Altered interaction between cardiac vagal influence and delta sleep EEG suggests an altered neuroplasticity in patients suffering from major depressive disorder

Graph-based EEG approach for depression prediction: integrating time-frequency complexity and spatial topology

Depression has become the prevailing global mental health concern. The accuracy of traditional depression diagnosis methods faces challenges due to diverse factors, making primary identification a complex task. Thus, the imperative lies in developing a method that fulfills objectivity and effectiveness criteria for depression identification. Current research underscores notable disparities in brain activity between individuals with depression and those without. The Electroencephalogram (EEG), as a biologically reflective and easily accessible signal, is widely used to diagnose depression. This article introduces an innovative depression prediction strategy that merges time-frequency complexity and electrode spatial topology to aid in depression diagnosis. Initially, time-frequency complexity and temporal features of the EEG signal are extracted to generate node features for a graph convolutional network. Subsequently, leveraging channel correlation, the brain network adjacency matrix is employed and calculated. The final depression classification is achieved by training and validating a graph convolutional network with graph node features and a brain network adjacency matrix based on channel correlation. The proposed strategy has been validated using two publicly available EEG datasets, MODMA and PRED+CT, achieving notable accuracy rates of 98.30 and 96.51%, respectively. These outcomes affirm the reliability and utility of our proposed strategy in predicting depression using EEG signals. Additionally, the findings substantiate the effectiveness of EEG time-frequency complexity characteristics as valuable biomarkers for depression prediction.

A Depression Diagnosis Method Based on the Hybrid Neural Network and Attention Mechanism

Depression is a common but easily misdiagnosed disease when using a self-assessment scale. Electroencephalograms (EEGs) provide an important reference and objective basis for the identification and diagnosis of depression. In order to improve the accuracy of the diagnosis of depression by using mainstream algorithms, a high-performance hybrid neural network depression detection method is proposed in this paper combined with deep learning technology. Firstly, a concatenating one-dimensional convolutional neural network (1D-CNN) and gated recurrent unit (GRU) are employed to extract the local features and to determine the global features of the EEG signal. Secondly, the attention mechanism is introduced to form the hybrid neural network. The attention mechanism assigns different weights to the multi-dimensional features extracted by the network, so as to screen out more representative features, which can reduce the computational complexity of the network and save the training time of the model while ensuring high precision. Moreover, dropout is applied to accelerate network training and address the over-fitting problem. Experiments reveal that the 1D-CNN-GRU-ATTN model has more effectiveness and a better generalization ability compared with traditional algorithms. The accuracy of the proposed method in this paper reaches 99.33% in a public dataset and 97.98% in a private dataset, respectively.

A Depression Prediction Algorithm Based on Spatiotemporal Feature of EEG Signal

Depression has gradually become the most common mental disorder in the world. The accuracy of its diagnosis may be affected by many factors, while the primary diagnosis seems to be difficult to define. Finding a way to identify depression by satisfying both objective and effective conditions is an urgent issue. In this paper, a strategy for predicting depression based on spatiotemporal features is proposed, and is expected to be used in the auxiliary diagnosis of depression. Firstly, electroencephalogram (EEG) signals were denoised through the filter to obtain the power spectra of the three corresponding frequency ranges, Theta, Alpha and Beta. Using orthogonal projection, the spatial positions of the electrodes were mapped to the brainpower spectrum, thereby obtaining three brain maps with spatial information. Then, the three brain maps were superimposed on a new brain map with frequency domain and spatial characteristics. A Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) were applied to extract the sequential feature. The proposed strategy was validated with a public EEG dataset, achieving an accuracy of 89.63% and an accuracy of 88.56% with the private dataset. The network had less complexity with only six layers. The results show that our strategy is credible, less complex and useful in predicting depression using EEG signals.

Associations between Heart Rate Variability and Brain Activity during a Working Memory Task: A Preliminary Electroencephalogram Study on Depression and Anxiety Disorder

Heart rate variability (HRV) has been suggested to reflect executive function and related neural activity. Executive dysfunction has been suggested to play an important role in the pathophysiology of emotional disorders. The purpose of this study was to investigate whether HRV showed a significant correlation with electroencephalogram (EEG) during a working memory performance in patients with depressive or anxiety disorder. A retrospective analysis was conducted with data from 61 patients with depressive disorder (43 women and 18 men) and 59 patients with anxiety disorder (35 women and 24 men). HRV was measured in the resting state, and EEG was recorded in the resting state and during the execution of a working memory task. It was performed in patients with depressive and anxiety disorder, and the paired sample t-test between resting state and task performance, as well as the partial correlation analysis between HRV and EEG, was conducted. Both depressed and anxious patients showed weaker beta relative power during the working memory task compared to the rest period. The resting-state EEG did not correlate with HRV parameters in both groups. In depressed patients, HRV showed a positive correlation with delta power during the task and a negative correlation with beta relative power during the task. In patients with anxiety disorder, HRV showed a significant positive correlation with theta power of the right frontal region during the task. Our results suggest that HRV would be related to executive-function-related neural activity in patients with depressive or anxiety disorder. Future studies with more subjects, including healthy controls, are needed to verify the correlation between HRV and EEG and to come up with a more comprehensive picture of neurobiological changes in emotional disorders.

Prenatal Exposure to PM2.5 and Cardiac Vagal Tone during Infancy: Findings from a Multiethnic Birth Cohort

BACKGROUND

The autonomic nervous system plays a key role in maintaining homeostasis and responding to external stimuli. In adults, exposure to fine particulate matter (PM2.5) has been associated with reduced heart rate variability (HRV), an indicator of cardiac autonomic control.

OBJECTIVES

Our goal was to investigate the associations of exposure to fine particulate matter (PM2.5) with HRV as an indicator of cardiac autonomic control during early development.

METHODS

We studied 237 maternal-infant pairs in a Boston-based birth cohort. We estimated daily residential PM2.5 using satellite data in combination with land-use regression predictors. In infants at 6 months of age, we measured parasympathetic nervous system (PNS) activity using continuous electrocardiogram monitoring during the Repeated Still-Face Paradigm, an experimental protocol designed to elicit autonomic reactivity in response to maternal interaction and disengagement. We used multivariable linear regression to examine average PM2.5 exposure across pregnancy in relation to PNS withdrawal and activation, indexed by changes in respiration-corrected respiratory sinus arrhythmia (RSAc)-an established metric of HRV that reflects cardiac vagal tone. We examined interactions with infant sex using cross-product terms.

RESULTS

In adjusted models we found that a 1-unit increase in PM2.5 (in micrograms per cubic meter) was associated with a 3.53% decrease in baseline RSAc (95% CI: -6.96, 0.02). In models examining RSAc change between episodes, higher PM2.5 was generally associated with reduced PNS withdrawal during stress and reduced PNS activation during recovery; however, these associations were not statistically significant. We did not observe a significant interaction between PM2.5 and sex.

DISCUSSION

Prenatal exposure to PM2.5 may disrupt cardiac vagal tone during infancy. Future research is needed to replicate these preliminary findings. https://doi.org/10.1289/EHP4434.

Dynamic coupling between the central and autonomic nervous systems during sleep: A review

Sleep is characterized by coordinated cortical and cardiac oscillations reflecting communication between the central (CNS) and autonomic (ANS) nervous systems. Here, we review fluctuations in ANS activity in association with CNS-defined sleep stages and cycles, and with phasic cortical events during sleep (e.g., arousals, K-complexes). Recent novel analytic methods reveal a dynamic organization of integrated physiological networks during sleep and indicate how multiple factors (e.g., sleep structure, age, sleep disorders) affect "CNS-ANS coupling". However, these data are mostly correlational and there is a lack of clarity of the underlying physiology, making it challenging to interpret causality and direction of coupling. Experimental manipulations (e.g., evoking K-complexes or arousals) provide information on the precise temporal sequence of cortical-cardiac activity, and are useful for investigating physiological pathways underlying CNS-ANS coupling. With the emergence of new analytical approaches and a renewed interest in ANS and CNS communication during sleep, future work may reveal novel insights into sleep and cardiovascular interactions during health and disease, in which coupling could be adversely impacted.

Altered nocturnal blood pressure profiles in women with insomnia disorder in the menopausal transition

OBJECTIVE

Insomnia disorder is a risk factor for cardiovascular (CV) pathology. It is unknown whether insomnia that develops in the context of the menopausal transition (MT) impacts the CV system. We assessed nocturnal blood pressure (BP) and heart rate (HR) profiles in women with insomnia disorder in the MT.

METHODS

Twelve women meeting DSM-IV criteria for insomnia in the MT (age, mean ± SD: 50.5 ± 3.6 y) and 11 controls (age, mean ± SD: 49.0 ± 3.0 y) had polysomnographic recordings on one or two nights during which beat-to-beat BP and HR were assessed and analyzed hourly from lights-out across the first 6 hours of the night and according to sleep stage. Physiological hot flashes were identified from fluctuations in sternal skin conductance.

RESULTS

Women with insomnia and controls had similar distributions of sleep stages and awakenings/arousals across hours of the night, although insomnia participants tended to have more wakefulness overall. More women in the insomnia group (7 of 12) than in the control group (2 of 11) had at least one physiological hot flash at night (P < 0.05). Both groups showed a drop in BP in the first part of the night; however, systolic and diastolic BP patterns diverged later, remaining low in controls but increasing in insomnia participants 4 to 6 hours after lights-out (P < 0.05). Both groups showed a similar pattern of decline in HR across the night.

CONCLUSIONS

Our findings suggest altered regulatory control of BP during sleep in the MT insomnia. The causes and long-term consequences of this altered nocturnal BP profile remain to be determined.

Sleep electroencephalography and heart rate variability interdependence amongst healthy subjects and insomnia/schizophrenia patients

The quantification of interdependencies within autonomic nervous system has gained increasing importance to characterise healthy and psychiatric disordered subjects. The present work introduces a biosignal processing approach, suggesting a computational resource to estimate coherent or synchronised interactions as an eventual supportive aid in the diagnosis of primary insomnia and schizophrenia pathologies. By deploying linear, nonlinear and statistical methods upon 25 electroencephalographic and electrocardiographic overnight sleep recordings, the assessment of cross-correlation, wavelet coherence and [Formula: see text]:[Formula: see text] phase synchronisation is focused on tracking discerning features amongst the clinical cohorts. Our results indicate that certain neuronal oscillations interact with cardiac power bands in distinctive ways responding to standardised sleep stages and patient groups, which promotes the hypothesis of subtle functional dynamics between neuronal assembles and (para)sympathetic activity subject to pathophysiological conditions.

The EEG as an index of neuromodulator balance in memory and mental illness

There is a strong correlation between signature EEG frequency patterns and the relative levels of distinct neuromodulators. These associations become particularly evident during the sleep-wake cycle. The monoamine-acetylcholine balance hypothesis is a theory of neurophysiological markers of the EEG and a detailed description of the findings that support this proposal are presented in this paper. According to this model alpha rhythm reflects the relative predominance of cholinergic muscarinic signals and delta rhythm that of monoaminergic receptor effects. Both high voltage synchronized rhythms are likely mediated by inhibitory Gαi/o-mediated transduction of inhibitory interneurons. Cognitively, alpha and delta EEG measures are proposed to indicate automatic and flexible strategies, respectively. Sleep is associated with marked changes in relative neuromodulator levels corresponding to EEG markers of distinct stages. Sleep studies on memory consolidation present some of the strongest evidence yet for the respective roles of monoaminergic and cholinergic projections in declarative and non-declarative memory processes, a key theoretical premise for understanding the data. Affective dysregulation is reflected in altered EEG patterns during sleep.

An integrative assessment of the psychophysiologic alterations in young women with recurrent major depressive disorder

OBJECTIVE

Alterations in neuroelectrical activities coincide with major depressive disorder (MDD). This study examines the pattern of cerebral activity and cardiac autonomic parameters of euthymic women with recurrent MDD.

METHODS

Resting electroencephalograms and electrocardiograms were recorded from 20 women with MDD receiving escitalopram and 40 matched and healthy women. We computed frontal alpha asymmetry to evaluate the interhemispheric balance. Parameters of heart rate variability were extracted to assess cardiac autonomic control. Sample entropy was used to assess the complexity of neurocardiac dynamics. The relationship between cardiovagal activity and alpha electroencephalogram was examined with a coherence analysis.

RESULTS

Multivariable analysis of variance revealed a differential pattern of psychophysiologic variables between MDD patients and controls (p = .03). MDD was associated with a tendency toward lower left frontal activity (-0.06 [standard deviation = 0.14] versus 0.04 [0.17] lnμV(2), p = .04). Discriminant analysis demonstrated more right frontal activation, a lower high-frequency heart rate power spectrum, and a higher ratio of the low- to high-frequency heart rate power spectrum in patients with MDD compared with controls. Residual depressive symptoms (r = -0.09 to 0.11, p = .63-.99) and escitalopram dosage (r = -0.09 to 0.28, p = .22-.84) were not correlated with autonomic measures. Coherence between normalized high-frequency component of the heart rate power spectrum and alpha power was not significant (F3, p = .27; F4, p = .16).

CONCLUSIONS

Euthymic women with recurrent MDD have a distinctive psychophysiologic profile. This profile may reflect altered frontal activation and a reduced cardiovagal tone in depression.

Sleep and cardiovascular regulation

Normal sleep has a profound effect on the cardiovascular system, reducing cardiovascular activity throughout non-rapid eye movement sleep; changes that are modified and augmented by circadian system influence. There is also evidence that sleep-initiated changes in autonomic balance may in turn modify the development of sleep within a night, particularly the development of slow wave sleep. It is assumed that the cardiovascular changes that accompany sleep reflect a functional aspect of sleep, although the precise functional role has not been agreed upon. Nevertheless, there is good evidence that the cardiovascular changes that occur during normal sleep are beneficial for the cardiovascular system. Arousals from sleep, which are common even in normal sleep, are associated with a surge in activity in cardiorespiratory systems, with marked effects on the sleep-related pattern of cardiovascular activity when they occur frequently. Despite the importance of this aspect of sleep, controversy remains as to both the nature of the activation response and the circumstances under which it is elicited. The concept that sleep-related changes in cardiovascular activity are beneficial leads to the corollary that sleep disturbance would result in adverse cardiovascular consequences. While there is strong empirical evidence for such a relationship, it remains unclear whether this is a direct effect or, as has been suggested recently, the effect of disturbed sleep is mediated via stress-related modification of neuroendocrine systems.

EEG delta oscillations as a correlate of basic homeostatic and motivational processes

Functional significance of delta oscillations is not fully understood. One way to approach this question would be from an evolutionary perspective. Delta oscillations dominate the EEG of waking reptiles. In humans, they are prominent only in early developmental stages and during slow-wave sleep. Increase of delta power has been documented in a wide array of developmental disorders and pathological conditions. Considerable evidence on the association between delta waves and autonomic and metabolic processes hints that they may be involved in integration of cerebral activity with homeostatic processes. Much evidence suggests the involvement of delta oscillations in motivation. They increase during hunger, sexual arousal, and in substance users. They also increase during panic attacks and sustained pain. In cognitive domain, they are implicated in attention, salience detection, and subliminal perception. This evidence shows that delta oscillations are associated with evolutionary old basic processes, which in waking adults are overshadowed by more advanced processes associated with higher frequency oscillations. The former processes rise in activity, however, when the latter are dysfunctional.