Innovative Projects Realized

Sensory Disinhibition, Cognitive Flexibility and the Neural Correlates Underlying Post-Traumatic Stress Disorder (PTSD): A Simultaneous EEG-fMRI Study

Individuals with post-traumatic stress disorder (PTSD) often struggle to relax, or simply be at ‘rest.’ In particular, recent research has revealed participants with PTSD show hyper-activity across the visual cortex while at rest. Referred to as sensory hyper-activity and disinhibition, altered resting-state patterns are thought to be underlying hypervigilance and re-living sensations clinically, as well as cognitive deficits, which may be produced when cognitive resources are overwhelmed by sensory hyper-activity. Nonetheless, research has been slow to characterize sensory hyper-activity and disinhibition, as well as any downstream effects related to such a sensory pathology. Therefore, we plan to analyze the neural correlates underlying sensory hyper-activity and disinhibition, as well as cognitive flexibility, since cognitive flexibility has been revealed to serve as a protective factor post-trauma(s). In particular, we plan to record electroencephalography (EEG) and functional MRI (fMRI) simultaneously, where we may reveal the temporal and the spatial correlates related to the above. Simultaneous EEG-fMRI, critically, has never been used to study PTSD and may be analyzed by advanced machine learning algorithms to reveal the biomarkers relevant to trauma-related psychopathology. Homewood Research Institute conducts research on trauma-exposed populations and stands to benefit by advancing more personalized, research-guided therapy for participants with PTSD.

Hyperpolarized 129Xe Cerebral Perfusion MRI Imaging Development – Year two

Imaging of cerebral perfusion (the delivery of blood to a capillary bed in the brain tissue) is significant for diagnosing a variety of diseases. There are multiple challenges associated with perfusion imaging which significantly limits the quality of cerebral perfusion images. I am planning to develop a novel approach of cerebral perfusion magnetic resonance imaging (MRI) using hyperpolarized (HP) xenon-129 (129Xe).
To conduct HP 129Xe perfusion imaging, I am developing a novel HP MRI Time-of-Flight (TOF) pulse sequence. This imaging technique allows for the acquisition of perfusion images with a high signal-to-noise ratio and contrast-to-noise ratio. Furthermore, this imaging methodology allows for the acquisition of quantitative perfusion images in less than 20 s.
HP 129Xe TOF imaging has the potential to overcome the challenges of current cerebral perfusion imaging and become a powerful tool for early-stage diagnosis of perfusion related neurological diseases such as Alzheimer’s and Parkinson diseases. This will allow for better treatment planning, which will be beneficial for modern clinical practice. Furthermore, HP 129Xe TOF imaging can significantly improve the understanding of the mechanism of perfusion-related diseases, which will allow for the development of highly efficient treatments for further translation into clinics.

Hyperpolarized 129Xe Cerebral Perfusion MRI Imaging Development

Imaging of cerebral perfusion (the delivery of blood to a capillary bed in the brain tissue) is significant for diagnosing a variety of diseases. There are multiple challenges associated with perfusion imaging which significantly limits the quality of cerebral perfusion images. I am planning to develop a novel approach of cerebral perfusion magnetic resonance imaging (MRI) using hyperpolarized (HP) xenon-129 (129Xe).
To conduct HP 129Xe perfusion imaging, I am developing a novel HP MRI Time-of-Flight (TOF) pulse sequence. This imaging technique allows for the acquisition of perfusion images with a high signal-to-noise ratio and contrast-to-noise ratio. Furthermore, this imaging methodology allows for the acquisition of quantitative perfusion images in less than 20 s.
HP 129Xe TOF imaging has the potential to overcome the challenges of current cerebral perfusion imaging and become a powerful tool for early-stage diagnosis of perfusion related neurological diseases such as Alzheimer’s and Parkinson diseases. This will allow for better treatment planning, which will be beneficial for modern clinical practice. Furthermore, HP 129Xe TOF imaging can significantly improve the understanding of the mechanism of perfusion-related diseases, which will allow for the development of highly efficient treatments for further translation into clinics.

Understanding the neural underpinnings of a novel PTSD therapy: Goal Management Therapy

A wide range of cognitive faculties are commonly compromised in numerous trauma-exposed populations, leading to impaired functioning, and reduced treatment response rates. There is a dire need for novel therapies that can directly target such deficits in top-down cognitive control. Goal management training (GMT) is one such cognitive remediation therapy found to be effective for some other psychopathologies. Despite its success, the neural underpinnings of GMT-linked cognitive improvements remain shrouded in mystery. This proposal aims to study the impact of a 9-week GMT randomized control trial on the structure and function of neural systems known to be dysregulated in PTSD. This study will utilize a trauma-relevant emotional working memory task to test the participant’s executive processes in the context of their trauma, while also collecting structural and functional neuroimaging data using Canada’s only operational 7T MRI. The functional MRI (fMRI) data will probe therapy-linked changes in three cognitively relevant brain networks, while the structural MRI data will investigate therapy-linked changes in patterns of myelination. Taken together, these results will be the first characterization of the neural changes associated with GMT therapy-driven resolution of PTSD symptoms, and can position the Homewood Research Institute as a pioneer of novel psychiatric treatments.

The Narreme Engine: A Tool for Narrative Analysis and Construction – Year two

In my PhD dissertation, I proposed a renewed theory of the “narreme” – the minimum basic unit of narrative structure, a concept which originated in the 1960s but which has been largely neglected since then. I suggested that a Narreme Theoretic approach could be tremendously useful not only for the purposes of narrative analysis, but also for the construction of narratives in various creative media, as well as a pedagogical tool for teaching reading comprehension and literacy, and as a method for narrative-based psychotherapies. Can Narreme Theory be applied practically for these purposes? That is the question I aim to address in my postgraduate research.
My project would attempt to continue the work I have done in my dissertation, seeking to fill the gap left by the neglect of narreme-based approaches to story research; to this end, in collaboration with my partner organization, I will attempt to develop a software-based “Narreme Engine” for analysis and construction of story networks. The success of such an engine would help establish the utility of my approach, and provide a useful tool for moving forward in the fields of narratology and narrative media.

The Narreme Engine: A Tool for Narrative Analysis and Construction

In my PhD dissertation, I proposed a renewed theory of the “narreme” – the minimum basic unit of narrative structure, a concept which originated in the 1960s but which has been largely neglected since then. I suggested that a Narreme Theoretic approach could be tremendously useful not only for the purposes of narrative analysis, but also for the construction of narratives in various creative media, as well as a pedagogical tool for teaching reading comprehension and literacy, and as a method for narrative-based psychotherapies. Can Narreme Theory be applied practically for these purposes? That is the question I aim to address in my postgraduate research.
My project would attempt to continue the work I have done in my dissertation, seeking to fill the gap left by the neglect of narreme-based approaches to story research; to this end, in collaboration with my partner organization, I will attempt to develop a software-based “Narreme Engine” for analysis and construction of story networks. The success of such an engine would help establish the utility of my approach, and provide a useful tool for moving forward in the fields of narratology and narrative media.

The Application of Single Domain Antibodies in Biasing GPCR Signal Pathways – A Case Study with the Adenosine A2A Receptor – Year two

Single domain antibodies (sdAbs) represent a versatile class of heavy chain antibodies lacking a paired light chain and encoded by VHH germline genes typically expressed in camelids. KisoJi Biotech has developed a transgenic mouse line in which each mouse expresses multiple camelid VHH genes capable of specifically binding any antigen of interest. The small molecular weight of sdAbs (15 kDa) provides new opportunities to utilize these as specific ligands of G Protein-Coupled Receptors (GPCRs). One-third of current pharmaceuticals target GPCRs – the largest family of membrane proteins in the human genome and mediators of diverse biological processes through signal transduction. The adenosine A2A receptor (A2AR) is a prototypical class A GPCR and a target for the treatment of many diseases (1). Studies reveal A2ARs can stimulate multiple G protein pathways, with differing efficacies, depending on tissue type, ligand, and presence of other receptors. Our goal is to demonstrate the therapeutic utility of sdAbs to: i) achieve greater specificity and allosteric control of activation, ii) target specific functional states of the receptor, iii) achieve longer serum half-lives, and iv) improve tissue partitioning to obviate classical side-effects. This technology will then serve as a general platform for sdAbs to class A GPCRs.

The Application of Single Domain Antibodies in Biasing GPCR Signal Pathways – A Case Study with the Adenosine A2A Receptor

Single domain antibodies (sdAbs) represent a versatile class of heavy chain antibodies lacking a paired light chain and encoded by VHH germline genes typically expressed in camelids. KisoJi Biotech has developed a transgenic mouse line in which each mouse expresses multiple camelid VHH genes capable of specifically binding any antigen of interest. The small molecular weight of sdAbs (15 kDa) provides new opportunities to utilize these as specific ligands of G Protein-Coupled Receptors (GPCRs). One-third of current pharmaceuticals target GPCRs – the largest family of membrane proteins in the human genome and mediators of diverse biological processes through signal transduction. The adenosine A2A receptor (A2AR) is a prototypical class A GPCR and a target for the treatment of many diseases (1). Studies reveal A2ARs can stimulate multiple G protein pathways, with differing efficacies, depending on tissue type, ligand, and presence of other receptors. Our goal is to demonstrate the therapeutic utility of sdAbs to: i) achieve greater specificity and allosteric control of activation, ii) target specific functional states of the receptor, iii) achieve longer serum half-lives, and iv) improve tissue partitioning to obviate classical side-effects. This technology will then serve as a general platform for sdAbs to class A GPCRs.

An introduction to robust and efficient statistical learning algorithms with applications in actuarial science

The big data era represents an opportunity for statistical methods to shine, through applications relevant to a wide spectrum of fields, including actuarial science. In order to seize and make the most out of this opportunity, researchers and practitioners must, however, effectively manage the challenges that big data pose. The intern will be exposed to two challenges: data quality (taking the more specific form of data bases containing outliers because of data with gross errors or extreme values) and scalability of the numerical methods required for inference. The intern will be introduced to novel Bayesian robust methods and Markov chain Monte Carlo algorithms to address these issues. The intern will explore the benefits of applying these methods and algorithms in actuarial contexts, in particular in the field of general insurance. This last part will represent a contribution and may lead to a paper in an actuarial journal.

3D spine imaging using tracked ultrasound and artificial intelligence

Obtaining accurate images of the spine is important for different medical purposes. For example, to measure how deformed the spine is in patients with scoliosis and select the most suitable treatment. Images can also be used to guide a clinician while inserting a needle to reach a exact location in the spine to administer anesthesia. Another application is to plan and guide the insertion of screws in each vertebra, which are required to correct malformations of the spine.

Currently, the most common way to obtain images of the spine is by using technologies that require the use of X-rays. However, X-rays are known to increase the risk of developing cancer. A safer and more affordable alternative is to use sound waves. Nevertheless, the images obtained with sound waves are not as clear as the ones obtained with X-rays. Consequently, we propose the use of computers and complex strategies based on artificial intelligence. With those tools, we will be able to increase the quality of images obtained with sound waves, making them comparable to X-ray images for practical purposes. A product with these characteristics would be benefitial for the society, while generating profit for the partner company.

Advanced Analytical Techniques integrated with Machine Learning for Proactive Raw material Characterization for Vaccine Production – Year two

Over the last several decades, challenges in the development, production, supply, and use of vaccines have been raised and by consequence had led to increasing concern around the world. As a result, an increase in research and innovation in the vaccine industry is needed. In this sense, one of the most critical factors in the vaccine production industry is the raw material and its quality. It is well known, that in order to obtain high yields of the target compounds in vaccines, well-characterized and homogenized raw material is needed. Hence, a raw material characterization, optimization, and control process are critical before raw materials are used in the fermentation that we can refer to as a biological process. The use of advanced analytical techniques such as Raman and Nuclear Magnetic Resonance spectroscopy are suitable alternatives to characterize complex matrix as they offer a deeply detailed composition. Furthermore, this data can be integrated with Machine Learning for better-quality control and automatization of the process. The main aim of this work is to characterize and optimize the raw material used in vaccine production through advanced analytical techniques and integrating it with Machine Learning to improve vaccine production and decrease the cost.

Advanced Analytical Techniques integrated with Machine Learning for Proactive Raw material Characterization for Vaccine Production

Over the last several decades, challenges in the development, production, supply, and use of vaccines have been raised and by consequence had led to increasing concern around the world. As a result, an increase in research and innovation in the vaccine industry is needed. In this sense, one of the most critical factors in the vaccine production industry is the raw material and its quality. It is well known, that in order to obtain high yields of the target compounds in vaccines, well-characterized and homogenized raw material is needed. Hence, a raw material characterization, optimization, and control process are critical before raw materials are used in the fermentation that we can refer to as a biological process. The use of advanced analytical techniques such as Raman and Nuclear Magnetic Resonance spectroscopy are suitable alternatives to characterize complex matrix as they offer a deeply detailed composition. Furthermore, this data can be integrated with Machine Learning for better-quality control and automatization of the process. The main aim of this work is to characterize and optimize the raw material used in vaccine production through advanced analytical techniques and integrating it with Machine Learning to improve vaccine production and decrease the cost.