Lage Carmen, López-García Sara, Bejanin Alexandre, Kazimierczak Martha, Aracil-Bolaños Ignacio, Calvo-Córdoba Alberto, Pozueta Ana, García-Martínez María, Fernández-Rodríguez Andrea, Bravo-González María, Jiménez-Bonilla Julio, Banzo Ignacio, Irure-Ventura Juan, Pegueroles Jordi, Illán-Gala Ignacio, Fortea Juan, Rodríguez-Rodríguez Eloy, Lleó-Bisa Alberto, García-Cena Cecilia E, Sánchez-Juan Pascual
Institute for Research Marqués de Valdecilla (IDIVAL), University of Cantabria and Department of Neurology, Marqués de Valdecilla University Hospital, Santander, Spain.
Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain.
Front Aging Neurosci. 2021 Feb 4;12:603790. doi: 10.3389/fnagi.2020.603790. eCollection 2020.
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.
眼球运动行为能够为广泛的皮质网络的完整性提供深入了解，这可能有助于阿尔茨海默病与额颞叶痴呆之间的鉴别诊断。三组患者，分别为患有阿尔茨海默病、行为变异型额颞叶痴呆（bvFTD）和语义变异型原发性进行性失语（svPPA），以及一组认知未受损的老年人样本接受了眼动追踪评估。发现样本中的所有参与者，包括对照组，均有生物标志物支持的诊断。探索了用18F-FDG PET评估的神经心理学和脑代谢的眼动相关指标。训练机器学习分类算法以区分阿尔茨海默病、bvFTD和对照组。共有93名受试者（33名阿尔茨海默病患者、24名bvFTD患者、7名svPPA患者和29名对照组）纳入研究。在所有测试中，阿尔茨海默病组受损最严重，并且在一些视觉引导的扫视参数方面表现出特定异常，如追踪误差和水平前扫视潜伏期，理论上这些与后脑区域密切相关。bvFTD患者尤其在认知要求最高的任务中表现出缺陷，即反扫视和记忆扫视测试，这些任务需要额叶区域的精细控制。除了正确记忆扫视次数较少外，svPPA患者在大多数参数上的表现与对照组相似。追踪误差与右侧颞中回后部的结构性实践和执行功能的认知测量以及代谢显著相关。分类算法在区分阿尔茨海默病与对照组时曲线下面积为97.5%，区分bvFTD与对照组时为96.7%，区分阿尔茨海默病与bvFTD时为92.5%。总之，阿尔茨海默病、bvFTD和svPPA患者表现出不同的眼球运动模式，这些模式反映了每种疾病病理的特征性神经解剖分布，因此其评估在他们的诊断检查中可能有用。机器学习方法可以促进眼动追踪在临床实践中的应用。
