Introduction
Stroke is the leading cause of acquired disabilities in adults.
1
Stroke-related impairments cause drastic reductions in patients’ daily
living activities and quality of life. To regain independence and
quality of life after stroke, effective rehabilitation planning is
essential. Recovery prediction can help clinicians design individually
tailored rehabilitation plans, including realistic discharge planning
and appropriate allocation of time and resources. In addition, it allows
patients to set realistic goals.
2
Neuroimaging-based
brain connectivity analyses are already used in recovery prediction,
and several predictors have been identified.
3–7
Neurologic research has emphasized that the effects of neurological
disorders are exerted over an entire network because the brain is
organized in networks of connections among many neurons.
8–10
Damage caused by stroke can diffuse through the brain networks and
influence the function of distant brain regions even when the damage to
the brain structure is a focal lesion.
9,11
Therefore, using a brain connectivity analysis for recovery prediction
is an appropriate approach. However, prediction remains difficult
because of inter-individual variability.
Previous clinical studies have used various predictive markers. Among them, initial motor function is the most representative.
2,12,13 However, it has limitations in predicting motor recovery in patients with severe stroke.
13
Furthermore, for clinical purposes, an accurate prediction model beyond
initial motor function itself is needed. Our aim in this study was to
use magnetic resonance imaging (MRI) data and initial motor function in a
brain connectivity analysis to propose a new predictor that can
accurately predict recovery from stroke.
Previous studies have
demonstrated widespread remote changes in connectivity in regions in
both hemispheres as the result of a focal lesion.
8,14,15
Also, motor learning after stroke is performed by widespread networks
in the whole brain, without the need for a motor-related central region,
and many regions in widespread networks compensate for learning
success.
16
In this respect, an investigation of the overall connectivity in the
whole brain and the indirect connectivity of the damaged area, beyond
the direct connectivity of the damaged area, might be important for
understanding recovery after stroke.
The second link-step
connectivity of a lesion network (the next link-step beyond a lesion’s
direct connectivity) was obtained from resting-state functional MRI
(fMRI) and investigated using the following considerations: (a) second
link-step connectivity is likely to be highly affected by a lesion
because connectivity is adjacently connected to a lesion when
considering the spread of damage throughout the entire network; (b) the
connectivity forms a wider brain network and broadly covers more brain
regions than first link-step connectivity in terms of information
spreading within a network structure. Therefore, by quantifying second
link-step connectivity, the impact of the focal lesion on the whole
brain network can be assessed according to points (a) and (b).
Furthermore, this connectivity is expected to actively contribute to
recovery after stroke onset because it does not suffer actual physical
damage from the focal lesion. During the recovery period, a lesion with a
low impact on connectivity enables cost-effective reorganization to
allow recovery across the whole brain network, so second link-step
connectivity might indicate the potential for functional recovery.
Therefore, we hypothesized that patients whose lesions had a low impact
on second link-step connectivity would be more likely to recover from
stroke damage, as reflected by better motor recovery, than patients
whose lesions had high impacts on second link-step connectivity.
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