TY - JOUR
T1 - Reduced habit-driven errors in Parkinson’s Disease
AU - Bannard, Colin
AU - Leriche, Mariana
AU - Bandmann, Oliver
AU - Brown, Christopher H.
AU - Ferracane, Elisa
AU - Sánchez-Ferro, Álvaro
AU - Obeso, José
AU - Redgrave, Peter
AU - Stafford, Tom
N1 - Funding Information:
This work was made possible by funding from the Michael J Fox Foundation (TS). Additional support was received via The University of Sheffield’s Crucible programme’s and an Early Career Researcher grant to ML. We would like to thank everyone at the Foundation who help with the administration of the grant; everyone at the hospitals in Sheffield and Madrid who helped with testing, especially Jodie Keyworth; All participants in the experiments; Rui Akaike and Amie Tran at the University of Texas at Austin who helped with stimulus coding; and Danielle Matthews read a draft of this manuscript. This research was supported/funded by the NIHR Sheffield Biomedical Research Centre (BRC)/NIHR Sheffield Clinical Research Facility (CRF). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.
Publisher Copyright:
© 2019, The Author(s).
PY - 2019/12/1
Y1 - 2019/12/1
N2 - Parkinson’s Disease can be understood as a disorder of motor habits. A prediction of this theory is that early stage Parkinson’s patients will display fewer errors caused by interference from previously over-learned behaviours. We test this prediction in the domain of skilled typing, where actions are easy to record and errors easy to identify. We describe a method for categorizing errors as simple motor errors or habit-driven errors. We test Spanish and English participants with and without Parkinson’s, and show that indeed patients make fewer habit errors than healthy controls, and, further, that classification of error type increases the accuracy of discriminating between patients and healthy controls. As well as being a validation of a theory-led prediction, these results offer promise for automated, enhanced and early diagnosis of Parkinson’s Disease.
AB - Parkinson’s Disease can be understood as a disorder of motor habits. A prediction of this theory is that early stage Parkinson’s patients will display fewer errors caused by interference from previously over-learned behaviours. We test this prediction in the domain of skilled typing, where actions are easy to record and errors easy to identify. We describe a method for categorizing errors as simple motor errors or habit-driven errors. We test Spanish and English participants with and without Parkinson’s, and show that indeed patients make fewer habit errors than healthy controls, and, further, that classification of error type increases the accuracy of discriminating between patients and healthy controls. As well as being a validation of a theory-led prediction, these results offer promise for automated, enhanced and early diagnosis of Parkinson’s Disease.
UR - http://www.scopus.com/inward/record.url?scp=85062424931&partnerID=8YFLogxK
U2 - 10.1038/s41598-019-39294-z
DO - 10.1038/s41598-019-39294-z
M3 - Article
C2 - 30833640
AN - SCOPUS:85062424931
SN - 2045-2322
VL - 9
JO - Scientific Reports
JF - Scientific Reports
IS - 1
M1 - 3423
ER -