Search

Lawson Health Research Institute has been awarded over $2 million in federal funding for a project focusing on two unique medical imaging systems designed to give unparalleled insight into brain function and disease.  

The Right Honourable Justin Trudeau, Prime Minister of Canada, announced more than $518 million in research infrastructure support through the Canada Foundation for Innovation (CFI).

“Canada's researchers and scientists are some of the brightest and most skilled in the world. These investments will ensure that they have the cutting-edge laboratories and equipment needed to help us build a Canada that is healthier, greener, and more competitive," says The Rt. Hon. Justin Trudeau, Prime Minister of Canada. 

In a live conversation, the Honourable François-Philippe Champagne, Minister of Innovation, Science and Industry, shared more about how the funding will support 102 state-of-the-art projects at 35 post-secondary institutions and research hospitals across the country - helping Canada remain at the forefront of exploration, innovation and discovery. 

Image

“Imaging technologies, such as MRI and PET scanners, have revolutionized our understanding and treatment of major neurological diseases, including dementia and mental illness, by allowing us to study disease mechanisms and their impact on brain health,” says Dr. Keith St. Lawrence, Lawson Scientist and project co-lead. “We are developing and testing two leading-edge systems that will enable imaging of key vascular, metabolic and molecular factors linked to disease.”  

The first system is a head-only PET (positron emission tomography) insert that can be placed in any clinical MRI machine. Combining the molecular specificity of PET with the structural and functional capabilities of MRI, the possibilities for brain imaging will be greatly enhanced. The head-only PET insert being tested in London has been developed by Cubresa Inc., located in Winnipeg, Manitoba.  

Image

“Combined with deep-learning approaches, we could achieve a highly improved sensitivity of the PET insert and reduce the radiation dose by up to 50 times compared to whole-body imaging,” explains Dr. Jonathan Thiessen, Lawson Scientist and project co-lead. “This will be the first commercially available high-resolution brain PET/MRI installed in the world.”  

For example, better diagnosis of different forms of dementias would be possible and researchers could closely compare protein abnormalities in the brain with cognitive function. The low radiation dose allows for long-term studies investigating changes in the brain and neuroinflammation that can lead to major psychiatric diseases and cognitive degeneration or disability. 

The second system uses portable, state-of-the-art optical imaging to increase the reliability of bedside brain monitoring to provide rapid assessment of brain health in restrictive environments.   

“We believe this technology will demonstrate how biomedical optics can improve neurological outcome for surgery and patients in intensive care, provide an accessible technology for assessing neurovascular health, and become a clinically relevant tool for monitoring changes in brain function,” says Dr. St. Lawrence.  

With current systems, the type of data is limited and extremely vulnerable to signal contamination from the scalp which can overshadow signals from the brain. Using the team’s specialized detection approach, the scalp signal contributions could be greatly reduced to get more accurate information on markers of brain activity, such as cerebral blood flow and energy metabolism.  

The team will study use of the system in surgical and intensive-care settings to monitor for cerebral ischemia and metabolic stress, which are the major causes of brain injury. They will also monitor treatment in patients with schizophrenia and study dysfunction in the brain associated with negative symptoms, as cognitive impairment deteriorates with age for some people with the disease. Another goal is to develop a brain-computer interface for patients who are incapable of physical communication. 

“With this funding, we can develop a truly unique advancement that has the potential to test the limits of optics for brain applications,” adds Dr. Thiessen.

These two imaging platforms build on previous investments in Lawson Imaging that now exceed over $40 million in research funds since the initial CFI support in hybrid imaging in 2007. London is home to Canada’s first whole body PET/MRI scanner that was installed at St. Joseph’s Hospital, part of St. Joseph’s Health Care London. 

Mood disorders like major depressive disorder (MDD) and bipolar disorder are often complex and hard to diagnose, especially among youth when the illness is just evolving. This can make decisions about medication difficult. In a collaborative study by Lawson Health Research Institute, The Mind Research Network and Brainnetome Center, researchers have developed an artificial intelligence (AI) algorithm that analyzes brain scans to better classify illness in patients with a complex mood disorder and help predict their response to medication.

Image

The full study included 78 emerging adult patients from mental health programs at London Health Sciences Centre (LHSC), primarily from the First Episode Mood and Anxiety Program (FEMAP). The first part of the study involved 66 patients who had already completed treatment for a clear diagnosis of either MDD or bipolar type I (bipolar I), which is a form of bipolar disorder that features full manic episodes, as well as an additional 33 research participants with no history of mental illness. Each individual participated in scanning to examine different brain networks using Lawson’s functional magnetic resonance imaging (fMRI) capabilities at St. Joseph’s Health Care London. 

The research team analyzed and compared the scans of those with MDD, bipolar I and no history of mental illness, and found the three groups differed in particular brain networks. These included regions in the default mode network, a set of regions thought to be important for self-reflection, as well as in the thalamus, a ‘gateway’ that connects multiple cortical regions and helps control arousal and alertness.

The data was used by researchers at The Mind Research Network to develop an AI algorithm that uses machine learning to examine fMRI scans to classify whether a patient has MDD or bipolar I. When tested against the research participants with a known diagnosis, the algorithm correctly classified their illness with 92.4 per cent accuracy. 

The research team then performed imaging with 12 additional participants with complex mood disorders for whom a diagnosis was not clear. They used the algorithm to study a participant’s brain function to predict his or her diagnosis and, more importantly, examined the participant’s response to medication. 

“Antidepressants are the gold standard pharmaceutical therapy for MDD while mood stabilizers are the gold standard for bipolar I,” says Dr. Elizabeth Osuch, a clinician-scientist at Lawson, medical director at FEMAP and co-lead investigator on the study. “But it becomes difficult to predict which medication will work in patients with complex mood disorders when a diagnosis is not clear. Will they respond better to an antidepressant or to a mood stabilizer?”

The research team hypothesized that participants classified by the algorithm as having MDD would respond to antidepressants while those classified as having bipolar I would respond to mood stabilizers. When tested with the complex patients, 11 out of 12 responded to the medication predicted by the algorithm. 

“Machine learning is an approach that learns in a data-centric way, providing information that can be used to predict future data sets. In this case, that’s the prediction of MDD from bipolar I,” says Dr. Vince Calhoun, President of The Mind Research Network; Distinguished Professor, Departments of Electrical and Computer Engineering, Neurosciences, Computer Science, and Psychiatry at The University of New Mexico; and co-lead investigator on the study. “There are multiple layers of algorithms in this project. The first layer includes an approach that automatically extracts brain networks from the data provided and the second layer includes automatically identifying which combinations of networks are most sensitive or predictive of MDD and bipolar I.”

Image

Above: Dr. Vince Calhoun

“This study takes a major step towards finding a biomarker of medication response in emerging adults with complex mood disorders,” says Dr. Osuch. “It also suggests that we may one day have an objective measure of psychiatric illness through brain imaging that would make diagnosis faster, more effective and more consistent across health care providers.”

Psychiatrists currently make a diagnosis based on the history and behavior of a patient. Medication decisions are based on that diagnosis. “This can be difficult with complex mood disorders and in the early course of an illness when symptoms may be less well-defined,” says Dr. Osuch. “Patients may also have more than one diagnosis, such as a combination of a mood disorder and a substance abuse disorder, further complicating diagnosis. Having a biological test or procedure to identify what class of medication a patient will respond to would significantly advance the field of psychiatry.”

The study, “Complexity in mood disorder diagnosis: fMRI connectivity networks predicted medication-class of response in complex patients,” is published online in Acta Psychiatrica Scandinavica. Local support included donor funding through London Health Sciences Foundation. 

Learn more about this research:

 

Image

Above: Drs. Elizabeth Osuch and Jean Théberge, Lawson scientists