Canadian Consortium on Neurodegeneration in Aging (CCNA) Partners Forum and Science Days 2021: Abstracts from the trainee poster competition

Abstract

Consensus methods have been used in health care for a long time to reach agreement among experts when there is a lack of information or conflicting information on a health topic. The Delphi and nominal group techniques are extensively used in health research. Although both consensus methods are transparent in developing health research agendas, their emphasis on clinical and academic experts is problematic in Indigenous research. Another consensus approach named Glaser’s state-of-the-art is being used in Indigenous research. In this approach, a panel of experts identifies additional experts who collectively engage in iterative rounds to develop a consensus statement based on current research. We will be using a modified Glaser’s state-of-the-art approach to develop an informant-based functional assessment tool to assess the instrumental activities of daily living in people living with dementia. In the first phase, we will form a core research team, set up an Indigenous community advisory group (CAG), and conduct a focus group with health professionals and in-depth interviews with caregivers to develop a draft functional assessment tool. In the second phase, we will refine the tool using a consensus-building process that corresponds to Glaser’s stateof-the-art approach. Using community-engaged research, we aim to shift the focus from expert panels to individuals and communities with lived caregiver experiences from Indigenous perspectives. We will engage with the Indigenous communities and utilize Indigenous data analysis to develop a first-ever culturally grounded functional assessment tool in partnership with Indigenous caregivers. Lay Abstract: We will be using community-engaged research to develop an informant based functional assessment tool to assess Instrumental Activities of Daily Living (IADL) in Indigenous population. While developing the tool, our consensus-based approach will shift the focus from expert panel to individuals and communities with lived experiences from Indigenous perspectives. CATEGORY: MASTER’S TRAINEE High-Resolution Diffusion Tensor Imaging of the Hippocampus Shows Differences Between Parkinson’s Disease and Healthy Controls Alexandra Budd1, Myrlene Gee2, Krista Nelles2, Christian Beaulieu3, Richard Camicioli2 . 1University of Alberta; 2Department of Medicine, Division of Neurology and Neuroscience, University of Alberta; 3Department of Biomedical Engineering, University of Alberta. Question Addressed: Do measures obtained using highresolution diffusion tensor imaging (DTI) of the hippocampus differ between patients with Parkinson’s disease (PD) and healthy elderly controls? Additionally, are these measures associated with age and global cognition? Methods: Manual hippocampal tracing was performed on novel high-resolution DTI scans in 36 individuals with PD (mean age: 68.86 years ± 7.97) and 35 controls (mean age: 66.66 years ± 6.80). Diffusion measures [fractional anisotropy (FA) and mean diffusivity (MD)] and global cognition [Montreal Cognitive Assessment (MoCA)] were compared across groups using independent samples t-tests. Within each group, the association between diffusion and aging as well as MoCA score was assessed using Pearson correlations. Results: Average MoCA scores were 25.42 ± 4.31 for the PD group and 27.07 ± 1.58 for controls with available MoCA scores (n = 15), trending towards significance (p = 0.051). While hippocampal MD did not differ, FA was significantly lower in the PD group (mean = 0.17 ± 0.01) than in the control group (mean = 0.18 ± 0.02; p = 0.016). Age and FA were negatively correlated within the control group (r = -0.39, p = 0.022). Research Implications: High-resolution DTI found decreased hippocampal FA between PD and control groups, suggesting ABSTRACTS Canadian Consortium on Neurodegeneration in Aging (CCNA) Partners Forum and Science Days 2021: Abstracts from the trainee poster competition COLLABORATION FOR CONNECTIVITY ; OCTOBER 12-15, 2021 https://doi.org/10.5770/cgj.25.596 CANADIAN CONSORTIUM ON NEURODEGENERATION IN AGING (CCNA) 111 CANADIAN GERIATRICS JOURNAL, VOLUME 25, ISSUE 1, MARCH 2022 that FA may be more sensitive than MD to hippocampal changes in PD. While age and global cognition were not associated with FA in PD, other factors may be linked to these hippocampal changes and should be explored. Acknowledgements: We thank CCNA for funding this project. Lay Abstract: The hippocampus may be affected in Parkinson’s disease. This study used diffusion tensor imaging, an MRI-based method that provides indirect measures of tissue integrity, to compare the hippocampi of patients and healthy controls. One of these measures was lower in patients, suggesting decreased hippocampal integrity compared to controls. Machine Learning for the Prediction of Cognitive Decline in Parkinson’s Disease Milton Camacho1, Hannes Almgren1, Zahinoor Ismail1, Richard Camicioli2, Oury Monchi1, Nils Forkert1. 1University of Calgary, 2University of Alberta. Background: Parkinson’s Disease (PD) is the second most common neurodegenerative disease, associated with both motor and non-motor symptoms (NMS). Various studies have highlighted the link between cognitive decline, one of the most severe NMS, and neuropsychiatric symptoms (NPS) in PD, however, their relationship is not well understood. Machine Learning (ML) is able to identify complex non-linear patterns in high-dimensional data, which can potentially identify the PD population at risk of developing dementia. Methods: The aim was to develop ML models to classify PD, PD with Mild Cognitive Impairment (PD-MCI) and PD with dementia patients (PD-Dementia) using deep phenotyping information collected in the Canadian Consortium on Neurodegeneration in Aging COMPASS-ND study (PD=33/ PD-MCI=22/PD-Dementia=18;Male=50/Female=23). Available data included demographics, and also cognitive, clinical, and neuropsychiatric measurements (i.e., letter fluency tests, blood pressure, and NPS, respectively). Two separate models, incorporating both a ReliefF feature selector and the random forest classifier, were developed. One model was trained using all available features and the second was trained using only demographics and NPS. Results: The best results were achieved using all available features. A 10-fold cross-validation evaluation revealed that the model achieved 80.8% accuracy classifying individuals diagnosed with PD, PD-MCI, and PD-Dementia. The second model performed worse with 65.7% accuracy. Conclusions: The results of this study clearly showed the benefit of multi-modal data for classification of PD patients with and without cognitive decline. Neuropsychiatric features were found to be promising when differentiating between PD and PD-Dementia, however, in this study they proved being insufficient to properly classify PD-MCI patients. Lay Abstract: Parkinson’s disease patients have a higher risk of developing dementia, however, it remains a challenge to predict their evolution. This work applied Machine learning to investigate the use of non-motor symptoms in Parkinson’s disease achieving 80.8% accuracy for the identification of the Parkinson’s disease population at risk of developing dementia. Investigating Changes in Cognition associated with the use of CPAP in Cognitive Impairment and Dementia: A Retrospective Study Yakdehikandage S. Costa, Andrew Lim, Sandra E. Black, Mark I. Boulos. University of Toronto. Objective: To characterize the impact of 2-12 months of CPAP use on cognition in a clinical cohort with obstructive sleep apnea (OSA) and cognitive impairment due to neurodegenerative and/or vascular etiologies after controlling for baseline sleepiness. Methods: We retrospectively analyzed 158 patients with cognitive impairment due to a neurodegenerative and/or vascular etiology and an OSA diagnosis confirmed with in-laboratory polysomnography or home sleep apnea testing (mean age 69.9 ± 10.7; 69% male). Baseline Epworth Sleepiness Scores (ESS) and relevant comorbidities were obtained from selfreported questionnaires. Baseline and follow-up Montreal Cognitive Assessment (MoCA), and Mini-Mental Status Examination (MMSE) scores were obtained from clinical and research visits conducted 2-12 months apart. Adherence was defined as CPAP use ≥4 hr/night, 7 days/week at followup. Associations between CPAP adherence and follow-up cognitive scores were analyzed using multivariable linear mixed-effects models. Results: After adjusting for age, sex, body mass index, ESS, relevant comorbidities and the random effect of study cohort, CPAP adherence was significantly associated with a 2.9 point increase in follow-up MoCA scores (p>0.001, n=116) and a 1.2 point increase in follow-up MMSE scores (p=0.03, n=130). Research Implications: In patients with OSA and cognitive impairment due to a neurodegenerative and/or vascular etiology, cognitive function may be stabilized or reversed with the use of CPAP. The findings of this study will aid in motivating patients to use CPAP and support future randomized controlled trials in this area. Lay Abstract: Obstructive sleep apnea (OSA) is known to worsen cognition in patients with cognitive impairment. Our objective is to see if treating OSA by using CPAP can reverse the impact of OSA on cognition. In our clinical cohort, we demonstrate that the use of CPAP is associated with improvements in cognition. CANADIAN CONSORTIUM ON NEURODEGENERATION IN AGING (CCNA) 112 CANADIAN GERIATRICS JOURNAL, VOLUME 25, ISSUE 1, MARCH 2022 Towards a Better Understanding of Tele-Administration Validity From Researchers’ Perspectives Shirley Dumassais1, Gabrielle Aubin1, Karl Grewal2, Megan O’Connell2, Natalie Phillips3, Walter Wittich1 . 1University of Montreal, 2University of Saskatchewan, 3Concordia University. Abstract: The aging population requires the consideration of the accessibility and modalities of research activities. Telehealth appears as a solution to keep research activities going in a safe way, ev