The content discusses the use of FSA and HMM to analyze questionnaire data on older adult mental health during the COVID-19 pandemic and fMRI data on late-life depression treatment. The approach highlights the importance of understanding underlying rules driving output signals for a deeper insight into mental health dynamics. By modeling brain changes associated with treatment response, the study offers a novel perspective on analyzing mental health data using computational models like cvHMM.
Longitudinal assessments and fMRI analysis are key components, emphasizing the significance of finite state controllers in explaining complex systems. The study showcases how HMM can enhance signal representation by removing noise, providing valuable insights into behavior and neural activity related to depression. The comparison between Viterbi-generated sequences and k-means induced sequences demonstrates the effectiveness of the HMM approach in capturing meaningful relationships within the data.
Overall, the research presents a robust framework for studying mental health dynamics through a combination of FSA theory and computational models like cvHMM. It suggests potential applications in diverse populations and conditions, offering new avenues for developing effective interventions based on a deeper understanding of mental health processes.
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by Yuanzhe Huan... às arxiv.org 03-07-2024
https://arxiv.org/pdf/2403.03414.pdfPerguntas Mais Profundas