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.
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.
Authors: Johannes Gehrig; Heinrich Johannes Bergmann; Laura Fadai; Dilara Soydaş; Christian Buschenlange; Marcus J Naumer; Jochen Kaiser; Stefan Frisch; Marion Behrens; Christian Foerch; Yavor Yalachkov Journal: Front Neurol Date: 2022-02-14 Impact factor: 4.003
Authors: Jianfeng Wu; Dongfang Jiao; Chunfu Lu; Chengmin Li; Xiaofang Huang; Suzan Weng Journal: Int J Environ Res Public Health Date: 2022-04-08 Impact factor: 4.614
Authors: Sanna Hannonen; Sami Andberg; Virve Kärkkäinen; Minna Rusanen; Juha-Matti Lehtola; Toni Saari; Ville Korhonen; Laura Hokkanen; Merja Hallikainen; Tuomo Hänninen; Ville Leinonen; Kai Kaarniranta; Roman Bednarik; Anne M Koivisto Journal: J Alzheimers Dis Date: 2022 Impact factor: 4.160