Reattribution of Semantic Verse

Feature Group MeetingVanessa Sochat

July 18, 2013

Hypothesis

Reattribution of terms of well known semantic verse can be useful in expressing an agreed

upon, mutual sentiment shared by a common cohort.

Background

Background1

2

3

Background4

Hypothesis

Reattribution of terms of well known semantic verse can be useful in expressing an agreed

upon, mutual sentiment shared by a common cohort.

Hypothesis

Reattribution of terms of well known semantic verse can be useful in expressing an agreed

upon, mutual sentiment shared by a common cohort.

“We, the Feature Group”

Hypothesis

Reattribution of terms of well known semantic verse can be useful in expressing an agreed

upon, mutual sentiment shared by a common cohort.

“Present with a lunchbox of wisdom”

Hypothesis

Reattribution of terms of well known semantic verse can be useful in expressing an agreed

upon, mutual sentiment shared by a common cohort.

“Dan, we are going to miss you!”

Conclusions

On the 18th day of July the Feature group gave to me...

Conclusions18 horse power car,17 ounce jail rock16 hairy eyeballs

15 inch party-stick14 Matlab hotshots

13 neon straw things12 ounces caffeine11 invisible friends

10 ounces beard cream9 months of planning8 wheeled Caltrain

7 shiny pens6 hour hand warmth

5 (plus one!) GOLDEN OREOS!4 desktop friends

3 barf bags2 heavy duty sponges

and a lunch box packed with all this wisdom!

Data Driven Neuropsychiatric Profiling

Qualifying Exam PresentationVanessa SochatAugust 19, 2013

Outline

• Background• Hypothesis and Specific Aims• Methods• Conclusion

– Biomedical Contribution– Informatics Contribution

Outline

• Background• Hypothesis and Specific Aims• Methods• Conclusion

– Biomedical Contribution– Informatics Contribution

Autism Spectrum Disorder:A childhood development disorder

• Afflicts 1 in 100 children • Economic burden of $126 billion annually• Social, communication, and cognitive deficits,

repetitive behaviors and interests

Unsolved Problem:

data-driven subtyping of autism spectrum disorders for early diagnosis and tailored, effective treatment

Autism Spectrum Disorder:Our knowledge is limited

• Genetics• Behavior• Neuroimaging

Challenges:

Results not reproducibleNo clinical applicability

Outline

• Background• Hypothesis and Specific Aims• Methods• Conclusion

– Biomedical Contribution– Informatics Contribution

Outline

• Background• Hypothesis and Specific Aims• Methods• Conclusion

– Biomedical Contribution– Informatics Contribution

Hypothesis

Structuring and mining behavioral and imaging data will define clinically-useful disease subtypes of ASD better than currently possible using DSM

alone.

Specific Aims

Aim 1: to develop a computational representation of ASD phenotypes based on imaging and behavioral data

Aim 2: to develop informatics methods to identify subtypes of ASD patients

Aim 3: to evaluate the methods

BEHAVIOR & COGNITION

Big Picture

ASD MRI HC MRI1. Start with groups2. Collect data3. Find differences4. Inconsistent results

1. Collect data2. Standardize behavior3. Local brain phenotype4. Relate5. Patterns of relation =

subtypesMRI

Outline

• Background• Hypothesis and Specific Aims• Methods• Conclusion

– Biomedical Contribution– Informatics Contribution

Outline

• Background• Hypothesis and Specific Aims• Methods• Conclusion

– Biomedical Contribution– Informatics Contribution

Aim 1: develop a computational representation of ASD phenotypes based on imaging and behavioral data

A Standard Representation of behavior and cognition

A Standard Representation of behavior and cognition

A Standard Representation of behavior and cognition

• Structure data• Query• Cognitive phenotype

C1 C2 C3 …. CN

Data driven identification of Local Differences in Brain Structure

(OBSERVED DATA)

(MIXING MATRIX) (ORIGINAL DATA)

X = A SX

S = A-1 XX

n x m n x n n x m

fMRI data

time

time

space spacecomponents

components

spatial maps

Independent Component AnalysisOf fMRI to define functional networks

n x m n x n n x m

sMRI data spatial maps

brains

brains

components

components

space space

Independent Component AnalysisOf sMRI to discover structural patterns

set of weights belonging to one person, each one telling us the relative contribution of the person’s brain to a particular pattern of brain structure

Aim 2: develop informatics methods to identify subtypes of ASD patients

C1 C2 C3 …. CN

cognitive phenotype +

brain phenotype = neuropsychiatricprofile

Aim 2: develop informatics methods to identify subtypes of ASD patients: ideas

cognitive phenotype +

brain phenotype = neuropsychiatricprofile

Goal

find specific patterns of brain structure that can predict a personality trait, or an intelligence metric.

Decision Support Means

subtype diagnosis based on brain structure

Aim 2: develop informatics methods to identify subtypes of ASD patients: ideas

cognitive phenotype +

brain phenotype = neuropsychiatricprofile

Do combined autism + healthy control decompositionapply some threshold to define a group for each component evaluate these groups.

1

Do combined autism + healthy control decompositionapply some threshold to define a group for each component do second decomposition to get “cleaner” resultevaluate these groups.

2

Start by splitting data based on some behavioral metricDo decomposition for each groupsomehow compare output, and evaluate groups

3

Aim 2: develop informatics methods to identify subtypes of ASD patients: ideas

cognitive phenotype +

brain phenotype = neuropsychiatricprofile

• How do we classify a new case? Need to do ICA again?

• Can weights / spatial maps have meaning outside of a decomposition?

• Ideal: make comparisons between different decompositions

• Not ideal: running ICA all over again with entire data + new dataset

Aim 3: evaluate the method: ideas

demonstrate that the subtypes of ASD defined by our methods have greater homogeneity among individuals within the subtypes than subtypes defined by the current gold standard DSM

two sample T-test with my groups to assess voxel-wise differences in structure compared to same test with DSM labels

want to see our groups have clusters of just ASD or just HC

1

2

3

4 validation by producing known results from literature

Outline

• Background• Hypothesis and Specific Aims• Methods• Conclusion

– Biomedical Contribution– Informatics Contribution

Outline

• Background• Hypothesis and Specific Aims• Methods• Conclusion

– Biomedical Contribution– Informatics Contribution

Conclusion

• Informatics Contributions– Extending Big Data paradigms to neuroscience– Novel KR of behavioral and cognitive metrics– Novel KR of local brain phenotype– Methods to make inferences over these KR

• Biological Contributions– Discovery of biomarkers of disorder– Definition of disorder subtypes– Decision support about treatment

Acknowledgements

Advisors and PanelDaniel RubinRuss AltmanMark MusenAntonio Hardan

ColleaguesKaustubh SupekarFeature GroupThe MIND Institute

Support StaffJohn DiMarioMary Jeanne & Nancy

FundingMicrosoft ResearchSGF and NSF

Friends and Fellow BMIRebecca SawyerLinda SzaboKatie PlaneyTiffany Ting LuFrancisco GimenezDiego MunozLuke Yancy Jr.Jonathan MortensenThe M&Ms previously known as first years

Thank you!

CAM: Cognitive Atlas Markup