The Role of Post-Stroke Time and Corticospinal Tract Injury in Cortical Activity and Motor Recovery
Poster No:
1313
Submission Type:
Abstract Submission
Authors:
Célia Delcamp1, Zhibin Zhou2, Anne Schwarz1, Ramesh Srinivasan2, Steven Cramer1
Institutions:
1Dept. Neurology, University of California Los Angeles, Los Angeles, CA, 2Dept. Cognitive Sciences, University of California Irvine, Irvine, CA
First Author:
Co-Author(s):
Introduction:
Patients show some degree of recovery after stroke in a time-dependent manner. Time after stroke (TAS) is thus related to motor status, likely through changes that include cortical plasticity (Wu et al., 2016). This highlights the need to better understand various forms of cortical activity in relation to TAS and their potential mediating role in the TAS-motor status relationship. Similarly, % cortico-spinal injury (%CST) impacts motor status (Lin et al., 2019), emphasizing the need to study its effects on cortical activity while accounting for TAS, and exploring the potential mediating roles of cortical activity in the %CST-motor behavior relationship. Considering time-related changes may improve %CST-based motor predictive models and deepen understanding of recovery mechanisms.
Methods:
100 patients with stroke (TAS=3-3,435 days) and 45 healthy controls were included. Electroencephalography (256-lead cap EEG, 3 minutes eyes open) and anatomical MRI (n=60) were analyzed.
EEG was filtered (0.25-50Hz), noisy epochs rejected (>2.5 SD), EEG was then re-referenced, and ICA was applied.
Normalized spectral powers (δ, θ, α, β, γ), ratios (δ/α , (δ + θ)/(α+β)), and asymmetry indexes ((PowIpsi-PowContra)/(|PowIpsi+PowContra|)) were computed for whole brain, plus each hemisphere, motor cortex, and parietal lobe.
Motor status was assessed with the Fugl-Meyer test (FM), and %CST was quantified from MRIs.
Factorial analysis was used to reduce data dimensionality. To study the relationship between TAS, %CST, and EEG metrics, partial Spearman correlations were performed. Group differences were tested with robust ANCOVAs. Mediation analyses evaluated EEG metrics as mediators between TAS and FM, and between %CST and FM. To explore the impact of TAS and %CST on FM, we performed a robust linear regression, and a Likelihood Ratio Test. Age and FM co-variables were added to these models. Statistical threshold (p<.05) was adjusted for multiple comparisons.
EEG was filtered (0.25-50Hz), noisy epochs rejected (>2.5 SD), EEG was then re-referenced, and ICA was applied.
Normalized spectral powers (δ, θ, α, β, γ), ratios (δ/α , (δ + θ)/(α+β)), and asymmetry indexes ((PowIpsi-PowContra)/(|PowIpsi+PowContra|)) were computed for whole brain, plus each hemisphere, motor cortex, and parietal lobe.
Motor status was assessed with the Fugl-Meyer test (FM), and %CST was quantified from MRIs.
Factorial analysis was used to reduce data dimensionality. To study the relationship between TAS, %CST, and EEG metrics, partial Spearman correlations were performed. Group differences were tested with robust ANCOVAs. Mediation analyses evaluated EEG metrics as mediators between TAS and FM, and between %CST and FM. To explore the impact of TAS and %CST on FM, we performed a robust linear regression, and a Likelihood Ratio Test. Age and FM co-variables were added to these models. Statistical threshold (p<.05) was adjusted for multiple comparisons.
Results:
Factorial analysis isolated 6 factors explaining 88% of variance, grouping power variables by frequency bands and isolating the brain asymmetry.
TAS correlated positively with the β and γ factors (resp. ρ=.55, p<.001; ρ=.55, p<.001) and negatively with the θ factor (ρ=-.62, p<.001) (Fig. 1). Other cortical factors did not correlate (ρ<.15, p>.14).
%CST correlated only with the brain asymmetry factor (ρ=.27, p=.04), with greater power in the ipsilesional hemisphere.
ANCOVAs revealed group differences (F(3,141)>8.6; p<.001): acute-phase differences in β, γ, and θ factors compared to controls (p<.005) were absent in chronic phase (p>.55) (Fig. 2).
TAS and %CST each showed a direct effect on motor status (resp. p<.04; p<.001), which were not mediated by EEG metrics (resp. p>.79; p=.48).
%CST alone predicts 17% of the variance in motor status (p<001). Adding TAS to the model explains 23% (p=.002), significantly improving this model prediction (p=.035).
TAS correlated positively with the β and γ factors (resp. ρ=.55, p<.001; ρ=.55, p<.001) and negatively with the θ factor (ρ=-.62, p<.001) (Fig. 1). Other cortical factors did not correlate (ρ<.15, p>.14).
%CST correlated only with the brain asymmetry factor (ρ=.27, p=.04), with greater power in the ipsilesional hemisphere.
ANCOVAs revealed group differences (F(3,141)>8.6; p<.001): acute-phase differences in β, γ, and θ factors compared to controls (p<.005) were absent in chronic phase (p>.55) (Fig. 2).
TAS and %CST each showed a direct effect on motor status (resp. p<.04; p<.001), which were not mediated by EEG metrics (resp. p>.79; p=.48).
%CST alone predicts 17% of the variance in motor status (p<001). Adding TAS to the model explains 23% (p=.002), significantly improving this model prediction (p=.035).
·Figure 1
·Figure 2
Conclusions:
Post-stroke time affects β, γ, and θ cortical activity, with differences relative to controls in the acute phase diminishing in the chronic phase, suggesting neuroplasticity. These finding highlight the crucial role of time post-stroke in studying cortical and motor dynamics.
TAS and %CST are independently associated with motor status. While resting EEG metrics did not mediate these effects, further analyses are underway to explore the potential role of cortical and cortico-muscular coherences (Delcamp et al., 2024) in the TAS-FM and %CST-FM relationships.
Finally, including TAS improves %CST-based predictive model, highlighting the role of time-related phenomena in neuroplasticity, whether spontaneous or induced by therapy.
In summary, EEG power in key bands normalizes over time. EEG metrics were related to neural injury and time post-stroke but did not explain their effects on motor status. These results deepen our understanding of post-stroke neurophysiology and may improve motor predictive models for personalized rehabilitation strategies.
TAS and %CST are independently associated with motor status. While resting EEG metrics did not mediate these effects, further analyses are underway to explore the potential role of cortical and cortico-muscular coherences (Delcamp et al., 2024) in the TAS-FM and %CST-FM relationships.
Finally, including TAS improves %CST-based predictive model, highlighting the role of time-related phenomena in neuroplasticity, whether spontaneous or induced by therapy.
In summary, EEG power in key bands normalizes over time. EEG metrics were related to neural injury and time post-stroke but did not explain their effects on motor status. These results deepen our understanding of post-stroke neurophysiology and may improve motor predictive models for personalized rehabilitation strategies.
Modeling and Analysis Methods:
Classification and Predictive Modeling
EEG/MEG Modeling and Analysis 1
Task-Independent and Resting-State Analysis 2
Keywords:
Cerebrovascular Disease
Data analysis
Electroencephaolography (EEG)
Motor
Plasticity
STRUCTURAL MRI
1|2Indicates the priority used for review
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Provide references using APA citation style.
Lin, D. J., Cloutier, A. M., Erler, K. S., Cassidy, J. M., Snider, S. B., Ranford, J., Parlman, K., Giatsidis, F., Burke, J. F., Schwamm, L. H., Finklestein, S. P., Hochberg, L. R., & Cramer, S. C. (2019). Corticospinal Tract Injury Estimated from Acute Stroke Imaging Predicts Upper Extremity Motor Recovery after Stroke. Stroke, 50(12), 3569.
Wu, J., Srinivasan, R., Burke Quinlan, E., Solodkin, A., Small, S. L., & Cramer, S. C. (2016). Utility of EEG measures of brain function in patients with acute stroke. Journal of Neurophysiology, 115(5), 2399–2405.