(11.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e48.48\u003c/p\u003e\n \u003cp\u003e(11.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e5.55\u003c/p\u003e\n \u003cp\u003e(\u003cstrong\u003e0.005\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e③\u0026gt;①\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e2.22 (0.085)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e③\u0026gt;②\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e3.32 (\u003cstrong\u003e0.003\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e①\u0026gt;②\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.78 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eVRB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e34.13\u003c/p\u003e\n \u003cp\u003e(9.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e33.29\u003c/p\u003e\n \u003cp\u003e(10.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e42.88\u003c/p\u003e\n \u003cp\u003e(8.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e8.04\u003c/p\u003e\n \u003cp\u003e(\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e③\u0026gt;①\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e2.47 (\u003cstrong\u003e0.044\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e③\u0026gt;②\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e4.01 (\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e①\u0026gt;②\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1.18 (0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eVIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e40.49\u003c/p\u003e\n \u003cp\u003e(8.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e40.9\u003c/p\u003e\n \u003cp\u003e(8.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e46.05\u003c/p\u003e\n \u003cp\u003e(7.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e2.89\u003c/p\u003e\n \u003cp\u003e(0.059)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e③\u0026gt;①\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e2.03 (0.133)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e③\u0026gt;②\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e2.24 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e①\u0026lt;②\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.10 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: MCCB, MATRICS consensus cognitive battery; MDDNSI, first-episode major depressive disorder patient without suicidal ideation; MDDSI, first-episode major depressive disorder patient with suicidal ideation; HCs, healthy controls; SOP, speed of processing domain; AV, attention/vigilance; WM, working memory; VRB, verbal learning; VIS, visual learning.\u003c/p\u003e\n\u003cp\u003eTabel 3. Partial correlation analysis between TD-BHI and clinical characteristics.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eGroups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eClinical characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eTD-BHI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cem\u003er\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eMDDNSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eMCCB-VRB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003elow-\u0026theta;-Pz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.48 (\u003cstrong\u003e0.002\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ehigh-\u0026theta;-Pz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.48 (\u003cstrong\u003e0.002\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ehigh-\u003cimg width=\"9\" height=\"29\" src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1772571167.gif\" alt=\"image\"\u003e-CP5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.34 (\u003cstrong\u003e0.028\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003ePSQI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003elow-\u0026theta;-CP6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.45 (\u003cstrong\u003e0.014\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ehigh-\u0026theta;-CP6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.46 (\u003cstrong\u003e0.014\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eMDDSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eMCCB-AV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003elow-\u0026theta;-PZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.17 (\u003cstrong\u003e0.033\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eMCCB-VRB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ehigh-\u0026beta;-C4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.26 (\u003cstrong\u003e0.044\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ehigh-\u0026beta;-CP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.25 (\u003cstrong\u003e0.046\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eMCCB-VIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003elow-\u0026beta;-CP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.28 (\u003cstrong\u003e0.023\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ehigh-\u0026beta;-CP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.28 (\u003cstrong\u003e0.020\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003ePSQI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003elow-\u0026beta;-P3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.22 (\u003cstrong\u003e0.011\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ehigh-\u0026beta;-P3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.22 (\u003cstrong\u003e0.010\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: TD-BHI, temporal dynamic brain-heart interactions; MDDNSI, first-episode major depressive disorder patient without suicidal ideation; MDDSI, first-episode major depressive disorder patient with suicidal ideation; MCCB, MATRICS consensus cognitive battery; VRB, verbal learning; AV, attention/vigilance; VIS, visual learning; PSQI, pittsburgh sleep quality index; low, low-frequency PSD of ECG; high, high-frequency PSD of ECG; \u003cimg width=\"39\" height=\"29\" src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1772571167.gif\" alt=\"image\"\u003e\u0026nbsp;means the PSD of various bands in EEG; P3, Pz, T7, CP5, P4 means the position of electrode; low-\u0026beta;-P3 means interaction between low-frequency PSD of ECG and the PSD of \u0026beta; in EEG on the scalp position of P3.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Major depressive disorder, Brain-heart interactions, Suicidal ideation, EEG, ECG","lastPublishedDoi":"10.21203/rs.3.rs-8839132/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8839132/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePrevious studies have indicated abnormal brain activation or heart rate variability in patients with major depressive disorder (MDD). Suicidal ideation, as one of the main concerns of MDD, is a serious public health problem. However, the interactions between brain and heart of MDD patients with and without suicidal ideation remain largely unknown.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this study, resting-state EEG and ECG data were simultaneously collected from 42 healthy controls (HCs), 69 MDD patients with suicidal ideation (MDDSI), and 39 MDD patients without suicidal ideation (MDDNSI). We proposed a novel methodology for analyzing temporal dynamic brain-heart interactions (TD-BHI). Then, we calculated the correlations between abnormalities of TD-BHI and cognitive performance, as well as sleep quality.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe found that the TD-BHI values in the MDDSI group significantly increased in the parietal lobes and decreased in the right parietal lobes, compared with those in the HCs group. Additionally, the attention / vigilance score of the MATRICS consensus cognitive battery (MCCB) and the Pittsburgh sleep quality index scores were significantly correlated with the TD-BHI values in the MDDSI group.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eDistinct abnormalities of TD-BHI may mediate suicidal ideation in MDD patients and can serve as a reliable biomarker for the diagnosis of MDDSI.\u003c/p\u003e","manuscriptTitle":"Temporal dynamic brain-heart interaction alterations associated with suicidal ideation in major depressive disorder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-08 14:51:50","doi":"10.21203/rs.3.rs-8839132/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-08T04:51:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-06T04:24:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-16T07:34:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"255559234004841652900112739817123818590","date":"2026-03-14T10:20:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4317925763194153957677715360817492618","date":"2026-03-02T02:16:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-26T13:43:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-26T12:47:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-16T09:53:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-14T03:42:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2026-02-14T03:38:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0c10d1f7-af68-46bf-b468-d2d30500c679","owner":[],"postedDate":"March 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T11:09:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-08 14:51:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8839132","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8839132","identity":"rs-8839132","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}