Neuroimaging, genetic, clinical, and demographic predictors of treatment response in patients with social anxiety disorder.
Adult
Amygdala
/ diagnostic imaging
Anxiety Disorders
/ diagnosis
Citalopram
/ therapeutic use
Cognitive Behavioral Therapy
Demography
Female
Gyrus Cinguli
/ diagnostic imaging
Humans
Magnetic Resonance Imaging
Male
Middle Aged
Neuroimaging
Phobia, Social
/ diagnosis
Selective Serotonin Reuptake Inhibitors
/ therapeutic use
Treatment Outcome
CBT
Pattern recognition
Personalized medicine
SSRI
SVM
Social phobia
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 01 2020
15 01 2020
Historique:
received:
06
04
2019
revised:
30
08
2019
accepted:
19
10
2019
pubmed:
28
10
2019
medline:
26
1
2021
entrez:
27
10
2019
Statut:
ppublish
Résumé
Correct prediction of treatment response is a central goal of precision psychiatry. Here, we tested the predictive accuracy of a variety of pre-treatment patient characteristics, including clinical, demographic, molecular genetic, and neuroimaging markers, for treatment response in patients with social anxiety disorder (SAD). Forty-seven SAD patients (mean±SD age 33.9 ± 9.4 years, 24 women) were randomized and commenced 9 weeks' Internet-delivered cognitive behavior therapy (CBT) combined either with the selective serotonin reuptake inhibitor (SSRI) escitalopram (20 mg daily [10 mg first week], SSRI+CBT, n = 24) or placebo (placebo+CBT, n = 23). Treatment responders were defined from the Clinical Global Impression-Improvement scale (CGI-I ≤ 2). Before treatment, patients underwent functional magnetic resonance imaging and the Multi-Source Interference Task taxing cognitive interference. Support vector machines (SVMs) were trained to separate responders from nonresponders based on pre-treatment neural reactivity in the dorsal anterior cingulate cortex (dACC), amygdala, and occipital cortex, as well as molecular genetic, demographic, and clinical data. SVM models were tested using leave-one-subject-out cross-validation. The best model separated treatment responders (n = 24) from nonresponders based on pre-treatment dACC reactivity (83% accuracy, P = 0.001). Responders had greater pre-treatment dACC reactivity than nonresponders especially in the SSRI+CBT group. No other variable was associated with clinical response or added predictive accuracy to the dACC SVM model. Small sample size, especially for genetic analyses. No replication or validation samples were available. The findings demonstrate that treatment outcome predictions based on neural cingulate activity, at the individual level, outperform genetic, demographic, and clinical variables for medication-assisted Internet-delivered CBT, supporting the use of neuroimaging in precision psychiatry.
Sections du résumé
BACKGROUND
Correct prediction of treatment response is a central goal of precision psychiatry. Here, we tested the predictive accuracy of a variety of pre-treatment patient characteristics, including clinical, demographic, molecular genetic, and neuroimaging markers, for treatment response in patients with social anxiety disorder (SAD).
METHODS
Forty-seven SAD patients (mean±SD age 33.9 ± 9.4 years, 24 women) were randomized and commenced 9 weeks' Internet-delivered cognitive behavior therapy (CBT) combined either with the selective serotonin reuptake inhibitor (SSRI) escitalopram (20 mg daily [10 mg first week], SSRI+CBT, n = 24) or placebo (placebo+CBT, n = 23). Treatment responders were defined from the Clinical Global Impression-Improvement scale (CGI-I ≤ 2). Before treatment, patients underwent functional magnetic resonance imaging and the Multi-Source Interference Task taxing cognitive interference. Support vector machines (SVMs) were trained to separate responders from nonresponders based on pre-treatment neural reactivity in the dorsal anterior cingulate cortex (dACC), amygdala, and occipital cortex, as well as molecular genetic, demographic, and clinical data. SVM models were tested using leave-one-subject-out cross-validation.
RESULTS
The best model separated treatment responders (n = 24) from nonresponders based on pre-treatment dACC reactivity (83% accuracy, P = 0.001). Responders had greater pre-treatment dACC reactivity than nonresponders especially in the SSRI+CBT group. No other variable was associated with clinical response or added predictive accuracy to the dACC SVM model.
LIMITATIONS
Small sample size, especially for genetic analyses. No replication or validation samples were available.
CONCLUSIONS
The findings demonstrate that treatment outcome predictions based on neural cingulate activity, at the individual level, outperform genetic, demographic, and clinical variables for medication-assisted Internet-delivered CBT, supporting the use of neuroimaging in precision psychiatry.
Identifiants
pubmed: 31655378
pii: S0165-0327(19)30886-9
doi: 10.1016/j.jad.2019.10.027
pii:
doi:
Substances chimiques
Serotonin Uptake Inhibitors
0
Citalopram
0DHU5B8D6V
Banques de données
ISRCTN
['ISRCTN24929928']
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
230-237Informations de copyright
Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.