Title : Computational analysis of neurological biomarkers for mental health disorders
Abstract:
This research project focuses on a computational analysis of neurological biomarkers for mental health disorders, with an emphasis on epilepsy. Epilepsy, characterized by recurrent seizures, affects neurological and psychological well-being. Our study aims to identify and analyze correlations between neurological biomarkers and psychological variables in epilepsy to enhance the understanding and management of the disorder. The project is structured chronologically, beginning with the selection of epilepsy based on its prevalence and available research data. We conducted an extensive literature review using databases such as PubMed and Google Scholar to identify potential biomarkers and psychological variables associated with epilepsy. Publicly available datasets were sourced from repositories like the National Institutes of Health (NIH) and Kaggle. Data preprocessing involved cleaning, normalizing, and handling missing values using Python libraries such as pandas and NumPy. We conducted exploratory data analysis (EDA) to identify patterns and relationships within the data. Descriptive statistics, including means and standard deviations, were calculated to summarize the data. For the correlation analysis, we employed Pearson correlation coefficients (r) to examine the relationships between neurological biomarkers and psychological variables. Significant correlations were identified at p < 0.05. Additionally, multiple linear regression models were used to predict psychological outcomes based on biomarkers, with R-squared values indicating the proportion of variance explained by the models. Machine learning algorithms, including logistic regression and decision trees, were utilized to predict mental health outcomes. Feature selection methods such as Principal Component Analysis (PCA) and LASSO (Least Absolute Shrinkage and Selection Operator) were applied to identify the most influential biomarkers. The performance of predictive models was evaluated using metrics such as accuracy, precision, recall, and F1-score. Preliminary results indicated significant correlations between specific biomarkers, such as changes in brain region activities and neurochemical levels, and psychological variables like stress and anxiety (r = 0.65, p < 0.01). Regression models demonstrated that these biomarkers could explain a substantial proportion of the variance in psychological outcomes (R² = 0.58). The study also involves the development of a mental health app prototype designed to support high school students. Features include mood tracking, stress relief exercises, and access to educational content. The app development process incorporates user-centered design, data privacy, and security measures. Iterative testing and user feedback ensure the app meets the needs of its target audience. This project advances the understanding of epilepsy biomarkers and their psychological implications, offering practical applications through an innovative mental health app. The findings have the potential to impact mental health awareness and support among high school students, providing tools for better management of epilepsy and related psychological challenges.
Audience Take Away Notes:
- Key Neurological Biomarkers for Epilepsy: Identification and significance of biomarkers like specific brain regions and genetic markers, and their correlations with neurological symptoms and psychological variables.
- Correlation Analysis Techniques: Understanding the use of Pearson correlation coefficients and multiple linear regression models to analyze relationships between biomarkers and psychological factors, with insights into statistical significance (p-values).
- Machine Learning for Predicting Mental Health Outcomes: Application of machine learning algorithms (e.g., logistic regression, decision trees) to predict mental health outcomes based on biomarkers, including model performance
- metrics.
- Development of a Mental Health App Prototype: Features and benefits of the mental health app prototype designed for high school students, including mood tracking, stress relief exercises, and educational content.
- Practical Implications for Mental Health Support: Practical applications of research findings for mental health awareness and support, particularly in educational settings, and the potential impact on student mental health
- management.
