McIntosh lab

Dr. Randy McIntosh’s research program seeks to unify our understanding of normal and pathological operations of the brain, through the notion of dynamical network function. I lead an international consortium, The Virtual Brain (TVB), which is an evolving brain simulation platform. TVB is a large-scale neural modeling platform that directly uses neuroimaging data to parameterize a model. Because individual data can be used, any person’s brain can become the Virtual Brain. This research will lead to a coherent framework for linking brain disorders to functional deficits, explaining individual variation and predicting recovery.

Additionally, our research also involves the development of analytic tools that are optimized to quantify neural interactions. One of these is Partial Least Squares, which was originally developed in collaboration with Dr. Fred Bookstein at the University of Michigan.


Dr. Anthony McIntosh

Program Managers

Lab Manager

  • Maria Karachalios

Associate Scientist

  • Kelly Shen

Post-doctoral Fellows

  • Josie-Anne Bertrand
  • John Griffiths
  • Etay Hay
  • Stephanie Hutka
  • Valorie Salimpoor

Graduate Students

  • Sarah Carpentier
  • Zainab Fatima
  • Erin Gibson
  • Tyler Good
  • Michele Korostil
  • Joelle Zimmerman

TVBTHEVIRTUALBRAIN (TVB) represents a new solution, providing new tools to facilitate the collaboration between experimentalists and modelers by exposing both a comprehensive simulator for brain dynamics and an integrative framework for the management, analysis simulation of structural and functional data in an accessible, web-based interface. 

TVB delivers the first open and realistic simulation of the human brain. By employing novel concepts from neuroscience, TVB is completely customized to an individual patient’s brain. Our vision is to safely and effectively devise, benchmark and test therapies before pharmaceutical or surgical application to provide a revolutionary health care experience in the form of personalized neuroscience.

TVB takes a network approach on the largest scale: By manipulating brain network parameters, particularly it’s connectivity; TVB will simulate brain behavior as it is commonly observed in clinical settings. TVB will be the first framework to offer such an innovative approach to brain science thanks to the following unique features:

 Simulation system will be remotely accessible through a simple web browser, including impressive 3D visualization. No need to have supercomputers or large databases on-site.
• TVB will deliver the same neuroimaging results as a patient’s brain in the clinic – its design will allow for complete customization and validation.
• By intentionally “damaging” TVB, predictions will be possible about the potential success of surgical or pharmaceutical interventions for any individual presenting brain damage or disease.
• The participation of many clinics and research centers around the world is a beneficial feedback-loop, refining TVB continuously with growing experimental data validated against refined models.


Randy McIntosh

For more information, please see my profile

Josie-Anne Bertrand

Tanya Brown 

Sarah Carpentier
My research focuses on neural network dynamics associated with memory and brain-environment interactions. Specifically, I am primarily interested in how humans integrate information from our environments with our memories to produce higher order perceptions and our subjective, conscious appreciation of the world around us. To this end, I have used electroencephalography and magnetoencephalography and taken a whole-brain network connectivity approach to studying the neural systems associated with musical training, perception, and preferences, as well as second language fluency and acquisition.

John Eusebio
I recently graduated from Western University with a H.BA in Psychology, specializing in Developmental Cognitive Neuroscience. My primary research interest involves the initiation of cognitive control, and the formation of functional networks to facilitate task performance under varying levels of cognitive load. I am also interested in neuroplasticity and the role of resting state networks in exploring the brain’s dynamic repertoire. I will be exploring these areas in more detail in my graduate work, which is co-supervised by Dr. McIntosh & Dr. Stephen Strother. 

Zainab Fatima

Erin Gibson
My research focuses on the functional role of fluctuations in neural activity.  While conventional wisdom holds that under ideal conditions the brain should behave as an orderly and machine-like system, recent theoretical and empirical evidence suggests that the optimal working point for the brain may be at, or near, a region of instability. To explore this possibility, I am using EEG to examine whether fluctuations in neural activity are driven or damped by the effects of stimulus presentation, task engagement and skill learning.  This work aims to characterize the extent to which neural activity is stabilized or destabilized by perceptual and cognitive events.

John Griffiths

Tyler Good, Master’s Candidate, University of Toronto
I graduated from the University of Waterloo with an H.B.Sc in Biomedical Science and Psychology in 2014. Currently, I am a MA candidate in Psychology at the University of Toronto in conjunction with the Rotman Research Institute, Baycrest Hospital. My research explores the structural and functional connectivity of the brain and how it changes following brain injury. Specifically, I am interested in how structural and functional dynamics relate following sports concussion, and the repeated sub-concussive impacts sustained over the course of a sports season. To this end, I conduct neuroimaging analysis of structural (DTI) and functional (fMRI) data. Furthermore, I combine subject-specific neuroimaging data with mathematical modeling in The Virtual Brain (TVB) to produce simulated functional dynamics. These simulations provide a unique insight into the brain’s structure-function relationship.

Etay Hay
My research aims at discovering and analyzing stable connections in the brain using data-driven modeling. I constrain whole-brain models with fMRI activity data and structural connectivity estimated by diffusion tensor imaging, and use LASSO regularized regression to derive the connection weights. I analyze the network of stable connections indicated by the models, in different brain states and across subject age.

Stefanie Hutka, PhD Candidate, University of Toronto
My main line of research focuses on the association between music and language in the brain. I see this association as a window into understanding brain plasticity. I am particularly interested in exploring how pitch processing experience in musicians and tone language speakers is related to benefits in perceptual and cognitive processing. My methodologies include electroencephalography (EEG) to capture linear and nonlinear dependencies in the data, as well as psychophysical and cognitive testing. I also hold an ARCT in Violin Performance from the Royal Conservatory of Music, and when not at the lab, enjoy performing everything from classical to Top 40 on violin with my ensemble, Strings in Motion.

Maria Karachalios
I am the primary person who organizes and oversees the entire process of preparing and executing an MRI, EEG, or MEG study. My responsibilities also involve the acquisition and pre-processing of MRI, EEG, and MEG data. My experience with a specialized step in the pre-processing of MRI data – Independent Components Analysis (ICA) – lead to the creation of a guidelines document that is available for reference (please email me).

Michele Korostil
Michele’s research focuses on the spatiotemporal dynamics of learning in psychotic disorders. She is currently completing a project using fMRI to explore how verbal learning unfolds in the brain during practice in persons with schizophrenia. Challenges with learning are a key aspect of the illness schizophrenia that can impede optimal functioning. A better understanding of the brain networks engaged in practice-related learning will help inform development of remediating and rehabilitative treatments for cognition in schizophrenia. Michele is additionally collaborating on projects using EEG measures to identify biomarkers in adolescents who are at high risk for developing psychosis in the hope that better early identification will help efforts to prevent conversion to full-blown psychotic episodes.
Michele is also a psychiatrist practicing medicine at the Centre for Addiction and Mental Health (CAMH) in Toronto. She works with people who have complex mental illnesses including treatment-refractory psychoses and early psychotic disorders. She was honoured as CAMH’s Physician of the Year in 2013.

Natasha Kovacevic
With background in Neuroscience, Mathematics, Art and Design, Natasha is the originator  of My Virtual Dream (MVD), as an art-science project and public art installation. Her multi-disciplinary point of view allows her to uncover and formulate links between neuroimaging data and artistic rendering of the sensory world. Natasha’s neuroscience contributions are in brain computer interface, methods, visualization and computing (specifically processing pipelines for EEG, MRI, MEG, genetic SNP) and general methods (PLS, group ICA). Related to Neuroscience Natasha’s art/design contributions are: Virtual Brain sculpture, MVD game design, MVD set design, BCI-to-Visuals & Music linkage in MVD.

Valorie Salimpoor
My research interests involve understanding why we like music using various brain imaging and behavioral techniques.  I am particularly interested in examining why different individuals have different musical preferences.  To better understand this, we examine how music that people listen to in the past can impact their current musical preferences and how the brain associates emotions and pleasure with musical sequences.  We also examine interactions between different brain systems that give rise to musical pleasure.  My second area of research involves cognitive training.  I am interested in the role of dopamine (reward and motivation) in learning and memory and how this can enhance cognitive training through video games.

Kelly Shen
My research seeks to understand the neural basis of human cognition by investigating the neural networks that underlie flexible cognitive function. Specifically, I am interested in how the interactions between several neurocognitive networks — for example, those that mediate attention, learning, and memory – determine behaviour. Toward this end, I combine behavioural, eyetracking and neuroimaging techniques with state-of-the-art methods for network analysis. My research approach is to consider cognitive processes not in isolation but as a complex and interwoven collection that together influence ongoing behavior. I adopt a complementary approach to neuroimaging consistent with an emerging perspective in the field in which brain connectivity is considered across brain regions and, equally importantly, across time. Together, these methods hold great promise for uncovering not only the fundamental neural processes underlying cognition, but also the changes they undergo in aging and neurodegenerative illness.

Joelle Zimmerman
Joelle Zimmermann is a Ph.D. student at the University of Toronto, studying Psychology with a focus on Neuroscience in collaboration with the Rotman Research Institute, Baycrest Hospital. She received an Honours B.Sc. (2009-2013), and M.A. (2013-2014) in Psychology at the University of Toronto. Her research involves combining neuroimaging methods with mathematical modeling to investigate structural (DTI) and functional (fMRI) network properties using The Virtual Brain (TVB) simulator of brain dynamics. The goal is to investigate subject-specificity of TVB simulations, whether individual structural networks can predict the individual modeled functional connectivity. Her primary interest lies in understanding structure-function connectivity coupling changes in aging. In addition, she is conducting research in motor learning during aging.




Zainab Fatima
My primary research interests revolve around studying the spatial and temporal dynamics of large-scale interactions that occur in the brain due to convergence of top-down (attentional) and bottom-up (stimulus-driven) processing. I use functional neuroimaging (fMRI) data to investigate how sensorimotor and cognitive systems are organized. By changing task demands and cognitive load, sensorimotor systems can change their interactions with cognitive systems and vice versa. I’m interested in studying the neural network properties of such dynamic interactions using multivariate statistical techniques such as partial least squares (PLS) and structural equation modeling (SEM).

My secondary interests lie in examining how the brain organization changes as a result of learning. I’m specifically interested in how the prefrontal cortex, parts of the basal ganglia and the hippocampus interact with each other at the beginning of learning a task and as learning progresses. I’m also fascinated by changing network dynamics in learning due to the type of strategy individuals’ use. I will be exploring this area in more detail in further graduate work.



Tanya Brown
I recently graduated from the University of Toronto with an H.B.Sc, specifically with a Major in both Psychology and Biology. My predominant area of interest is in the field of neuropsychology with a focus on the neurobiological correlates of various pathological behaviors caused by dementia and other aging-related brain diseases, which is what I plan to make the focus of my graduate school studies. I am currently in the process of working on multiple projects which employ various brain imaging techniques, including functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG) and electroencephalography (EEG) as well as other methodologies, such as eye-tracking and behavioral testing. I am involved in all steps of research projects, from the preparatory steps of experimental design, programming experiment paradigms, subject recruitment and administration of patient and control testing sessions, to data analysis and publication submissions. The ultimate goal of this research is to provide insight into the neural networks that are responsible for specific human behaviors and cognition. The more comprehensive understanding we have of a normally functioning brain can lend to a more efficacious understanding and treatment of compromised neural systems.