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Computational Psychiatry

Computational Psychiatry

Psychiatric disorders impose a heavy burden on patients and their families. These disorders also cost the Canadian economy $51 billion each year (Lim 2008, Chronic Dis Can 28(3): 92-98). Economic costs of psychiatric illnesses are similarly gigantic in other countries.

Treating psychiatric illnesses is challenging in several ways. Psychiatric diagnosis is often not straight-forward, and this can make treatment less effective. For example, the early stages of major depressive disorder and bipolar disorder are often indistinguishable. Differentiating these two diseases early is highly desirable as the treatments are very different.

Psychiatric prognosis is also difficult. While effective psychiatric drug treatments are available, individual patients react differently to a given drug, making it difficult to predict the best treatment for an individual. Psychiatrists may try various drugs before finding an effective combination. This trial-and-error process is especially problematic as many drugs have unpleasant side-effects and most drugs take several weeks to become therapeutic. Psychiatric patients often become disillusioned and discontinue drug treatment that would have helped them.

We are working on technologies for improving diagnosis for psychiatric patients and for providing more accurate prognostic predictions of how patients will respond to treatment. The goal is to provide tools to assist medical doctors in making more accurate diagnoses and more effective treatment decisions. This should improve patients’ response to treatment, improve adherence to pharmacotherapy, and improve patient outcomes.

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We are a multidisciplinary group based in Alberta, Canada, including clinicians and scientists who work in the fields of mental health, neuroscience, population health, and machine learning. We have an active collaboration with the IBM Centers for Advanced Studies (CAS), CAS Alberta.

Our team (led by Sunil Kalmady) published an article describing a way to diagnose never-treated Schizophrenia. Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning in Nature Schizophrenia.

Also, this research was featured in –

A story on this research was aired on german national TV show nano (channel 3Sat) – AI against schizophrenia (Time: 21:50) – 2 Oct 2019

​[Research was conducted in collaboration with National Institute of Mental Health and Neurosciences, India and funded by IBM CAS Alberta, MITACS, Wellcome Trust/DBT India Alliance, DST, ICMR, Alberta Machine Intelligence Institute, NSERC]

The research was led by our team member Bo Cao [UofA’s Department of Psychiatry], with the collaboration of Xiang Yang Zhang [University of Texas Health Science Center at Houston]​​ “Treatment response prediction and individualized identification of first-episode drug-naïve schizophrenia using brain functional connectivity” was published in Molecular Psychiatry.

Our team (led by Mina Gheiratmand [UofA] and Irina Rish [IBM]) published an article Learning stable and predictive network-based patterns of schizophrenia and its clinical symptoms (in Nature Schizophrenia) describing a way to diagnose schizophrenia.

See also Metrics

[Research sponsored by IBM CAS Alberta, Alberta Innovates-Health Solutions (AIHS), AMII, CIHR, NSERC]

​Prof. Greiner was interviewed for article in Toronto Star: here (9 May 2016)

​Interviewed for article at UofA FoS page: here (1 Apr 2016)​

Global’s Su-Ling Goh talks to Professors Russ Greiner and Matt Brown about their research into using computers to analyze MRI brain scans to diagnose and treat mental illness (in collaboration with IBM).

Global News Clip (Video [ 1:30 ] – 2015.10.14)

Dr. Russ Greiner and members of his lab at the University of Alberta are applying machine-learning approaches to find patterns in brain imaging that will help predict or diagnose brain dysfunctions such as ADHD, Alzheimers and schizophrenia. “We’re helping to advance the emerging field of ‘computational psychiatry.’ Both diagnostic and prognostic tools have high potential for commercialization, to be further developed in companies in Alberta, and elsewhere,” says Dr. Greiner. Currently diagnoses are typically subjective as they are based on a professional’s assessment of whether a patient exhibits a benchmark combination of behaviours on list of criteria. Using IBM technology, researchers at University of Alberta and the University of Calgary will instead build a bio-based system to help identify and develop better, faster, more reliable treatments for mental health, one of the most expensive disease categories in the developed world.

See IBM News Release (2015.06.24) Also Moods magazine (2015.09.01)

International undergrads can join U of Alberta research groups for the summer, with funding provided by the Mitacs Globalink program. My lab had the honor of hosting two Mitacs Globalink Summer Students (July 2014).

This was covered by

Using MRIs to map gender differences in the brain (neuroscience meets computing science)…

Apps for health monitoring (computing science meets public health)…

Our team (led by Mina Gheiratmand [UofA] and Irina Rish [IBM]) published an article Learning stable and predictive network-based patterns of schizophrenia and its clinical symptoms (in Nature Schizophrenia) describing a way to diagnose schizophrenia.

See also Metrics

[Research sponsored by IBM CAS Alberta, Alberta Innovates-Health Solutions (AIHS), AMII, CIHR, NSERC]

Dr. Russ Greiner and members of his lab at the University of Alberta are applying machine-learning approaches to find patterns in brain imaging that will help predict or diagnose brain dysfunctions such as ADHD, Alzheimers and schizophrenia. “We’re helping to advance the emerging field of ‘computational psychiatry.’ Both diagnostic and prognostic tools have high potential for commercialization, to be further developed in companies in Alberta, and elsewhere,” says Dr. Greiner. Currently diagnoses are typically subjective as they are based on a professional’s assessment of whether a patient exhibits a benchmark combination of behaviours on list of criteria. Using IBM technology, researchers at University of Alberta and the University of Calgary will instead build a bio-based system to help identify and develop better, faster, more reliable treatments for mental health, one of the most expensive disease categories in the developed world.

See IBM News Release (2015.06.24) Also Moods magazine (2015.09.01)

International undergrads can join U of Alberta research groups for the summer, with funding provided by the Mitacs Globalink program. My lab had the honor of hosting two Mitacs Globalink Summer Students (July 2014).

This was covered by

Using MRIs to map gender differences in the brain (neuroscience meets computing science)…

Apps for health monitoring (computing science meets public health)…

International undergrads can join U of Alberta research groups for the summer, with funding provided by the Mitacs Globalink program. My lab had the honor of hosting two Mitacs Globalink Summer Students (July 2014).

This was covered by

Using MRIs to map gender differences in the brain (neuroscience meets computing science)…

Apps for health monitoring (computing science meets public health)…

International undergrads can join U of Alberta research groups for the summer, with funding provided by the Mitacs Globalink program. My lab had the honor of hosting two Mitacs Globalink Summer Students (July 2014).

This was covered by

Using MRIs to map gender differences in the brain (neuroscience meets computing science)…

Apps for health monitoring (computing science meets public health)…

International undergrads can join U of Alberta research groups for the summer, with funding provided by the Mitacs Globalink program. My lab had the honor of hosting two Mitacs Globalink Summer Students (July 2014).

This was covered by

Using MRIs to map gender differences in the brain (neuroscience meets computing science)…

Apps for health monitoring (computing science meets public health)…

International undergrads can join U of Alberta research groups for the summer, with funding provided by the Mitacs Globalink program. My lab had the honor of hosting two Mitacs Globalink Summer Students (July 2014).

This was covered by

Using MRIs to map gender differences in the brain (neuroscience meets computing science)…

Apps for health monitoring (computing science meets public health)…

International undergrads can join U of Alberta research groups for the summer, with funding provided by the Mitacs Globalink program. My lab had the honor of hosting two Mitacs Globalink Summer Students (July 2014).

This was covered by

Using MRIs to map gender differences in the brain (neuroscience meets computing science)…

Apps for health monitoring (computing science meets public health)…

Television

CTV:  April 8th, 2022

Written News

More Written News

Original technical article

“Detecting Presence of PTSD Using Sentiment Analysis From Text Data”,
Jeff Sawalha, Muhammad Yousefnezhad, Zehra Shah, Matthew R. G. Brown, Andrew J. Greenshaw and Russell Greiner,
Frontiers in Psychiatry, 01 February 2022.

Research Scientists / PDFs

Department of Accounting and Business Analytics

Research Scientist, Dept.
Psychiatry Adjunct Professor
Dept. Computing Science

Department of Biostatistics
Harvard University

Postdoctoral Fellow
Faculty of Medicine & Dentistry
Psychiatry Dept

Postdoctoral Fellow
Faculty of Science

Grad Research Asst
Fellowship Faculty of Medicine & Dentistry
Department of Psychiatry

Postdoctoral Fellow
Faculty of Medicine & Dentistry
Medicine Department

Associates

Department of Accounting and Business Analytics

Research Scientist, Dept. Psychiatry Adjunct Professor
Dept. Computing Science

Grad Research Asst Fellowship Faculty of Medicine & Dentistry
Department of Psychiatry

Graduate Students

Alumni

Grad Students

Elvan Ciftci ​(Postdoctoral Fellow, 2019)
Animesh Kumar Paul ​(MSc, 2019)
Farzane Aminmansour ​(MSc, 2019)
James Benoit (PhD, 2018)
​Michel Juhas (PhD, 2018)
Mina Gheiratmand (Postdoctoral Fellow, 2018)
Amir Forouzandehmoghadam ​(MSc, 2017)
Reyhaneh Ghoreishiamiri (MSc, 2017)
Neil Borle (MSc, 2017)
Bhaskar Sen (MSc, 2015)
Sina Ghiassian (MSc, 2014)
Gagan Sidhu (MSc, 2012)

Interns / Visitors

Jianshan Chen (Psychiatry Resident, 2019—2020)
Nitin Choudhary (UARE undergrad intern, Summer 2018)
Joseph Mann (Guest researcher, Starting 2018)
Ezgi Ince Guliyev (Psychiatry Resident, Fall 2017)
Alex Rutar (undergrad intern, Summer 2017)
Johannes Langer (UARE grad intern, Summer 2017)
Kunal Singh (UARE grad intern, Summer 2017 )
Patrick Schwaferts (UARE grad intern, Winter 2017)
Sugai Liang (Summer 2016)
Siddharth Muthukumar (UARE undergrad intern, Summer 2016)
Ian Smith (HIP student intern, Summer 2016)