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Cognitive dysfunction is the most common and important nonmotor symptom in Parkinson’s disease (PD) and can occur at any stage. However, there is still a lack of effective biomarkers to evaluate the decline in cognitive function and predict the progression of the disease, especially in the early stage. At present, the cognitive scale is widely used to evaluate the cognitive function of patients with PD, but its sensitivity and accuracy are relatively limited, especially in the early identification of mild cognitive impairment. Eye movement tracking is an advanced neurophysiological measurement method that serves as a powerful means to study the relationship between behavior and neural mechanisms. In recent years, eye movement tracking has been found to provide a nonverbal and less cognitive method to measure the disease progress of patients with cognitive impairment. Moreover, there is a good correlation between eye movement tracking and the traditional cognitive assessment scale, indicating that eye movement tracking can be used to evaluate and monitor the cognitive status, disease severity, and disease progression of patients with PD. Compared to the traditional cognitive scale, the eye movement detected by the instrument has better objectivity and repeatability. Existing studies have found that executive dysfunction is one of the most important manifestations of cognitive dysfunction in patients with PD and is related to an increase in the error rate of the saccade, an increase in the disinhibition of the delayed saccade task, and a prolongation of the saccade reaction time. This suggests that eye movement measurement plays an important role in the early diagnosis, progression, and differential diagnosis of PD and may even help to predict the disease progression of patients with PD and cognitive impairment. In this article, we review the correlation between cognitive impairment and eye movement disorder in patients with PD.
Cognitive dysfunction is the most common and important nonmotor symptom in Parkinson’s disease (PD) and can occur at any stage. However, there is still a lack of effective biomarkers to evaluate the decline in cognitive function and predict the progression of the disease, especially in the early stage. At present, the cognitive scale is widely used to evaluate the cognitive function of patients with PD, but its sensitivity and accuracy are relatively limited, especially in the early identification of mild cognitive impairment. Eye movement tracking is an advanced neurophysiological measurement method that serves as a powerful means to study the relationship between behavior and neural mechanisms. In recent years, eye movement tracking has been found to provide a nonverbal and less cognitive method to measure the disease progress of patients with cognitive impairment. Moreover, there is a good correlation between eye movement tracking and the traditional cognitive assessment scale, indicating that eye movement tracking can be used to evaluate and monitor the cognitive status, disease severity, and disease progression of patients with PD. Compared to the traditional cognitive scale, the eye movement detected by the instrument has better objectivity and repeatability. Existing studies have found that executive dysfunction is one of the most important manifestations of cognitive dysfunction in patients with PD and is related to an increase in the error rate of the saccade, an increase in the disinhibition of the delayed saccade task, and a prolongation of the saccade reaction time. This suggests that eye movement measurement plays an important role in the early diagnosis, progression, and differential diagnosis of PD and may even help to predict the disease progression of patients with PD and cognitive impairment. In this article, we review the correlation between cognitive impairment and eye movement disorder in patients with PD.
ObjectiveTo evaluate the effectiveness of multimodal features based on gait analysis and eye tracking for elderly people screening with subjective cognitive decline in the community.MethodsIn the study, 412 cognitively normal older adults aged over 65 years were included. Among them, 230 individuals were diagnosed with non-subjective cognitive decline and 182 with subjective cognitive decline. All participants underwent assessments using three screening tools: the traditional SCD9 scale, gait analysis, and eye tracking. The gait analysis involved three tasks: the single task, the counting backwards dual task, and the naming animals dual task. Eye tracking included six paradigms: smooth pursuit, median fixation, lateral fixation, overlap saccade, gap saccade, and anti-saccade tasks. Using the XGBoost machine learning algorithm, several models were developed based on gait analysis and eye tracking to classify subjective cognitive decline.ResultsA total of 161 gait and eye-tracking features were measured. 22 parameters, including 9 gait and 13 eye-tracking features, showed significant differences between the two groups (p < 0.05). The top three eye-tracking paradigms were anti-saccade, gap saccade, and median fixation, with AUCs of 0.911, 0.904, and 0.891, respectively. The gait analysis features had an AUC of 0.862, indicating better discriminatory efficacy compared to the SCD9 scale, which had an AUC of 0.762. The model based on single and dual task gait, anti-saccade, gap saccade, and median fixation achieved the best efficacy in SCD screening (AUC = 0.969).ConclusionThe gait analysis, eye-tracking multimodal assessment tool is an objective and accurate screening method that showed better detection of subjective cognitive decline. This finding provides another option for early identification of subjective cognitive decline in the community.
BackgroundAbnormal eye movements occur at the early stages of Alzheimer’s disease (AD). However, the characteristics of abnormal eye movements of patients with AD and their relationship with clinical symptoms remain inconsistent, and their predictive value for diagnosing and monitoring the progression of AD remains unclear.MethodsA total of 42 normal controls, 63 patients with mild cognitive impairment due to AD (AD-MCI), and 49 patients with dementia due to AD (AD-D) were recruited. Eye movements were assessed using the EyeKnow eye-tracking and analysis system. Cognitive function, neuropsychiatric symptoms, and activities of daily living were evaluated using various rating scales, and correlation analyses and receiver operating characteristic curves were performed.ResultsPatients with AD exhibited increased number of offsets and offset degrees, prolonged offset duration, and decreased accuracy in lateral fixation; reduced accuracy, prolonged saccadic duration, and decreased velocity in prosaccade; decreased accuracy and corrected rate, prolonged corrected antisaccadic duration, and reduced velocity in antisaccade; and reduced accuracy and increased inhibition failures in memory saccade. Eye movement parameters were correlated with global cognition and the cognitive domains of memory, language, attention, visuospatial ability, execution function, and activities of daily living. Subgroup analysis indicated that the associations between eye movements and clinical symptoms in patients with AD were influenced by disease severity and history of diabetes. In the AD-D and AD with diabetes groups, these associations diminished. Nevertheless, the associations persisted in the AD-MCI and AD without diabetes groups. The areas under the curves for predicting AD, AD-MCI, and AD-D were 0.835, 0.737, and 0.899, respectively (all p < 0.05).ConclusionPatients with AD exhibit distinct patterns of abnormal eye movements. Abnormal eye movements are significantly correlated with global cognition, multiple cognitive domains, and activities of daily living. Abnormal eye movements have a considerable predictive value for the diagnosis and progression of AD.
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