Temerty-Tanz-TDRA Seed Fund
Description
The Tanz Centre for Research in Neurodegenerative Diseases (Tanz) and the Toronto Dementia Research Alliance (TDRA) at the Temerty Faculty of Medicine are pleased to announce a third Temerty-Tanz-TDRA seed fund competition for 2023. These awards – valued at up to $70,000 – will be granted to collaborative projects that advance scientific knowledge in the field of dementia. There will be three seed funds available as in previous years. However, this year, and differently from the past two competitions, one will be reserved for projects that focus on the link between depression and dementia, and two for projects that focus on neurodegenerative disease/dementia in general.
Apply
TDRA is not currently accepting applications for the Temerty-Tanz-TDRA Seed Fund Award Competition.
For more information please contact tdra@utoronto.ca
Meet the 2023 Seed Fund Award Recipients
Project 1: Harnessing vagal nerve function to curb depression and dementia
Principal Investigators: Minna Woo (UHN) & Thomas Prevot (CAMH)
Dementia, and associated cognitive deficits, is a major health problem that is expected to affect 150 million people globally by 2050. This is often accompanied by depression. Yet, the exact mechanisms of how depression and dementia co-occur remain poorly understood. Inflammation in the brain is a key driving factor for dementia, which is also a shared risk factor for depression.
Research has shown that a major branch of the nervous system, known as the vagus nerve, plays a critical role in suppressing inflammation. Dementia risk increases with aging, which coincides, interestingly, with the decline in vagal function. Thus, the focus of our study is to investigate the mechanistic role of vagal function in preclinical models of dementia and depression.
This study will test the hypothesis that vagal nerve function regulates the emergence of depression and cognitive decline. Researchers anticipate that subjects with enhanced vagal function will be protected against increased neuroinflammation, depression and cognitive deficits. Better understanding of the molecular mechanisms that regulate vagal function will have implications for application of vagal nerve stimulation, which is already approved for refractory (or hard to treat) depression.
Project 2: Using functional imaging to evaluate the effect of photobiomodulation in patients with Mild Cognitive Impairment
Principal Investigators: Corinne Fischer (UHT), Simon Graham (Sunnybrook), Tom Schweizer (UHT)
This study focuses on investigating the efficacy and underlying mechanisms of photobiomodulation (PBM), a novel non-invasive candidate treatment that delivers infrared light to the brain and that has shown promise in preclinical studies and case series. Researchers will study the suggested mechanisms of PBM by utilizing state-of-the-art in-vivo 31 phosphorous MR spectroscopy (MRS) and MRI to measure brain neural metabolites and structural and functional brain alterations. Additionally, they will investigate the association between cognition and MRS findings and blood-biomarkers (lactate and pyruvate) commonly used to estimate cellular energy levels indirectly.
The study will recruit 30 older adults with Mild Cognitive Impairment (MCI) who will receive home-based PBM treatment for six weeks (15 active, 15 sham). Alongside the treatment, researchers will conduct comprehensive cognitive assessments, analyze blood lactate, and employ advanced MRI and MRS to examine pre- and post-treatment changes in brain structure and function. This experimental protocol presents a unique opportunity to gain a deeper understanding of the PBM's mechanisms of action on brain health and function, and to assess the potential of the collected data to provide biomarkers of PBM utility.
Project 3: On the Road to Acceptance: Optimizing Naturalistic Driving Monitoring Systems for Individuals with Dementia
Principal Investigators: Mark Rapoport (Sunnybrook), Gary Naglie (Baycrest), & Sayeh Bayat (U of Calgary)
A major challenge in dementia care is determining the point at which driving safety becomes significantly compromised for drivers with dementia. While a diagnosis of dementia directly impacts driving abilities, it is insufficient for revoking one’s driving privileges. This is particularly true for individuals in the early stages of dementia who may retain the ability to drive safely for a certain period. On-road driving assessments are currently the gold standard for assessing fitness-to-drive in this population. However, these assessments have limitations, including lack of standardization across various centers, and challenges related to availability, generalizability, and cost.
This study aims to investigate the acceptability and usability of naturalistic driving monitoring systems to enhance driving-related decision-making in individuals with dementia. Specifically, the primary focus will be understanding the perceptions of community-based drivers with dementia regarding these modern driving monitoring technologies. Moreover, the study will explore their perspectives on the acceptability of decisions based on such technologies compared to those based on traditional on-road driving assessments. Focus group discussions will be employed to assess the technology's acceptability and identify potential barriers to its adoption.
When fully developed, driving monitoring technologies could serve as early indicators of declining driving performance. This would enable a more evidence-based approach to decision-making regarding driving in dementia. The present study will allow us to anticipate how this technology can be implemented in practice.
2022 Seed Fund Award Recipients
Using machine learning to differentiate Alzheimer’s disease from depression
Primary investigators: Mary Pat McAndrews& Jennifer Rabin
Lay description: Alzheimer disease (AD) and depression are two of the most common health conditions in older adults. The two disorders are associated with an overlapping set of symptoms that include memory difficulties. As a result, it can be difficult to determine whether a person’s memory problems are due to the early stages of AD, depression, or both. Providing an accurate diagnosis, as early as possible, is important to ensure that people receive the most effective treatment and care.
This project will use machine learning techniques to create a method for accurately determining whether someone has AD, depression, both conditions, or neither condition. A machine learning model will be trained to use cognitive test data (e.g., memory, language, attention scores) and brain data derived from an MRI scan to accurately make these classifications. The cognitive test data and brain data will come from four large cohort studies of older adults. The analysis will then be replicated on a separate dataset to evaluate the generalizability of the algorithm. If successful, this study will provide an affordable diagnostic tool that clinicians can use to accurately diagnose people with AD, depression, or both conditions.
Targeting α-synuclein with a novel peptide inhibitor to treat cognitive impairment and depression in Parkinson’s disease
Primary Investigators: Clement Hamani, Lorraine Kalia, Suneil Kalia, Philip Kim
Lay description: Parkinson’s disease results from the death of brain cells which are necessary for many of our daily functions, including movement. Loss of movement control (i.e., motor symptoms) is a defining feature of Parkinson’s disease. However, people living with Parkinson’s disease also suffer from depression and dementia that can lead to memory problems (i.e., non-motor symptoms). Depression often occurs before motor symptoms, while dementia affects about 80% of people just 10 years after being diagnosed with Parkinson’s disease. Treatment for these non-motor symptoms could drastically improve the quality of lives of people affected by Parkinson’s disease.
Many people living with Parkinson’s disease have an abnormal accumulation of a normally occurring protein in the brain, called alpha-synuclein. The proteins clump together to form Lewy Bodies, which can cause brain cells to die. This project will examine the relationship between alpha-synuclein accumulation, brain cell death, depression, and memory problems in an animal model in which features of Parkinson’s disease are caused by rotenone, a pesticide associated with increased risk of Parkinson’s disease in humans. A recently discovered peptide, called PDpep1.3, has been shown to reduce alpha-synuclein accumulation in the brain, and is associated with decreases in brain cell death and motor symptoms. This project will test whether PDpep1.3 can also reduce depression and memory problems in the rotenone model of Parkinson’s disease. Results from this project will begin to define the potential interactions between depression and dementia in Parkinson’s disease.
2021 Seed Fund Award Recipients
Impact of lipopolysaccharide on immune response & cerebral amyloid deposition in older adults with a history of major depressive disorder
Primary Investigators: Damien Gallagher & Ariel Graff-Guerrero
Lay Description: Activation of the inflammatory response is known to play a role in causing amyloid-beta to accumulate. Study investigators previously found that over one third of older adults living with depression have persistent inflammation. One cause for this may be breakdown of the gut barrier, which helps ensure that bacteria living in our gut remain separate from the rest of our body. Depression has been associated with breakdown of the gut barrier and increased immune response to lipopolysaccharide (LPS), which is a product released from the cell membrane of bacteria that live in our gut. This study aims to determine if LPS is a key driver of inflammation and increased accumulation of amyloid-beta protein in the brain, thereby precipitating depression and subsequent cognitive decline in a proportion of older adults living with depression.
The contribution of cerebrovascular disease to depression in patients with & without Alzheimer’s disease
Primary Investigators: Angela Golas & Carmela Tartaglia
Lay Description: This study aims to better understand the interrelationship of depression, neurodegeneration, and cerebrovascular disease in patients with and without positive biomarkers for Alzheimer disease (AD). The investigators hypothesize that patients living with depression will have larger white matter hyperintensities volume (associated with small vessel disease), and that this will relate to cognitive impairment in both AD and non-AD patients. The study will also compare the interrelationship between depression, cerebrovascular disease, and AD between males and females. The results could provide evidence for implicating cerebrovascular disease in depression and cognitive impairment in AD and non-AD patients. This could provide rationale for primary prevention of depression with control of vascular risk factors.
Read publication.
Assessment of heart rate variability in older adults with lifetime history of depression or mild cognitive impairment
Primary Investigators: Jean Chen & Linda Mah
Lay Description: Higher heart rate variability (HRV) is associated with greater emotional well-being and cognitive function. Study investigators have demonstrated that increasing the duration of exhalation-to -inhalation breathing ratio leads to increased HRV in healthy older and young adults. Autonomic dysfunction is well-established in major depressive disorder (MDD) and Alzheimer’s disease (AD), and emerging data shows evidence of the same in mild cognitive impairment (MCI). The goal of this study is to assess autonomic function through measurement of HRV in remitted MDD (rMDD), amnestic MCI (aMCI), rMDD with comorbid aMCI (rMDD+aMCI), and cognitively unimpaired (CU) older adults at rest and in response to a physiological challenge. Results from this study will contribute to our knowledge of the association between autonomic dysfunction and AD risk, and could identify potential autonomic targets for AD prevention.