Understanding the Cognitive Demands of the Purdue Pegboard Test: An fNIRs Study
- 663 Downloads
To evaluate manual dexterity in clinical settings Purdue Pegboard test (PPT) is widely used and consists of four uni- and bi-manual sub-tests. The objective of this study is to deep dive into PPT and parse out the cognitive component of the subtests using functional Near Infrared Spectroscopy (fNIRS). The prefrontal cortex (PFC) of 16 participants was assessed using fNIRS technique while performing PPT. Repeated measures one-way ANOVA was performed on average ∆HbR and ∆HbO in PFC regions (left DLPFC, left vmPFC, right vmPFC, right DLPFC). A Bonferroni correction was used to account for multiple comparisons. Results from both outcome measures taken together showed that assembly sub-task uses significantly higher resources in the left PFC. The findings can be summarized as follows: PPT measures more than manual dexterity. Subtask complexity affects the cognitive demands in the PFC. The Assembly may be the most sensitive subtest to the changes in PFC activity.
KeywordsfNIRS Purdue Pegboard Fine motor Cognitive
We sincerely thank Hosein Bakhshipour for all his support and encouragement.
- 6.Ortiz-Rubio, A., Cabrera-Martos, I., Rodríguez-Torres, J., Fajardo-Contreras, W., Díaz-Pelegrina, A., Valenza, M.C.: Effects of a home-based upper limb training program in patients with multiple sclerosis: a randomized controlled trial. Arch. Phys. Med. Rehabil. 97(12), 2027–2033 (2016)CrossRefGoogle Scholar
- 13.Maroni, T., Dawson, B., Dennis, M., Naylor, L., Brade, C., Wallman, K.: Effects of half-time cooling using a cooling glove and jacket on manual dexterity and repeated-sprint performance in heat. J. Sports Sci. Med. 17(3), 485–491 (2018)Google Scholar
- 21.Dashtestani, H., Cui, J., Harrison, D., Gandjbakhche, A.: Application of machine learning techniques in investigating the relationship between neuroimaging dataset measured by functional near infra-red spectroscopy and behavioral dataset in a moral judgment task. Clin. Transl. Neurophotonics 10864, 32 (2019)Google Scholar