A Possible Common Neurophysiologic Basis for MDD, Bipolar Disorder, and Schizophrenia: Lessons from Electrophysiology

How to Solve Clinical Challenges in Mood Disorders; Machine Learning Approaches Using Electrophysiological Markers

Differentiating between the diagnoses of mood disorders and other psychiatric disorders, and predicting treatment response in depression has long been a concern for clinicians. Machine learning (ML) is one part of artificial intelligence that focuses on instructing computers to mimic the cognitive abilities of the human brain through training. This study will review the research on the use of ML techniques to differentiate diagnoses and predict treatment responses in mood disorders based on electroencephalography (EEG) data. There have been several attempts to differentiate between the diagnoses of bipolar disorder and major depressive disorder , mood disorders, and other psychiatric disorders using ML techniques found on EEG markers. Previous studies have shown that accuracy varies depending on which EEG markers are used, the sample size, and the ML technique. Also, precise and improved ML approaches can be developed by adapting the various feature selection and validation methods that reflect each disease's characteristics. Although ML faces some limitations and challenges in solving for consistent and improved accuracy in the diagnosis and treatment of mood disorders, it has a great potential to understand mood disorders better and provide valuable tools to personalize both identification and treatment.

Insomnia-related brain functional correlates in first-episode drug-naïve major depressive disorder revealed by resting-state fMRI

Introduction

Insomnia is a common comorbidity symptom in major depressive disorder (MDD) patients. Abnormal brain activities have been observed in both MDD and insomnia patients, however, the central pathological mechanisms underlying the co-occurrence of insomnia in MDD patients are still unclear. This study aimed to explore the differences of spontaneous brain activity between MDD patients with and without insomnia, as well as patients with different level of insomnia.

Methods

A total of 88 first-episode drug-naïve MDD patients including 44 with insomnia (22 with high insomnia and 22 with low insomnia) and 44 without insomnia, as well as 44 healthy controls (HC), were enrolled in this study. The level of depression and insomnia were evaluated by HAMD-17, adjusted HAMD-17 and its sleep disturbance subscale in all subjects. Resting-state functional and structural magnetic resonance imaging data were acquired from all participants and then were preprocessed by the software of DPASF. Regional homogeneity (ReHo) values of brain regions were calculated by the software of REST and were compared. Finally, receiver operating characteristic (ROC) curves were conducted to determine the values of abnormal brain regions for identifying MDD patients with insomnia and evaluating the severity of insomnia.

Results

Analysis of variance showed that there were significant differences in ReHo values in the left middle frontal gyrus, left pallidum, right superior frontal gyrus, right medial superior frontal gyrus and right rectus gyrus among three groups. Compared with HC, MDD patients with insomnia showed increased ReHo values in the medial superior frontal gyrus, middle frontal gyrus, triangular inferior frontal gyrus, calcarine fissure and right medial superior frontal gyrus, medial orbital superior frontal gyrus, as well as decreased ReHo values in the left middle occipital gyrus, pallidum and right superior temporal gyrus, inferior temporal gyrus, middle cingulate gyrus, hippocampus, putamen. MDD patients without insomnia demonstrated increased ReHo values in the left middle frontal gyrus, orbital middle frontal gyrus, anterior cingulate gyrus and right triangular inferior frontal gyrus, as well as decreased ReHo values in the left rectus gyrus, postcentral gyrus and right rectus gyrus, fusiform gyrus, pallidum. In addition, MDD patients with insomnia had decreased ReHo values in the left insula when compared to those without insomnia. Moreover, MDD patients with high insomnia exhibited increased ReHo values in the right middle temporal gyrus, and decreased ReHo values in the left orbital superior frontal gyrus, lingual gyrus, right inferior parietal gyrus and postcentral gyrus compared to those with low insomnia. ROC analysis demonstrated that impaired brain region might be helpful for identifying MDD patients with insomnia and evaluating the severity of insomnia.

Conclusion

These findings suggested that MDD patients with insomnia had wider abnormalities of brain activities in the prefrontal-limbic circuits including increased activities in the prefrontal cortex, which might be the compensatory mechanism underlying insomnia in MDD. In addition, decreased activity of left insula might be associated with the occurrence of insomnia in MDD patients and decreased activities of the frontal-parietal network might cause more serious insomnia related to MDD.

Intraoperative EEG-based monitors: are we looking under the lamppost?

PURPOSE OF REVIEW

While electroencephalogram (EEG)-based depth of anesthesia monitors have been in use clinically for decades, there is still a major debate concerning their efficacy for detecting awareness under anesthesia (AUA). Further utilization of these monitors has also been discussed vividly, for example, reduction of postoperative delirium (POD).It seems that with regard to reducing AUA and POD, these monitors might be applicable, under specific anesthetic protocols. But in other settings, such monitoring might be less contributive and may have a 'built-it glass ceiling'.Recent advances in other venues of electrophysiological monitoring might have a strong theoretical rationale, and early supporting results, to offer a breakthrough out of this metaphorical glass ceiling. The purpose of this review is to present this possibility.

RECENT FINDINGS

Following previous findings, it might be concluded that for some anesthesia protocols, the prevailing depth of anesthesia monitors may prevent incidences of AUA and POD. However, in other settings, which may involve other anesthesia protocols, or specifically for POD - other perioperative causes, they may not. Attention-related processes measured by easy-to-use real-time electrophysiological markers are becoming feasible, also under anesthesia, and might be applicable for more comprehensive prevention of AUA, POD and possibly other perioperative complications.

SUMMARY

Attention-related monitoring might have a strong theoretical basis for the prevention of AUA, POD, and potentially other distressing postoperative outcomes, such as stroke and postoperative neurocognitive disorder. There seems to be already some initial supporting evidence in this regard.

The differences of event-related potential components in patients with comorbid depression and anxiety, depression, or anxiety alone

BACKGROUND

There was a high comorbidity rate of major depressive disorder (MDD) and generalized anxiety disorder (GAD), showing a poor prognosis and significant detrimental impact on functioning. The study aimed to find whether patients with comorbid GAD and MDD had some differences in cognitive functions from patients with MDD or GAD alone.

METHODS

360 adult patients were enrolled from inpatient department of psychiatry from 2020 to 2022. They were divided into three groups with 120 patients for each group: MDD, GAD, and MDD + GAD. All the patients completed psychological evaluation scales including patient health questionnaire-9 (PHQ-9) and 7-item generalized anxiety disorder (GAD-7). All the patients underwent examinations of auditory brainstem response and event-related potentials (ERPs).

RESULTS

In MDD + GAD group, P3b latency was significantly longer than patients with MDD alone, and P300 reaction time was positively correlated with total score of GAD-7 and PHQ-9, and PHQ-9 total score was also significantly positively correlated with P2-P3b amplitude (all p < 0.05). In addition, MDD patients had significantly longer P300 reaction time and lower P2-P3b amplitude than the GAD group (p < 0.05).

LIMITATIONS

It was a single-center and cross-sectional study, and we used self-report scales as assessment tools.

CONCLUSIONS

Patients with MDD and GAD comorbidity might have a worse cognitive function than MDD patients, and the severity of cognitive impairments was positively correlated with the severity of anxiety and depression symptoms.

Negative Air Ions in Neuropsychiatric Disorders

BACKGROUND

Air ions (AIs) are clusters of ionized particles present in the atmosphere, carrying an electrical charge of negative or positive polarity. Past speculations suggested that exposure to positive air ions may be harmful, while exposure to negative air ions (NAIs) may be associated with beneficial health effects. Increasing attention has been directed towards investigating the potential effect of NAIs on human brain activities since initial observations of their beneficial effects on some cognitive processes and mood.

AIMS

Given the paucity and scattered literature, our paper aims to review the available studies on potential positive effects of NAIs exposure on cognitive performances and depression.

DISCUSSION

The review of the literature seems to confirm the effects of NAIs on several brain functions. Indeed, a significant association between NAIs exposure and both well-being and high cognitive performances has been described. Furthermore, exposure to high concentrations of NAIs could be related to the improvement of depressive symptoms.

CONCLUSION

A growing evidence of data, although not yet conclusive, would suggest that NAIs might improve cognitive processes. These findings require specific and urgent controlled trials adopting systems based on AIs release to possibly prevent and treat cognitive dysfunctions present in a broad range of neuropsychiatric conditions.

Real-time monitoring of barriers to patient engagement for improved rehabilitation: a protocol and representative case reports

PURPOSE

There is ample evidence that patient engagement is of major clinical importance in rehabilitation, and it seems this engagement is based upon effective allocation of attention to the tasks during the rehabilitation session. It is possible to discern two types of barriers which hinder attentive engagement: (1) dysfunctional affective coping and (2) limited cognitive recruitment and specifically attention deficit. These barriers might be general for a given patient, due to pre-morbid or co-morbid dysfunctions. But more often they are evoked by tasks or challenges during the rehabilitation session which might be too complicated or stressing for the specific patient who copes with potentially grave impairments. These barriers hinder rehabilitation progress and should be monitored and overcome, by the therapist, throughout the session.

METHODS

We have developed an easy-to-use tool for monitoring a patient's attentive engagement in real-time throughout a rehabilitation session based on analysing the electrophysiological signal sampled from a simple headset. The tool then analyzes the dynamics of the marker over time to identify cognitive and affective barriers during the session. It enables the therapist to insert feedback regarding the patient's functional performance and to combine it with the analysed barriers, in order to derive automatic recommendations for overcoming the cognitive and affective barriers (if identified) for significant enhancement of the rehabilitation session.

RESULTS AND CONCLUSIONS

In this work we present the principles of the tool as well as three detailed case reports to demonstrate its potential usefulness.IMPLICATIONS FOR REHABILITATIONCognitive and affective barriers hinder patient engagment and rehabilitation success.In this work we present an easy to use electrophysiology-based tool which monitors these barriers.Based on the measured barriers and patient's performance, the tool derives treatment suggestions.

Event-related potentials to rare visual targets and negative symptom severity in a transdiagnostic psychiatric sample

OBJECTIVES

Negative psychiatric symptoms are often resistant to treatments, regardless of the disorder in which they appear. One model for a cause of negative symptoms is impairment in higher-order cognition. The current study examined how particular bottom-up and top-down mechanisms of selective attention relate to severity of negative symptoms across a transdiagnostic psychiatric sample.

METHODS

The sample consisted of 130 participants: 25 schizophrenia-spectrum disorders, 26 bipolar disorders, 18 unipolar depression, and 61 nonpsychiatric controls. The relationships between attentional event-related potentials following rare visual targets (i.e., N1, N2b, P2a, and P3b) and severity of the negative symptom domains of anhedonia, avolition, and blunted affect were evaluated using frequentist and Bayesian analyses.

RESULTS

P3b and N2b mean amplitudes were inversely related to the Positive and Negative Syndrome Scale-Negative Symptom Factor severity score across the entire sample. Subsequent regression analyses showed a significant negative transdiagnostic relationship between P3b amplitude and blunted affect severity.

CONCLUSIONS

Results indicate that negative symptoms, and particularly blunted affect, may have a stronger association with deficits in top-down mechanisms of selective attention.

SIGNIFICANCE

This suggests that people with greater severity of blunted affect, independent of diagnosis, do not allocate sufficient cognitive resources when engaging in activities requiring selective attention.

Using EEG to Predict Clinical Response to Electroconvulsive Therapy in Patients With Major Depression: A Comprehensive Review

Introduction: An important approach to improve the therapeutic effect of electroconvulsive therapy (ECT) may be to early characterize patients who are more likely to respond. Our objective was to explore whether baseline electroencephalography (EEG) settings before the beginning of ECT treatment can predict future clinical response to ECT in patients with depressive disorder. Methods: We conducted a systematic search in the MEDLINE, EMBASE, PsycINFO, Web of Science, and Cochrane Central Register of Controlled Trials (CENTRAL) databases to identify studies using EEG in adults with depressive disorder treated by ECT. To investigate the predictive value of baseline EEG on clinical outcomes of ECT, we extracted from the retrieved studies and qualitatively described the association between the baseline EEG markers characteristics and the rates of future responders and/or remitters to ECT. Results: The primary search yielded 2,531 potentially relevant citations, and 12 articles were selected according to inclusion criteria. Most of the studies were prospective studies with small sample size. Sociodemographic and clinical characteristics of patients, ECT settings, EEG settings, and outcomes were heterogeneous. Event-related potential (ERP) paradigms were used in three studies, polysomnography was used in three studies, and the six other studies used EEG to measure cerebral connectivity and activity. Conclusions: P300 amplitude, coherence, and connectivity measures were correlated with remission in patients with depression treated by ECT. Sleep EEG recordings seemed not to be correlated with remission after ECT. Further prospective studies with large sample size are needed to determine optimal EEG parameters associated with clinical response to ECT in depressive disorder. Systematic Review Registration: PROSPERO CRD42020181978.

The Effects of Anesthetics on the Cortex-Lessons From Event-Related Potentials

Consciousness while under general anesthesia is a dreadful condition. Various electroencephalogram (EEG)-based technologies have been developed, on the basis of empirical evidence, in order to identify this condition. However, certain electrophysiological phenomena, which seem strongly related with depth of anesthesia in some drugs, appear less consistent with those of other anesthetic drugs. There is a gap between the complexity of the phenomenon of consciousness and its behavioral manifestations, on the one hand, and the empirical nature of the reported electrophysiological markers, which are associated with it, on the other hand. In fact, such a gap might prevent us from progressing toward unified electrophysiological markers of consciousness while under anesthesia, which are applicable to all anesthetic drugs. We believe that there is a need to bridge this conceptual gap. Therefore, in this work, we will try to present a theoretical framework for such bridging. First, we suggest focusing on neuropsychological processes, which seem to have a clear role in the behavioral manifestations of consciousness while under anesthesia but seem, nevertheless, better defined than consciousness itself-such as perception and attention. Then, we suggest analyzing the effects of anesthesia upon these neuropsychological processes, as they are manifested in the EEG signal. Specifically, we will focus on the effects of anesthesia on event-related potentials (ERPs), which seem more easily associable with neuropsychological modeling.

Neuropsychiatric Disorders as Erratic Attention Regulation - Lessons from Electrophysiology

Attention related electrophysiological waves, such as P300, often deviate from norm in various populations of neuropsychiatric patients. For example, the amplitude is often smaller and the latency is often longer in major depressive disorder, in bipolar disorder and in schizophrenia. On the other hand, in other neuropsychiatric populations, it is often possible to note the opposite phenomena of larger P300 amplitude and shorter latency in comparison with norm, but only for a specific subset of stimuli. This is often reported in various anxiety disorders, substance abuse and various chronic pain syndromes. These findings in the various clinical populations, on their commonalities and differences, are presented in this work. The prevalence of these two types of deviations in the electrophysiological markers of attention, shared by multiple neuropsychiatric populations, raise interesting questions regarding the role of attention deviation and regulation in neuropsychiatry. We present these questions and outline a possible hypothesis in this regard. Furthermore, such potential sensitivity of the attention-related markers to clinical dynamics suggests they could be candidates for monitoring and, potentially, early-sensing of clinical dynamics. Therefore, we discuss the potential usability of such markers.

A Pilot Study of Possible Easy-to-Use Electrophysiological Index for Early Detection of Antidepressive Treatment Non-Response

INTRODUCTION

The evaluation of response to pharmacological treatment in MDD requires 4-8 weeks. Therefore, the ability to predict response, and especially lack of response to treatment, as early as possible after treatment onset or change, is of prime significance. Many studies have demonstrated significant results regarding the ability to use EEG and ERP markers, including attention-associated markers such as P300, for early prediction of response to treatment. But these markers are derived from long EEG/ERP samples, often from multiple channels, which render them impractical for frequent sampling.

METHODS AND RESULTS

We developed a new electrophysiological attention-associated marker from a single channel (two electrodes), using 1-min samples with auditory oddball stimuli. This work presents an initial evaluation of the ability to use this marker's dynamics between repetitive measures for early (<2 weeks) differentiation between responders and non-responders to antidepressive treatment, in 26 patients with various levels of depression and heterogeneous treatment interventions. The slope of change in the marker between early consecutive samples was negative in the non-responders, but not in the responders. This differentiation was stronger for patients suffering from severe depression (p < 0.001).

CONCLUSION

This pilot study supports the feasibility of the EEG marker for early recognition of treatment-resistant depression. If verified in large-scale prospective studies, it can contribute to research and clinical work.