Translational Postdoctoral Fellow Research Award Results

Niveen Fulcher

Translational Postdoctoral Fellow
PhD, Neuroscience - Western University

Safety assessment of low-intensity electric field treatment of glioblastoma in a rat neuro-oncology model

Supervisor(s): Dr. Eugene Wong and Dr. Matthew Hebb

Research Information:
Glioblastoma multiforme (GBM) is the deadliest brain cancer in human adults and currently incurable. Existing treatment only extends life by ~14 months; evidently, research into new therapies is imperative. Our team has developed a biotechnology called Intratumoral Modulation Therapy (IMT) to deliver low-intensity electric field therapy. Our preliminary in vivo data, together with in vitro work have shown IMT efficacies on treating tumours. To achieve the clinical vision of providing IMT after standard chemo-radiotherapy to prevent GBM recurrence, IMT – delivered via implanted bioelectrodes within GBM tissue – has to be safe.

In this proposal, we focus on evaluating the neurological impacts related to the delivery of IMT in vivo in a rat model to define safety and confirm effectiveness of IMT. With an electric field from IMT that is predicted to attenuate GBM progression in vivo, we seek potential IMT adversities to the brain and behaviour and dependence on sex. We will determine safety to the brain following IMT in male and female rats with tumours, via imaging and immunohistology, and hypothesize that IMT producing ~6.7 V/cm electric field will not result in excess activated glial cells, or general damage to surrounding cells, compared to sham in both sexes, while tumours in female rats respond better to IMT than males. Additionally, we will determine behavioural effects during and following IMT in male and female rats without tumours, via behavioural tasks, and hypothesize that compared to shams, IMT at 200 kHz producing aforementioned electric field will not affect locomotor, anxiety, or asymmetrical sensorimotor outputs. The results of this project will provide novel key information to the field of neuroscience, while bringing us one step closer to offering new therapy options to GBM patients.

Manoj Medapati

Translational Postdoctoral Fellow
PhD, Innate Immunity & Cell Biology - University of Manitoba

Molecular and metabolic profiling in disease-associated microglia (DAM) and corresponding extracellular vesicles

Supervisor(s): Dr. Shawn Whitehead and Dr. Stephen Pasternak

Research Information:
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder associated with gradual cognitive decline in the elderly population. AD is characterized by aggregation of abnormally folded proteins in the brain called plaques. These plaques are infiltrated and cleared by immune cells in the brain known as microglia.  However, AD progression leads to development of abnormal microglia that are no longer able to clear plaques. These abnormal microglia are associated with advanced AD and have uniquely different metabolism compared to healthy microglia.  Hence targeting abnormal microglia metabolism in AD brain is of significant therapeutic benefit. However, the presence of normal microglia along with abnormal microglia makes targeting challenging. We aim to distinguish normal and abnormal microglia using blood-based biomarkers. In brain, microglia release particles called extracellular vesicles that traverse into blood. These particles in blood contain markers that emulate markers of brain microglia.

In this study we will isolate microglia derived particles based on markers that are unique to normal and abnormal microglia. These isolated particles will be used to identify metabolites that discriminate between normal and abnormal microglia. Next, we will study how metabolism affects abnormal microglia functions by using animal models and in immune cells from normal and AD patients. The results from this study identify novel biomarkers for AD and will help develop novel therapeutic strategies.  

Ruizhi Wang

Translational Postdoctoral Fellow
PhD, Medical Neuroscience - Indiana University

The role of Pin1/IRF3 signaling in governing neuroinflammation and Alzheimer’s disease pathogenesis

Supervisor(s): Dr. Qi Zhang and Dr. Kun Ping Lu

Research Information:
Pin1 (peptidyl-prolyl cis-trans isomerase) isomerizes specific phospo-Ser/Thr-Pro motifs to alter target protein conformations with high efficiency. Through this process, various biological and pathological pathways are controlled by Pin1, including Alzheimer’s disease. Alzheimer’s Disease (AD) is characterized by amyloid-β (Aβ) peptide and hyperphosphorylated protein tau, both of which are regulated by Pin1. The inhibition of Pin1 results in elevated AD pathological changes. On top of that, Pin1 also regulates innate inflammatory responses via mediating interferon regulatory factor 3 (IRF3) and type I interferon production. Emerging evidence have demonstrated that prolonged neuroinflammation is a key driver of AD pathogenesis. However, the role of Pin1 in neuroinflammation is not well understood.

Our preliminary data shows that inhibition of Pin1 leads to an inflammatory gene signature and constitutively activated IRF3. Therefore, we hypothesize that Pin1 inhibition leads to reduced IRF3 degradation via RAUL (RTA-Associated Ubiquitin Ligase), which results in elevated neuroinflammation in AD pathogenesis. To validate the hypothesis, we propose two specific aims.

Specific Aim 1: Demonstrate the mechanism by which Pin1 degrades IRF3 in vitro. Our working hypothesis is that Pin1 targets IRF3 Ser339–Pro motif for IRF3 polyubiquitination and proteasome-dependent degradation via E3 ubiquitin ligase RAUL.

Specific Aim 2: Assess the role of the Pin1 and IRF3 in AD pathologies using human AD patient samples. Our working hypothesis is that the Pin1/IRF3 signaling is correlated with neuroinflammation in human AD patient samples.

Through these proposed studies, we will elucidate the detailed molecular mechanisms by which Pin1 degrades IRF3 and identify how the dysregulation of Pin1/IRF3 signaling leads to neuroinflammation in AD. This proposal fits our long-term goal which is to reveal the CDK4/Cdh1/Pin1/IRF3 signaling axis and its anti-neuroinflammatory function in AD. This study will provide new therapeutic avenues such as novel Pin1 stabilizers to treat AD patients.

Wenyao Xia

Translational Postdoctoral Fellow
PhD, Medical Biophysics - Western University

Optimizing Neurosurgical Resection: Enhancing Precision through Intraoperative Augmented Reality Data Fusion

Supervisor(s): Dr. Terry Peters and Dr. Jonathan Lau

Research Information:
Neurosurgery, a field navigating the complexities of the nervous system, requires the utmost precision and meticulous planning. Our research aims to enhance the precision and performance of neurosurgical resection, the removal of diseased brain tissue, by merging different types of imaging data during surgery.
Our process begins with extracting cortical surface information from pre-operative MRI scans, and then registering this cortical surface onto the intra-operative image scene captured by high-definition cameras. Once such alignment is established, we can link pre-operative surgical planning and functional mapping coordinates (data indicating active brain areas) to the live surgical scene, providing various types of data overlays in the form of Augmented Reality.

The integration of multi-modal imaging data can significantly lessen the cognitive load on surgeons, enabling them to focus more on the procedure itself. This technique also has the potential to increase surgical accuracy and advance neurological research, particularly in areas like epilepsy, by providing more precise mapping of brain function.

For validation, we use expert labels as benchmarks, placing landmarks on the pre-operative scans and their corresponding locations on the intra-operative image. The time and accuracy for surgeons of varying experience levels to identify specific landmarks on the intra-operative image will be evaluated. Moreover, we will compare the landmarks' virtual display using our registration approach with experts' labels to evaluate the accuracy of the proposed method.

Roberto Budzinski

Clinical/Research Postdoctoral Fellow
PhD, Physics - Federal University of Paraná, Curitiba, Paraná, Brazil.

Spatiotemporal dynamics in epilepsy: analysis and models for cortical stimulation in iEEG

Supervisor(s): Dr. Lyle Muller, Dr. Seyed Mirsattari, and Dr. Ján Mináč 

Research Information:
Epilepsy affects millions of people in the entire world and it is one of the most common neural diseases. In the case of pharmacoresistant epilepsy, the seizures cannot be controlled with medication. The standard procedure for these cases has been surgery intervention, in which a portion of the brain (responsible for the generation of the seizures) is removed. It is urgent to better understand the mechanisms behind this issue in order to create new diagnostics and treatments for this neurological disease. In this project, we aim to combine mathematical modeling and computational neuroscience with clinical, empirical data to understand the properties of pathological synchronization during the disease state. To do so, we will use a large-population model (Kuramoto oscillators) with data for long-range connectivity in the human brain. The central hypothesis of the model is that both structural connectivity and time delays due to axonal conduction shape large-scale cortical dynamics during epilepsy. Our model will allow us to understand better the spatiotemporal dynamics in epilepsy and also the effect of cortical stimulation in this phenomenon. Finally, we will collaborate with Dr. Mirsattari, where we will study the predictions of our model with empirical data. This allows us to test our hypothesis and further understand the properties and mechanism of this important neurological disease.

Kathleen Lyons

Clinical/Research Postdoctoral Fellow
PhD, Psychology - Western University, London, Ontario, Canada.

Investigating how sensory phenotypes in neurodevelopmental disorders relate to clinical symptomology, cognitive abilities, and neural features

Supervisor(s): Dr. Ryan Stevenson and Dr. Rob Nicolson

Research Information:
One prevalent feature of both autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) is atypical sensory processing; autistic children and those with ADHD sense and perceive their world differently from typically developing children. Although sensory differences are commonly reported in both ASD and ADHD, the presentation of these symptoms vary drastically across individuals. Despite its pervasiveness, differences in sensory processing have yet to be directly compared between these two diagnostic categories. Comparing these distinct sensory profiles may allow for the discovery of transdiagnostic mechanisms shared between autism and ADHD. Additionally, characterizing this heterogeneity in sensory processing will help inform targeted strategies of support. The aim of this proposed work is to compare sensory phenotypes in ASD and ADHD, and to investigate how these profiles relate to cognitive abilities, clinical symptomatology, and neural features. To do this, we will be using the behavioral, clinical, and neural measures from the Province of Ontario Neurodevelopmental Disorders Network (POND) dataset. This dataset includes a cohort of autistic individuals (N = 1,203), individuals with attention-deficit/hyperactivity disorder (N = 1,065), and individuals without any diagnoses (N =295). Cluster analysis will be used to identify sensory features that commonly co-occur within a sample of ASD and ADHD participants separately, allowing for classification of distinct groups of individuals who share a similar sensory profile. We will then use hierarchical linear models and machine learning methods to investigate if clinical features, cognitive abilities, and differences in functional connectivity predict the sensory clusters we obtain. This will be the first large-scale assessment of sensory profiles using both parental reports and behavioral sensory data comparing autism and ADHD. Moreover, we will map out how these sensory profiles relate to differences in clinical, cognitive, and brain features, which may have implications for treatment.

Uma Venkatasubramanian

Clinical/Research Postdoctoral Fellow
PhD, Neuroscience/Signal Processing - University of Otago, Christchurch, New Zealand.

Characterizing functional and effective brain connectivity in critically ill children at-risk for delirium

Supervisor(s): Dr. Rishi Ganesan and Dr. Yalda Mohsenzadeh

Research Information:
One out of three critically ill children develop alterations in awareness and attention known as delirium. Children who develop delirium stay in the hospital longer, have worse outcomes after hospital discharge and increase healthcare costs. Moreover, families of children with delirium often suffer from acute distress and post-traumatic stress. However, there are significant gaps in our current ability to predict or diagnose delirium in the pediatric intensive care unit (PICU). Critical care providers presently rely on intermittent and infrequent assessments of behaviour to diagnose delirium. But this approach is subjective, prone to errors and misses the subtype of delirium that is atypical but common in critically ill children.

It is now known that our awareness and attention emerge from how different areas of the brain connect and communicate with each other. As part of the proposed research, we plan to study how electrical signals from different brain regions are related in critically ill children with severe infections at-risk of delirium. By studying brain’s electrical connectivity before, during and after delirium, we will be able to evaluate if connectivity changes precede or accompany clinical delirium. This interdisciplinary project will be the first to describe brain connectivity during the acute phase of critical illness in children; understand how functional and effective connectivity measures changes over time in critically ill children; and evaluate how connectivity measures correlate with clinical delirium and predict long-term functional outcomes. We will also be able to generate effective connectivity-based brain network models to identify the network changes happening in brain during delirium. This first-of-its-kind clinical research could lead to more accurate prediction and more objective diagnosis of delirium in critically ill children.