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Motor Recovery After Subcortical Stroke Depends on Modulation of Extant Motor Networks.

Sharma N, Baron JC - Front Neurol (2015)

Bottom Line: Whether these are a result of stroke-specific cognitive processes or reflect modulation of existing movement-related networks is unknown.ICs were only included if the subject scores were significant (for either EM or MI).This highlights that recovery of motor function after subcortical stroke involves preexisting cortical networks that could help identify more effective restorative therapies.

View Article: PubMed Central - PubMed

Affiliation: Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge , Cambridge , UK ; MRC Unit for Lifelong Health and Ageing, University College London , London , UK ; The National Hospital for Neurology and Neurosurgery , London , UK.

ABSTRACT

Introduction: Stroke is the leading cause of long-term disability. Functional imaging studies report widespread changes in movement-related cortical networks after stroke. Whether these are a result of stroke-specific cognitive processes or reflect modulation of existing movement-related networks is unknown. Understanding this distinction is critical in establishing more effective restorative therapies after stroke. Using multivariate analysis (tensor-independent component analysis - TICA), we map the neural networks involved during motor imagery (MI) and executed movement (EM) in subcortical stroke patients and age-matched controls.

Methods: Twenty subcortical stroke patients and 17 age-matched controls were recruited. They were screened for their ability to carry out MI (Chaotic MI Assessment). The fMRI task was a right-hand finger-thumb opposition sequence (auditory-paced 1 Hz; 2, 3, 4, 5, 2…). Two separate runs were acquired (MI and rest and EM and rest; block design). There was no distinction between groups or tasks until the last stage of analysis, which allowed TICA to identify independent components (ICs) that were common or distinct to each group or task with no prior assumptions.

Results: TICA defined 28 ICs. ICs representing artifacts were excluded. ICs were only included if the subject scores were significant (for either EM or MI). Seven ICs remained that involved the primary and secondary motor networks. All ICs were shared between the stroke and age-matched controls. Five ICs were common to both tasks and three were exclusive to EM. Two ICs were related to motor recovery and one with time since stroke onset, but all were shared with age-matched controls. No IC was exclusive to stroke patients.

Conclusion: We report that the cortical networks in stroke patients that relate to recovery of motor function represent modulation of existing cortical networks present in age-matched controls. The absence of cortical networks specific to stroke patients suggests that motor adaptation and other potential confounders (e.g., effort and additional muscle use) are not responsible for the changes in the cortical networks reported after stroke. This highlights that recovery of motor function after subcortical stroke involves preexisting cortical networks that could help identify more effective restorative therapies.

No MeSH data available.


Related in: MedlinePlus

The figures show the involvement of each IC across the whole brain with a standard threshold of p > 0.5 (alternative hypothesis test) and the variance it accounts for out of the total explained variance. In four stroke patients, the images were flipped so that the left hemisphere is always contralateral to executed movement/motor imagery. The left hemisphere equates to the ipsilesional hemisphere. The scales show the transformed z-score, orange is activation, and blue is deactivation. The normalized time course response is shown for each task and the full model fit (full model fit = blue, executed movement = red, and motor imagery = green). The mean subject scores with standard error bars are shown for each task and differences highlighted (executed movement = red, motor imagery = green). The time course and subject score for each task are shown.
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Figure 2: The figures show the involvement of each IC across the whole brain with a standard threshold of p > 0.5 (alternative hypothesis test) and the variance it accounts for out of the total explained variance. In four stroke patients, the images were flipped so that the left hemisphere is always contralateral to executed movement/motor imagery. The left hemisphere equates to the ipsilesional hemisphere. The scales show the transformed z-score, orange is activation, and blue is deactivation. The normalized time course response is shown for each task and the full model fit (full model fit = blue, executed movement = red, and motor imagery = green). The mean subject scores with standard error bars are shown for each task and differences highlighted (executed movement = red, motor imagery = green). The time course and subject score for each task are shown.

Mentions: Figures 1 and 2 show the whole brain activations and deactivations, the time course (BOLD), subject scores, and percentage of total variance explained. Table 1 summarizes the areas involved [labeled using the Jülich Atlas (29)].


Motor Recovery After Subcortical Stroke Depends on Modulation of Extant Motor Networks.

Sharma N, Baron JC - Front Neurol (2015)

The figures show the involvement of each IC across the whole brain with a standard threshold of p > 0.5 (alternative hypothesis test) and the variance it accounts for out of the total explained variance. In four stroke patients, the images were flipped so that the left hemisphere is always contralateral to executed movement/motor imagery. The left hemisphere equates to the ipsilesional hemisphere. The scales show the transformed z-score, orange is activation, and blue is deactivation. The normalized time course response is shown for each task and the full model fit (full model fit = blue, executed movement = red, and motor imagery = green). The mean subject scores with standard error bars are shown for each task and differences highlighted (executed movement = red, motor imagery = green). The time course and subject score for each task are shown.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4644932&req=5

Figure 2: The figures show the involvement of each IC across the whole brain with a standard threshold of p > 0.5 (alternative hypothesis test) and the variance it accounts for out of the total explained variance. In four stroke patients, the images were flipped so that the left hemisphere is always contralateral to executed movement/motor imagery. The left hemisphere equates to the ipsilesional hemisphere. The scales show the transformed z-score, orange is activation, and blue is deactivation. The normalized time course response is shown for each task and the full model fit (full model fit = blue, executed movement = red, and motor imagery = green). The mean subject scores with standard error bars are shown for each task and differences highlighted (executed movement = red, motor imagery = green). The time course and subject score for each task are shown.
Mentions: Figures 1 and 2 show the whole brain activations and deactivations, the time course (BOLD), subject scores, and percentage of total variance explained. Table 1 summarizes the areas involved [labeled using the Jülich Atlas (29)].

Bottom Line: Whether these are a result of stroke-specific cognitive processes or reflect modulation of existing movement-related networks is unknown.ICs were only included if the subject scores were significant (for either EM or MI).This highlights that recovery of motor function after subcortical stroke involves preexisting cortical networks that could help identify more effective restorative therapies.

View Article: PubMed Central - PubMed

Affiliation: Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge , Cambridge , UK ; MRC Unit for Lifelong Health and Ageing, University College London , London , UK ; The National Hospital for Neurology and Neurosurgery , London , UK.

ABSTRACT

Introduction: Stroke is the leading cause of long-term disability. Functional imaging studies report widespread changes in movement-related cortical networks after stroke. Whether these are a result of stroke-specific cognitive processes or reflect modulation of existing movement-related networks is unknown. Understanding this distinction is critical in establishing more effective restorative therapies after stroke. Using multivariate analysis (tensor-independent component analysis - TICA), we map the neural networks involved during motor imagery (MI) and executed movement (EM) in subcortical stroke patients and age-matched controls.

Methods: Twenty subcortical stroke patients and 17 age-matched controls were recruited. They were screened for their ability to carry out MI (Chaotic MI Assessment). The fMRI task was a right-hand finger-thumb opposition sequence (auditory-paced 1 Hz; 2, 3, 4, 5, 2…). Two separate runs were acquired (MI and rest and EM and rest; block design). There was no distinction between groups or tasks until the last stage of analysis, which allowed TICA to identify independent components (ICs) that were common or distinct to each group or task with no prior assumptions.

Results: TICA defined 28 ICs. ICs representing artifacts were excluded. ICs were only included if the subject scores were significant (for either EM or MI). Seven ICs remained that involved the primary and secondary motor networks. All ICs were shared between the stroke and age-matched controls. Five ICs were common to both tasks and three were exclusive to EM. Two ICs were related to motor recovery and one with time since stroke onset, but all were shared with age-matched controls. No IC was exclusive to stroke patients.

Conclusion: We report that the cortical networks in stroke patients that relate to recovery of motor function represent modulation of existing cortical networks present in age-matched controls. The absence of cortical networks specific to stroke patients suggests that motor adaptation and other potential confounders (e.g., effort and additional muscle use) are not responsible for the changes in the cortical networks reported after stroke. This highlights that recovery of motor function after subcortical stroke involves preexisting cortical networks that could help identify more effective restorative therapies.

No MeSH data available.


Related in: MedlinePlus