Executive function impairments and the progression to Alzheimer's Disease: using computational methods for eye movement data analysis

• Grant number: 17/12339-3
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• Start date: November 01, 2017
• End date: March 31, 2018
• Field of knowledge: Health Sciences - Medicine - Psychiatry
• Principal Investigator: Orestes Vicente Forlenza
• Grantee: Marta Luísa Gonçalves de Freitas Pereira
• Supervisor: Marc Pomplun
• Host Institution: Instituto de Psiquiatria Doutor Antonio Carlos Pacheco e Silva (IPq). Hospital das Clínicas da Faculdade de Medicina da USP (HCFMUSP). Secretaria da Saúde (São Paulo - Estado). São Paulo , SP, Brazil
• Institution abroad: University of Massachusetts Boston (UMass Boston), United States
• Associated to the scholarship: 15/19632-2 - Executive functioning as an indicator of conversion to Alzheimer's Disease: contribution of the eye movement analysis, BP.DR

Abstract

Background: Eye movement analysis has proved to be a very accurate and cost/time effective method in the detection and classification of cognitive decline in many neurodegenerative conditions, specifically in Alzheimer's disease (AD) and in Mild Cognitive Impairment (MCI). However, little is known about eye movement patterns as a result of executive impairments in these conditions. Recently, computational science methods have shown to be very efficient in analyzing the large amount of data produced by eye movement studies, being able to accurately distinguish MCI/AD from control participants. Objectives: To determine an eye movement profile for each group of participants, specifically which parameters / combination of parameters best detect a similar eye movement pattern between participants and best differentiate between them; to determine if the eye movement profile of each group matches its clinical profile, namely neuropsychological, imaging and cerebrospinal fluid (CSF) data. Methods: computer science methods will be used to determine the most relevant oculomotor parameters that best distinguish the three groups. In addition, classification algorithms will be tested to identify the best one that predicts the participants group from their behavioral data. Results: The present study expects to determine an eye movement cognitive profile for each group of participants, based on their performance on eye movement task performance. It also seeks to measure executive functioning decline in the MCI and AD, by combining eye movement with other neuropsychological and clinical data. Altogether, the study pretends to demonstrate the effectiveness of applying computer science techniques to the detection of cognitive decline early in the course AD. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

GONCALVES DE FREITAS PEREIRA, MARTA LUISA; DE ARRUDA CAMARGO, MARINA VON ZUBEN; RIBEIRO BELLAN, ARIELLA FORNACHARI; TAHIRA, ANA CAROLINA; DOS SANTOS, BERNARDO; DOS SANTOS, JESSICA; MACHADO-LIMA, ARIANE; NUNES, FATIMA L. S.; FORLENZA, ORESTES VICENTE. Visual Search Efficiency in Mild Cognitive Impairment and Alzheimer's Disease: An Eye Movement Study. JOURNAL OF ALZHEIMER'S DISEASE, v. 75, n. 1, p. 261-275, 2020. (13/23135-9, 17/12339-3, 14/20913-3, 15/19632-2)

DE ARRUDA CAMARGO, MARINA VON ZUBEN; PAIS, MARCOS VASCONCELOS; RIBEIRO BELLAN, ARIELLA FORNACHARI; TAHIRA, ANA CAROLINA; DOS SANTOS, BERNARDO; FIDALGO GARCEZ SANT'ANA, LIVEA CARLA; RADANOVIC, MARCIA; FORLENZA, ORESTES VICENTE. Impact of Cognitive Demand on Eye Movement Pattern in Patients with Alzheimer's Disease. JOURNAL OF ALZHEIMER'S DISEASE, v. 90, n. 1, p. 11-pg., 2022-01-01. (17/12339-3, 13/23135-9, 15/19632-2, 14/20913-3)