Diagnostic Performance of an Eye-Tracking Assisted Visual Inference Language Test in the Assessment of Cognitive Decline due to Alzheimer's Disease

BACKGROUND

The assessment of language changes associated with visual search impairment can be an important diagnostic tool in the Alzheimer's disease (AD) continuum.

OBJECTIVE

Investigate the performance of an eye-tracking assisted visual inference language task in differentiating subjects with mild cognitive impairment (MCI) or AD dementia from cognitively unimpaired older adults (controls).

METHODS

We assessed a group of 95 older adults (49 MCI, 18 mild dementia due to AD, and 28 controls). The subjects performed the same task under multiple experimental conditions which generate correlated responses that need to be taken into account. Thus, we performed a non-parametric repeated measures ANOVA model for verbal answers, and a linear mixed model (LMM) or its generalized version for the analysis of eye tracking variables.

RESULTS

Significant differences were found in verbal answers across all diagnostic groups independently of type of inference, i.e., logic or pragmatic. Also, eye-tracking parameters were able to discriminate AD from MCI and controls. AD patients did more visits to challenge stimulus (Control-AD, -0.622, SE = 0.190, p = 0.004; MCI-AD, -0.514, SE = 0.173, p = 0.011), more visits to the correct response stimulus (Control-AD, -1.363, SE = 0.383, p = 0.002; MCI-AD, -0.946, SE = 0.349, p = 0.022), more fixations on distractors (Control-AD, -4.580, SE = 1.172, p = 0.001; MCI-AD, -2.940, SE = 1.070, p = 0.020), and a longer time to first fixation on the correct response stimulus (Control-AD, -0.622, SE = 0.190, p = 0.004; MCI-AD, -0.514, SE = 0.173, p = 0.011).

CONCLUSION

The analysis of oculomotor behavior along with language assessment protocols may increase the sensitivity for detection of subtle deficits in the MCI-AD continuum, representing an important diagnostic tool.

Impact of Cognitive Demand on Eye Movement Pattern in Patients with Alzheimer’s Disease

Background: Eye-movement behavior has been used as a reliable tool to identify cognitive and behavioral patterns in individuals with different neuropsychiatric disorders including Alzheimer’s disease (AD). Most studies in the field have been dedicated to evaluating eye-movement behavior during cognitive tasks in different protocols using multiple parameters. Objective: We aimed to evaluate the differences of eye-movement behavior in healthy subjects, subjects with mild cognitive impairment (MCI), and those with AD in a simple color task with and without cognitive demand. Methods: 91 subjects: 18 AD, 47 MCI, and 26 healthy controls had their oculomotor parameters assessed during baseline (no cognitive demand involved) and during a simple computational color memory task using an eye-tracker. Results: Baseline showed statistically different and heterogeneous results between normal cognition and MCI groups. Familiarization phase of the task could not discriminate between groups in any of the analyzed parameters. AD subjects made longer fixations and visits on distractors, and more frequent fixations and visits on the target areas than other groups during the response phase. Conclusion: Eye-tracking time-related parameters differentiate AD subjects from other groups under cognitive demand even in a simple color memory task.

Textual Inference Comprehension in Mild Cognitive Impairment: The Influence of Semantic Processing and Verbal Episodic Memory

Language complaints, especially in complex tasks, may occur in mild cognitive impairment (MCI). Various language measures have been studied as cognitive predictors of MCI conversion to Alzheimer's type dementia. Understanding textual inferences is considered a high-demanding task that recruits multiple cognitive functions and, therefore, could be sensitive to detect decline in the early stages of MCI. Thus, we aimed to compare the performance of subjects with MCI to healthy elderly in a textual inference comprehension task and to determine the best predictors of performance in this ability considering one verbal episodic memory and two semantic tasks. We studied 99 individuals divided into three groups: (1) 23 individuals with amnestic mild cognitive impairment (aMCI), (2) 42 individuals with non-amnestic mild cognitive impairment (naMCI), (3), and (4) 34 cognitively healthy individuals for the control group (CG). A reduced version of The Implicit Management Test was used to assess different types of inferential reasoning in text reading. MCI patients performed poorer than healthy elderly, and there were no differences between MCI subgroups (amnestic and non-amnestic). The best predictors for inference-making were verbal memory in the aMCI and semantic tasks in the naMCI group. The results confirmed that the failure to understand textual inferences can be present in MCI and showed that different cognitive skills like semantic knowledge and verbal episodic memory are necessary for inference-making.

Visual Search Efficiency in Mild Cognitive Impairment and Alzheimer's Disease: An Eye Movement Study

BACKGROUND

Visual search abilities are essential to everyday life activities and are known to be affected in Alzheimer's disease (AD). However, little is known about visual search efficiency in mild cognitive impairment (MCI), a transitive state between normal aging and dementia. Eye movement studies and machine learning methods have been recently used to detect oculomotor impairments in individuals with dementia.

OBJECTIVE

The aim of the present study is to investigate the association between eye movement metrics and visual search impairment in MCI and AD.

METHODS

127 participants were tested: 43 healthy controls, 51 with MCI, and 33 with AD. They completed an eyetracking visual search task where they had to find a previously seen target stimulus among distractors.

RESULTS

Both patient groups made more fixations on the screen when searching for a target, with longer duration than controls. MCI and AD fixated the distractors more often and for a longer period of time than the target. Healthy controls were quicker and made less fixations when scanning the stimuli for the first time. Machine-learning methods were able to distinguish between controls and AD subjects and to identify MCI subjects with a similar oculomotor profile to AD with a good accuracy.

CONCLUSION

Results showed that eye movement metrics are useful for identifying visual search impairments in MCI and AD, with possible implications in the early identification of individuals with high-risk of developing AD.