Rehabilitation and Biomarkers of Stroke Recovery: Study Protocol for a Randomized Controlled Trial

The minimal clinically important difference of two multifaceted fatigue evaluation questionnaires in chronic stroke

PURPOSE

This research sought to ascertain the Minimal Clinically Important Difference (MCID) and Robust Clinically Important Difference (RCID) of the Multidimensional Fatigue Symptom Inventory-Short Form (MFSI-SF) and Chalder Fatigue Questionnaire-11 (CFQ-11) as two important concepts for the clinical interpretation of the results in chronic post-stroke population.

METHODS

A total of 128 subjects with chronic post-stroke completed the MFSI-SF and CFQ-11 before and after six weeks of intervention. The MCIDs were derived using both anchor- and distribution-based methods; however, only anchor-based methods were used to estimate RCIDs.

RESULTS

Anchor-based MCIDs for MFSI-SF and CFQ-11 were in the range of -5 to -6.28 and -2 to -4.56, respectively. Distribution-based MCIDs in MFSI-SF and CFQ-11 were calculated in the range of -4.17 to -24.05 and -1.72 to -7.68, respectively. RCID ranges of -10 to -15 were obtained for the MFSI-SF and -6 to -7.33 for the CFQ-11.

CONCLUSION

These findings may have implications for clinical experts in the clinical interpretation of fatigue changes observed in MFSI-SF and CFQ-11 in individuals with chronic stroke.

Neuroimaging Techniques as Potential Tools for Assessment of Angiogenesis and Neuroplasticity Processes after Stroke and Their Clinical Implications for Rehabilitation and Stroke Recovery Prognosis

Stroke as the most frequent cause of disability is a challenge for the healthcare system as well as an important socio-economic issue. Therefore, there are currently a lot of studies dedicated to stroke recovery. Stroke recovery processes include angiogenesis and neuroplasticity and advances in neuroimaging techniques may provide indirect description of this action and become quantifiable indicators of these processes as well as responses to the therapeutical interventions. This means that neuroimaging and neurophysiological methods can be used as biomarkers—to make a prognosis of the course of stroke recovery and define patients with great potential of improvement after treatment. This approach is most likely to lead to novel rehabilitation strategies based on categorizing individuals for personalized treatment. In this review article, we introduce neuroimaging techniques dedicated to stroke recovery analysis with reference to angiogenesis and neuroplasticity processes. The most beneficial for personalized rehabilitation are multimodal panels of stroke recovery biomarkers, including neuroimaging and neurophysiological, genetic-molecular and clinical scales.

Neuromechanical Biomarkers for Robotic Neurorehabilitation

One of the current challenges for translational rehabilitation research is to develop the strategies to deliver accurate evaluation, prediction, patient selection, and decision-making in the clinical practice. In this regard, the robot-assisted interventions have gained popularity as they can provide the objective and quantifiable assessment of the motor performance by taking the kinematics parameters into the account. Neurophysiological parameters have also been proposed for this purpose due to the novel advances in the non-invasive signal processing techniques. In addition, other parameters linked to the motor learning and brain plasticity occurring during the rehabilitation have been explored, looking for a more holistic rehabilitation approach. However, the majority of the research done in this area is still exploratory. These parameters have shown the capability to become the “biomarkers” that are defined as the quantifiable indicators of the physiological/pathological processes and the responses to the therapeutical interventions. In this view, they could be finally used for enhancing the robot-assisted treatments. While the research on the biomarkers has been growing in the last years, there is a current need for a better comprehension and quantification of the neuromechanical processes involved in the rehabilitation. In particular, there is a lack of operationalization of the potential neuromechanical biomarkers into the clinical algorithms. In this scenario, a new framework called the “Rehabilomics” has been proposed to account for the rehabilitation research that exploits the biomarkers in its design. This study provides an overview of the state-of-the-art of the biomarkers related to the robotic neurorehabilitation, focusing on the translational studies, and underlying the need to create the comprehensive approaches that have the potential to take the research on the biomarkers into the clinical practice. We then summarize some promising biomarkers that are being under investigation in the current literature and provide some examples of their current and/or potential applications in the neurorehabilitation. Finally, we outline the main challenges and future directions in the field, briefly discussing their potential evolution and prospective.