YORKTOWN, N.Y. and EDMONTON, Alberta, July 21, 2017
/PRNewswire/ -- IBM (NYSE: IBM) scientists and the
University of Alberta in Edmonton, Canada, have published new data in
Nature's partner journal, Schizophrenia1, demonstrating
that AI and machine learning algorithms helped predict instances of
schizophrenia with 74% accuracy. This retrospective analysis also
showed the technology predicted the severity of specific symptoms
in schizophrenia patients with significant correlation, based on
correlations between activity observed across different regions of
the brain. This pioneering research could also help scientists
identify more reliable objective neuroimaging biomarkers that could
be used to predict schizophrenia and its severity.
Schizophrenia is a chronic and debilitating neurological
disorder that affects 7 or 8 out of every 1,000 people. Those with
schizophrenia can experience hallucinations, delusions or thought
disorders, along with cognitive impairments, such as an inability
to pay attention and physical impairments, such as movement
disorders2.
"This unique, innovative multidisciplinary approach opens new
insights and advances our understanding
of the neurobiology of schizophrenia, which may help to
improve the treatment and management of the disease," says Dr.
Serdar Dursun, a Professor of
Psychiatry & Neuroscience with the University of Alberta. "We've discovered a number
of significant abnormal connections in the brain that can be
explored in future studies, and AI-created models bring
us one step closer to finding objective neuroimaging-based patterns
that are diagnostic and prognostic markers of schizophrenia."
In the paper, researchers analyzed de-identified brain
functional Magnetic Resonance Imaging (fMRI) data from the open
data set, Function Biomedical Informatics Research Network (fBIRN)
for patients with schizophrenia and schizoaffective disorders,
as well as a healthy control group. fMRI measures brain activity
through blood flow changes in particular areas of the brain.
Specifically, the fBIRN data set reflects research done on brain
networks at different levels of resolution, from data gathered
while study participants conducted a common auditory test.
Examining scans from 95 participants, researchers used machine
learning techniques to develop a model of schizophrenia that
identifies the connections in the brain most associated with the
illness.
The results of the IBM and University of
Alberta research demonstrated that, even on more challenging
neuroimaging data collected from multiple sites (different
machines, across different groups of subjects etc.) the machine
learning algorithm was able to discriminate between patients with
schizophrenia and the control group with 74% accuracy using the
correlations in activity across different areas of the
brain.
Additionally, the research showed that functional network
connectivity could also help determine the severity of several
symptoms after they have manifested in the patient, including
inattentiveness, bizarre behavior and formal thought disorder, as
well as alogia, (poverty of speech) and lack of motivation. The
prediction of symptom severity could lead to a more quantitative,
measurement-based characterization of schizophrenia; viewing the
disease on a spectrum, as opposed to a binary label of diagnosis or
non-diagnosis. This objective, data-driven approach to severity
analysis could eventually help clinicians identify treatment plans
that are customized to the individual.
"The ultimate goal of this research effort is to identify and
develop objective, data-driven measures for characterizing mental
states, and apply them to psychiatric and neurological disorders,"
said Ajay Royyuru, Vice President of
Healthcare & Life Sciences, IBM Research. "We also hope to
offer new insights into how AI and machine learning can be used to
analyze psychiatric and neurological disorders to aid psychiatrists
in their assessment and treatment of patients."
The Research Domain Criteria (RDoC) initiative of NIMH
emphasizes the importance of objective measurements in psychiatry.
This field, often referred to as "computational psychiatry", aims
to use modern technology and data driven approaches to improve
evidence-based medical decision making in psychiatry, a field that
often relies upon subjective evaluation approaches.
As part of the ongoing partnership, researchers will continue to
investigate areas and connections in the brain that hold
significant links to schizophrenia. Work will continue on improving
the algorithms by conducting machine learning analysis on larger
datasets, and by exploring ways to extend these techniques to other
psychiatric disorders such as depression or post-traumatic stress
disorder.
About IBM Research
For more than seven decades, IBM Research has defined the future
of information technology with more than 3,000 researchers in 12
labs located across six continents. Scientists from IBM Research
have produced six Nobel Laureates, 10 U.S. National Medals of
Technology, five U.S. National Medals of Science, six Turing
Awards, 19 inductees in the National Academy of Sciences and 20
inductees into the U.S. National Inventors Hall of Fame. For more
information about IBM Research, visit www.ibm.com/research.
The IBM Alberta Centre for Advanced Studies (CAS) is a
multi-year innovation collaboration between the Alberta Government,
University of Alberta, University of Calgary and IBM Canada Ltd. which
engages Alberta's academic and
industrial communities with IBM research and development groups
around the globe.
About the University of
Alberta
The University of Alberta in
Edmonton is one of Canada's top teaching and research
universities, with an international reputation for excellence
across the humanities, sciences, creative arts, business,
engineering, and health sciences. Home to 39,000 students and
15,000 faculty and staff, the university has an annual budget of
$1.84 billion and attracts nearly
$450 million in sponsored research
revenue. The U of A offers close to 400 rigorous undergraduate,
graduate, and professional programs in 18 faculties on five
campuses—including one rural and one francophone campus. The
university has more than 275,000 alumni worldwide. The university
and its people remain dedicated to the promise made in 1908 by
founding president Henry Marshall
Tory that knowledge shall be used for "uplifting the whole
people."
Contact(s) information
Adrienne Sabilia
IBM Media Relations
1 (914) 945-1420
acsabili@us.ibm.com
Spencer Murray
Alberta Machine Intelligence Institute Communications Associate
t: 780.492.8448 | c: 780.991.7136
spencer@amii.ca
Jinna Kim
IBM Canada Media Relations
(905) 316-2179
jinnak@ca.ibm.com
1 Gheiratmand, M. et al. Learning stable and predictive
network-based patterns of schizophrenia and its clinical symptoms.
NPJ Schizophr 3, 22, doi:10.1038/s41537-017-0022-8 (2017).
http://rdcu.be/r1Zy
2 National Institute of Mental Health, "Schizophrenia"
https://www.nimh.nih.gov/health/publications/schizophrenia-booklet/index.shtml
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