Modular Courses

Instructor: Pamela Plant 

Session Offered: 20249
Dates: September 19, 2024 - November 28, 2024 (Thursdays - alternating weeks) Time: 3:00 - 5:00 pm, Location: In person (MS3287)

Description: MSc graduates from the IMS continue their education in PhD programs and professional schools, volunteer/intern, or enter the job market. As graduate students they become familiar with an area of research, learn technical skills as applied to their research project and develop their communication and critical thinking skills. This course focuses on creating a healthy student-supervisor relationship, project and time management, developing transferable skills, and building a professional network. Each session will feature an interactive presentation, in class activities and peer evaluations. Students will be expected to read resources provided and prepare materials before each class. Complementing the course will be a series of Friday afternoon interactive career development panels and networking events.

Instructor: Pamela Plant

Session Offered: 20251
Dates: January 16, 2025 - March 27, 2025, Time: 3-5 pm, Location: In person (UC 55)

Description: PhD graduates from the Institute for Medical Sciences (IMS) continue to be employed as professors and scientists in research institutes but increasingly are finding jobs in other sectors of the economy. This course module is designed to enable PhD students to assess and develop their leadership and transferable skills in order to take better advantage of the opportunities available to them in academia and beyond. Each session will feature an interactive presentation and in-class activities. Complementing the course will be IMS Connects, a series of Friday afternoon interactive career development panels and networking events designed for IMS students featuring alumni working in various sectors.

Instructor: Shree Bhalerao

Session Offered: TBD
Start Date: TBD, Location: TBD

Description: Traumatic brain injury (TBI) has become very popular in the fields of neurology, neurosurgery, psychiatry and in the media. Most studies suggest that fifty percent of TBIs are not reported and thus go untreated. Despite this, most people are unaware that the risk for psychiatric disorders can double following a TBI. Thus, a team of two psychiatrists and a neuroscientist will present a module on the psychiatric sequalae following TBI. The course will xamine six main psychiatric issues associated with TBI: depression, anxiety, addiction, agitation, aggression, and the stigma associated. Each session will utilize didactic, interactive, multimedia elements and group activities to explore diagnostic, neurological, and management issues associated with TBI

Instructor: TBD

Session Offered: TBD

Dates: TBD, Time: TBD, Location: TBD

Description: CIHR defines Knowledge translation (KT) as “the exchange, synthesis and ethically-sound application of knowledge—within a complex system of interactions among researchers and users—to accelerate the capture of the benefits of research for Canadians through improved health, more effective services and products, and a strengthened health care system”. A prominent characteristic of KT is that it encompasses all steps between the creation of new knowledge and its application to yield beneficial outcomes for society. Essentially, KT is an interactive process underpinned by effective exchanges between researchers who create new knowledge and those who use it. KT can narrow the gaps between health research, health practice and health policy. KT concepts are universal and broadly applicable; yet teaching and learning about how to do effective KT has long been a challenge.

This introductory course in Knowledge Translation is designed to give students brief background knowledge in the importance of knowledge  translation in their research, prominent knowledge translation theories and models, strategies and measures to support research use in various fields and the importance of multi-faceted KT approaches. Our approach to KT is rooted in theory and science, but our application of KT is uniquely pragmatic and focused on real-world impact.

Instructor: Carmela Tartaglia, Filsy Samuel; permission is required to enroll in this course. Please send a brief statement of interest/background via email for enrollment approval.

Session Offered: 20251
Dates: January 21, 2025 - March 11, 2025 (Tuesdays), Time 4:00 - 6:00 pm, Location: In person (HS 108)

Description: A clinical and research overview of neurodegenerative diseases with a special focus on Alzheimer’s and Parkinson’s Disease. The module will provide students with a thorough understanding of clinical presentation, genetic and environmental causes, molecular underpinnings, and advances in therapeutic and biomarker research.

Instructor: TBD

Session Offered: TBD
Dates: TBD Time: TBD Location: TBD

Description: To provide IMS students with an introduction to clinical trials.

Instructor: Teresa To

Session Offered: 20251
Dates: February 3, 2025 - March 23, 2025, Location: Online Asynchronous 

Description: Each week over 7 weeks, basic concepts will be introduced in a 2-hour session at the GREAT Network SharePoint Server. To facilitate learning, exercises or tutorials will be given to trainees to work at their own time. You will be given instructions on attending the sessions via a log on user account and password. Course materials (reading materials, PowerPoint Presentations and handouts) will be downloadable from our SharePoint Server which will be opened for access to all participants after registration. To receive the certificate of completion, attendance of all 7 lectures is required. Each lecture will be opened to access for a week and participants can review the lecture at his/her own time and pace. At the end of each week’s lecture, participants are required to take a small quiz (comprises of about 5 multiple choice or short questions). Completion of the quiz is required to move forward to the next lecture.

Instructor: Pascal Tyrrell

Session Offered: TBD
Start Date: TBD, Time: TBD (alternating weeks), Location: In person (TBD)

Description: This short course is primarily for students in a MSc, PhD, or residency program that has a requirement of a data analysis. Students learn about important fundamental statistical principles by studying the analysis of real data.  Link to discussions:  http://www.tyrrell4innovation.ca/biostatistics-in-a-nutshell

Instructor: Gaspard Montandon

Session Offered: TBD
Start Date: TBD, Time: TBD,  Location: In person (TBD)

Description: Animal Models of Human Diseases is a short course covering a wide variety of animal models which aim at reproducing human diseases and identify new therapeutic solutions.

Syllabus

Instructor: Ben Dunkley

Session Offered: TBD
Start Date: TBD Time: TBD Location: TBD

Description: This module will provide a general survey of the major imaging modalities used in neuroimaging research. Several lecturers will talk about how these imaging methods have improved our understanding of various neurological disease conditions, with examples of where this has translated into improved clinical practice. We will review lesion studies, transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS), which all speak to the importance of understanding the spatial localization of brain function.

Instructor: Shane Harding , C. Anne Koch 

Session Offered: TBD
Start Date: TBD, Time: TBD,  Location: TBD

Description:  The overall objective of this course is to develop an understanding of the transdisciplinary nature of radiation science both in practical clinical application and in research. This foundational knowledge will be built upon by placing radiation science in the context of the greater scientific enterprise academically and within society. 

Each interactive session is led by an expert in the topic who gives an introduction, followed by discussion or a group activity. 

Brainstorm Sessions 2021-2022

Brainstorm Sessions 2021-2022 (104.4 KB)

Sessions include: 

• Group presentations of trainee's projects 
• Introduction to radiation biology, radiation oncology and physics for radiotherapy 
• Science communication 
• Knowledge translation 
• Introduction to "Hot topics" in cancer research with a focus on radiation medicine (examples include personalized medicine, trial design, machine learning) 
• Entrepreneurship 
• Team science 
• Career path

Instructor: Mina Tadrous

Session Offered: TBD
Dates: TBD, Time: TBD; Location: Online

Description: A systematic review of the literature is essential when conducting evidence-based research, especially in the health and life sciences. There is an increase in the number of systematic reviews, scoping reviews, or similar methodological reviews of the literature known as knowledge syntheses (KS) published each year. Learning outcomes for this module stress students’ critical understanding of the why, how and to what ends the search for a KS affects the overall quality of a review.

Instructor: Farzad Khalvati

Session Offered: TBD
Dates: TBD, Time: TBD, Location: In person 

Description:  This is a 6-week modular course (0.25 FCE) designed to provide an understanding of fundamentals of Artificial Intelligence / Machine Learning (AI/ML) for wide applications in medical imaging. Medical Imaging, which is an important specialty in medicine for diagnosis, prognosis, and intervention of different types of diseases including cancer, is increasingly moving toward quantitative approaches. AI/ML algorithms are playing a key role in quantitative medical imaging analytics for disease diagnosis (detection) and prognosis (prediction). With the help of recent advances in AI/ML and computer vision, novel predictive models are capable of diagnosing a disease with high accuracy and consistency, and predicting clinical outcomes (e.g., response to treatment) with an accuracy, which is beyond existing clinical methods.

Instructor: Pascal Tyrrell

Session Offered: 20249
Start Date: October 9, 2024 (Wednesdays), Time: 6:00 - 7:30pm, Location: In person (HS 108)

Description: Artificial Intelligence (AI) is a rapidly developing field and has been revolutionizing the medical field to benefit medicine. This course uses recent literature, papers, and examples to look at the applications of AI tools to see how they can directly assist patients and medical professionals. This 0.25 FCE course is primarily for students in a MSc or PhD program that would like to gain insight on the emerging field. Some topics discussed include challenges in the medical field and how AI can help, applications of AI/ML, and future of AI in medicine

Instructor: TBD

Session Offered: TBD

Dates: TBD, Time: TBD, Location: (In person)

Description:  Each of the 6 sessions will consist of a theoretical component and a hands on component using sample data sets to facilitate the development of adequate image manipulation and analysis skills.

Enrollment Instructions: Permission is required to enroll in this course. Please email caterina.diciano-oliveira@unityhealth.to and copy cc.medscience@utoronto.ca. 

Description: An independent study course on topics of interest to the student that is not already offered at IMS. Student usually meet weekly to read, criticize and discuss current literature in the field and write essay assignments. This course can also be used to conduct field projects. Independent study course topics can be relevant to the area of research the student is conducting but should NOT be on the exact same topic as the student’s thesis, and the student’s supervisor may NOT serve as instructor of the independent study course. It is the responsibility of the student and instructor to prepare a course outline, grading scheme, etc. The student needs to fill out this form, and obtain approval to enroll in this course. The student can work with the IMS office to develop a suitable course.

The student and the course instructor are expected to meet on a weekly basis. If a student would like to drop the course, they can drop the course before 15% of the course is completed after consulting with the course instructor.

Instructor: Caterina Di Ciano-Oliveira

Session Offered: 20251

Dates: January 14, 2025 - February 18, 2025 (Tuesdays), Time: 2:00 - 4:00 pm, Location: BF316 (In person)

Description: The course will consist of theoretical (75%) and hands-on components (25%) utilizing microscopes.

Enrollment Instructions: Permission is required to enroll in this course. Please email caterina.diciano-oliveira@unityhealth.to and copy cc.medscience@utoronto.ca. 

Instructor: Farzad Khalvati

Session Offered: TBD

Start Date: TBD, Time: TBD, Location: TBD

Description: This is a 6-week modular course (0.25 FCE) designed to provide an understanding of fundamentals of Natural Language Processing (NLP) for wide applications in medicine.

Natural Language and text is among the oldest form of data, available in different fields including medicine. With the advent of Artificial Intelligence (AI) and Machine Learning (ML), NLP is in an established stage, applicable to text filtration, classification, clustering, and context generation. In medicine, NLP can be applied to electronic patient records for prediction of patient outcomes, augmenting hospital triage system, and early diagnosis of disease. Hence, learning NLP fundamentals is becoming a necessity for healthcare professionals and researchers.

Instructor: Sanaa Choufani 

Session Offered: 20245F

Dates: May 15, 2024 - June 19, 2024 (Wednesdays), Time: 3-5 pm, Location: MS3287

Description: This 6-week modular course is designed to expose graduate students to the field of epigenetics, its applications in normal human development and to examine the different aspects of epigenetic alterations that could lead to disease. The course is designed to address the knowledge and skills gap in the emerging field of epigenetics. It will provide hands-on tutorials to help graduate students understand how, where, when and why epigenetic marks are important and how to study them.

Instructor: TBD

Description: This course is targeted at graduate students enrolled in basic science research programs that plan to utilize real time PCR. The course is designed to inform students of the steps involved in the full workflow of gene expression profiling: isolation of RNA, bioanalysis, primer design and validation, the qPCR set-up and run and data analysis.

Instructor: Evan Tannenbaum

Session Offered: 20249

Dates: September 10, 2024 - October 15, 2024 (Tuesdays), Time: 9-11 am, Location: In person (MS 3287)

Description: This is a mixed-media course designed to educate and familiarize graduate students to the uniqueness of the clinical environment to support their clinically-focused graduate research. 

Enrollment Instructions: Permission is required to enroll in this course. Please fill out this form if you are interested in taking the course. Do NOT enroll in the course on ACORN until you receive an email indicating you are approved to take the course. 

Syllabus

Instructor: Pascal Tyrrell

Session Offered: 20251

Dates: January 29, 2025 - March 5, 2025 (Wednesdays), Time: 6:00 - 7:30 pm, Location: MS 4279

Description: Has your research or personal exploration sparked an idea you're eager to pursue but feel overwhelmed on where to start and how to go about it? Well look no further as this course combines the technical fortitudes surrounding startups, specifically in the field of medical sciences. In addition to this, students will learn about the necessary soft skills of many successful founders. This 0.25 FCE course is primarily for students in a MSc or PhD program that would like to gain insight about startups. Topics discussed in the course include startup structure, value proposition, market and industry analyses, business planning, funding, and regulatory hurdles.

Syllabus

Instructor: Mohammad Kaviul Khan

Session Offered: 20251

Dates: January 23, 2025 - February 27, 2025 (Thursdays), Time: 6-9 pm, Location: Online

Description: Categorical data is a type of qualitative data that involves grouping observations into categories instead of measuring them numerically. It is a common form of data found in many branches of science, so the ability to work effectively with categorical data is an important skill for researchers to possess. This course will provide students with the knowledge and skills to perform and interpret categorical data analysis involving contingency tables and logistic regression models. The statistical software package R with the R Studio interface will be used. Both descriptive and inferential statistical methods will be covered.

Prerequisite Course: MSC1030H or equivalent undergraduate introductory statistics course. Please fill out this form if you are interested in taking the course.