Evaluating Robustness of Brain Stimulation Biomarkers for Depression: A Systematic Review of Magnetic Resonance Imaging and Electroencephalography Studies

Deep Learning-Based Artificial Intelligence Can Differentiate Treatment-Resistant and Responsive Depression Cases with High Accuracy

Background: Although there are many treatment options available for depression, a large portion of patients with depression are diagnosed with treatment-resistant depression (TRD), which is characterized by an inadequate response to antidepressant treatment. Identifying the TRD population is crucial in terms of saving time and resources in depression treatment. Recently several studies employed various methods on EEG datasets for automatic depression detection or treatment outcome prediction. However, no previous study has used the deep learning (DL) approach and EEG signals for detecting treatment resistance. Method: 77 patients with TRD, 43 patients with non-TRD, and 40 healthy controls were compared using GoogleNet convolutional neural network and DL on EEG data. Additionally, Class Activation Maps (CAMs) acquired from the TRD and non-TRD groups were used to obtain distinctive regions for classification. Results: GoogleNet classified the healthy controls and non-TRD group with 88.43%, the healthy controls and TRD subjects with 89.73%, and the TRD and non-TRD group with 90.05% accuracy. The external validation accuracy for the TRD-non-TRD classification was 73.33%. Finally, the CAM analysis revealed that the TRD group contained dominant features in class detection of deep learning architecture in almost all electrodes. Limitations: Our study is limited by the moderate sample size of clinical groups and the retrospective nature of the study. Conclusion: These findings suggest that EEG-based deep learning can be used to classify treatment resistance in depression and may in the future prove to be a useful tool in psychiatry practice to identify patients who need more vigorous intervention.

Bringing Imaging Biomarkers Into Clinical Reality in Psychiatry

Importance

Advancing precision psychiatry, where treatments are based on an individual's biology rather than solely their clinical presentation, requires attention to several key attributes for any candidate biomarker. These include test-retest reliability, sensitivity to relevant neurophysiology, cost-effectiveness, and scalability. Unfortunately, these issues have not been systematically addressed by biomarker development efforts that use common neuroimaging tools like magnetic resonance imaging (MRI) and electroencephalography (EEG). Here, the critical barriers that neuroimaging methods will need to overcome to achieve clinical relevance in the near to intermediate term are examined.

Observations

Reliability is often overlooked, which together with sensitivity to key aspects of neurophysiology and replicated predictive utility, favors EEG-based methods. The principal barrier for EEG has been the lack of large-scale data collection among multisite psychiatric consortia. By contrast, despite its high reliability, structural MRI has not demonstrated clinical utility in psychiatry, which may be due to its limited sensitivity to psychiatry-relevant neurophysiology. Given the prevalence of structural MRIs, establishment of a compelling clinical use case remains its principal barrier. By contrast, low reliability and difficulty in standardizing collection are the principal barriers for functional MRI, along with the need for demonstration that its superior spatial resolution over EEG and ability to directly image subcortical regions in fact provide unique clinical value. Often missing, moreover, is consideration of how these various scientific issues can be balanced against practical economic realities of psychiatric health care delivery today, for which embedding economic modeling into biomarker development efforts may help direct research efforts.

Conclusions and Relevance

EEG seems most ripe for near- to intermediate-term clinical impact, especially considering its scalability and cost-effectiveness. Recent efforts to broaden its collection, as well as development of low-cost turnkey systems, suggest a promising pathway by which neuroimaging can impact clinical care. Continued MRI research focused on its key barriers may hold promise for longer-horizon utility.

Advances in biosensors for major depressive disorder diagnostic biomarkers

Depression is one of the most common mental disorders and is mainly characterized by low mood or lack of interest and pleasure. It can be accompanied by varying degrees of cognitive and behavioral changes and may lead to suicide risk in severe cases. Due to the subjectivity of diagnostic methods and the complexity of patients' conditions, the diagnosis of major depressive disorder (MDD) has always been a difficult problem in psychiatry. With the discovery of more diagnostic biomarkers associated with MDD in recent years, especially emerging non-coding RNAs (ncRNAs), it is possible to quantify the condition of patients with mental illness based on biomarker levels. Point-of-care biosensors have emerged due to their advantages of convenient sampling, rapid detection, miniaturization, and portability. After summarizing the pathogenesis of MDD, representative biomarkers, including proteins, hormones, and RNAs, are discussed. Furthermore, we analyzed recent advances in biosensors for detecting various types of biomarkers of MDD, highlighting representative electrochemical sensors. Future trends in terms of new biomarkers, new sample processing methods, and new detection modalities are expected to provide a complete reference for psychiatrists and biomedical engineers.

Differences in Electroencephalography Power Levels between Poor and Good Performance in Attentional Tasks

Decreased attentional function causes problems in daily life. However, a quick and easy evaluation method of attentional function has not yet been developed. Therefore, we are searching for a method to evaluate attentional function easily and quickly. This study aimed to collect basic data on the features of electroencephalography (EEG) during attention tasks to develop a new method for evaluating attentional function using EEG. Twenty healthy young adults participated; we examined cerebral activity during a Clinical Assessment for Attention using portable EEG devices. The Mann-Whitney U test was performed to assess differences in power levels of EEG during tasks between the low- and high-attention groups. The findings revealed that the high-attention group showed significantly higher EEG power levels in the δ wave of L-temporal and bilateral parietal lobes, as well as in the β and γ waves of the R-occipital lobe, than did the low-attention group during digit-forward, whereas the high-attention group showed significantly higher EEG power levels in the θ wave of R-frontal and the α wave of bilateral frontal lobes during digit-backward. Notably, lower θ, α, and β bands of the right hemisphere found in the low-attention group may be key elements to detect attentional deficit.

Treatment of Depression with Acupuncture Based on Pathophysiological Mechanism

Depression is a prevalent mental disorder and has a profound impact on an individual's psychological and physical well-being. It is characterized by a persistently depressed mood, loss of interest, energy loss, and cognitive dysfunction. In recent years, more and more people have changed to mental diseases, such as depression, anxiety, mania and so on. In the incidence of depression, covering all ages, but still mainly young and middle-aged women. Traditional treatments for depression mainly rely on medication and psychotherapy, but these methods are not effective for all patients and are often accompanied by certain side effects. Therefore, finding safe and effective alternative or adjuvant treatments has become a priority. Here we highlight the research progress of acupuncture in the treatment of depression and to explore the mechanism of acupuncture in the treatment of depression. Acupuncture treatment of depression is an ancient and effective method, the mechanism involves multiple biological pathways, for example, by regulating neurotransmitter levels, regulating the neuroendocrine axis, improving neuroplasticity, anti-inflammatory and other effects, improving emotional state and play an antidepressant role. To provide evidence to support the widespread use of acupuncture in clinical practice. We hope to provide new treatment ideas and methods for patients with depression, and even reduce the incidence of depression.