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A mechanistic model for individualised treatment of anxiety disorders based on predictive neural biomarkers

Increased amygdala responsiveness is the hallmark of fear and a characteristic across patients with anxiety disorders. The amygdala is embedded in a complex regulatory circuit. Multiple different mechanisms may elevate amygdala responsiveness and lead to the occurrence of an anxiety disorder. While...

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Autores principales: Brehl, Anne-Kathrin, Kohn, Nils, Schene, Aart Herman, Fernández, Guillen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7168651/
https://www.ncbi.nlm.nih.gov/pubmed/32204741
http://dx.doi.org/10.1017/S0033291720000410
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author Brehl, Anne-Kathrin
Kohn, Nils
Schene, Aart Herman
Fernández, Guillen
author_facet Brehl, Anne-Kathrin
Kohn, Nils
Schene, Aart Herman
Fernández, Guillen
author_sort Brehl, Anne-Kathrin
collection PubMed
description Increased amygdala responsiveness is the hallmark of fear and a characteristic across patients with anxiety disorders. The amygdala is embedded in a complex regulatory circuit. Multiple different mechanisms may elevate amygdala responsiveness and lead to the occurrence of an anxiety disorder. While top-down control by the prefrontal cortex (PFC) downregulates amygdala responses, the locus coeruleus (LC) drives up amygdala activation via noradrenergic projections. This indicates that the same fearful phenotype may result from different neural mechanisms. We propose a mechanistic model that defines three different neural biomarkers causing amygdala hyper-responsiveness in patients with anxiety disorders: (a) inherent amygdala hypersensitivity, (b) low prefrontal control and (c) high LC drive. First-line treatment for anxiety disorders is exposure-based cognitive behavioural therapy, which strengthens PFC recruitment during emotion regulation and thus targets low-prefrontal control. A treatment response rate around 50% (Loerinc et al., 2015, Clinical Psychological Reviews, 42, 72–82) might indicate heterogeneity of underlying neurobiological mechanisms among patients, presumably leading to high variation in treatment benefit. Transforming insights from cognitive neuroscience into applicable clinical heuristics to categorise patients based on their underlying biomarker may support individualised treatment selection in psychiatry. We review literature on the three anxiety-related mechanisms and present a mechanistic model that may serve as a rational for pathology-based diagnostic and biomarker-guided treatment selection in psychiatry.
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spelling pubmed-71686512020-04-27 A mechanistic model for individualised treatment of anxiety disorders based on predictive neural biomarkers Brehl, Anne-Kathrin Kohn, Nils Schene, Aart Herman Fernández, Guillen Psychol Med Review Article Increased amygdala responsiveness is the hallmark of fear and a characteristic across patients with anxiety disorders. The amygdala is embedded in a complex regulatory circuit. Multiple different mechanisms may elevate amygdala responsiveness and lead to the occurrence of an anxiety disorder. While top-down control by the prefrontal cortex (PFC) downregulates amygdala responses, the locus coeruleus (LC) drives up amygdala activation via noradrenergic projections. This indicates that the same fearful phenotype may result from different neural mechanisms. We propose a mechanistic model that defines three different neural biomarkers causing amygdala hyper-responsiveness in patients with anxiety disorders: (a) inherent amygdala hypersensitivity, (b) low prefrontal control and (c) high LC drive. First-line treatment for anxiety disorders is exposure-based cognitive behavioural therapy, which strengthens PFC recruitment during emotion regulation and thus targets low-prefrontal control. A treatment response rate around 50% (Loerinc et al., 2015, Clinical Psychological Reviews, 42, 72–82) might indicate heterogeneity of underlying neurobiological mechanisms among patients, presumably leading to high variation in treatment benefit. Transforming insights from cognitive neuroscience into applicable clinical heuristics to categorise patients based on their underlying biomarker may support individualised treatment selection in psychiatry. We review literature on the three anxiety-related mechanisms and present a mechanistic model that may serve as a rational for pathology-based diagnostic and biomarker-guided treatment selection in psychiatry. Cambridge University Press 2020-04 2020-03-24 /pmc/articles/PMC7168651/ /pubmed/32204741 http://dx.doi.org/10.1017/S0033291720000410 Text en © The Author(s) 2020 http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review Article
Brehl, Anne-Kathrin
Kohn, Nils
Schene, Aart Herman
Fernández, Guillen
A mechanistic model for individualised treatment of anxiety disorders based on predictive neural biomarkers
title A mechanistic model for individualised treatment of anxiety disorders based on predictive neural biomarkers
title_full A mechanistic model for individualised treatment of anxiety disorders based on predictive neural biomarkers
title_fullStr A mechanistic model for individualised treatment of anxiety disorders based on predictive neural biomarkers
title_full_unstemmed A mechanistic model for individualised treatment of anxiety disorders based on predictive neural biomarkers
title_short A mechanistic model for individualised treatment of anxiety disorders based on predictive neural biomarkers
title_sort mechanistic model for individualised treatment of anxiety disorders based on predictive neural biomarkers
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7168651/
https://www.ncbi.nlm.nih.gov/pubmed/32204741
http://dx.doi.org/10.1017/S0033291720000410
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