A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms

Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in rehabilitation research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires significant neuroanatomical expertise. We previously released a large, open-source dataset of stroke T1w MRIs and manually segmented lesion masks (ATLAS v1.2, N=304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N=955), a larger dataset of T1w stroke MRIs and manually segmented lesion masks that includes both training (public) and test (hidden) data. Algorithm development using this larger sample should lead to more robust solutions, and the hidden test data allows for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke rehabilitation research.

Perceived study-induced influence on the control group in a randomized controlled trial evaluating a complex intervention to promote psychosocial well-being after stroke: a process evaluation

Background A commonly applied control condition in trials evaluating complex interventions in rehabilitation research is “usual care.” The main challenge is to ensure that the control group receives genuine usual care as delivered in everyday clinical practice. The assessment interviews and dialogues with the data collectors may influence the control group participants’ reflections on their condition and adjustments. This represents a threat to the internal validity of the trial. Thus, the aim of this study was to explore the perceived study-induced influence of assessment interviews on the adjustment of the members of a control group in a randomized clinical trial. The aim of the trial was to test a dialogue-based psychosocial intervention aiming at promoting the psychosocial well-being and adjustment of stroke survivors. Methods Fifteen participants in the control group of a multicenter stroke rehabilitation trial participated in narrative semi-structured interviews. Ricoeur’s interpretation theory guided the analysis. Results The perceived study-induced influence of the assessment interviews on the adjustment process of members of the control group varied considerably. The results demonstrated that the assessment interviews facilitated some participants’ feelings of control and their ability to cope. Other participants’ statements indicate that they relied on their existing personal capacity to cope and adjust and that the assessment interviews did not make any difference either on their coping ability or on their process of adjustment. Five themes were identified that described the perceived study-induced influence of the assessment interviews in the control group. The themes illustrated that the assessments served as a safety net, enhanced awareness and understanding, encouraged seeking support, allowed the opportunity to vent disappointment, or did not make any difference either way. Conclusions RCT assessment interviews may influence the adjustment process and represent a serious problem in measuring interventions over time in trials of complex interventions in rehabilitation research. To uphold rigor and stringency, the usual care control conditions should be thoroughly assessed and described. Informing participants only about the treatment they were allocated to receive might counteract the potential to dilute the difference between the two arms of the trial. Trial registration ClinicalTrials.gov NCT02338869. Registered on October 4, 2014

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.