quals practice presentation
TRANSCRIPT
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