Innovative Ideas Competition I

The first Innovative Ideas Competition launched in Fall 2017 and 85 people submitted Letters of Intent (LOI). Of these, 22 were invited to submit a full application for review by a panel of 25 national and international reviewers outside of McGill. Twelve projects were awarded funding from these 22 applications.

Funded projects

HBHL Research Theme 1
Deep learning approaches to tackle neuroimaging challenges
Developing vascularized brain organoids on chips for high-throughput screening
Estimating spatially resolved microstructural features of the brain based on quantitative MRI
High-complexity statistical modeling via optimal transportation
Marmoset Magnetic Resonance Imaging platform
HBHL Research Theme 2
Brain hemodynamic signals as a dynamic biomarker of dementia initiation, progression and treatment
In vivo cholinergic markers of preclinical Alzheimer’s disease progression
Measuring protein turnover in organoid and animal models of Parkinson’s disease
HBHL Research Theme 3
Understanding resilience and disease by using language and the brain to predict individual outcomes
Targeted plasticity for recovery from cortical visual impairment
HBHL Research Theme 4
A personalized approach to depression care: Discovering adaptive treatment strategies
When gender matters: Differential effects of prenatal adversity on the development of ADHD and related neurocognitive deficits

Funded project summaries

Deep learning approaches to tackle neuroimaging challenges

The goal of this project is to develop deep learning architectures for analyzing large-scale neuroimaging data, specifically the BigBrain 3D dataset, which is the result of a long-term collaboration between the Evans laboratory at McGill and the laboratory of Katrin Amunts in Jülich, Germany. The BigBrain 3D dataset is the most detailed 3D digital atlas of the human brain available.

HBHL Research Theme: 1

Principal Investigator: Alan Evans

Co-PI: Yoshua Bengio (Université de Montréal)

Collaborators: Konrad Wagstyl (University of Cambridge), Adriana Romero (Université de Montréal), Joseph Cohen (Université de Montréal), Katrin Amunts (Forschungszentrum Jülich), Timo Dickscheid (Forschungszentrum Jülich)

Funding Received (over 2 years)$161,500

Developing vascularized brain organoids on chips for high-throughput screening

Human induced pluripotent stem cells (hiPSC) can be differentiated into neural organoids (mini-brains). To improve organoid reproducibility, the team is testing novel stable polymer biomaterials. They are developing a 3D microfluidic chip to support organoid vascularization to increase organoid size and better model the multicellular complexity of the human brain. By improving neural organoid technology, researchers will be able to better model human neurodegenerative disease and develop new treatments.

HBHL Research Theme: 1

Principal Investigator: Timothy Kennedy

Co-PIs: Christopher Barrett (McGill), David Juncker (McGill)

Collaborators: Edward Fon (McGill), Thomas Durcan (McGill), Tanja Kuhlmann (Universitätsklinikum Münster), Amir Shmuel (McGill), Louis Collins (McGill), Kevin Petrecca (McGill), Edith Hamel (McGill)

Funding Received (over 2 years): $170,000

Estimating spatially resolved microstructural features of the brain based on quantitative MRI

The aim of this study is to map microstructural features of the brain, such as relative myelin content, cell density and fibre bundle orientation, using in-vivo quantitative MRI. This project will create a framework for comparing MRI-based estimates of brain microstructure to histological measurements, as well as improved estimation of relative myelin content, cell density and fibre bundle orientation from multi-modal structural and diffusion MRI.

HBHL Research Theme: 1

Principal Investigator: Amir Schmuel

Co-PIs: Ravi Menon (Western University), David Rudko (McGill)

Collaborators: Sergey Plis (University of New Mexico), Corey Baron (Western University). Louis Collins (McGill University)

Funding Received (over 2 years): $170,000

High-complexity statistical modeling via optimal transportation

This project developed new metrics for evaluating confidence/uncertainty measures for classification by Deep Neural Networks, which are useful for making accurate, model-free predictions based on massive datasets. Based on optimal transportation theory, the team created theoretical results and computational procedures. The goal is to facilitate the holistic and integrated analysis of various types of health data.

HBHL Research Theme: 1

Principal Investigator: David Stephens

Co-PIs: Adam Oberman (McGill)

Collaborators: Esteban Tabak (Courant Institute)

Funding Received (over 2 years): $147,900

Marmoset Magnetic Resonance Imaging platform

The goal of this proposal was to develop a platform for marmoset magnetic resonance imaging (MRI) on the human 3 Tesla scanner at the McConnell Brain Imaging Centre. MRI techniques were developed for in vivo anesthetized marmoset imaging. To create a multi-modal marmoset brain atlas, the team optimised several acquisition protocols and image analysis pipelines.

HBHL Research Theme: 1

Principal Investigator: Christine Tardif

Co-PIs: David Rudko (McGill), Philippe Huot (McGill), Louis Collins (McGill)

Collaborators: Stephen Frey (Rogue Research Inc.), Stephen Nuara (McGill), Ilana Leppert (McGill), Rick Hoge (McGill), Michael Petrides (McGill), Julien Doyon (McGill), Abbas Sadikot (McGill), Edward Fon (McGill)

Funding Received (over 2 years): $169,997

Brain hemodynamic signals as a dynamic biomarker of dementia initiation, progression and treatment

It is unclear whether brain imaging techniques, such as functional MRI, can detect disease onset, progression and response to therapy in pathologies such as Alzheimer's disease (AD) and vascular dementia. To test these hypotheses, this project employs animal models of Alzheimer's disease and vascular dementia, as well as a technique similar to functional MRI. The project's findings should provide a firm conclusion on the feasibility of using brain imaging data in the diagnosis and treatment of these two types of dementia.

HBHL Research Theme: 2

Principal Investigator: Edith Hamel

Co-PI: Frédéric Lesage (Université de Montréal)

Collaborators: Christophe Grova (Concordia)

Funding Received (over 2 years): $170,000

In vivo cholinergic markers of preclinical Alzheimer’s disease progression

This project seeks to determine whether the dysfunction of specific nerve cells following degeneration in the front and bottom of the brain precedes and predicts the spread of Alzheimer's disease pathology across the cerebrum's surface. The project team is collecting neuroimaging markers of brain structure and function, cholinergic system function and cognition in a group of asymptomatic participants who are at high risk of developing Alzheimer's. The project's goal is to stimulate new initiatives towards early diagnosis of prodromal syndromes using multimodal imaging.

HBHL Research Theme: 2

Principal Investigator: R. Nathan Spreng

Co-PIs: John Breitner (McGill), Sylvia Villeneuve (McGill), Judes Poirier (McGill), Jean-Paul Soucy (McGill), Pedro Rosa-Neto (McGill), Pierre Bellec (Université de Montréal)

Collaborators: Taylor Schmitz (McGill), Marc-André Bédard (UQAM)

Funding Received (over 2 years): $169,952

Measuring protein turnover in organoid and animal models of Parkinson’s disease

Several studies suggest that Parkinson's disease (PD) is caused by damage to mitochondria. Because PD neurons are especially vulnerable to mitochondrial damage, PD develops when they are not repaired. Notably, a protein called Parkin, which causes familial PD when mutated, functions by inducing the degradation of damaged mitochondria. This project team is the first to measure the rate of mitochondrial degradation in brain-like organoids derived from human stem cells with a Parkin deletion. This tool will be used to test new drugs that restore mitochondrial repair.

HBHL Research Theme: 2

Principal Investigator: Jean-Francois Trempe

Collaborators: Thomas Durcan (McGill), Edward Fon (McGill)

Funding Received (over 2 years): $85,000

Understanding resilience and disease by using language and the brain to predict individual outcomes

The use of computerised algorithms to analyse subtle speech variation can predict disease progression. This project team will create computerised algorithms to analyse speech and detect signs of cognitive ageing and disease risk. The project's outcomes could pave the way for the development of widely accessible and low-cost diagnostic tools for assessing disease risk.

HBHL Research Theme: 3

Principal Investigator: Denise Klein

Co-PIs: Michael Petrides (McGill), Shari Baum (McGill)

Collaborators: Morgan Sonderegger (McGill), Natalie Phillips (Concordia), Edward Fon (McGill)

Funding Received (over 2 years): $170,000

Targeted plasticity for recovery from cortical visual impairment

Following stroke or traumatic brain injury, many patients experience cortical visual impairment (CVI), which manifests as a large blind spot in their visual field. According to some studies, CVI patients can be trained to respond to simple stimuli in their blind fields. The goal of this project is to design and test new training protocols based on the expertise of PI Chritopher Pack's lab in the visual cortex.

HBHL Research Theme: 3

Principal Investigator: Christopher Pack

Co-PIs: Étienne de Villers Sidani (McGill)

Collaborators: Céline Odier (Université de Montréal), Rosa Sourial (MUHC), Marie-Josée Gagnon (Hôpital de réadaptation Villa Medica), Robert Gordon (The Article 19 Group, Inc.)

Funding Received (over 2 years): $146,356

A personalized approach to depression care: Discovering adaptive treatment strategies

Tailoring treatment to patients with depression requires evidence-based strategies. The goal of this study is to use electronic health records to discover adaptive treatment strategies that tailor sequential treatment decisions to patient characteristics such as symptom patterns, co-occurring conditions and response to previous treatments.

HBHL Research Theme: 4

Principal Investigator: Erica Moodie

Co-PIs: Samy Suissa (McGill), Christel Renoux (McGill)

Collaborators: Susan Shortreed (Kaiser Permanente), Gregory Simon (Kaiser Permanente)

Funding Received (over 2 years): $131,695 

When gender matters: Differential effects of prenatal adversity on the development of ADHD and related neurocognitive deficits

This study looks at how sex-dimorphism, prenatal programming, maternal mood, parenting and genetic risk for Attention Deficit Hyperactivity Disorder (ADHD) interact and manifest themselves across several cohorts from childhood to early adulthood. The project examines the severity of a multi-level ADHD construct to assess the multiple modal differences in boys and girls.

HBHL Research Theme: 4

Principal Investigator: Ashley Wazana

Co-PIs: J. Bruce Morton (Western University), Tim Oberlander (University of British Columbia), Celia Greenwood (McGill)

Collaborators: Michael Meaney (McGill), James Kennedy (University of Toronto), Jonathan Evans (University of Bristol), Henning Tiemeier (Erasmus)

Funding Received (over 2 years): $169,575

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