Executive functioning in adults with down syndrome: machine-learning-based prediction of inhibitory capacity

Jojoa Acosta, Mario Fernando; Signo, Sara; García-Zapirain, Begoña; Gimeno-Santos, Mercè; Méndez Zorrilla, Amaia; Vaidya, Chandan; Molins Sauri, Marta; Guerra-Balic, Myriam; Bruna i Rabassa, Olga

Executive functioning in adults with down syndrome: machine-learning-based prediction of inhibitory capacity

Archivos

jojoa_executive_2021.pdf (969.29 KB)

Fecha

2021-10-14

Autores

Jojoa Acosta, Mario Fernando

Signo, Sara

García-Zapirain, Begoña

Gimeno-Santos, Mercè

Méndez Zorrilla, Amaia

Vaidya, Chandan

Molins Sauri, Marta

Guerra-Balic, Myriam

Bruna i Rabassa, Olga

Editor

MDPI

Resumen

The study of executive function decline in adults with Down syndrome (DS) is important, because it supports independent functioning in real-world settings. Inhibitory control is posited to be essential for self-regulation and adaptation to daily life activities. However, cognitive domains that most predict the capacity for inhibition in adults with DS have not been identified. The aim of this study was to identify cognitive domains that predict the capacity for inhibition, using novel data-driven techniques in a sample of adults with DS (n = 188; 49.47% men; 33.6 ± 8.8 years old), with low and moderate levels of intellectual disability. Neuropsychological tests, including assessment of memory, attention, language, executive functions, and praxis, were submitted to Random Forest, support vector machine, and logistic regression algorithms for the purpose of predicting inhibition capacity, assessed with the Cats-and-Dogs test. Convergent results from the three algorithms show that the best predictors for inhibition capacity were constructive praxis, verbal memory, immediate memory, planning, and written verbal comprehension. These results suggest the minimum set of neuropsychological assessments and potential intervention targets for individuals with DS and ID, which may optimize potential for independent living.

Palabras clave

Aging
Artificial intelligence
Cognition
Down syndrome
Executive functions
Feature selection
Inhibition
Machine learning
Neuropsychology

Cita

Jojoa-Acosta, M. F., Signo-Miguel, S., Garcia-Zapirain, M. B., Gimeno-Santos, M., Méndez-Zorrilla, A., Vaidya, C. J., Molins-Sauri, M., Guerra-Balic, M., & Bruna-Rabassa, O. (2021). Executive functioning in adults with down syndrome: machine-learning-based prediction of inhibitory capacity. International Journal of Environmental Research and Public Health, 18(20). https://doi.org/10.3390/IJERPH182010785

URI

https://hdl.handle.net/20.500.14454/3346

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