Modeling and prediction of clinical symptom trajectories in Alzheimer’s disease

New research from our group describes a computational model that can be used for early detection of individuals at risk for clinical decline. This machine-learning model is capable of combining longitudinal, multimodal data (MRI, cognitive, genetic and demographic data) to predict the symptom progression patterns at the single subject level.

With the prevalence of Alzheimer’s disease rapidly increasing, the identification of the declining individuals a priori would provide a critical window for early intervention and preventative treatment planning. While this work holds crucial clinical utility for Alzheimer’s disease, this model can be applicable to other neurodegenerative diseases .

For more information about this research, click the link to read the paper Bhagwat et al. (2018)

Camp CoBrA 2018!

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The CoBrA Lab returned to Camp Kanawana for their fourth annual lab retreat. Thanks to the unseasonably warm weather, after our daily meetings reviewing our accomplishments of the past year and brainstorming future directions for our laboratory, we spent most of our days lakeside, canoeing, kayaking, and paddle boarding. This retreat provides a great opportunity to reflect upon the last academic year, and re-energize for the new year, with our yearly fix of s’mores, and exciting new upcoming projects.

Congratulations to Dr. Christopher Steele on his new position at Concordia University

The CoBrA lab would like to congratulate Dr. Christopher Steele on becoming a new Assistant Professor in the Department of Psychology, Concordia University. During his post-doctoral fellowship at the CoBrA Lab, Chris worked on examining cerebellar anatomy and cerebellar and cerebello-cortical connectivity and its heritability. We look forward to watching Chris build his team and his research program!

Congratulations to Mila on winning Best Poster Presentation at the Undergrad Expo

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The CoBrA lab would like to congratulate fellow member Mila Urosevic on winning third place for best poster presentation at the 2018 Undergraduate Research Expo of the Douglas Institute Research Centre. Mila presented her work titled, “Investigating the effects of diet and exercise in a mouse model of Alzheimer’s disease”. Mila presented data on her summer project examining the impact of diet and exercise interventions (using new dimensionality reduction techniques) on performance in Alzheimer’s disease related behaviours in the 3xTg mouse model.

Congratulations to the other winners, and to all students who presented!

Detecting Alzheimer's disease early using Artificial intelligence

While standard MRI allows us to see advanced Alzheimer’s disease, such as atrophy of the hippocampus, detecting subtle alterations in the brain occuring long before people start experiencing confusion and memory loss is crucial in order to detect and diagnose the disease long before it’s too late. With the use of artificial intelligence however, trained computer algorithms can detect patterns across MRI images to recognize these changes early and identify patients at risk of developing this disease.

Check out the video below where Dr. Mallar Chakravarty explains artificial intelligence (AI) and shares recent research from our group in which describes how AI can help with the early detection and prevention of Alzheimer’s disease.

For more information about this research, click the link to read our paper Bhagwat et al. (2018)