Affiliations: [a] Home Base, A Red Sox Foundation and Massachusetts General Hospital Program, Boston, MA, USA
| [b] Department of Radiology, Brain Aging and Dementia (BAnD) Laboratory; MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| [c] Emory University Rollins School of Public Health, Atlanta, GA, USA
| [d] IBM Watson Health, Cambridge, MA, USA
| [e] Laboratory of Computational Neuroimaging; MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| [f] Harvard Medical School, Boston, MA, USA
| [g] Neuroimaging Research for Veterans (NeRVe) Center, VA Boston Healthcare System, Boston, MA, USA
Correspondence:
[*]
Correspondence to: Yue Hong, PsyD, Home Base, A Red Sox Foundation and Massachusetts
General Hospital Program, 1 Constitution Rd, Charlestown, MA 02129, USA. Tel.: +13475583658; Fax: +16176439715; E-mail: yhong3@mgh.harvard.edu.
Abstract: Background:Studies have found that individuals with mild cognitive impairment (MCI) exhibit a range of deficits outside the realm of primary explicit memory, yet the role of response speed and implicit learning in older adults with MCI have not been established. Objective:The current study aims to explore and document response speed and implicit learning in older adults with neuropsychologically defined MCI using a simple serial reaction (SRT) task. In addition, the study aims to explore the feasibility of a novel utilization of the simple cognitive task using machine learning procedures as a proof of concept. Method:Participants were 22 cognitively healthy older adults and 20 older adults with MCI confirmed through comprehensive neuropsychological evaluation. Two-sample t-test, multivariate regression, and mixed-effect models were used to investigate group difference in response speed and implicit learning on the SRT task. We also explored the potential utility of SRT feature analysis through random forest classification. Results:With demographic variables controlled, the MCI group showed overall slower reaction time and higher error rate compared to the cognitively healthy volunteers. Both groups showed significant simple motor learning and implicit learning. The learning patterns were not statistically different between the two groups. Random forest classification achieved overall accuracy of 80.9%. Conclusions:Individuals with MCI demonstrated slower reaction time and higher error rate compared to cognitively healthy volunteers but demonstrated largely preserved motor learning and implicit sequence learning. Preliminary results from random forest classification using features from SRT performance supported further research in this area.
