Objective: (1) To characterize the differential effects of PD on the two attentional networks and (2) To examine the ability of attentional measurements differentiate between PD patients and Healthy controls (HCs).

Background: One cognitive symptom of Parkinson’s disease (PD), often undiagnosed, is attention dysfunction. The orienting of attention can be divided into two different networks: The endogenous – voluntarily network, initiated by internal goals, and the exogenous – involuntarily network, initiated reflexively. Studies suggest that the attentional dysfunction in PD may be due to unequal impairment in thus networks. However, the exact changes and contributions are yet unknown.

Method: Twenty PD patients and 20 HCs performed the modified version of the Attention Network Test (ANT) remotely on their personal computers at home. Differences in ANT measures between and within groups were tested using Two-way ANOVAs. Multiple machine-learning feature selection and classification algorithms were applied to discriminate between PD and HCs.

Results: PD patients demonstrated worse performance in the attentional processes that require internal (endogenous) as opposed to external (exogenous) mechanisms (F (1,37) = 4.75, p = 0.036, η² = 4.75) compared to HCs. The Xgboost Machine learning demonstrated the highest discriminatory values between PD patients and HC with area under the curve (AUC) 0.93.

Conclusion: The endogenous – voluntarily attentional network is more susceptible to PD than the exogenous – involuntarily attentional network. Furthermore, applying machine-learning to multiple ANT features reveals that different measures of attention discriminate between patients with PD and HC.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/the-impact-of-parkinsons-disease-on-multifactorial-aspects-of-attention/