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

Abstract

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 methods: 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.

Keywords: biomarker; eye-hand coordination; neurodegeneration; preclinical AD; visuomotor integration.

FIGURE 1

Flowchart of the classification process.

FIGURE 2

Mean performance on eye latency for all groups, on all tasks. Error bars represent standard error. MCI*: MCI group was excluded because of insufficient data (N = 3).

FIGURE 3

Mean performance on hand latency and hand error for all groups, on all tasks. Error bars represent standard error. Significant differences for the anti-saccade anti-tapping task indicate differences for hand error; other indicated differences are for hand latency. Significant differences were indicated with ^∗p < 0.05, ^∗∗p < 0.01, ^∗∗∗p < 0.001. MCI^∗: MCI group was excluded because of insufficient data (N = 3).

FIGURE 4

Mean performance on pupil latency for all groups. Error bars represent standard error. Significant differences were indicated with ^∗p < 0.05, ^∗∗p < 0.01, ^∗∗∗p < 0.001.

FIGURE 5

Mean weighted performance over all trials for all groups, on all tasks. Error bars represent standard error. Significant differences were indicated with ^∗p < 0.05, ^∗∗p < 0.01, ^∗∗∗p < 0.001. MCI*: MCI group was excluded because of insufficient data (N = 3).

FIGURE 6

Learning curves for the healthy controls (HC) and the Alzheimer’s Disease (AD) patients and the Mild Cognitive Impairment (MCI) patients. The SVM (linear) for the HC—patients, HC—MCI, and HC—AD are plotted and of the ANN (6 neurons) the MCI—AD dataset is plotted.

FIGURE 7

After Henriques et al. (2018). The classification performance of algorithms using CSF or MRI markers to classify HC, MCI patients, and AD patients, compared to classification performance using EHC parameters.