Geed Lab

Neuroplasticity and Motor Function Recovery after Stroke

Sensitive measures of change for upper-extremity rehabilitation trials


Clinical trials in neurorehabilitation focus on measurement of change in motor function over time, typically using easily administered clinical scales. However, special considerations apply when using clinical scales to measure behavioral change over time. For instance, do items on the scale remain invariant over time? Do clinical subpopulations respond differently to specific items on a scale over time? Importantly, do clinical measures translate to meaningful behaviors in the community?

Given that neurorehabilitation trial effect sizes are small with a large amount of variability in motor function, it is crucial to develop robust outcomes that are psychometrically valid, reliable, and sensitive to behavioral change over time. To that end, work in the lab has focused on:
  • Optimizing pre-existing clinical scales for use in rehabilitation trials (e.g., Geed et al., 2020). 
  • Developing robust statistical methods so that 24x7 data from wearables like accelerometers is clinically valid, meaningful, and ready to be used in neurorehabilitation clinical trials (e.g., Geed et al. 2023).
  • Validating EEG outcomes with respect to longitudinal post-stroke recovery, individual differences, and environmental influences.

Publications


Concurrent validity of machine learning-classified functional upper extremity use from accelerometry in chronic stroke


Shashwati Geed, Megan L. Grainger, Abigail Mitchell, Cassidy C. Anderson, Henrike L. Schmaulfuss, Seraphina A. Culp, Eilis R. McCormick, Maureen R. McGarry, Mystee N. Delgado, Allysa D. Noccioli, Julia Shelepov, Alexander W. Dromerick, Peter S. Lum

Frontiers in Physiology, vol. 14, 2023


Inaccurate Use of the Upper Extremity Fugl Meyer Negatively Impacts UE Rehabilitation Trial Design: Findings from the ICARE RCT.


Shashwati Geed, C. Lane, Monica A. Nelsen, S. Wolf, C. Winstein, A. Dromerick

Archives of Physical Medicine and Rehabilitation, 2020


Machine Learning Improves Functional Upper Extremity Use Capture in Distal Radius Fracture Patients


Sean B. Sequeira, Megan Grainger, A. M. Mitchell, Cassidy C. Anderson, Shashwati Geed, P. Lum, A. Giladi

Plastic and Reconstructive Surgery, Global Open, 2022

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