Poster No:
1499
Submission Type:
Abstract Submission
Authors:
Valerie Wiemer1,2, Theresa Paul1,2, Lukas Hensel1, Matthew Cieslak3, Caroline Tscherpel4, Christian Grefkes4, Scott Grafton5, Gereon Fink1,2, Lukas Volz1
Institutions:
1Medical Faculty, University of Cologne, and Department of Neurology, University Hospital Cologne, Cologne, Germany, 2Institute of Neuroscience and Medicine, Cognitive Neuroscience (INM-3), Research Centre Juelich, Juelich, Germany, 3Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 4Department of Neurology, University Hospital Frankfurt, Goethe University Frankfurt, Frankfurt am Main, Germany, 5Department of Psychological & Brain Sciences, University of California, Santa Barbara, CA
First Author:
Valerie Wiemer
Medical Faculty, University of Cologne, and Department of Neurology, University Hospital Cologne|Institute of Neuroscience and Medicine, Cognitive Neuroscience (INM-3), Research Centre Juelich
Cologne, Germany|Juelich, Germany
Co-Author(s):
Theresa Paul
Medical Faculty, University of Cologne, and Department of Neurology, University Hospital Cologne|Institute of Neuroscience and Medicine, Cognitive Neuroscience (INM-3), Research Centre Juelich
Cologne, Germany|Juelich, Germany
Lukas Hensel
Medical Faculty, University of Cologne, and Department of Neurology, University Hospital Cologne
Cologne, Germany
Matthew Cieslak
Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania
Philadelphia, PA
Caroline Tscherpel
Department of Neurology, University Hospital Frankfurt, Goethe University Frankfurt
Frankfurt am Main, Germany
Christian Grefkes
Department of Neurology, University Hospital Frankfurt, Goethe University Frankfurt
Frankfurt am Main, Germany
Scott Grafton
Department of Psychological & Brain Sciences, University of California
Santa Barbara, CA
Gereon Fink
Medical Faculty, University of Cologne, and Department of Neurology, University Hospital Cologne|Institute of Neuroscience and Medicine, Cognitive Neuroscience (INM-3), Research Centre Juelich
Cologne, Germany|Juelich, Germany
Lukas Volz
Medical Faculty, University of Cologne, and Department of Neurology, University Hospital Cologne
Cologne, Germany
Introduction:
While motor recovery after stroke is thought to arise from functional reorganization of the motor network, the underlying mechanisms remain poorly understood (Grefkes & Fink, 2020). Structural cortico-cortical motor network connectivity assessed via diffusion MRI (dMRI) has recently been shown to be strongly associated with distinct aspects of motor control after stroke, highlighting that undamaged fiber tracts may serve as a structural reserve to enable reorganization and recovery (Paul et al., 2023a). However, it remains unclear whether and how the structural reserve of the motor network allows changes in information integration that may help to compensate for motor impairment. To address this critical question, we assessed dMRI-based structural and fMRI-based effective connectivity within a cortical motor network to characterize structure-function relationships of motor network reorganization in chronic stroke patients.
Methods:
Structural motor network connectivity was quantified using a novel approach combining diffusion spectrum imaging and compartment-wise analysis of anisotropy (Volz et al., 2018; Paul et al., 2023b). In the same cohort of chronic stroke patients (N=23), the modulation of effective motor network connectivity underlying the control of finger-tapping movements with the paretic hand was assessed using dynamic causal modeling (DCM). Spearman correlations between tractwise anisotropy and DCM coupling parameters were computed to test for structural and effective connectivity associations. Motor impairment was quantified via finger-tapping frequency during the fMRI task, the Motricity Index Arm Score reflecting basal motor control, and the Action Research Arm Test indicating complex motor control. Associations between multimodal motor network connectivity and impairment were assessed via Spearman correlations and multiple linear backward regressions. Finally, subgroup analyses were performed to disentangle the relationship between motor network connectivity and recovery from the acute to chronic phase after stroke (substantial vs. non-substantial recovery).
Results:
Besides the expected excitatory influences from premotor areas onto the ipsilesional primary motor cortex (M1), DCM results revealed a significant pathophysiological excitatory influence from ipsi- to contralesional M1 during paretic hand movements. No significant correlations between structural and effective connectivity were observed at the group level. While structural connectivity was highly indicative of motor impairment after stroke, effective connectivity showed no association with motor impairment. However, when combining structural and effective connectivity estimates as predictors in the regression models, we were able to meaningfully increase the explained variance for all outcome variables (all R2>74%, adj.R2>61%, P<.003). Notably, subgroup analyses revealed differential results for structure-function relationships concerning motor recovery: In contrast to substantially recovered patients, non-substantially recovered patients showed a negative association for the contra- to ipsilesional M1 connection, indicating recovery-dependent network alterations after stroke.
Conclusions:
We here provide the first integration of structural and task-related effective cortical motor network connectivity in chronic stroke patients. Our results support the notion that interhemispheric cortical routes may support motor control and emphasize that the cortical structural reserve may facilitate functional reorganization after stroke. In particular, interhemispheric excitatory input from the ipsi- to the contralesional hemisphere may help to access undamaged contralesional descending fiber pathways, offering new insights into conflicting views on the functional role of contralesional M1. The high behavioral variance explained by a combination of structural and effective connectivity underlines their potential as future biomarkers for motor recovery after stroke.
Learning and Memory:
Neural Plasticity and Recovery of Function 2
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Connectivity (eg. functional, effective, structural) 1
Diffusion MRI Modeling and Analysis
fMRI Connectivity and Network Modeling
Keywords:
Other - Stroke; Motor recovery; Functional reorganization; Diffusion MRI; Functional MRI; Structural connectivity; Effective connectivity; Structure-function relationships
1|2Indicates the priority used for review
Provide references using author date format
Grefkes, C. (2020), 'Recovery from stroke: current concepts and future perspectives', Neurological research and practice, 2(1), 1-10
Paul, T. (2023a) 'Interhemispheric structural connectivity underlies motor recovery after stroke', Annals of Neurology, 94(4), 785-797
Paul, T., (2023b) 'The role of corticospinal and extrapyramidal pathways in motor impairment after stroke', Brain Communications, 5(1), fcac301
Volz, L. J., (2018) 'A probabilistic atlas of fiber crossings for variability reduction of anisotropy measures', Brain Structure and Function, 223, 635-651