麻豆AV

Projects Funded in CONNECT Expertise Matchmaking

The Expertise Matchmaking cycle of the聽Cognitive Neuroscience kNowledge Exchange for Clinical Translation (CONNECT) initiative was the third in a three-year effort to do cognitive neuroscience differently.

This third session was focused on elevating the real-world impact of cognitive neuroscience by promoting interdisciplinary collaboration.

Project Lay Summaries

Correlative light and electron microscopy in lipoprotein identification in Alzheimer鈥檚 disease

PI: Lisa Munter

Co-PI: Mike Strauss

Funding: $50,000

Lay abstract: Alzheimer鈥檚 disease and other neurodegenerative diseases are impacted by lipoprotein particles that transport lipids between cells in aqueous environments. Importantly, lipoproteins come in different sizes and different lipid compositions, which makes them difficult to study in disease models. Using correlative light and electron microscopy, two existing imaging techniques that give complementary information, as well as machine learning, we aim to 鈥渂arcode鈥� the many different lipoprotein types and gain insight into the role of lipoproteins in disorders of the brain.

Measuring the effects of poor metabolic health on the brain

PI: Alain Dagher

Co-PIs: Claudine Gauthier, May Faraj, Christine Tardif, Xiao Wen Hu, Michael Tsoukas, Bratislav Misic, Filip Morys

Funding: $50,000

Lay abstract: This project will explore the impact of midlife obesity on brain health, particularly its role as a risk factor for dementia. Combining extensive MRI measures of brain physiology and metabolic phenotyping, we will create a dataset that will allow us to investigate the effects of adiposity on the brain and model the causal relationships between systemic metabolic factors and brain dysfunction. This novel collaborative effort, combining expertise in neuroscience, MRI, endocrinology and metabolism, seeks to enhance our understanding of obesity's effects on the brain and lay the groundwork for interventions targeting metabolic dysfunction and obesity to reduce dementia incidence.

Understanding academic readiness in preschool children through brain imaging

PI: Dr. Tasmia Hai

Co-PI: Dr. Mallar Chakravarty

Funding: $49,694

Lay abstract: The preschool years are pivotal in shaping children鈥檚 academic and social-emotional development. While Attention-Deficit/Hyperactivity Disorder (ADHD) is commonly diagnosed in school-aged children, a significant proportion of them display symptoms of impulsivity and attention problems in the preschool years. The current project will examine the neuroanatomical and neuropsychological markers associated with academic readiness skills in preschool children with attention and behaviour problems. The findings can offer insights into the neuroanatomical and cognitive functions that contribute to early learning challenges and mitigate future risks by delivering targeted interventions.

Exploring 鈥渄eep attention鈥�: Bridging neuroscientific and literary perspectives

PI: Jelena Ristic

Co-PIs: Miranda Hickman, Aaron Johnson

Funding: $50,000

Lay abstract: Literary theorists and experimental psychologists theorize about 鈥渄eep鈥�, 鈥渋ntellectual鈥� or 鈥減oetic鈥� attention鈥攁 state of focused attention that promotes transformative thought. We will investigate deep attention by having participants read poetry that engages deep attention while we measure their mind states using integrated mobile EEG, eye tracking, and physiological measures to understand if 鈥榙eep attention鈥� is marked by specific or synchronized responses from the mind and the body. This work will advance our understanding of the physiological markers of human mind states and inform how environmental conditions that promote those mind states may foster mental capacities that promote learning, resilience and well-being.

Leveraging the neuropsychiatric phenotype of Parkinson鈥檚 Disease to identify mechanisms of disease heterogeneity

PI: Madeleine Sharp

Co-PI: Yashar Zeighami

Funding: $50,000

Lay abstract: Parkinson's Disease (PD) exhibits significant clinical heterogeneity, hindering progress toward a cure. The rich neuropsychiatric symptoms in PD provide an opportunity to explore the underlying neurobiology and host factors contributing to this variation and provide insights into its mechanisms. Combining expertise in neurology, cognitive neuroscience and disease modelling, the collaboration will analyze large datasets to distinguish disease-specific factors from host factors, incorporating clinical, genetic and imaging data. This comprehensive modelling approach will examine the effect of disease variables, their heterogeneity and their evolution over time to develop a framework that will help identify modifiable factors, predict progression and identify candidate biomarkers.

Connecting cognitive neuroscience with Conversation User Interface design to combat loneliness in ageing populations

PI: Signy Sheldon

Co-PIs: Kate Dupuis, David Usher

Funding: $50,000

Lay abstract: Loneliness is on the rise in our ageing population, which is now known to exacerbate age-related cognitive decline and increase dementia risk. A promising way to combat loneliness is to use Conversational User Interfaces (CUIs), chatbots that allow a person to interact with a computer on human terms. There is a lack of knowledge of how to design CUIs specifically for older adults. This project will use our current understanding of age changes to cognition to design a CUI for the ageing population. This device can serve as an effective loneliness intervention tool for older adults, which in turn can enhance cognition and reduce dementia risk.

Creation of an artificial intelligence platform using speech to diagnose and track Parkinson鈥檚 Disease

PI: Denise Klein

Co-PIs: Signy Sheldon, Madeleine Sharp, Mirco Ravanelli, Shari Baum

Funding: $50,000

Lay abstract: Human speech samples are low-cost and readily available, and diagnosing diseases through speech using AI could prove a transformative step in precision medicine and accessibility. Repeat administrations over time can also be used to monitor the rate of disease progression or delineate specific phenotypes. The proposed project focuses on detection of near-imperceptible signs of speech problems using machine learning, linking behaviour with the brain (imaging and genetic information), which, when combined, would serve as a biomarker for disease and disease progression. We will take speech recordings of patients and controls already acquired as part of the Quebec Parkinson Network (QPN), linked with the participants鈥� medical profiles, to develop a more advanced machine-learning model. Our team brings together a new collaboration for expertise matchmaking to tackle this complex scientific challenge.

Leveraging preclinical models and computational methods to understand how changing internal states shape reward learning

PI: Rosemary Bagot

Co-PIs: Becket Ebitz, Leah Mayo

Funding: $50,000

Lay abstract:聽This new collaboration will pioneer a translational research program to dissect the neural mechanisms by which stress increases cognitive rigidity to disrupt reward learning and decision making and how psilocybin might preserve cognitive flexibility. Using computational models as a translational bridge to infer latent cognitive states from similar reward learning tasks in humans and mice, we will examine how these cognitive processes are impacted by stress and psilocybin.

Characterizing the mechanisms of climate-related behaviour change in cognitive aging

PI: Maiya Geddes

Co-PIs: Eric Galbraith, Bahareh Seyedi, Howard Chertkow, Nicole Anderson, Yasser Iturria Medina

Funding: $50,000

Lay abstract: This project addresses the critical knowledge gap in understanding the neurobehavioural mechanisms driving modifiable health and climate-related behaviours. Utilizing advanced behavioural and multimodal brain imaging techniques, we aim to define the neural substrates of behaviour change through machine learning neuroimaging methods. This novel, interdisciplinary collaboration will combine expertise in international sustainability policy, climate change and planetary science, translational cognitive neuroscience, human motivation, computational neuroscience and behavioural neurology to provide neurobiologically-informed strategies for enhancing behaviour change in older adults, contributing to the broader understanding of the impact of modifiable behaviours on brain health and climate consciousness.

Parsing the role of the endogenous opioid system in social attachment

PI: Jennifer Bartz

Co-PI: Anna Weinberg

Funding: $49,540

Lay abstract: This collaboration aims to investigate the role of the endogenous opioid system in social attachment. Using physiological markers (EEG), behavioural markers, genotyping of the 渭-opioid system and pharmacological probes (opioid receptor antagonist naltrexone), we will explore the causal role of endogenous opioids in social reward and threat processing in close bonded relationships. Physiological outcomes will also be linked to social behaviour during real-world interactions. The findings may contribute valuable insights into psychiatric conditions related to social deficits and enhance our understanding of the impact of impaired social attachments on mental and physical health.

Mapping the 鈥榬epresentative brain鈥�: A toolkit for population neuroscience

PI: Nathan Spreng

Co-PI: Sam Harper

Funding: $50,000

Lay abstract: This collaborative project aims to address the challenges of mapping the "representative brain" by creating a toolkit for population neuroscience. Combining expertise in cognitive neuroscience and epidemiology, we will develop criteria and methods for population sampling in neuroscience research, ensuring the recruitment of representative samples. The toolkit will be tested in a large, ongoing cognitive neuroscience study at the Neuro, to evaluate sampling success and address any outstanding challenges. This innovative initiative is among the first of its kind and will have immediate impact on ongoing projects, towards a longer-term goal of advancing efforts to map a truly representative brain, thereby improving the translational impact of cognitive neuroscience discoveries.

Understanding emotional responses to risky choice outcomes in problem gambling

PI: Ross Otto

Co-PI: Luke Clark

Funding: $49,625

Lay abstract: This study explores the emotional responses to the outcomes of risky choices in problem gamblers and individuals at risk of developing a gambling problem. In previous research, we have used facial muscle activity (facial electromyography, or fEMG) to try capturing these fast emotional responses and found exaggerated emotional responses to outcomes resulting from choices in a gambling-like task. This project will investigate whether such intense emotional responses can predict problem gambling severity. Through diverse community samples, we will analyze emotional reactivity during risky decision-making, offering an initial insight into predicting problem gambling development based on emotional responses to wins and losses.

Fathers Matter: Evaluation of a co-developed digital platform to promote mental health for dads during the perinatal and early parenting epochs

PI: Deborah Da Costa

Co-PI: Cindy Hovington

Funding: $50,000

Lay abstract: The negative impact of a father鈥檚 poor mental health on their child鈥檚 development is well documented, yet, support for paternal emotional and mental well-being remains a much neglected area in our society. Many questions remain regarding how to best support dads with their emotional health and how to best deliver this content to them during the early parenting period. Supporting emotional health can act as a preventative approach for optimizing men's mental health during this important life stage. This project will extend the work of Healthydads.ca to 1) evaluate the impact of a digital platform called Curious Neuron on how dads cope with their emotions and 2) will assess which factors lead dads to engage more with an evidence based digital platform designed to support their emotional well-being and teach them effective parenting practices.

Depth-cue invariant object representations in deep learning models and relating them to neuroimaging using topological analysis and visualization

PI: Reza Farivar

Co-PIs: Derek Nowrouzezahrai, Sara Faridi

Funding: $50,000
Lay abstract: The way AI systems see the world was modelled after how neuroscientists think we see the world, but both are wrong in one crucial way鈥攐ur brains extract 3-D depth information of objects and these existing models do not. This project is about developing models that understand 3-D. A second goal is to improve how we interpret data and to develop tools to understand the shape of data. Right now, we try to find distances between clusters of data and assume clusters, but data could be in strange shapes, including spheres or doughnuts鈥攊.e., with no data in the middle.