Abstract
Oculomotor behavior can provide insight into the integrity of widespread cortical networks, which may contribute to the differential diagnosis between Alzheimer's disease and frontotemporal dementia. Three groups of patients with Alzheimer's disease, behavioral variant of frontotemporal dementia (bvFTD) and semantic variant of primary progressive aphasia (svPPA) and a sample of cognitively unimpaired elders underwent an eye-tracking evaluation. All participants in the discovery sample, including controls, had a biomarker-supported diagnosis. Oculomotor correlates of neuropsychology and brain metabolism evaluated with 18F-FDG PET were explored. Machine-learning classification algorithms were trained for the differentiation between Alzheimer's disease, bvFTD and controls. A total of 93 subjects (33 Alzheimer's disease, 24 bvFTD, seven svPPA, and 29 controls) were included in the study. Alzheimer's disease was the most impaired group in all tests and displayed specific abnormalities in some visually-guided saccade parameters, as pursuit error and horizontal prosaccade latency, which are theoretically closely linked to posterior brain regions. BvFTD patients showed deficits especially in the most cognitively demanding tasks, the antisaccade and memory saccade tests, which require a fine control from frontal lobe regions. SvPPA patients performed similarly to controls in most parameters except for a lower number of correct memory saccades. Pursuit error was significantly correlated with cognitive measures of constructional praxis and executive function and metabolism in right posterior middle temporal gyrus. The classification algorithms yielded an area under the curve of 97.5% for the differentiation of Alzheimer's disease vs. controls, 96.7% for bvFTD vs. controls, and 92.5% for Alzheimer's disease vs. bvFTD. In conclusion, patients with Alzheimer's disease, bvFTD and svPPA exhibit differentiating oculomotor patterns which reflect the characteristic neuroanatomical distribution of pathology of each disease, and therefore its assessment can be useful in their diagnostic work-up. Machine learning approaches can facilitate the applicability of eye-tracking in clinical practice.
Highlights
One might think that through the assessment of eye movements we are just evaluating purely motor responses
Significant differences were observed in the Mini-Mental State Examination (MMSE) scoring, with the lowest performance in the Alzheimer’s disease group, as well as in the intake of potentially sedative drugs, with a higher intake in behavioral variant of frontotemporal dementia (FTD) (bvFTD) patients compared to the other three groups
We investigated for negative associations with horizontal prosaccade latency, horizontal pursuit error and horizontal corrected antisaccade latency; and for positive associations with the percentage of correct memory saccades
Summary
One might think that through the assessment of eye movements we are just evaluating purely motor responses. The simplest ocular movements are visually-guided responses to keep an object of interest fixed in the fovea, either a rapid gaze shift to an appearing stimulus or prosaccade, either a pursuit movement which allows tracking a moving target Such visually-guided ocular movements are controlled by a core frontoparietal network that includes the frontal and supplementary eye fields and diverse parietal regions like the intraparietal sulcus and superior parietal cortex, and which is supported by subcortical structures and connected to the superior colliculus, the saccade generator. The memory saccade paradigm is another cognitively demanding task in which spatial working memory is necessary to retain the precise location where the stimulus has previously appeared and to redirect the gaze to it based on an internal representation In this case, neuroimaging and lesion studies point to a recruitment activation of DLPFC together with basal ganglia and thalamocortical circuitries (Brown et al, 2004)
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