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7. Rotational dynamics in motor cortex are consistent with a feedback controller

Author

Kalidindi, Hari Teja, Cross, Kevin P, Lillicrap, Timothy P, Omrani, Mohsen, Falotico, Egidio, Sabes, Philip N, and Scott, Stephen H

Subjects
Biomedical and Clinical Sciences, Neurosciences, Neurological, Animals, Feedback, Sensory, Macaca mulatta, Models, Neurological, Motor Cortex, Somatosensory Cortex, population dynamics, motor cortex, fronto-parietal circuits, feedback control, recurrent neural networks, Rhesus macaque, computational biology, neuroscience, rhesus macaque, systems biology, Biochemistry and Cell Biology, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Recent studies have identified rotational dynamics in motor cortex (MC), which many assume arise from intrinsic connections in MC. However, behavioral and neurophysiological studies suggest that MC behaves like a feedback controller where continuous sensory feedback and interactions with other brain areas contribute substantially to MC processing. We investigated these apparently conflicting theories by building recurrent neural networks that controlled a model arm and received sensory feedback from the limb. Networks were trained to counteract perturbations to the limb and to reach toward spatial targets. Network activities and sensory feedback signals to the network exhibited rotational structure even when the recurrent connections were removed. Furthermore, neural recordings in monkeys performing similar tasks also exhibited rotational structure not only in MC but also in somatosensory cortex. Our results argue that rotational structure may also reflect dynamics throughout the voluntary motor system involved in online control of motor actions.

Published
2021

40. Additional file 1 of The use of machine learning and deep learning techniques to assess proprioceptive impairments of the upper limb after stroke

Author

Hossain, Delowar, Scott, Stephen H., Cluff, Tyler, and Dukelow, Sean P.

Abstract

Additional file 1: Supplementary Figure 1: Histogram showing the distribution of all parameters in healthy controls and participants with stroke. The percentage on the y-axis is the participant count in each bin normalized to the number of participants with stroke (n = 429) and control (n = 465).

Published
2023
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47. Clinical, Neuroimaging and Robotic Measures Predict Long-Term Proprioceptive Impairments following Stroke.

Author

Chilvers, Matthew J., Rajashekar, Deepthi, Low, Trevor A., Scott, Stephen H., and Dukelow, Sean P.

Subjects
STROKE, FUNCTIONAL independence measure, AKAIKE information criterion, BRAIN imaging, ROBOTICS, MUSCULAR sense, NEUROREHABILITATION
Abstract

Proprioceptive impairments occur in ~50% of stroke survivors, with 20–40% still impaired six months post-stroke. Early identification of those likely to have persistent impairments is key to personalizing rehabilitation strategies and reducing long-term proprioceptive impairments. In this study, clinical, neuroimaging and robotic measures were used to predict proprioceptive impairments at six months post-stroke on a robotic assessment of proprioception. Clinical assessments, neuroimaging, and a robotic arm position matching (APM) task were performed for 133 stroke participants two weeks post-stroke (12.4 ± 8.4 days). The APM task was also performed six months post-stroke (191.2 ± 18.0 days). Robotics allow more precise measurements of proprioception than clinical assessments. Consequently, an overall APM Task Score was used as ground truth to classify proprioceptive impairments at six months post-stroke. Other APM performance parameters from the two-week assessment were used as predictive features. Clinical assessments included the Thumb Localisation Test (TLT), Behavioural Inattention Test (BIT), Functional Independence Measure (FIM) and demographic information (age, sex and affected arm). Logistic regression classifiers were trained to predict proprioceptive impairments at six months post-stroke using data collected two weeks post-stroke. Models containing robotic features, either alone or in conjunction with clinical and neuroimaging features, had a greater area under the curve (AUC) and lower Akaike Information Criterion (AIC) than models which only contained clinical or neuroimaging features. All models performed similarly with regard to accuracy and F1-score (>70% accuracy). Robotic features were also among the most important when all features were combined into a single model. Predicting long-term proprioceptive impairments, using data collected as early as two weeks post-stroke, is feasible. Identifying those at risk of long-term impairments is an important step towards improving proprioceptive rehabilitation after a stroke. [ABSTRACT FROM AUTHOR]

Published
2023
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