scholarly journals Breathlessness in COPD: linking symptom clusters with brain activity

2021 ◽  
pp. 2004099
Author(s):  
Sarah L. Finnegan ◽  
Olivia K. Harrison ◽  
Catherine J. Harmer ◽  
Mari Herigstad ◽  
Najib M. Rahman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Functional Neuroimaging ◽  
Brain Activity ◽  
Symptom Burden ◽  
Functional Brain Imaging ◽  
Self Report ◽  
Machine Learning Techniques ◽  
Chronic Obstructive ◽  
Unsupervised Machine Learning ◽  
Learning Techniques

RationaleCurrent 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.MethodsA 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.ResultsWe 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).ConclusionsThis 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.

scholarly journals Neuropsychological stratification of Chronic Obstructive Pulmonary Disease: building affect into clinically relevant neuro-biomarkers of breathlessness

2019 ◽  
Author(s):  
Sarah L. Finnegan ◽  
Olivia K. Faull ◽  
Catherine J. Harmer ◽  
Mari Herigstad ◽  
Najib M. Rahman ◽  
...  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Disease ◽  
Brain Activity ◽  
Symptom Burden ◽  
Functional Brain Imaging ◽  
Related Word ◽  
Self Report ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Symptom Load

AbstractBackgroundChronic breathlessness profoundly affects quality of life for its sufferers. Often, reported breathlessness is inconsistent with airway pathophysiology and objective disease markers. 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 perceptual modulators. Therefore, here we have developed a model capable of exploring these relationships aiding patient stratification and revealing clinically-relevant neuro-biomarkers.MethodsA cohort of 100 participants with mild-to-moderate chronic obstructive pulmonary disease (COPD) underwent a comprehensive assessment that included functional brain imaging while viewing and rating breathlessness-related word cues, self-report questionnaires and clinical measures.ResultsUsing an exploratory factor analysis across psychological and physiological measures, we identified two distinctive neuropsychological behavioural profiles that differed across four key factors corresponding to mood, symptom burden, and two capability measures. These profiles stratified participants into high and low symptom groups, which did not differ in spirometry values. The low symptom load group demonstrated greater FMRI activity to breathlessness-related word cues in the anterior insula.ConclusionsOur findings reveal two clear groups of individuals within our COPD cohort, divided by behavioural rather than clinical factors. Furthermore, indices of depression, anxiety, vigilance and perceived capability were linked to differences in brain activity within key regions thought to be involved in monitoring bodily sensations (interoception). These findings demonstrate the complex relationship between affect and interoceptive processing, providing the foundations for the development of targeted treatment programmes that harness clinical and symptom-relevant biomarkers.

In-Network Data Aggregation for Information-Centric WSNs using Unsupervised Machine Learning Techniques

2021 ◽  
Author(s):  
Marcelo E. Pellenz ◽  
Rosana Lachowski ◽  
Edgard Jamhour ◽  
Glauber Brante ◽  
Guilherme Luiz Moritz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Aggregation ◽  
Machine Learning Techniques ◽  
Network Data ◽  
Unsupervised Machine Learning ◽  
Learning Techniques

scholarly journals A Novel Semi-Supervised Fuzzy C-Means Clustering Algorithm Using Multiple Fuzzification Coefficients

Algorithms ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 258
Author(s):  
Tran Dinh Khang ◽  
Manh-Kien Tran ◽  
Michael Fowler
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Machine Learning Techniques ◽  
Unsupervised Machine Learning ◽  
Practical Applications ◽  
Fuzzy C Means ◽  
Learning Techniques ◽  
Fuzzy C Means Clustering ◽  
Data Points ◽  
Data Elements

Clustering is an unsupervised machine learning method with many practical applications that has gathered extensive research interest. It is a technique of dividing data elements into clusters such that elements in the same cluster are similar. Clustering belongs to the group of unsupervised machine learning techniques, meaning that there is no information about the labels of the elements. However, when knowledge of data points is known in advance, it will be beneficial to use a semi-supervised algorithm. Within many clustering techniques available, fuzzy C-means clustering (FCM) is a common one. To make the FCM algorithm a semi-supervised method, it was proposed in the literature to use an auxiliary matrix to adjust the membership grade of the elements to force them into certain clusters during the computation. In this study, instead of using the auxiliary matrix, we proposed to use multiple fuzzification coefficients to implement the semi-supervision component. After deriving the proposed semi-supervised fuzzy C-means clustering algorithm with multiple fuzzification coefficients (sSMC-FCM), we demonstrated the convergence of the algorithm and validated the efficiency of the method through a numerical example.

scholarly journals Using Real-Time Data and Unsupervised Machine Learning Techniques to Study Large-Scale Spatio–Temporal Characteristics of Wastewater Discharges and their Influence on Surface Water Quality in the Yangtze River Basin

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1268 ◽  
Author(s):  
Zhenzhen Di ◽  
Miao Chang ◽  
Peikun Guo ◽  
Yang Li ◽  
Yin Chang
Keyword(s):  
Machine Learning ◽  
Surface Water ◽  
Real Time ◽  
Yangtze River Basin ◽  
Clustering Algorithms ◽  
Machine Learning Techniques ◽  
Unsupervised Machine Learning ◽  
Learning Techniques ◽  
The Yangtze River Basin ◽  
Spatio Temporal

Most worldwide industrial wastewater, including in China, is still directly discharged to aquatic environments without adequate treatment. Because of a lack of data and few methods, the relationships between pollutants discharged in wastewater and those in surface water have not been fully revealed and unsupervised machine learning techniques, such as clustering algorithms, have been neglected in related research fields. In this study, real-time monitoring data for chemical oxygen demand (COD), ammonia nitrogen (NH3-N), pH, and dissolved oxygen in the wastewater discharged from 2213 factories and in the surface water at 18 monitoring sections (sites) in 7 administrative regions in the Yangtze River Basin from 2016 to 2017 were collected and analyzed by the partitioning around medoids (PAM) and expectation–maximization (EM) clustering algorithms, Welch t-test, Wilcoxon test, and Spearman correlation. The results showed that compared with the spatial cluster comprising unpolluted sites, the spatial cluster comprised heavily polluted sites where more wastewater was discharged had relatively high COD (>100 mg L−1) and NH3-N (>6 mg L−1) concentrations and relatively low pH (<6) from 15 industrial classes that respected the different discharge limits outlined in the pollutant discharge standards. The results also showed that the economic activities generating wastewater and the geographical distribution of the heavily polluted wastewater changed from 2016 to 2017, such that the concentration ranges of pollutants in discharges widened and the contributions from some emerging enterprises became more important. The correlations between the quality of the wastewater and the surface water strengthened as the whole-year data sets were reduced to the heavily polluted periods by the EM clustering and water quality evaluation. This study demonstrates how unsupervised machine learning algorithms play an objective and effective role in data mining real-time monitoring information and highlighting spatio–temporal relationships between pollutants in wastewater discharges and surface water to support scientific water resource management.

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