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Edde M, Houde F, Theaud G, Dumont M, Gilbert G, Houde JC, Maltais L, Théberge A, Doumbia M, Beaudoin AM, Lapointe E, Barakovic M, Magon S, Descoteaux M. Impact of follow ups, time interval and study duration in diffusion & myelin MRI clinical study in MS. Neuroimage Clin 2023; 40:103529. [PMID: 37857232 PMCID: PMC10591008 DOI: 10.1016/j.nicl.2023.103529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 10/04/2023] [Accepted: 10/10/2023] [Indexed: 10/21/2023]
Abstract
It is currently unknown how quantitative diffusion and myelin MRI designs affect the results of a longitudinal study. We used two independent datasets containing 6 monthly MRI measurements from 20 healthy controls and 20 relapsing-remitting multiple sclerosis (RR-MS) patients. Six designs were tested, including 3 MRI acquisitions, either over 6 months or over a shorter study duration, with balanced (same interval) or unbalanced (different interval) time intervals between MRI acquisitions. First, we show that in RR-MS patients, the brain changes over time obtained with 3 MRI acquisitions were similar to those observed with 5 MRI acquisitions and that designs with an unbalanced time interval showed the highest similarity, regardless of study duration. No significant brain changes were found in the healthy controls over the same periods. Second, the study duration affects the sample size in the RR-MS dataset; a longer study requires more subjects and vice versa. Third, the number of follow-up acquisitions and study duration affect the sensitivity and specificity of the associations with clinical parameters, and these depend on the white matter bundle and MRI measure considered. Together, this suggests that the optimal design depends on the assumption of the dynamics of change in the target population and the accuracy required to capture these dynamics. Thus, this work provides a better understanding of key factors to consider in a longitudinal study and provides clues for better strategies in clinical trial design.
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Affiliation(s)
- Manon Edde
- Imeka Solutions, Inc., Sherbrooke, QC, Canada; Université de Sherbrooke, Sherbrooke, QC, Canada.
| | | | | | | | - Guillaume Gilbert
- MR Clinical Science, Philips Healthcare Canada, Mississauga, Ontario, Canada
| | | | | | - Antoine Théberge
- Université de Sherbrooke, Sherbrooke, QC, Canada; Videos & Images Theory and Analytics Laboratory (VITAL), Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Moussa Doumbia
- Université de Sherbrooke, CIUSSS de l'Estrie-CHUS Fleurimont, Sherbrooke, QC, Canada
| | - Ann-Marie Beaudoin
- Université de Sherbrooke, Sherbrooke, QC, Canada; Université de Sherbrooke, CIUSSS de l'Estrie-CHUS Fleurimont, Sherbrooke, QC, Canada
| | - Emmanuelle Lapointe
- Université de Sherbrooke, CIUSSS de l'Estrie-CHUS Fleurimont, Sherbrooke, QC, Canada
| | - Muhamed Barakovic
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel Switzerland, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Stefano Magon
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel Switzerland, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Maxime Descoteaux
- Imeka Solutions, Inc., Sherbrooke, QC, Canada; Université de Sherbrooke, Sherbrooke, QC, Canada
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Garro F, Chiappalone M, Buccelli S, De Michieli L, Semprini M. Neuromechanical Biomarkers for Robotic Neurorehabilitation. Front Neurorobot 2021; 15:742163. [PMID: 34776920 PMCID: PMC8579108 DOI: 10.3389/fnbot.2021.742163] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 09/22/2021] [Indexed: 02/06/2023] Open
Abstract
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.
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Affiliation(s)
- Florencia Garro
- Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy.,Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy
| | - Michela Chiappalone
- Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy.,Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy
| | - Stefano Buccelli
- Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy
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