PTSD Classification Based on Surface-Based Morphometry: Integrating SHAP Analysis for Key Feature Identification DOI Creative Commons
Yulong Jia, Beining Yang, Haotian Xin

и другие.

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Март 25, 2025

Abstract Background Post-Traumatic Stress Disorder (PTSD) is associated with neurobiological alterations, which can be examined using surface-based morphology (SBM). While machine learning (ML) approaches have shown potential in classifying PTSD based on SBM features, further exploration needed to improve interpretability and clinical relevance. Objectives This study seeks integrate ML-based classification of SHAP analysis identify important features their associations symptomatology, providing insights into the structural changes underlying PTSD. Methods High-resolution T1-weighted MRI data from 101 participants (62 PTSD, 39 healthy controls) were analyzed FreeSurfer’s pipeline, extracting cortical thickness, surface area, curvature aparc.a2009s atlas. Several ML models, including Random Forest, SVM, XGBoost, trained evaluated ten-fold cross-validation. was applied determine feature importance, correlation analyses conducted examine relationships between key symptom severity. Results Sixteen regions identified significant differences reduced thickness left lingual gyrus increased bilateral central sulcus. The Forest model achieved highest accuracy (91%) classification. highlighted parahippocampal as features. Correlation suggested links these specific clusters. Conclusion integration interpretable methods provides a promising approach for investigating brain validation needed, findings contribute better understanding neurobiology may support future research diagnostic therapeutic strategies.

Язык: Английский

Shared and distinct morphometric similarity network abnormalities in generalized anxiety disorder, posttraumatic stress disorder and social anxiety disorder DOI Creative Commons
Guifeng Tan, Minlan Yuan,

Lun Li

и другие.

BMC Psychiatry, Год журнала: 2025, Номер 25(1)

Опубликована: Янв. 2, 2025

The high comorbidity and symptom overlap of generalized anxiety disorder (GAD), posttraumatic stress (PTSD), social (SAD), has led to the study their shared disorder-specific neural substrates. However, morphometric similarity network (MSN) differences among these disorders remain unknown. MSN derived from T1-weighted images in patients GAD, PTSD, SAD, health controls (HC) using a Siemens 3T magnetic resonance imaging system. Covariance analysis post hoc tests were used investigate group differences. In addition, relationship between clinical characteristics was analyzed. Increased (MS) left bankssts (BA22, superior temporal cortex, STC) right precentral gyrus, decreased MS gyrus cuneus_part1/part2, rostral middle frontal cortex (rMFC) STC common GAD PTSD relative HC SAD. Compared other three groups, SAD exhibited alterations increased rMFC STC, cuneus inferior parietal cortex. Additionally, regional found compared A mild positive correlation value Hamilton Anxiety Rating Scale scores (uncorrected p = 0.041) PTSD. Our provides first evidence for distinct brain abnormalities underlying pathophysiology which may aid differential diagnosis determining potential intervention targets.

Язык: Английский

Процитировано

0
PTSD Classification Based on Surface-Based Morphometry: Integrating SHAP Analysis for Key Feature Identification DOI Creative Commons
Yulong Jia, Beining Yang, Haotian Xin

и другие.

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Март 25, 2025

Abstract Background Post-Traumatic Stress Disorder (PTSD) is associated with neurobiological alterations, which can be examined using surface-based morphology (SBM). While machine learning (ML) approaches have shown potential in classifying PTSD based on SBM features, further exploration needed to improve interpretability and clinical relevance. Objectives This study seeks integrate ML-based classification of SHAP analysis identify important features their associations symptomatology, providing insights into the structural changes underlying PTSD. Methods High-resolution T1-weighted MRI data from 101 participants (62 PTSD, 39 healthy controls) were analyzed FreeSurfer’s pipeline, extracting cortical thickness, surface area, curvature aparc.a2009s atlas. Several ML models, including Random Forest, SVM, XGBoost, trained evaluated ten-fold cross-validation. was applied determine feature importance, correlation analyses conducted examine relationships between key symptom severity. Results Sixteen regions identified significant differences reduced thickness left lingual gyrus increased bilateral central sulcus. The Forest model achieved highest accuracy (91%) classification. highlighted parahippocampal as features. Correlation suggested links these specific clusters. Conclusion integration interpretable methods provides a promising approach for investigating brain validation needed, findings contribute better understanding neurobiology may support future research diagnostic therapeutic strategies.

Язык: Английский

Процитировано

0