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

EXTRACTION CLUSTER

DISORDER

SUBTYPES

Evaluation

SPHERES FLATTENED AND LAYERED INTO

STRUCURAL + FUNCTIONAL REPRESENTATION