http://journal.frontiersin.org/article/10.3389/fneur.2015.00230/full?
- 1Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- 2MRC Unit for Lifelong Health and Ageing, University College London, London, UK
- 3The National Hospital for Neurology and Neurosurgery, London, UK
- 4INSERM U894, Centre Hospitalier Sainte-Anne, Sorbonne Paris Cite, Paris, France
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.
Introduction
Stroke remains a leading cause of long-term disability and carries a significant social and economic cost (1, 2). After stroke, functional imaging studies of movement report widespread changes in activation of the cortical networks (3–8).
The precise cognitive processes that determine these changes remain
unclear. In this study, we used a data-led method to explore if the
changes in movement-related networks are a result of processes specific
to stroke patients (i.e., use of additional muscles) or whether they
represent modulation of extent movement-related networks. Understanding
this distinction in neuroplasticity is likely to help establish the
driver of fMRI changes reported after stroke and help establish the most
effective restorative therapies for patients (9–11).
Using a variety of tasks, numerous groups have
reported changes in movement-related networks – importantly these remote
changes relate to the recovery of motor performance. Movement-related
fMRI activation in the ipsilesional primary motor cortex is associated
with better recovery (4, 7, 8, 12, 13). Indeed it is on this model that many restorative intervention studies are based (14) changes in movement-related networks are being used to predict response to therapies (15).
Yet it is possible that the changes in movement-related networks may
represent an epiphenomenon of the increased difficulty involved in
carrying out the task after a stroke (6).
There are several caveats when considering comparisons of patients with healthy volunteers (6).
For instance, the kinematics of movements, EMG patterns, motor
strategies (adaptation versus relearning), and whether movement involved
different body parts in different subjects have not been monitored
consistently in the MRI. In other words, it is possible that the
differences reported represent a composite of cognitive processes
specific to stroke patients that may not be directly related to the
recovery process as such.
Understanding whether there are networks specific to
stroke patients will greatly aid the understanding of the recovery
process after stroke. It may allow a more targeted approach to
rehabilitation as it could identify the most appropriate training
programs. We explored the extent to which the widely described changes
in motor networks after stroke are a result of specific processes (i.e.,
motor adaptation or use of different muscle) or whether they represent
modulation of extant motor performance. There are two key aspects to our
study.
First, to remove any biases produced by subtle
differences in motor performance, we studied both motor imagery (MI) and
executed movement (EM). MI is intrinsically linked to the motor system
and can be used to study the motor system without actual movement (16–19).
In stroke patients with normal activations during EM, we have reported
abnormal hemispheric lateralization during MI that related to recovery
of motor function. In other words, by studying MI as well as EM, we are
able to identify aspects of task-dependent activation that relate to
motor execution and those more “upstream” (20).
Second, we use a data-led approach using tensor-independent component analysis (TICA) (21).
Using TICA, we examine the cortical networks that are common to stroke
patients and aged-matched controls or exclusive to either. Unlike the
conventional mass univariate approach, TICA is a powerful data-led
approach that explores similarities as well as differences in cortical
networks. Importantly, both tasks (MI and EM) from both groups (stroke
and aged-matched controls) are considered the same. We are able to use a
“blinded task” during the production of the independent components
(ICs) as they have the same temporal profile. In other words, we make no
prior assumptions as to the extent of overlap, if any, between the
task-related networks in stroke patients and controls or between the MI
and EM. If the widely reported changes in movement-dependent networks
are related to a stroke-specific cognitive process, then this analytic
approach will likely produce separate components.
We hypothesize that in recovered subcortical stroke
patients, the task-related motor networks identified for both EM and MI
are shared with the age-matched controls. In keeping with our reports
from healthy volunteers, we expect to find networks related exclusively
to EM and others that are shared with MI. Finally, we expect that in
stroke patients, the task-related networks would correlate with measures
of motor recovery.
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