To Explore the Predictive Power of Visuomotor Network Dysfunctions in Mild Cognitive Impairment and Alzheimer’s Disease

Justine Staal, Francesco Mattace-Raso, Hennie A.M. Daniels, Johannes van der Steen, Johan J.M. Pel*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)
32 Downloads (Pure)


Background: Research into Alzheimer’s disease has shifted toward the identification of minimally invasive and less time-consuming modalities to define preclinical stages of Alzheimer’s disease. Method: Here, we propose visuomotor network dysfunctions as a potential biomarker in AD and its prodromal stage, mild cognitive impairment with underlying the Alzheimer’s disease pathology. The functionality of this network was tested in terms of timing, accuracy, and speed with goal-directed eye-hand tasks. The predictive power was determined by comparing the classification performance of a zero-rule algorithm (baseline), a decision tree, a support vector machine, and a neural network using functional parameters to classify controls without cognitive disorders, mild cognitive impaired patients, and Alzheimer’s disease patients. Results: Fair to good classification was achieved between controls and patients, controls and mild cognitive impaired patients, and between controls and Alzheimer’s disease patients with the support vector machine (77–82% accuracy, 57–93% sensitivity, 63–90% specificity, 0.74–0.78 area under the curve). Classification between mild cognitive impaired patients and Alzheimer’s disease patients was poor, as no algorithm outperformed the baseline (63% accuracy, 0% sensitivity, 100% specificity, 0.50 area under the curve). Comparison with Existing Method(s): The classification performance found in the present study is comparable to that of the existing CSF and MRI biomarkers. Conclusion: The data suggest that visuomotor network dysfunctions have potential in biomarker research and the proposed eye-hand tasks could add to existing tests to form a clear definition of the preclinical phenotype of AD.

Original languageEnglish
Article number654003
JournalFrontiers in Neuroscience
Publication statusPublished - 28 Jun 2021

Bibliographical note

Measurements conducted from 2010 to 2017 have been performed by Casper de Boer, Lauren Dautzenberg, Giulia Gennari, Simone Verheij, and Danya Muilwijk. Measurements conducted from 2017 onward have been performed by JuS and assisted by Michelle van Zon.

Publisher Copyright:
© Copyright © 2021 Staal, Mattace-Raso, Daniels, van der Steen and Pel.


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