In our study, we developed multivariate regression models for the prediction of early motor improvement in stroke patients with limb paresis, using routine admission clinical and CTP imaging data. These models could predict early improvement of paresis with 84%–92% accuracy. These prediction models not only have the potential to contribute prognostic information to the clinical or research setting, but also could enhance acute therapeutic triage. Specifically, our results have the potential for rapid and accurate stratification of patients, identifying those likely to do well independent of therapy (for whom the risk of treatment may outweigh the benefit—“too good to treat”), vs those likely to do poorly despite therapy (“too bad to treat”), for whom early transfer to an inpatient rehabilitation center may be most beneficial.7
We developed an automated method for location-weighted analysis of CTP scans, with minimal operator-dependent bias, and highly reproducible results. This image analysis approach could help to optimize the generalizability of predictive models. In addition, our results confirm that multivariate models integrating both clinical and imaging parameters can improve prognostication of early stroke outcome. However, long-term stroke recovery is complex, and there are many contributing factors, including potentially modifiable ones.3
Although many variables (including muscle mass, aerobic fitness prior to stroke, level of poststroke fatigue, and depression) play a role in long-term rehabilitation, early functional recovery in ischemic stroke is presumably largely attributable to reperfusion of eloquent brain regions. Therefore, integrative long-term predictive models might also include prestroke functional status, motor-evoked potentials in the early poststroke phase, genotyping, and neurophysiologic assessment of neural plasticity.3,8
In acute stroke, the observed clinical deficits may in part be attributable to dysfunctional, critically ischemic brain outside the region of the acute infarct “core.” Dynamic contrast perfusion scanning (CTP or magnetic resonance perfusion [MRP]) can detect such hypoperfused regions that not only are well correlated with the presenting physical examination, but that also reflect potentially salvageable still viable ischemic brain tissues (i.e., at-risk penumbra), although it should be noted that perfusion map parameters only reflect correlates of cerebral oxygenation, and should not be interpreted as cerebral oxygenation itself. CTP scans, compared to MRP, are more practical in terms of availability, speed, and cost. In addition, the linear relation between contrast concentration and attenuation in CT imaging makes perfusion quantification more reliable compared to that of MRP techniques.9
Previous studies have also suggested that the precise neuroanatomic localization of such dysfunctional regions could substantially improve the accuracy of the correlation between imaging findings and the clinical deficit. Indeed, it was shown that the incorporation of infarct location data into volume-based models of functional outcome significantly improved the correlation between imaging-derived severity scores and the observed NIHSS scores.2
In our study, rather than modeling tissue outcome per se, as in much of the perfusion imaging literature (“core” vs “penumbra” vs “benign oligemia”),4
we determined the recoverable function of the stroke patient. We could determine the relationship between specific spatial patterns of brain ischemia, and the probability of subsequent motor improvement.
Our multivariate models suggest that the perfusion status of specific cerebral regions is well correlated with single limb motor deficit improvement in stroke patients, although these areas may not contribute to motor function directly, and may be different for different limbs, as discussed below. Indeed, CTP parameters of some cerebral regions not directly contiguous with the corticospinal tract were among the independent predictors of motor deficit improvement that we identified. Similarly, an fMRI-based motor improvement pattern in stroke patients within 24–48 hours after stroke that included small clusters of voxels in the ipsilesional postcentral gyrus and cingulate cortex was recently reported.1
It has also been shown that infarction of the striatum, corona radiata, external capsule, posterior limb of the internal capsule, and middle frontal gyrus contribute to estimating the likelihood of motor deficit in stroke patients.10
Notably, previous studies reported that paresis was caused not only by damage to the precentral motor strip or its descending fibers but also by premotor, parietal, and striatothalamic lesions.11,12
Indeed, the insular ribbon may serve as an example of how local blood flow deficits may predict more global stroke outcome. The insula is highly sensitive to hypoperfusion.13
Severe insular ribbon ischemia is not only associated with a low probability of aphasia improvement,5
but also a high probability of infarct growth.14
We therefore speculate that severe admission hypoperfusion of the insula may be a “canary in the coal mine” for poor global stroke outcome.
In addition, diffusion tensor imaging studies have reported significant differences in fractional anisotropy of the retrolenticular portion of internal capsule in stroke patients with poorer motor skill recovery compared to controls.15
The superior parietal lobule (BA 7) and the parietal precuneus are also involved in remembering and executing the correct order of task components and visuomotor function.16,17
Moreover, the left superior temporal and bilateral inferior frontal gyri play a role in ideation of voluntary simple movement.18
The right-left asymmetry in our findings might simply be an epiphenomenon reflecting differences in vascular embolic distribution between the right and left circulations in our relatively small cohort. However, this difference could also be attributable to the variable sensitivity of different CTP parameters in different brain locations (cortical vs deep).13
In addition, there is a growing literature reporting right-left asymmetry in topographic predictive models of stroke patients.10,13
That some of the clinical variables listed in the univariate analysis of , including age and motor score, were significantly different in patients with right compared to left hemispheric stroke might also be due to the smaller number of patients with right hemispheric stroke in our cohort. Patient handedness and true functional/anatomic asymmetry might have also contributed to our findings.19,20
Unfortunately, data regarding handedness were not available in our cohort. However, it is plausible that symptoms due to cerebral ischemia are more apparent if language or dominant hand function is affected, mostly in left hemispheric strokes, whereas right hemispheric strokes are usually associated with neglect, which reduces awareness of such deficits. This phenomenon could lead to a spatially heterogenous distribution of stroke lesions between patients with right vs left hemispheric stroke.
In addition to CTP variables, admission NIHSS was an independent predictor of functional improvement in our study. In our series, total admission NIHSS was a stronger predictor of clinical outcome than the specific motor component of the test (i.e., NIHSS items 5 and 6). Moreover, right-hemispheric lesion volumes have a smaller effect on NIHSS scores than left hemispheric lesion volumes.21
This further underscores why different models should be developed for left vs right hemispheric stroke.
One limitation in our study was the lack of patient evaluation with a specialized test of motor function for each limb, such as the Fugl-Meyer. Although total NIHSS score may be a more robust predictor of clinical improvement as a global measure of stroke severity, a more detailed and dedicated motor function test battery score may have performed better as a predictor of individual limb recovery. As another caveat, patients' discharge dates, rather than fixed time frame intervals, were used as the clinical endpoint in our study, which also may have diluted the strength of our correlations.
Another limitation of our study is that the correlation between a specific brain region and clinical outcome may be unapparent if voxels in that region were not sufficiently scanned due to artifact, patient positioning, or hardware-related coverage limitations. There is also the caveat that with coregistration and segmentation of brain scans based on spatial coordinates of an “idealized” brain image and preset atlases (such MNI-125), at least a small degree of misregistration is unavoidable. The standardized atlases used for our brain parcellation were developed based on cerebral cortex cytoarchitecture (BAs) and deep subcortical DTI tractography.22
The presumption that these subregions best distinguish areas of different function/perfusion remains controversial. A voxel-based analysis might avoid this drawback; however, voxel-based studies are even more subject to problems of variability.23,24
We were able to develop accurate multivariate atlas-based models for prediction of motor improvement in acute stroke patients presenting with single limb paresis, based on localization of admission CTP hypoperfused regions and NIHSS examination. Accurate functional prediction is important for determining appropriate rehabilitation strategies and discussing expectations of functional improvement with patients and their families; in the future, such models may help identify patients with clinical penumbra who are potential targets for therapy. These preliminary models can serve as a “proof-of-concept” for prospective location-weighted imaging prediction of early clinical improvement in acute stroke.