Longitudinal Validation of a Computerized Cognitive Battery (Cognigram) in the Diagnosis of Mild Cognitive Impairment and Alzheimer's Disease

Overview

This research project will test two new computerized technologies in the detection of brain changes related to Mild Cognitive Impairment (MCI) and dementia due to Alzheimer's disease. These technologies are: 1. Computerized cognitive battery: Cognigram (CG) Computerized assessments have multiple advantages for the early detection of subtle changes in cognition in older adults. One of their main advantages is their higher precision when measuring accuracy and speed of responses, compared to pencil-and-paper tests. They also allow a greater reliability in measures, as tests are given in a standardized format without the interference of an evaluator. Finally, by including automatized instructions and reports, they are suitable for off-site or long-distance use. The present study aims to validate the Cognigram™ (CG) computerized cognitive tool, in a prospective and longitudinal fashion, determining if changes in the CG scores over 3, 6, 9, and 12 months, can predict progression to dementia at 1-year, 2-years, and 3-years, for patients with Mild Cognitive Impairment (MCI). 2. The NeuroCatch™ Platform (NCP) Event-related potentials (ERP) are non-invasive, low-cost, electrophysiological methods that allow recording of the electrical activity of the brain in vivo through an Electroencephalogram (EGG). They are free from cultural and educational influence and can provide insights into the cognitive processes. ERP could enable to detect brain changes and determine the prognosis of MCI subjects. The NCP, an investigational medical device system developed by NeuroCatch Inc., consists of an EEG software and hardware that captures brain health information. It offers a quick (i.e., 10 minutes for EEG preparation and 6 minutes for each task of EEG recording), simple (i.e., includes only 8 electrodes), and easy-to-use solution (i.e., includes a computerized software that automatically analyzes data and outputs graphs in less than 1 minute) for the acquisition of EEG and ERP.

Rationale Today's aging population brings an increase in the incidence of dementia. In Canada, there are approximately 564,000 persons diagnosed with dementia, with an expected two-fold increase in this number by the year 2031. In this context, the early detection and prediction of cognitive decline are both imperative for achieving the prevention and/or slowing of dementia. Standard pencil-and-paper neuropsychological tests are pivotal for the detection and follow-up of cognitive impairment; however they are labor-intensive and require the presence of a trained neuropsychologist on-site. In this regard, computerized testing may be better suited for cognitive screening in large epidemiologic studies and for longitudinal monitoring by primary care providers, due to their higher efficiency for serial assessments and their suitability for off-site or long-distance use. At the same time, computerized testing allows for higher precision in the recording of accuracy and speed of response, with a level of sensitivity not possible in standard administrations. A number of computerized cognitive batteries have been recently developed, though intended as research tools. There is a current demand for the validation of computerized cognitive batteries in the clinical setting. One such computerized battery is the Cognigram™ (CG), which measures processing speed, attention, working memory and learning. Previous cross-sectional studies have demonstrated the validity of CG for detecting MCI and various types of dementia. However, there is no current literature on the longitudinal validity of CG, and minimal longitudinal validation of other computerized cognitive batteries currently in existence. On the other hand, research and medicine is moving away from behavioral responses to assess brain health (e.g. verbal responses, reaction time, etc.) and are moving toward more neuroimaging focused measures. Biological tests could enable to detect pre-dementia and determine the prognosis of MCI subjects.A promising biological test is EEG/ERP. The investigators have previously shown group differences in ERPs for patients with MCI and CN. Other studies have reported promising ERP markers of pre-dementia and progression of MCI to dementia. However, ERP can be complex to process and labor-intensive, limiting its value in the clinical setting. For example, the usual time for an ERP series measuring multiple cognitive domains typically lasts 1 hour, another 25 minutes for applying the EEG cap and ensuring all electrodes are connected, and some 30 minutes per paradigm (x2-3 paradigms). The NeuroCatch™ Platform (NCP) offers key competitive advantages compared to other EEG platforms, having a rapid test time with automated processing, analysis and results display - benefitting patients and clinicians being significantly easier to use than current EEG systems, and it alleviates training and ramp up costs amongst EEG users. In this study, the investigators will use the NPC to explore for ERPs that could predict progression to dementia in patients with MCI. The present study will make an initial assessment of the capacity of the NCP to detect cognitive decline and predict conversion to dementia in patients with MCI. Hypotheses: In Cognigram, the investigators hypothesize that the following will occur: i. Significant differences in the CG scores at baseline will be found between MCI and CN groups ii. MCI subjects will show greater longitudinal changes than the CN subjects iii. Intra-individual Significant longitudinal changes in the CG performances at 3 and/or 6 months and/or 9 months and/or 12 months, will relate to the prospective clinical outcome at 12-months, and/or 24-months, and/or 36-months. iv. The CG will show a higher sensitivity in detecting cognitive changes, and a higher predictive power of longitudinal clinical outcome than the pencil-and-paper MoCA test. In NeuroCatch, the investigators hypothesize that the following will occur: I. Significant differences in the ERP parameters (amplitude and latency of ERPs such as N100, N400, P300) at baseline will be found between MCI and CN groups II. MCI subjects will show greater longitudinal changes in ERP parameters than the CN subjects III. Intra-individual significant longitudinal changes in ERP parameters at 6 months and/or 12 months and/or 24 months, will relate to the prospective clinical outcome at 12-months, and/or 24-months, and/or 36-months. IV. The NCP will show a higher sensitivity in detecting cognitive changes, and a higher predictive power of longitudinal clinical outcome than the pencil-and-paper MoCA test. The objectives of the project are as follows: i. To discriminate CG longitudinal changes related to neural damage from normal variations in cognitively normal (CN) older adults ii. To determine if CG baseline score is related to the clinical outcome at 12-months, 24-months, and 36-months iii. To determine if intra-individual CG changes at 3, 6, 9, and 12 months, are related to the clinical outcome at 12-months, 24-months, and 36-months. iv. To compare the powers of CG and MoCA for predicting longitudinal clinical outcomes. v. To discriminate ERP longitudinal changes (e.g., changes in N100, N400, P300 amplitudes and latencies) related to neural damage from normal variations in cognitively normal (CN) older adults. vi. To determine if ERP baseline parameters (e.g. amplitude of N100, N400, P300) is related to the clinical outcome at 12-months, 24-months, and 36-months vii. To determine if intra-individual changes in ERP parameters at 6, 12, and 24 months, are related to the clinical outcome at 12-months, 24-months, and 36-months. viii. To compare the powers of NCP and MoCA for predicting longitudinal clinical outcomes. For the purpose of these project, a total sample of 30 MCI and their study partners, and 30 cognitively normal subjects (CN), will be recruited. Study partners of MCI subjects will answer the functional questionnaire and the cognitive questionnaire required for defining the clinical status of participants. It is mandatory for MCI participants to have a study partner available in order to participate in this study. For the CN project, the participants will undergo CG and Montreal Cognitive Assessment (MoCA) testing sessions at baseline, 3-months, 6-months, 9-months, and 12-months. The capacities for predicting clinical longitudinal outcomes (i.e., reversion to normal cognition, significant decline within MCI spectrum, or progression to dementia) of CG and MoCA will be compared. The clinical outcome will be assessed using a Neuropsychological battery, a functional assessment and a brief cognitive questionnaire at baseline, 12-months, 24-months and 36-months. It is expected that changes in CG scores will be sensitive to cognitive decline, allowing an early prediction of progression of the cognitive impairment. For the NCP project, the participants will undergo NCP (Appendix 14: NCP) and MoCA testing sessions at baseline, 6-months, 12-months, 24-months, and 36-months. The capacities for predicting clinical outcomes (i.e., reversion to normal cognition, significant decline within MCI spectrum, or progression to dementia) of the NCP and MoCA tests will be compared. The clinical outcome will be assessed using a Neuropsychological battery, a functional assessment and a brief cognitive questionnaire at baseline, 12-months, 24-months, and 36-months. It is expected that changes in ERP will be sensitive to cognitive decline, allowing an early prediction of progression of the cognitive impairment. Prospective longitudinal validation, if demonstrated, would greatly increase confidence in using CG and NCP in a clinical setting for the assessment of MCI patients and would allow early prediction of future clinical outcomes. This could help to develop preventive therapeutic strategies, while providing additional time for patients and their families to prepare for the future (e.g., financial arrangements, treatment options, and community services). CG and NCP would be of greatest use in primary care, where multiple constraints make cognitive assessments problematic (e.g., lack of time to perform cognitive testing, and lack of access to expensive neuropsychological testing not covered by OHIP) and EEG/ERP assessments prohibitive (e.g., lack of time to perform EEG, lack of access to trained experts for analyzing and interpreting the results of the EEG/ERP).