Poster No:
1225
Submission Type:
Abstract Submission
Authors:
Fabienne Windel1, Elena Beanato1, Beatrice Lugli1, Philipp Koch2, Gabriel Girard1, Lisa Fleury1, Friedhelm Hummel1
Institutions:
1EPFL, Lausanne, Switzerland, 2University Hospital Schleswig-Holstein, Lübeck, Germany
First Author:
Co-Author(s):
Philipp Koch
University Hospital Schleswig-Holstein
Lübeck, Germany
Introduction:
Fatigue is a common symptom among stroke survivors, with prevalence rates ranging from 38-77% (Lerdal et al., 2009). Unlike acute fatigue resulting from physical or mental exertion, post-stroke fatigue (PSF) is persistent and unresponsive to rest (Zedlitz et al., 2012). PSF has been shown to adversely affect rehabilitation outcomes, functional abilities, and quality of life (Radman et al., 2012). Despite these significant impacts, PSF remains under-researched and poorly characterized. Existing studies have provided inconclusive evidence regarding its underlying mechanisms, with no clear association between lesion location and PSF (Kutlubaev et al., 2012) and indirect measures of disconnectivity failing to predict fatigue (Ulrichsen et al., 2021). However, evidence linking PSF to acute caudate infarcts (Tang et al., 2013) and thalamic lesions (Wang et al. 2022) suggests a possible involvement of the basal ganglia and thalamus.
Methods:
We investigated PSF in a cohort of chronic stroke patients (N=53), recruited for motor impairment. Fatigue was assessed with the Multidimensional Fatigue Inventory (MFI) using the combined score, incorporating all five subscales as recommended for clinical populations (Bakalidou et al. 2022). The association of lesion location with PSF was assessed via lesion overlays, splitting the cohort into patients with high and low fatigue. To quantify this first step, multivariate lesion symptom mapping analysis (MLSM, Zhang et al. 2014) was conducted. To probe disconnection of a potential underlying network, individual diffusion-weighted imaging data using whole brain probabilistic tractography was weighted by the COMMIT algorithm (Koch et al. 2021) and overlayed with the binary lesion masks to create disconnectome matrices. The relationship of disconnectomes and the MFI was then investigated via seed-based whole-brain disconnection in a multivariate regression model, considering global disconnection as well as age and lesion volume, and between literature informed regions of interest (ROI) including the above-mentioned basal ganglia and thalamus, via Pearson Correlation.
Results:
In this cohort, 20.8% of chronic stroke patients exhibited significant fatigue (MFI > 60), with a mean MFI score of 46.28. Lesion overlays suggest that different areas are associated to high fatigue and low fatigue. When quantifying this qualitative inspection using MLSM however, none of the clusters survived permutation testing. The multivariate model demonstrated that whole-brain disconnection, lesion volume and age were not associated with variations on the MFI score. Directed ROI to ROI disconnection between the basal ganglia and thalamus show a trending, but not significant, correlation with the MFI score.
Conclusions:
Fatigue is a common and persistent symptom among stroke survivors, evident even at the chronic stage and in cohorts recruited for other primary symptoms. Our findings suggest that lesion location alone does not fully account for differences in fatigue severity. Although structural disconnection offers a potentially more nuanced framework for understanding PSF, no significant associations were identified in this cohort. These results underscore the complexity of PSF and the need for further studies investigating the neural underpinnings of this symptom, particularly regarding network-based disconnection.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Diffusion MRI Modeling and Analysis
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity
Novel Imaging Acquisition Methods:
Diffusion MRI
Keywords:
Basal Ganglia
Cerebrovascular Disease
Multivariate
Neurological
STRUCTURAL MRI
Structures
Sub-Cortical
Thalamus
Tractography
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
I accept
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information.
Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
I do not want to participate in the reproducibility challenge.
Please indicate below if your study was a "resting state" or "task-activation” study.
Other
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Patients
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
Structural MRI
Diffusion MRI
Neuropsychological testing
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
FSL
Free Surfer
Other, Please list
-
MRtrix
Provide references using APA citation style.
Bakalidou, D., Krommydas, G., Abdimioti, T., Theodorou, P., Doskas, T., & Fillopoulos, E. (2022). The dimensionality of the multidimensional fatigue inventory (MFI-20) derived from healthy adults and patient subpopulations: a challenge for Clinicians. Cureus, 14(6).
Lerdal, A., Bakken, L. N., Kouwenhoven, S. E., Pedersen, G., Kirkevold, M., Finset, A., & Kim, H. S. (2009). Poststroke fatigue—a review. Journal of pain and symptom management, 38(6), 928-949.
Koch, P. J., Park, C. H., Girard, G., Beanato, E., Egger, P., Evangelista, G. G., ... & Hummel, F. C. (2021). The structural connectome and motor recovery after stroke: predicting natural recovery. Brain, 144(7), 2107-2119.
Kutlubaev, M. A., Duncan, F. H., & Mead, G. E. (2012). Biological correlates of post‐stroke fatigue: a systematic review. Acta Neurologica Scandinavica, 125(4), 219-227.
Radman, N., Staub, F., Aboulafia-Brakha, T., Berney, A., Bogousslavsky, J., & Annoni, J. M. (2012). Poststroke fatigue following minor infarcts: a prospective study. Neurology, 79(14), 1422-1427.
Tang, W. K., Liang, H. J., Chen, Y. K., Chu, W. C., Abrigo, J., Mok, V. C. T., ... & Wong, K. S. (2013). Poststroke fatigue is associated with caudate infarcts. Journal of the neurological sciences, 324(1-2), 131-135.
Ulrichsen, K. M., Kolskår, K. K., Richard, G., Alnæs, D., Dørum, E. S., Sanders, A. M., ... & Westlye, L. T. (2021). Structural brain disconnectivity mapping of post-stroke fatigue. NeuroImage: Clinical, 30, 102635.
Wang, J., Gu, M., Xiao, L., Jiang, S., Yin, D., He, Y., ... & Liu, X. (2022). Association of lesion location and fatigue symptoms after ischemic stroke: a VLSM study. Frontiers in Aging Neuroscience, 14, 902604.
Zedlitz, A. M., Rietveld, T. C., Geurts, A. C., & Fasotti, L. (2012). Cognitive and graded activity training can alleviate persistent fatigue after stroke: a randomized, controlled trial. Stroke, 43(4), 1046-1051.
Zhang, Y., Kimberg, D. Y., Coslett, H. B., Schwartz, M. F., & Wang, Z. (2014). Multivariate lesion‐symptom mapping using support vector regression. Human brain mapping, 35(12), 5861-5876
No