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Developing and implementing biomarkers that distinguish patient subgroups will help address these issues and unravel the factors important to the recovery process. The goal of the present paper is to provide a consensus statement regarding the current state of the evidence for stroke recovery biomarkers. Biomarkers of motor, somatosensory, cognitive and language domains across the recovery timeline post-stroke are considered; with focus on brain structure and function, and exclusion of blood markers and genetics. We provide evidence for biomarkers that are considered ready to be included in clinical trials, as well as others that are promising but not ready and so represent a developmental priority.
We conclude with an example that illustrates the utility of biomarkers in recovery and rehabilitation research, demonstrating how the inclusion of a biomarker may enhance future clinical trials. In this way, we propose a way forward for when and where we can include biomarkers to advance the efficacy of the practice of, and research into, rehabilitation and recovery after stroke. Stroke is a heterogeneous condition, making choice of treatment, and prediction of outcome and treatment response, difficult. Biomarkers can greatly inform patient selection for trials in general medical research, and this is equally true for stroke recovery.
In practical terms, biomarkers should improve our ability to predict long-term outcomes after stroke across multiple domains. This is beneficial for: a patients, caregivers and clinicians; b planning subsequent clinical pathways and goal setting; and c identifying whom and when to target, and in some instances at which dose, with interventions for promoting stroke recovery. Unpredictable outcomes after stroke, particularly in those who present with the most severe impairment 3 mean that clinical trials of rehabilitation interventions need hundreds of patients to be appropriately powered.
Use of biomarkers would allow incorporation of accurate information about the underlying impairment, and thus the size of these intervention trials could be considerably reduced, 4 with obvious benefits. These principles are no different in the context of stroke recovery as compared to general medical research. Interventions fall into two broad mechanistic : 1 behavioural interventions that take advantage of experience and learning-dependent plasticity e.
Our goal is to provide a consensus statement regarding the evidence for SRBs that are helpful in outcome prediction and therefore identifying subgroups for stratification to be used in trials.
Finally, we provide an example of how inclusion of a clinical trial-ready biomarker might have benefitted a recent phase III trial. As there is generally limited evidence at this time for blood or genetic biomarkers, we do not discuss these, but recommend they are a developmental priority. Table 1. Summary of possible brain biomarkers to measure brain structure or function. Table 2. Scope of functional domains considered given existing literature. A challenge across the reviewed literature was to determine where the biomarker data explained recovery beyond that denoted by clinical outcome measures, such as the proportional recovery rule that has been demonstrated using motor, 313 visuospatial neglect 14 and language 15 outcomes.
Given the recency of these models, we were unable to address this in this paper and urge people to consider this in their future trial de. Further, to fully understand the predictive capacity of biomarkers, we need to move beyond cross-sectional studies, which constitute the bulk of the biomarker literature at present, and conduct mechanistic studies that go beyond simple correlations, as well as conduct longitudinal studies that provide data useful for predicting outcome or treatment response.
Neuroimaging biomarkers include quantitative characterisation of the stroke lesion itself, as well as the structure and function of non-lesioned brain areas. There is little consensus regarding the usefulness of characterizing the ischemic penumbra at the hyper-acute stage in relation to predicting motor outcomes. Recent data suggest that the site of ischemic penumbra, rather than volume, could predict outcome or treatment response i. Measures of corticospinal tract CST white matter integrity in the acute stage may predict motor outcome. Early measurement of CST fiber via diffusion tensor imaging DTIa reflection of white matter integrity, predicts motor outcome Fugl—Meyer score at 12 months, especially for patients with initially more severe impairment.
Data also support the potential utility of the asymmetry between the ipsilesional and contralesional CST FA to predict treatment gains in the chronic stage. Measures of the extent of CST injury in the acute stage, such as via CST lesion load, 24 also have predictive value for poor motor outcome. A model including this biomarker improved prediction of Fugl—Meyer motor score at three months post-stroke that was beyond what could be determined by baseline behavioral assessment, age or infarct volume.
Several studies have found that in the chronic stage, the extent of CST injury also helps predict treatment gains. Other regions distant from the lesion influence motor recovery. While measures related to distant regions require further development to reach the stage of valid biomarker, several useful observations have been published in the chronic stage, including those related to contralesional CST, 28 corpus callosum, 29 precentral gyral, 30 and superior longitudinal fasciculi.
There is broad consensus that the presence of an upper limb motor evoked potential MEP in response to transcranial magnetic stimulation TMS at the hyperacute and acute stages strongly predicts good motor outcome 3738 and that shorter MEP latencies and central motor conduction times are associated with better outcome. Resting state functional connectivity rsFC findings in the early and late subacute phases converge on the conclusion that interhemispheric connectivity is of particular importance to motor control.
Cross-sectional studies have demonstrated decreased rsFC correlates with the degree of motor impairment. from a ridge regression machine-learning algorithm analysis of a large sample of early subacute stroke patients suggest that rsFC may explain a smaller amount of the behavioral variance observed than the amount of structural damage to the CST. Quantitative indices extracted from functional MRI fMRI in the early and late subacute stage, such as the laterality index from M1, and the study of its change over time, show that stroke is associated with a less lateralized pattern of activation as compared to healthy subjects, a finding that is exaggerated among patients with poorer outcomes.
As there are fewer long-term studies of the laterality index, and it often shows ificant biological associations, this is an area of priority for development. Magnetoencephalography MEG or electroencephalography EEGnon-invasive measures of cortical neuronal oscillations, are sensitive to alterations in both GABAergic and glutamatergic alling that are important for plasticity and recovery after stroke.
Acutely, lower beta-rebound in response to tactile finger stimulation which indicates increased early post-stroke sensorimotor excitability 66 and increased somatosensory map size 67 predict good recovery after stroke. Combining neuroimaging and neurophysiology biomarkers may be useful for predicting motor outcomes and therapy response. One recent study emphasized that combining neuroimaging measures of neural injury and neural function was key to best predicting response to a standardized robotic therapy in the chronic stage.
In summary, neuroimaging and neurophysiology CST biomarkers can predict motor outcome and response to therapy after stroke, and are recommended for use in clinical trials, e. Table 4. Expert consensus biomarker recommendations. Currently, there are few studies of structural or functional biomarkers conducted to understand outcome, predict recovery or predict treatment response in the somatosensory functioning domain in the hyperacute or acute phases post-stroke. Most work on structural biomarkers involving the non-lesioned brain has focused on understanding outcome by mapping the structural integrity of residual pathways.
Feasibility of visualisation of sensorimotor systems by tracking fibres has been demonstrated in hyperacute, acute and early subacute phases for somatosensory symptoms. Only one study noted associations between the structure of somatosensory regions and motor outcome in the chronic phase. Failure to activate the somatosensory cortex during median nerve stimulation in the acute stage predicts poor clinical recovery at three months. In addition, somatosensation function in the chronic phase correlates with activity in the ipsilesional and contralesional primary sensorimotor cortex 86 and a more distributed pattern of activity involving parietal cortex.
Changes have also been reported in association with training of touch discrimination, 88 passive proprioception 89 and sensorimotor function, 90 with a focus on tracking outcomes and mechanisms, rather than prediction. There is insufficient evidence to recommend the use of any specific biomarkers of somatosensory system function in clinical trials; however, several candidates are suggested.
The recovery of somatosensation is often overlooked despite well-documented observations that impaired sensation is an impediment to optimal recovery. Indeed, studies that readily identify structure—function relationships for phonology and semantic processing, and often fail to find an equivalent for executive function.
Frontal and basal ganglia region microbleeds were associated with executive dysfunction outcome in the chronic phase, 95 and another study found that mean diffusivity of normal appearing white matter whole brain in non-lesioned areas correlated with outcomes for executive function among individuals with ischaemic leukoaraiosis plus a lacunar stroke. Though functional imaging methods may offer the best hope of generating robust biomarkers for executive function, there is little published work. Available associations are correlative and from cross-sectional studies, rather than predictions of outcomes or more complex evaluation of biological hypotheses.
In the late subacute phase, executive functioning correlates with alpha band functional connectivity between the left fronto-opercular cortex and the rest of the brain. The default mode network DMN has emerged as a key biological substrate in the context of cognitive functioning. Resting state studies have provided robust examples of disruption of interhemispheric connectivity associated with domain-specific cognitive deficits 4751, and recovery.
Multiple studies have confirmed this connectivity is much depressed in the acute stage after stroke in persons with USN and returns toward normal in association with the resolution of symptoms, with the largest current longitudinal study retaining 64 stroke patients at 12 months. Similar relationships have been reported for the DMN and other networks. In the domain of spatial cognition, multiple moderately sized studies of right hemisphere injury confirm that damage to different major long range white matter tracts may predict chronic persistence of unilateral spatial neglect.
Two well-deed longitudinal studies implicate the inferior occipitofrontal fasciculus and uncinate fasciculus and decreased FA in the left and right superior longitudinal fasciculus II, and forceps major of the corpus callosum with neglect scores. In cross-sectional studies, linear regression shows an association of unilateral spatial neglect with damage to the fronto-parietal segment of the arcuate fasciculus, and that Based on this evidence, biomarkers of cognitive function, including executive functioning, are not ready for immediate broad implementation in clinical trials.
Thus, further study and validation of biomarkers that explain current state and future chante in cognitive functions are a ificant priority area for development. Resting state FC is a promising candidate biomarker Table 4cand study of its utility as a biomarker of recovery is emphasized here.
There are a of studies identifying a relationship between lesion site and aphasia, anatomical findings that suggest potential metrics to evaluate as biomarkers. Measures of functional connectivity may also be useful in this phase, as a recent study of acute lacunar stroke patients observed that increased resting state FC between the left and right superior temporal gyri was correlated with poorer language function.
In the early subacute phase, there are relationships between lesion location and aphasia symptoms that suggest potential biomarkers. Kreisler et al. Forkel et al. Recent work by Geranmayeh et al. Functional MRI activity in the early subacute phase shows promise as a predictor of long-term recovery when analyzed using a multivariate machine learning technique. Saur et al. In the largest case-control study of subacute stroke patients with aphasia to date, Yang et al.
Of note is the observation that the interhemispheric hyperconnectivity of the superior temporal gyrus was inversely correlated with the aphasia quotient, indicating that greater connectivity was associated with worse aphasia.
Voxel-based analyses in the chronic phase has established utility in multiple brain systems and specifically have identified structural damage associated with particular aphasic symptoms, distinguishing between semantic and phonological processes and recognition versus production.
Using this approach and covariate factors of time of stroke, volume, and 35 different brain regions, predictions of language outcome, and within subject changes in speech production, have been identified. Posterior middle temporal lobe damage can negatively affect aphasia therapy outcome in the chronic phase. Bonilha et al. Several small studies identified a relationship between therapy success and integrity of the left arcuate fasciculus, right arcuate fasciculus and white matter in proximity to the hippocampus.
Fridriksson identified a ificant relationship between treatment-induced naming improvements and fMRI activity in the both a posterior cluster including parietal lobe and precuneus and an anterior cluster including middle frontal gyrus and pars opercularis. Subsequent analyses showed that altered activity in perilesional areas was associated with increased naming accuracy, but measures of pre-treatment brain activity as opposed to changes in activity predicted improvement in semantic errors, suggesting additional factors contribute to treatment outcome.
Resting state FC has also shown potential for understanding and predicting aphasia recovery in the subacute and chronic phases including treatment induced improvements ; however, larger studies are required to validate this approach. In summary, in the acute and early subacute stages, the use of structural MRI provides insights into the neural basis of language deficits, but there are not sufficient large studies demonstrating that these methods clearly improve prediction of recovery or treatment response.
Functional brain assessments such as via fMRI show potential at the early subacute stage for ificantly improving prediction of outcome; however, this approach needs validation. Structural MRI and DTI may forecast recovery at the late subacute and chronic stage, suggesting the possible use of these techniques to stratify patients for clinical trials, understand therapy mechanism and predict outcome.
It should be noted that: 1 there is still considerable variability in outcome that is not ed for by these methods, 2 each method uses a unique and complex analysis technique, 3 different aphasia treatments may engage unique networks Table 4dand 4 detailed studies examining the combined utility of anatomical and functional brain measures for predicting language recovery are warranted. How might biomarker data be incorporated into future stroke recovery research?
Biomarkers present a way forward to subgroup or stratify patients in order to reduce variance and increase power, allowing for smaller sample sizes. Thus, a patient exploiting all possible compensatory brain mechanisms might have little room to improve, while a similar patient who uses no compensatory mechanisms might achieve benefit.
Clinical trials therefore need to base participant eligibility on more than presence of a stroke, or behavioural status. Instead, patient selection should include appropriate biomarkers; ideally, these will be linked with preclinical methods as well as the biological mechanism of the therapy or treatment under investigation.
A useful example comes from the recent phase III Everest trial, which relied on behavioural assessments to determine participant eligibility, and ultimately found that patients randomized to epidural motor cortex stimulation did not reach the primary efficacy endpoint more often than patients in the control group. We believe that this example is highly useful in illustrating the utility of biomarkers in recovery and rehabilitation research and expect that the inclusion of biomarkers will enhance future clinical trials.
An unrestricted educational grant was provided by Ipsen Pharma. International Journal of Stroke. Article Menu. Download PDF. Open EPUB.
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