Biomedical Informatics Retreat 2013, Stanford University

1. Evaluate BRAIN PHENOTYPE by using COGNITIVE PHENOTYPE to reproduce known brain-behavioral relationships
A pattern of brain structure relevant to the amygdala can predict level of anxiety
• Evaluate disorder subtype clustering by showing that it captures the current gold standard, the DSM, and demonstrating that
subtypes defined with this method can be used with traditional analyses to better identify group differences
Method
Cognitive and Brain Phenotypes? Holy “Neuropsychiatric Profiling,” Batman!
Using cognitive phenotypes and brain phenotypes to redefine subclasses of autism spectrum disorder
Why Develop Cognitive Phenotypes?
• The Diagnostic & Statistical Manual of Mental Disorders (DSM) 5 has obliterated subtypes of
many disorders, presenting opportunity for data-driven methods to define subtypes.
• Efforts to use any kind of data (genetic, imaging, clinical) to define subtypes are only possible
with some “gold standard,” but we cannot use the DSM.
• Normalized, structured “trait scores” extracted from behavioral assessments hold promise for
a new standard of evaluation for hypothesized subtypes.
• These “cognitive phenotypes” are humanly interpretable scores that can be used to validate
subtypes from more complex genetic, imaging, and clinical analysis.
Supported by
NSF, SGF
Contact
vsochat@stanford.edu
cognitive phenotype
V. Sochat, Rubin Lab, Stanford University School of Medicine, Stanford CA
brain phenotype neuropsychiatric profile
Method
BEHAVIORAL &
COGNITIVE METRICS
STRUCTURED TRAIT
SCORE DATABASE
Why Develop Brain Phenotypes?
Patterns of aberrant volume, cortical thickness, and resting brain function can serve as
unbiased biomarkers of neuropsychiatric disorder.
• The inability to identify these robust biomarkers is due to using DSM defined groups to drive
analysis, and assuming homogeneity in healthy controls and disorder groups.
• Instead, we should derive groups from the data, and validate them with reliable “trait scores”
from our “cognitive phenotypes.”
• Patterns of brain structure and function associated with specific behavioral outcomes can then
be used for prognosis by a clinician.
STRUCTURAL MRI
VOXEL BASED
MORPHOMETRY
NORMALIZED
TISSUE VOLUMES
AND SURFACES
FUNCTIONAL MRI
TIME
PREPROCESSING
& NORMALIZATION
FUNCTIONAL BRAIN
NETWORKS
QUERY
DATABASE
COGNITIVE
PHENOTYPES
ICA
SURFACE
EXTRACTION
SPHERICAL
MAPPING
BRAIN
PHENOTYPES
FEATURE
EXTRACTIONCLUSTER
DISORDER
SUBTYPES
Evaluation
SPHERES FLATTENED AND LAYERED INTO
STRUCURAL + FUNCTIONAL REPRESENTATION