2022 Research

Regulation of Ubiquitin Specific Protease 8 (USP8) by AMPK-Mediated Phosphorylation

Parkinson’s disease (PD) affects greater than 1% of people over 60 and is the second most common neurodegenerative disorder. PD diagnoses are predicted to double in the next 20 years, thus highlighting a critical socioeconomic need to identify the cellular components and pathways responsible for onset and progression. A major goal is to expand the available therapeutics and to develop novel prophylactic strategies. We have recently identified two enzymes, USP8 and AMPK, that interact and may be directly involved in PD-associated mechanisms. AMPK primarily functions as a metabolic energy sensor for cells, helping to regain homeostasis under low energy or stress conditions. AMPK may be linked to PD through its ability to orchestrate specialized autophagy, the removal of damaged cellular components. Similarly, USP8 is also a PD-associated target as it has been shown to contribute to the formation of Lewy bodies, the pathological plaques in the neuronal tissue of the brain—a hallmark of PD.

Using a multidisciplinary approach, my work aims to characterize the AMPK-USP8 interaction to determine its role in PD and associated cellular processes. We believe AMPK regulates the activity of USP8 directly. Such disruption contributes to pathological states resembling PD phenotypes. These novel findings will shed light on a new regulatory axis and a potential target system of significance to PD patients.

Developing Diagnostic MRI Biomarkers for Parkinson’s Disease

PD is a progressive disorder that is known for its motor symptoms like slowness of movement and tremor. These symptoms are caused by the death of cells in the brain that produce the chemical dopamine. The symptoms of PD are extremely variable between patients, and there are many disorders that resemble PD in the early stages of the disease. This causes patients with similar disorders (e.g., essential tremor) to be incorrectly diagnosed with PD and referred to a movement disorder specialist for treatment. It is essential to improve the specificity of diagnostic tools available to physicians. This will prevent patients from receiving the wrong treatment, unnecessarily taking up limited spots to see specialists and being incorrectly recruited into clinical trials for PD which compromises the validity of the results. Therefore, this study aims to identify biomarkers (i.e., indicators) of PD onset and progression that can be seen with Magnetic Resonance Imaging (MRI), which will help physicians differentiate PD patients from healthy people, as well as patients with essential tremor. Twenty patients from each patient group and twenty healthy controls will be recruited for this study. The MacDonald and Khan labs have developed a novel MRI technique to identify changes in volume and structural connectivity in the striatum and SNc/VTA. This reliably differentiates PD patients from healthy controls with unprecedented accuracy. As an extension to this research, the current study will test the ability of this MRI technique to differentiate PD from other movement disorders, with the hopes of providing physicians an accessible and reliable diagnostic tool. 

Predictive Phenotyping of Alzheimer’s and Parkinson’s Disease from Multifactor Biomarker and Neuroimaging Data

Individuals diagnosed with either Alzheimer’s disease (AD) or PD typically exhibit a distinctive profile of progressive impairments. AD primarily affects cognitive functions (e.g., dementia), whereas PD affects motor functions (e.g. tremor). However, in many cases, this symptomatic distinction is not apparent, because some AD and PD patients experience a mixture of cognitive and motor functions of varying severities. These observations have called into question our current definitions of AD and PD as being two separate diseases. An alternative framework proposes that AD and PD may represent extreme phenotypes of a continuum where mixed AD/PD subtypes lie in the middle. To test this, I will obtain longitudinal neuroimaging, protein pathology and clinical test data of AD and PD patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Parkinson’s Progression Markers Initiative (PPMI), respectively. Then, I will use a novel data integration technique, called similarity network fusion, to combine all the different data types, producing networks of patients who share common patterns in their data. I expect to see unique clusters of patients corresponding to the distinct disease subtypes. Overall, my findings may reveal novel links between the different mixed subtypes of AD and PD and their underlying mechanisms, ultimately leading to developing better intervention strategies for these diseases. 

Investigating the Feasibility of Temporal Interference Stimulation for Humans

Deep Brain Stimulation (DBS) is a useful technique for clinicians and can be used to help improve symptoms of various neurological diseases, including PD. DBS involves implanting an electrode deep into the brain. This procedure has a small but serious risk of complications, such as hemorrhage and infection. Recently, a new stimulation technique, called temporal interference stimulation, has been developed. In the mouse, temporal interference stimulation can target deep brain structures without activating the overlying brain regions. This technique involves using two electric fields, that on their own do not affect brain activity. However, within a small region of the brain, where the two fields interact in the ideal way, the interaction of these fields can influence brain activity. While the work in mice is promising, whether it will work in humans is unclear, given differences in brain size and skull thickness. Using a combination of computational and experimental techniques, my research aims to investigate whether temporal interference stimulation will be feasible in humans. If so, this new stimulation technique may hold promise as a new treatment technique for people living with PD.

Statistical and Ethical Implications of Pimavanserin Drug Trials Published with Unexplained or Missing Datasets on Parkinson’s Disease

PD may present an array of symptoms, ranging from rigidity, tremors, psychiatric disorders and sleep issues. Levodopa remains the primary drug prescribed to manage PD. However, its use is accompanied by many side effects, one of which is PD psychosis. The complementary use of antipsychotic medications remains contested, with their use related to the worsening of PD. In 2016, a new medication called Pimavanserin was approved by the Food and Drug Administration (FDA) through their accelerated approval process, with the aim of overcoming prior shortcomings of antipsychotic medications. Many patients and their families may have heard about Pimavanserin and expressed interest in having it approved in Canada. 

Although Pimavanserin performed well in drug studies initially, there are now questions emerging as to its safety and efficacy. An independent organization called the Institute of Safe Medicine Practices (ISMP) published a report in 2017 raising concerns as to the validity of the Pimavanserin drug trials. They cited hallucinations (21.8%), drug effectiveness (14.9%), confusion (11.5%), and death (10.9%) as the most frequently reported adverse events related to the drug. 

This project’s primary aim is to examine in further detail the statistical rigour of Pimavanserin’s drug trials. This will help to better understand whether Pimavanserin is safe for Canadians, and if it should be considered for approval as a treatment option. As well, a secondary aim of this study is to examine the utility and ethical implications of the FDA’s accelerated approval process. 

Mobile Brain Imaging and Mobility in Parkinson’s Disease

Attention, an important aspect of human cognition, is needed for safe mobility and navigation through the environment. With age, the ability to move and navigate through the world requires greater cognitive resources. Previous brain imaging research in older adults and adults with PD have shown that mobility impairments are associated with reduced attention. However, previous research was limited to assessing attention while participants were immobile and/or in unnatural settings, such as the laboratory. This does not necessarily translate to what would occur in the real world. Mobile brain imaging techniques have made it possible to observe brain activity outside standard laboratory environments while participants are in motion. My research will use mobile electroencephalography (EEG) to compare brain activity across laboratory and real-world environments. In the naturalistic setting, participants will walk outside while their brain activity is recorded by a mobile EEG headband. Participants will be required to pay attention to natural occurrences in the environment, such as pedestrians, curbs, crosswalks and various forms of transportation. Brain activity will be compared between younger adults, older adults (with and without a history of falls) and adults diagnosed with PD. The findings from my research have the potential to expand current understanding of brain function in PD, human mobility and risk of falling, using real-world methods and technology.

Indirect DBS Targeting Using Anatomical Landmarks and Machine Learning 

Deep Brain Stimulation (DBS) involves the constant delivery of electricity applied via electrodes to specific regions within the brain. It is performed to manage motor symptoms of PD like stiffness, slowness and tremor when medications become less effective. In DBS, deviations of more than 2 millimeters from ideal electrode position can lead to suboptimal therapeutic benefit (up to 60% difference in some cases). For some of the most common DBS targets used to treat PD (e.g., subthalamic nucleus), imaging acquired at the clinic pre-operatively does not allow for their clear visualization in relation to other surrounding regions. This makes it hard to accurately place DBS electrodes. It is like trying to plug in your charger with the lights turned off. In the same way you can navigate a dark room by identifying known landmarks (perhaps the edge of the table or chair), we plan to help surgeons find the DBS targets of interest by relating them to 32 points in the brain. This can be found very easily on pre-operative imaging (we call them anatomical landmarks). We also plan to use a powerful magnetic resonance imaging scanner (with a 7 Tesla magnet), available at the Robarts Research Institute in Western University, to develop an automatic tool to help find DBS targets in relation to the 32 landmarks. This tool will help surgeons that have lower quality imaging to still perform DBS with a high degree of accuracy.