Accounting for the valley of recovery during post-stroke rehabilitation training via a model-based analysis of macaque manual dexterity.
Jun Izawa, Noriyuki Higo, Yumi Murata
PMID 36644290WHAT IT FOUND
In two monkeys with motor cortex lesions, a computational model explained the temporary performance drop when switching from a compensatory grip to a precision grip.
The model suggests that training that shares overlapping neural pathways between these two skills can prevent this drop.
Key findings
01The model identified that the temporary drop in performance during the switch from compensatory to precision grip is driven by the interaction between the two motor skills.
02Sensitivity analysis showed that increasing the interaction term between the two skills in the model erased the recovery valley, suggesting that overlapping neural representations facilitate recovery.
03The study authors suggest that clinical interventions could aim to increase this interaction, potentially through methods that broaden the overlap of neural representations.
STILL TO COME
How it was doneWhat they foundWhat it means for PTs
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What it does not show
The study used only two monkeys, which is a very small sample size. The results are based on a computational model of animal behavior, not on human clinical trials. The authors state that the model does not capture the heterogeneous phenomena of human stroke recovery. The decision-making process behind why the monkeys switched grips was not modeled. The lesion areas and recovery profiles differed between the two monkeys.
Declared interests
The authors declare no competing interests. The work was supported by grants from JSPS KAKENHI and the Japan Agency for Medical Research and Development.
The easy way to misread this
Do not apply this model to human patients as a tested intervention. The findings are derived from a computational analysis of two monkeys and the authors explicitly state that generalizing these conclusions to human stroke recovery has significant limitations.