Prediction of prognosis in patients with panic disorder using pre-treatment brain white matter features.
Fractional anisotropy
Panic disorder
Prediction
Prognosis
Regression
White matter
Journal
Journal of affective disorders
ISSN: 1573-2517
Titre abrégé: J Affect Disord
Pays: Netherlands
ID NLM: 7906073
Informations de publication
Date de publication:
15 09 2022
15 09 2022
Historique:
received:
28
03
2022
revised:
15
06
2022
accepted:
27
06
2022
pubmed:
6
7
2022
medline:
20
7
2022
entrez:
5
7
2022
Statut:
ppublish
Résumé
The early identification of patients with panic disorder (PD) with a poor prognosis is important for improving treatment outcomes; however, it is challenging due to a lack of objective biomarkers. We investigated the reliability of characterizing structural white matter (WM) connectivity and its ability to predict PD prognosis after pharmacotherapy. A total of 138 patients (59 men) with PD and 153 healthy controls (HCs; 73 men) participated in this study. PD symptom severity was measured using the Panic Disorder Severity Scale (PDSS) at baseline and follow-up periods of 8 weeks, 6 months, and 1 year. The least absolute shrinkage and selection operator (Lasso) was utilized to identify prognosis-related WM regions on diffusion imaging features. Lasso identified seven prognosis-related WM regions: the bilateral posterior corona radiata, bilateral posterior limb of the internal capsule, the left retrolenticular part of the internal capsule, the left sagittal stratum, and the right fornix/stria terminalis. Some of these regions showed lower mean fractional anisotropy (FA) values in patients with PD than in HCs. The predicted PDSS scores using FA from these regions consistently correlated with the actual prognosis in all periods. This study had limited ability to evaluate individual longitudinal changes in detail owing to the data acquisition time and brain atlas resolution. Our findings suggest the possibility of using structural WM connectivity as a biomarker for the clinical characterization of PD. Our findings will expand our understanding of the neurobiology of PD and improve biomarker-based prognosis prediction in clinical practice.
Sections du résumé
BACKGROUND
The early identification of patients with panic disorder (PD) with a poor prognosis is important for improving treatment outcomes; however, it is challenging due to a lack of objective biomarkers. We investigated the reliability of characterizing structural white matter (WM) connectivity and its ability to predict PD prognosis after pharmacotherapy.
METHODS
A total of 138 patients (59 men) with PD and 153 healthy controls (HCs; 73 men) participated in this study. PD symptom severity was measured using the Panic Disorder Severity Scale (PDSS) at baseline and follow-up periods of 8 weeks, 6 months, and 1 year. The least absolute shrinkage and selection operator (Lasso) was utilized to identify prognosis-related WM regions on diffusion imaging features.
RESULTS
Lasso identified seven prognosis-related WM regions: the bilateral posterior corona radiata, bilateral posterior limb of the internal capsule, the left retrolenticular part of the internal capsule, the left sagittal stratum, and the right fornix/stria terminalis. Some of these regions showed lower mean fractional anisotropy (FA) values in patients with PD than in HCs. The predicted PDSS scores using FA from these regions consistently correlated with the actual prognosis in all periods.
LIMITATIONS
This study had limited ability to evaluate individual longitudinal changes in detail owing to the data acquisition time and brain atlas resolution.
CONCLUSIONS
Our findings suggest the possibility of using structural WM connectivity as a biomarker for the clinical characterization of PD. Our findings will expand our understanding of the neurobiology of PD and improve biomarker-based prognosis prediction in clinical practice.
Identifiants
pubmed: 35780964
pii: S0165-0327(22)00752-2
doi: 10.1016/j.jad.2022.06.092
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
214-221Informations de copyright
Copyright © 2022 Elsevier B.V. All rights reserved.