Pre-treatment frontal beta events are associated with executive dysfunction improvement after repetitive transcranial magnetic stimulation for depression: A preliminary report

Transdiagnostic Attentional Deficits Are Associated with Depressive and Externalizing Symptoms in Children and Adolescents with Neuropsychiatric Disorders

OBJECTIVE

Although inattention, impulsivity, and impairments to vigilance are most associated with attention-deficit/hyperactivity disorder (ADHD), transdiagnostic attentional deficits are prevalent across all psychiatric disorders. To further elucidate this relationship, the present study investigated parent-reported neuropsychiatric symptom correlates of attention deficits using the factor structure of the Conners' Continuous Performance Test (CPT-II), a neuropsychological test of attention.

METHOD

Two-hundred and eighteen children and adolescents (7-21 years old) completed the CPT-II as part of standard clinical protocol during outpatient pediatric neuropsychology visits. The factor structure of the CPT-II was determined with a principal component analysis (PCA) using Promax rotation. Pearson correlation analyses and regression models examined the relationship between the generated factor structure, parent-reported clinical symptoms, and pre-determined clinical diagnoses.

RESULTS

Results from the PCA suggested a three-factor model best supported the structure of the CPT-II, and were subsequently defined as inattention, impulsivity, and vigilance. Performance-based inattention was significantly correlated with parent-reported hyperactivity, aggression, conduct problems, and depression. Parent-reported depressive symptoms and conduct problems were the strongest correlates of performance-based inattention, not hyperactivity or aggression. Performance-based inattention was significantly associated with an ADHD diagnosis but not a depression or anxiety diagnosis.

CONCLUSIONS

Findings suggest attentional deficits are not specific to any one disorder. To enhance the identification, classification, and treatment of neuropsychiatric disorders, both researchers and clinicians alike must diminish the importance of categorical approaches to child/adolescent psychopathology and continue to consider the dimensionality of transdiagnostic characteristics such as inattention.

Effects of transcranial magnetic stimulation on axonal regeneration in the corticospinal tract of female rats with spinal cord injury

BACKGROUND

This study investigates the potential of transcranial magnetic stimulation (TMS) to enhance spinal cord axon regeneration by modulating corticospinal pathways and improving motor nerve function recovery in rats with spinal cord injury (SCI).

NEW METHOD

TMS is a non-invasive neuromodulation technique that generates a magnetic field to activate neurons in the brain, leading to depolarization and modulation of cortical activity. Initially utilized for brain physiology research, TMS has evolved into a diagnostic and prognostic tool in clinical settings, with increasing interest in its therapeutic applications. However, its potential for treating motor dysfunction in SCI has been underexplored.

RESULTS

The TMS intervention group exhibited significant improvements compared to the control group across behavioral assessments, neurophysiological measurements, pathological analysis, and immunological markers.

COMPARISON WITH EXISTING METHODS

Unlike most studies that focus on localized spinal cord injury or muscle treatments, this study leverages the non-invasive, painless, and highly penetrating nature of TMS to focus on the corticospinal tracts, exploring its therapeutic potential for SCI.

CONCLUSIONS

TMS enhances motor function recovery in rats with SCI by restoring corticospinal pathway integrity and promoting axonal regeneration. These findings highlight TMS as a promising therapeutic option for SCI patients with currently limited treatment alternatives.

Multiband EEG signature decoded using machine learning for predicting rTMS treatment response in major depression

Major depressive disorder (MDD) is a global health challenge with high prevalence. Further, many diagnosed with MDD are treatment resistant to traditional antidepressants. Repetitive transcranial magnetic stimulation (rTMS) offers promise as an alternative solution, but identifying objective biomarkers for predicting treatment response remains underexplored. Electroencephalographic (EEG) recordings are a cost-effective neuroimaging approach, but traditional EEG analysis methods often do not consider patient-specific variations and fail to capture complex neuronal dynamics. To address this, we propose a data-driven approach combining iterated masking empirical mode decomposition (itEMD) and sparse Bayesian learning (SBL). Our results demonstrated significant prediction of rTMS outcomes using this approach (Protocol 1: r=0.40, p<0.01; Protocol 2: r=0.26, p<0.05). From the decomposition, we obtained three key oscillations: IMF-Alpha, IMF-Beta, and the remaining residue. We also identified key spatial patterns associated with treatment outcomes for two rTMS protocols: for Protocol 1 (10Hz left DLPFC), important areas include the left frontal and parietal regions, while for Protocol 2 (1Hz right DLPFC), the left and frontal, left parietal regions are crucial. Additionally, our exploratory analysis found few significant correlations between oscillation specific predictive features and personality measures. This study highlights the potential of machine learning-driven EEG analysis for personalized MDD treatment prediction, offering a pathway for improved patient outcomes.

Resting-State EEG Alterations of Practice-Related Spectral Activity and Connectivity Patterns in Depression

BACKGROUND

Depression presents with altered energy regulation and neural plasticity. Previous electroencephalography (EEG) studies showed that practice in learning tasks increases power in beta range (13-30 Hz) in healthy subjects but not in those with impaired plasticity. Here, we ascertain whether depression presents with alterations of spectral activity and connectivity before and after a learning task.

METHODS

We used publicly available resting-state EEG recordings (64 electrodes) from 122 subjects. Based on Beck Depression Inventory (BDI) scores, they were assigned to either a high BDI (hBDI, BDI > 13, N = 46) or a control (CTL, BDI < 7, N = 75) group. We analyzed spectral activity, theta-beta, and theta-gamma phase-amplitude coupling (PAC) of EEG recorded at rest before and after a learning task.

RESULTS

At baseline, compared to CTL, hBDI exhibited greater power in beta over fronto-parietal regions and in gamma over the right parieto-occipital area. At post task, power increased in all frequency ranges only in CTL. Theta-beta and theta-gamma PAC were greater in hBDI at baseline but not after the task.

CONCLUSIONS

The lack of substantial post-task growth of beta power in depressed subjects likely represents power saturation due to greater baseline values. We speculate that inhibitory/excitatory imbalance, altered plasticity mechanisms, and energy dysregulation present in depression may contribute to this phenomenon.

Frontoparietal beta event characteristics are associated with early life stress and psychiatric symptoms in adults

Recent work has found that the presence of transient, oscillatory burst-like events, particularly within the beta band (15-29 Hz), is more closely tied to disease state and behavior across species than traditional electroencephalography (EEG) power metrics. This study sought to examine whether features of beta events over frontoparietal electrodes were associated with early life stress (ELS) and the related clinical presentation. Eighteen adults with documented ELS (n = 18; ELS + ) and eighteen adults without documented ELS (n = 18; ELS-) completed eyes-closed resting state EEG as part of their participation in a larger childhood stress study. The rate, power, duration, and frequency span of transient oscillatory events were calculated within the beta band at five frontoparietal electrodes. ELS variables were positively associated with beta event rate at Fp2 and beta event duration at Pz, in that greater ELS was associated with higher resting rates and longer durations. These beta event characteristics were used to successfully distinguish between ELS + and ELS- groups. In an independent clinical dataset (n = 25), beta event power at Pz was positively correlated with ELS. Beta events deserve ongoing investigation as a potential disease marker of ELS and subsequent psychiatric treatment outcomes.

EEG-based major depressive disorder recognition by neural oscillation and asymmetry

Background

Major Depressive Disorder (MDD) is a pervasive mental health issue with significant diagnostic challenges. Electroencephalography (EEG) offers a non-invasive window into the neural dynamics associated with MDD, yet the diagnostic efficacy is contingent upon the appropriate selection of EEG features and brain regions.

Methods

In this study, resting-state EEG signals from both eyes-closed and eyes-open conditions were analyzed. We examined band power across various brain regions, assessed the asymmetry of band power between the hemispheres, and integrated these features with clinical characteristics of MDD into a diagnostic regression model.

Results

Regression analysis found significant predictors of MDD to be beta2 (16-24 Hz) power in the Prefrontal Cortex (PFC) with eyes open (B = 20.092, p = 0.011), beta3 (24-40 Hz) power in the Medial Occipital Cortex (MOC) (B = -12.050, p < 0.001), and beta2 power in the Right Medial Frontal Cortex (RMFC) with eyes closed (B = 24.227, p < 0.001). Asymmetries in beta1 (12-16 Hz) power with eyes open (B = 28.047, p = 0.018), and in alpha (8-12 Hz, B = 9.004, p = 0.013) and theta (4-8 Hz, B = -13.582, p = 0.008) with eyes closed were also significant predictors.

Conclusion

The study confirms the potential of multi-region EEG analysis in improving the diagnostic precision for MDD. By including both neurophysiological and clinical data, we present a more robust approach to understanding and identifying this complex disorder.

Limitations

The research is limited by the sample size and the inherent variability in EEG signal interpretation. Future studies with larger cohorts and advanced analytical techniques are warranted to validate and refine these findings.