Predicting treatment outcomes in patients with panic disorder: Cross-sectional and two-year longitudinal structural connectome analysis using machine learning methods.

Journal: Journal of anxiety disorders

PMID: 39121510

Abstract

PURPOSE: This study examined the relationship between structural brain networks and long-term treatment outcomes in patients with panic disorder (PD) using machine learning methods.

Authors

Chongwon Pae

Center for Systems and Translational Brain Sciences, Institute of Human Complexity and Systems Science, Yonsei University, Republic of Korea; BK21 PLUS Project for Medical Science, Yonsei University College of Medicine, Republic of Korea; Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
Hyun-Ju Kim

Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea.
Minji Bang

Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea.
Chun Il Park

Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea.
Sang-Hyuk Lee

Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea. Electronic address: drshlee@cha.ac.kr.

Keywords

Adult Brain Cerebral Cortex Connectome Cross-Sectional Studies Diffusion Tensor Imaging Female Humans Longitudinal Studies Machine Learning Magnetic Resonance Imaging Male Middle Aged Panic Disorder Treatment Outcome White Matter

External Resources

View on PubMed Access via DOI PubMed (39121510)

Predicting treatment outcomes in patients with panic disorder: Cross-sectional and two-year longitudinal structural connectome analysis using machine learning methods.

Abstract

Authors

Keywords

External Resources

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