Whole Person and Population Modelling logo

Whole Person& Population Modelling

Modelling the whole person, and whole populations, to understand brain health and mental illness across the lifespan.

We integrate genomics, neuroimaging, wearable and clinical data with machine learning to discover who is at risk for mental illness and neurodegeneration, and why, across the lifespan, from youth to late life.

Led by Daniel Felsky, PhD · Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH) · University of Toronto

Get in touch
9
Active research themes
19+
Lab members & trainees
100+
Peer-reviewed papers
4,000+
Citations

Our approach

Modelling the whole person

We bring together genes, brain, body, behaviour, and environment, integrating molecular, imaging, cognitive, wearable, and social data into unified models of mental and brain health across the lifespan, from youth to late life.

The Whole Person Modelling workflow: curating and harmonizing external datasets, internal and external modelling, and translating multi-domain predictions into a clinical dashboard.
From multi-domain data to predictions and clinical tools: the Whole Person Modelling workflow.

Research

What we work on

Our program spans psychiatric and neurodegenerative conditions, united by a single idea: that mental and brain health emerge from the interaction of genes, brain, body, behaviour, and environment. These are our active research themes.

01

Youth Mental Health & the Psychosis Spectrum

Using the Toronto Adolescent & Youth (TAY) CAMH Cohort Study, we model how psychosis-spectrum symptoms emerge in young people and search for blood- and MRI-based markers that predict who is at risk.

TAY CohortNeuroimagingBlood biomarkers
02

Integrated Youth Services & Learning Health Systems

We build federated data platforms and predictive models that help integrated youth-mental-health services learn from their own data, improving care, equity, and measurement across Ontario and nationally.

YWHO / IYSEHR & service dataEquity
03

Late-Life Depression, Treatment Response & Dementia Risk

We develop genomic, neuroimaging, and sleep-based machine-learning models to predict antidepressant response and treatment-resistant depression in older adults, and to map the shared roots of depression and dementia.

Machine learningPolygenic scoresMAPLE-D
04

Addictions & Cognitive Dysfunction

Through the Cognitive Dysfunction in the Addictions (CDiA) program, we combine genetics, blood, MRI, and wearable data to understand why some people with substance-use disorders lose cognitive function over time.

CDiAWearablesIntegrative modeling
05

Alzheimer's Disease, Neurodegeneration & Brain Aging Genomics

Our core genomics program builds polygenic, transcriptomic, and single-cell models of risk and resilience in Alzheimer's disease and cognitive aging, spanning the gut-immune-brain axis to tau spreading and brain reserve.

ROS/MAP & ADNISingle-nucleus RNA-seqPET & PRS
06

Bipolar Disorder

We pair single-cell transcriptomics with genetics to build gene-program-specific risk scores for bipolar disorder, and study its neuroimaging correlates and sex-specific cardiovascular comorbidity.

snRNA-seqGWASSex differences
07

Body Image & Eating Disorders

With collaborators, we are building a comprehensive brain-and-behaviour model of how the self-body image forms, while discovering biomarkers of its disturbance in eating disorders and body dysmorphic disorder.

Functional MRIBehaviour
08

AI Methods, Digital Twins & Reproducible Frameworks

We develop reproducible AI benchmarking platforms, digital-twin approaches to neurodegeneration risk and prevention, and open tools for multi-omic data integration (similarity network fusion).

Digital twinsmetaSNFOpen science
09

Environment & Gene-Environment Interaction

How environmental exposures and early-life experience interact with genetic risk to shape brain development and mental health, including prenatal air pollution and childhood adversity. This is the environment pillar of whole-person modelling.

Gene-environmentEarly-life exposuresChild development

People

Our team

A transdisciplinary group of scientists and trainees spanning genomics, neuroimaging, biostatistics, computer science, and clinical research.

The Whole Person and Population Modelling lab group.
The Whole Person & Population Modelling group, 2025.
Society of Biological Psychiatry Annual Meeting, New York, May 2026.
Society of Biological Psychiatry Annual Meeting, New York (NY), May 2026.

Principal Investigator

Daniel Felsky, PhDDF
Daniel Felsky, PhD
Principal Investigator · Scientist, KCNI-CAMH
Associate Professor, Department of Psychiatry & Division of Biostatistics (Dalla Lana School of Public Health), University of Toronto. Develops whole-person, multi-modal models of mental illness and brain aging.

Postdoctoral Fellows

Earvin TioET
Earvin Tio
Postdoctoral Fellow
Suicide risk & whole-person machine learning
Mohamed AbdelhackMA
Mohamed Abdelhack
Postdoctoral Fellow
MRI, sleep, cognition & bipolar disorder
Samar ElsheikhSE
Samar Elsheikh
Postdoctoral Fellow · STAGE Mentee
Polygenic scores & antidepressant response in late-life depression
Mai GamalMG
Mai Gamal
Incoming Postdoctoral Fellow
Neural correlates of social factors (CDiA)

Graduate Students

Tianyi (Amanda) LiuAL
Tianyi (Amanda) Liu
PhD · IMS
Genome-wide Interaction Study (GWIS) of bipolar disorder
Polina LekhnitskayaPL
Polina Lekhnitskaya
PhD · IMS
Language deficits, neurodegeneration & TBI
Fardin NabizadehFN
Fardin Nabizadeh
Incoming PhD · IMS
Tau pathology spreading in Alzheimer's disease
Mary Anne PanoyanMP
Mary Anne Panoyan
PhD (co-supervised)
Tandem repeats, brain health, and neurodegeneration
Ashley Moo-ChoyAM
Ashley Moo-Choy
PhD (co-supervised)
Genomic Structural Equation Modelling of PTSD
Hongrui LiuHL
Hongrui Liu
MSc · IMS
Gut-immune-brain axis in neurodegeneration
Tesam AhmedTA
Tesam Ahmed
MSc · IMS
Latent multi-trajectory modelling of psychotic-like experiences
Rivka Van KleiRV
Rivka Van Klei
MSc · IMS
Polygenic scores for brain change & resilience
Isabella ChawrunIC
Isabella Chawrun
PhD · UWaterloo
Autism in adulthood & machine learning
Yuan TianYT
Yuan Tian
PhD · UofT
Multimodal neuroimaging & GWAS in Alzheimer's

Research Staff & Undergraduate Trainees

Ayesha SyedaAS
Ayesha Syeda
Research Assistant
Multimodal classification of treatment-resistant late-life depression
Megan DingMD
Megan Ding
Undergraduate · BCB430
Single-cell transcriptomic risk scores for depression
Amelia WuAW
Amelia Wu
Undergraduate · BCB330
Machine learning & digital twins for lifespan risk
Lucy KangLK
Lucy Kang
Undergraduate · BCB330
Research placement

Alumni

Postdoctoral Fellows

  • Stuart Matan-Lithwick 2021-2024
  • Sejal Patel 2021-2022
  • Peter Zhukovsky

Doctoral Students

  • Akshay Mohan
  • Tara Henechowicz now postdoc, Colorado

Master's Students

  • Denise Sabac 2022-2024
  • Mu Yang 2022-2023
  • Rachel Bercovitch 2022-2023
  • Amin Kharaghani 2019-2022
  • Alyssa Cannitelli 2021
  • Katrina Hueniken 2020-2021
  • Emily Wiljer defended June 2026
  • Anna Mouzenian defended June 2026

Doctor of Medicine Students

  • Ben Khoo 2023
  • Roberta Dolling-Boreham 2021

Undergraduate Students

  • Feiyang Sun
  • Alameen Salim 2024
  • Lu (Evelyn) Huang 2024
  • Angela Uzelac 2023-2024
  • Jun Ni (Jenny) Du 2022-2023
  • Yihan (Cathlyn) Chen 2022-2023
  • Micaela Consens 2019-2021
  • Wing Chung (Jessie) Lam 2020-2021
  • Bianca Pokhrel 2020-2021

Research Analysts

  • Rajith Wickramatunga
  • Melissa Misztal 2022-2024
  • Milos Milic 2020-2022

Publications

Selected Publications

A selection of recent papers from the lab. Trainee first authors are common; full lists are available via ORCID and Google Scholar.

Full, up-to-date list: Google Scholar · ORCID.

Resources

Resources & links

Datasets, analysis tools, and collaborator labs we draw on most often. Links open in new tabs.

Are we missing a resource your project relies on? Send it to daniel.felsky@camh.ca and we'll add it.

Location & Contact

Find us at the KCNI

Krembil Centre for Neuroinformatics

The Krembil Centre for Neuroinformatics (KCNI) at the Centre for Addiction and Mental Health brings together computational neuroscience, data science, and clinical research to understand the brain basis of mental illness and to accelerate discovery through open, data-driven methods. Our group is part of the KCNI community.

Centre for Addiction and Mental Health (CAMH)
12th Floor, 250 College Street
Toronto, Ontario, Canada  M5T 1R8

CAMH is located in downtown Toronto, near the University of Toronto St. George campus.

Get in touch

Office416-535-8501 ×33587

Prospective students and collaborators are warmly welcome to reach out.

Opportunities

Programs, scholarships, and placements for students who want to train with us, across the University of Toronto, CAMH, the Institute of Medical Science, and Dalla Lana.