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      Lesioned hemisphere‐specific phenotypes of post‐stroke fatigue emerge from motor and mood characteristics in chronic stroke

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          Abstract

          Background and purpose

          Post‐stroke fatigue commonly presents alongside several comorbidities. The interaction between comorbidities and their relationship to fatigue is not known. In this study, we focus on physical and mood comorbidities, alongside lesion characteristics. We predict the emergence of distinct fatigue phenotypes with distinguishable physical and mood characteristics.

          Methods

          In this cross‐sectional observational study, in 94 first time, non‐depressed, moderate to minimally impaired chronic stroke survivors, the relationship between measures of motor function (grip strength, nine‐hole peg test time), motor cortical excitability (resting motor threshold), Hospital Anxiety and Depression Scale and Fatigue Severity Scale‐7 (FSS‐7) scores, age, gender and side of stroke was established using Spearman's rank correlation. Mood and motor variables were then entered into a k‐means clustering algorithm to identify the number of unique clusters, if any. Post hoc pairwise comparisons followed by corrections for multiple comparisons were performed to characterize differences among clusters in the variables included in k‐means clustering.

          Results

          Clustering analysis revealed a four‐cluster model to be the best model (average silhouette score of 0.311). There was no significant difference in FSS‐7 scores among the four high‐fatigue clusters. Two clusters consisted of only left‐hemisphere strokes, and the remaining two were exclusively right‐hemisphere strokes. Factors that differentiated hemisphere‐specific clusters were the level of depressive symptoms and anxiety. Motor characteristics distinguished the low‐depressive left‐hemisphere from the right‐hemisphere clusters.

          Conclusion

          The significant differences in side of stroke and the differential relationship between mood and motor function in the four clusters reveal the heterogenous nature of post‐stroke fatigue, which is amenable to categorization. Such categorization is critical to an understanding of the interactions between post‐stroke fatigue and its presenting comorbid deficits, with significant implications for the development of context‐/category‐specific interventions.

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          Most cited references38

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          Control of goal-directed and stimulus-driven attention in the brain.

          We review evidence for partially segregated networks of brain areas that carry out different attentional functions. One system, which includes parts of the intraparietal cortex and superior frontal cortex, is involved in preparing and applying goal-directed (top-down) selection for stimuli and responses. This system is also modulated by the detection of stimuli. The other system, which includes the temporoparietal cortex and inferior frontal cortex, and is largely lateralized to the right hemisphere, is not involved in top-down selection. Instead, this system is specialized for the detection of behaviourally relevant stimuli, particularly when they are salient or unexpected. This ventral frontoparietal network works as a 'circuit breaker' for the dorsal system, directing attention to salient events. Both attentional systems interact during normal vision, and both are disrupted in unilateral spatial neglect.
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            Identification of Distinct Psychosis Biotypes Using Brain-Based Biomarkers.

            Clinical phenomenology remains the primary means for classifying psychoses despite considerable evidence that this method incompletely captures biologically meaningful differentiations. Rather than relying on clinical diagnoses as the gold standard, this project drew on neurobiological heterogeneity among psychosis cases to delineate subgroups independent of their phenomenological manifestations.
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              Prevalence and predictors of post-stroke mood disorders: A meta-analysis and meta-regression of depression, anxiety and adjustment disorder.

              To ascertain the prevalence and predictors of mood disorders, determined by structured clinical interviews (ICD or DSM criteria) in people after stroke.
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                Author and article information

                Contributors
                a.kuppuswamy@ucl.ac.uk
                Journal
                Eur J Neurol
                Eur J Neurol
                10.1111/(ISSN)1468-1331
                ENE
                European Journal of Neurology
                John Wiley and Sons Inc. (Hoboken )
                1351-5101
                1468-1331
                09 December 2023
                March 2024
                : 31
                : 3 ( doiID: 10.1111/ene.v31.3 )
                : e16170
                Affiliations
                [ 1 ] Department of Clinical and Movement Neuroscience, Institute of Neurology University College London London UK
                [ 2 ] Department of Biomedical Sciences University of Leeds Leeds UK
                Author notes
                [*] [* ] Correspondence

                Annapoorna Kuppuswamy, Department of Clinical and Movement Neuroscience, Institute of Neurology, University College London, Box 146, 33 Queen Square, London WC1N 3BG, UK.

                Email: a.kuppuswamy@ 123456ucl.ac.uk

                Author information
                https://orcid.org/0000-0001-7835-6937
                https://orcid.org/0000-0002-4288-0814
                Article
                ENE16170 EJoN-23-1953.R1
                10.1111/ene.16170
                11141786
                38069662
                16ee11fa-4cf1-47f1-a6fc-0432ecb3b639
                © 2023 The Authors. European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 03 November 2023
                : 18 September 2023
                : 14 November 2023
                Page count
                Figures: 2, Tables: 2, Pages: 9, Words: 5154
                Funding
                Funded by: Wellcome Trust , doi 10.13039/100010269;
                Categories
                Original Article
                Stroke
                Custom metadata
                2.0
                March 2024
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.4.3 mode:remove_FC converted:31.05.2024

                Neurology
                k‐means clustering,motor cortex excitability,phenotypes,post‐stroke fatigue
                Neurology
                k‐means clustering, motor cortex excitability, phenotypes, post‐stroke fatigue

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