Distinctive Oculomotor Behaviors in Alzheimer's Disease and Frontotemporal Dementia

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.

Keywords: Alzheimer's disease; antisaccade; biomarkers; frontotemporal dementia; oculomotor; semantic dementia; smooth pursuit.

Figure 1

Flow chart to find the most suitable combination between eye movement features and machine learning algorithm. AUC, area under the curve; FDR, Fisher discriminant ratio; ML, machine learning; PLSR, partial less square regression.

Figure 2

Loop implemented to test the confidence interval of the selected machine learning algorithm. AUC, area under the curve; I, iterations; ML, machine learning; ROC, receiver operating characteristic.

Figure 3

Performance of controls and dementia groups in oculomotor parameters related to time (A), success (B), and spatial accuracy (C,D). Asterisks mark those dementia groups which show significant differences (P < 0.05) compared to controls. ms, milliseconds; °, degrees.

Figure 4

Some clinical examples of oculomotor evaluations. (A,B) Show the performance of a patient with Alzheimer's disease in an horizontal prosaccade test (A) and an horizontal sinusoidal smooth pursuit test (B), with great difficulties in overlapping his gaze with the moving target in the latter. (C,D) Illustrate the different performance in the horizontal antisaccade test between a patient with bvFTD (C), with some successful responses but that are slow and inaccurate; and a patient with svPPA (D), who also makes some errors but followed by fast corrections in this case. In the ordinate axis, 0 indicates the center, positive values the right side and negative values the left side. Blue lines represent the patient's ocular movement and dotted lines the expected location of the gaze, which corresponds to the target position in the prosaccade and sinusoidal smooth pursuit tests, and to the opposite position of the target in the antisaccade test. Blinks are marked in yellow and pupil detection failure (usually also due to blinks) in red. deg, degrees; ms, milliseconds.

Figure 5

Relationship between brain metabolism and horizontal prosaccade latency (A), horizontal corrected antisaccade latency (B) and horizontal pursuit error (C). SUVR, Standard Uptake Value Rate.

Figure 6

Useful oculomotor parameters for the differential diagnosis between Alzheimer's disease and the other two dementia groups (A), and between bvFTD and svPPA (B). AS, antisaccade; MS, memory saccade; PS, prosaccade.