Author: Sarah L Finnegan1, Olivia K Harrison2,3,4, Catherine J Harmer5,6, Mari Herigstad7, Najib M Rahman8,9, Andrea Reinecke4,5,6, Kyle T S Pattinson2
Affiliation: <sup>1</sup> Wellcome Centre for Integrative Neuroimaging and Nuffield Division of Anaesthetics, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK sarah.finnegan@ndcn.ox.ac.uk.
<sup>2</sup> Wellcome Centre for Integrative Neuroimaging and Nuffield Division of Anaesthetics, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
<sup>3</sup> Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland.
<sup>4</sup> School of Pharmacy, University of Otago, Dunedin, New Zealand.
<sup>5</sup> Department of Psychiatry, Medical Sciences, University of Oxford, Oxford, UK.
<sup>6</sup> Oxford Health NHS foundation Trust, Warneford Hospital, Oxford, UK.
<sup>7</sup> Department of Biosciences and Chemistry, Sheffield Hallam University, Sheffield, UK.
<sup>8</sup> Nuffield Department of Medicine, University of Oxford, Oxford, UK.
<sup>9</sup> NIHR Oxford Biomedical Research Centre, Oxford, UK.
Conference/Journal: Eur Respir J
Date published: 2021 Apr 19
Other:
Special Notes: doi: 10.1183/13993003.04099-2020. , Word Count: 261
Rationale:
Current models of breathlessness often fail to explain disparities between patients' experiences of breathlessness and objective measures of lung function. While a mechanistic understanding of this discordance has thus far remained elusive, factors such as mood, attention and expectation have all been implicated as important modulators of breathlessness. Therefore, we have developed a model to better understand the relationships between these factors using unsupervised machine learning techniques. Subsequently we examined how expectation-related brain activity differed between these symptom-defined clusters of participants.
Methods:
A cohort of 91 participants with mild-to-moderate chronic obstructive pulmonary disease (COPD) underwent functional brain imaging, self-report questionnaires and clinical measures of respiratory function. Unsupervised machine learning techniques of exploratory factor analysis and hierarchical cluster modelling were used to model brain-behaviour-breathlessness links.
Results:
We successfully stratified participants across four key factors corresponding to mood, symptom burden and two capability measures. Two key groups resulted from this stratification, corresponding to high and low symptom burden. Compared to the high symptom load group, the low symptom burden group demonstrated significantly greater brain activity within the anterior insula, a key region thought to be involved in monitoring internal bodily sensations (interoception).
Conclusions:
This is the largest functional neuroimaging study of COPD to date and is the first to provide a clear model linking brain, behaviour and breathlessness expectation. Furthermore, it was possible to stratify participants into groups, which then revealed differences in brain activity patterns. Together, these findings highlight the value of multi-modal models of breathlessness in identifying behavioural phenotypes, and for advancing understanding of differences in breathlessness burden.
PMID: 33875493 DOI: 10.1183/13993003.04099-2020