Early adverse life events (EALs) increase susceptibility to depression and impair cognitive performance, but the physiological mechanisms are still unclear. The target of this article is to clarify the impact of adverse childhood experiences on emotional and cognitive performance from the perspective of the heart–brain axis. We used the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) to test cognitive function and the Childhood Trauma Questionnaire (CTQ) to assess adverse childhood experiences. Heart rate variability (HRV) and electroencephalograms (EEG) were acquired at rest. We observed that subjects with depression had experienced more traumatic events during their childhood. Furthermore, they exhibited lower heart rate variability and higher power in the delta, theta, and alpha frequency bands. Moreover, heart rate variability partially mediated the association between childhood trauma exposure and depressive symptoms. Our findings suggested that adverse life events in childhood could influence the development of depression in adulthood, which might be linked to cardiac autonomic dysfunction and altered brain function.

Background: Evidence from functional magnetic resonance imaging (fMRI) studies of schizophrenia suggests that interindividual variation in the stationary striatal functional circuit may be correlated with antipsychotic treatment response. However, little is known about the role of the dynamic striatum-related network in predicting patients’ clinical improvement. The spontaneous coactivation pattern (CAP) technique has recently been found to be important for elucidating the non-stationary nature of functional brain networks.Methods: Forty-two drug-naive first-episode schizophrenia patients underwent fMRI and T1W imaging before and after 8 weeks of risperidone monotherapy. The striatum was divided into 3 subregions, including the putamen, pallidum, and caudate. Spontaneous CAPs and CAP states were utilized to measure the dynamic characteristics of brain networks. We used DPARSF and Dynamic Brain Connectome software to analyze each subregion-related CAP and CAP state for each group and then compared the between-group differences in the neural network biomarkers. We used Pearson’s correlation analysis to determine the associations between the neuroimaging measurements with between-group differences and the improvement in patients’ psychopathological symptoms.Results: In the putamen-related CAPs, patients showed significantly increased intensity in the bilateral thalamus, bilateral supplementary motor areas, bilateral medial, and paracingulate gyrus, left paracentral lobule, left medial superior frontal gyrus, and left anterior cingulate gyrus compared with healthy controls. After treatment, thalamic signals in the putamen-related CAP 1 showed a significant increase, while the signals of the medial and paracingulate gyrus in the putamen-related CAP 3 revealed a significant decrease. The increase in thalamic signal intensity in the putamen-related CAP 1 was significantly and positively correlated with the percentage reduction in PANSS_P.Conclusion: This study is the first to combine striatal CAPs and fMRI to explore treatment response-related biomarkers in the early phase of schizophrenia. Our findings suggest that dynamic changes in CAP states in the putamen-thalamus circuit may be potential biomarkers for predicting patients’ variation in the short-term treatment response of positive symptoms.

Modified Electric Convulsive Therapy (MECT) is an efficacious physical therapy in treating mental disorders. The occurrence of epilepsy is a crucial benchmark for evaluating therapeutic effectiveness. However, the medical field still lacks relevant research on automatically detecting epileptic waves in MECT. Therefore, this article proposes a novel automatic detection method of epileptic waves in MECT. In this article, EEG local features (time, frequency, and time-frequency domains) and global features (Pearson correlation coefficient) are combined for epileptic wave detection with SVM (Support Vector Machine). We researched the system with 15 EEG detection channels. The dataset under investigation contains EEG data from 22 patients who received MECT and presented with epileptic seizures. The results revealed that LA (Logarithm of Activity) feature exhibits the best classification significance. When epileptic waves appear, there is a decrease in the power ratio of delta waves and an increase in the power ratio of theta waves. Additionally, the complexity of EEG decreases while the correlation between EEG channels increases. The Cz, F4, and P3 channels exhibit the highest classification significance among all EEG channels. Furthermore, based on the channel classification significance, the EEG detection channels number can be reduced to 8. Similarly, based on the feature classification significance, the local feature number can be reduced from 9 to 3. These conclusions can improve detection efficiency and reduce the cost for MECT. Moreover, the method we proposed can effectively detect epileptic waves in MECT. This work can provide physicians with a reference for evaluating the effectiveness of MECT.

Introduction: Electroconvulsive therapy (ECT) is one of the main strategies for major depressive disorder (MDD). Recently, the use of esketamine in the treatment of depression due to the rapid antidepressant effects has been highlighted. The present study hypothesizes that 1) adjunctive esketamine during ECT will produce greater improvement in depressive symptoms compared to placebo; 2) the esketamine–ciprofol combination will demonstrate superior antidepressant efficacy and fewer adverse events relative to the esketamine–propofol combination.Methods and analysis: This is a prospective, randomized, double-blind, placebo-controlled, repeated-measures trial with factorial design, planned to be conducted in Renmin Hospital of Wuhan University from 1 May 2024 to 31 May 2025. A total of 168 cases with MDD undergoing scheduled ECT will be randomized in a ratio of 1:1:1:1 to receive propofol or ciprofol sedation with or without esketamine (0.25 mg/kg) treatment. The primary outcome is the changes from baseline to day 28 in HAMD-24. Secondary outcomes include the rates of response (a 50% or greater reduction in HAMD-24 total scores) and remission (a score of 8 or less in the HAMD-24 total scores), along with the rate of reduction in the HAMD-24 total scores from baseline, at the end of the trial. In addition, the incidence of adverse events and the details of ECT will also be recorded. Standard intention-to-treat (ITT) analyses will be performed after handling missing data using multiple imputation methods. The predefined subgroup analysis on primary outcomes will be conducted according to age and sex. The generalized estimating equation (GEE) will be utilized to analyze the outcomes. This study will address two critical questions in ECT practice: whether ECT with adjunctive esketamine achieves clinically superior outcomes to ECT alone, and whether anesthetic choice (ciprofol versus propofol) modulates the antidepressant efficacy of esketamine. The findings from this randomized controlled trial (RCT) will provide novel evidence to optimize sedation regimens during ECT in patients with MDD, specifically addressing the risk–benefit ratio of adjunctive esketamine administration.Ethics and dissemination: This trial was approved by the local Institutional Review Board (No. WDRY2024-K018) and conducted following the guidelines of the Declaration of Helsinki. Results of this trial will be publicly disclosed in a peer-reviewed journal.Clinical Trial Registration: clinicaltrials.gov, identifier ChiCTR2400083664.