Neuroimaging, genetic, clinical, and demographic predictors of treatment response in patients with social anxiety disorder

Andreas Frick, Jonas Engman, Iman Alaie, Johannes Björkstrand, Malin Gingnell, Elna-Marie Larsson, Elias Eriksson, Kurt Wahlstedt, Mats Fredrikson, Tomas Furmark

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Resumé

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.

OriginalsprogEngelsk
TidsskriftJournal of Affective Disorders
Vol/bind261
Sider (fra-til)230-237
ISSN0165-0327
DOI
StatusUdgivet - jan. 2020

Fingeraftryk

Neuroimaging
Gyrus Cinguli
Serotonin Uptake Inhibitors
Internet
Placebos
Social Phobia
Citalopram
Genetic Markers
Sample Size
Support Vector Machine

Citer dette

Frick, Andreas ; Engman, Jonas ; Alaie, Iman ; Björkstrand, Johannes ; Gingnell, Malin ; Larsson, Elna-Marie ; Eriksson, Elias ; Wahlstedt, Kurt ; Fredrikson, Mats ; Furmark, Tomas. / Neuroimaging, genetic, clinical, and demographic predictors of treatment response in patients with social anxiety disorder. I: Journal of Affective Disorders. 2020 ; Bind 261. s. 230-237.
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title = "Neuroimaging, genetic, clinical, and demographic predictors of treatment response in patients with social anxiety disorder",
abstract = "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.",
author = "Andreas Frick and Jonas Engman and Iman Alaie and Johannes Bj{\"o}rkstrand and Malin Gingnell and Elna-Marie Larsson and Elias Eriksson and Kurt Wahlstedt and Mats Fredrikson and Tomas Furmark",
note = "Copyright {\circledC} 2019 The Authors. Published by Elsevier B.V. All rights reserved.",
year = "2020",
month = "1",
doi = "10.1016/j.jad.2019.10.027",
language = "English",
volume = "261",
pages = "230--237",
journal = "Journal of Affective Disorders",
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Frick, A, Engman, J, Alaie, I, Björkstrand, J, Gingnell, M, Larsson, E-M, Eriksson, E, Wahlstedt, K, Fredrikson, M & Furmark, T 2020, 'Neuroimaging, genetic, clinical, and demographic predictors of treatment response in patients with social anxiety disorder', Journal of Affective Disorders, bind 261, s. 230-237. https://doi.org/10.1016/j.jad.2019.10.027

Neuroimaging, genetic, clinical, and demographic predictors of treatment response in patients with social anxiety disorder. / Frick, Andreas; Engman, Jonas; Alaie, Iman; Björkstrand, Johannes; Gingnell, Malin; Larsson, Elna-Marie; Eriksson, Elias; Wahlstedt, Kurt; Fredrikson, Mats; Furmark, Tomas.

I: Journal of Affective Disorders, Bind 261, 01.2020, s. 230-237.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Neuroimaging, genetic, clinical, and demographic predictors of treatment response in patients with social anxiety disorder

AU - Frick, Andreas

AU - Engman, Jonas

AU - Alaie, Iman

AU - Björkstrand, Johannes

AU - Gingnell, Malin

AU - Larsson, Elna-Marie

AU - Eriksson, Elias

AU - Wahlstedt, Kurt

AU - Fredrikson, Mats

AU - Furmark, Tomas

N1 - Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.

PY - 2020/1

Y1 - 2020/1

N2 - 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.

AB - 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.

U2 - 10.1016/j.jad.2019.10.027

DO - 10.1016/j.jad.2019.10.027

M3 - Journal article

VL - 261

SP - 230

EP - 237

JO - Journal of Affective Disorders

JF - Journal of Affective Disorders

SN - 0165-0327

ER -