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My first 100 Tb of data. STATISTICAL METHODS FOR NEW TECHNOLOGY WORKING GROUP. Ciprian M. Crainiceanu Johns Hopkins University http://www.biostat.jhsph.edu/smnt. Members of the group. Key personnel C.M. Crainiceanu, B.S. Caffo, A.-M. Staicu, S. Greven, D. Ruppert, C.-Z. Di Senior Students

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my first 100 tb of data

My first 100 Tb of data

STATISTICAL METHODS FOR NEW TECHNOLOGY WORKING GROUP

Ciprian M. Crainiceanu

Johns Hopkins University

http://www.biostat.jhsph.edu/smnt

members of the group
Members of the group
  • Key personnel
    • C.M. Crainiceanu, B.S. Caffo, A.-M. Staicu, S. Greven, D. Ruppert, C.-Z. Di
  • Senior Students
    • V. Zipunnikov, J.-A. Goldsmith
  • Other statisticians (>20)
  • Scientific collaborators
    • Direct collaboration
    • Solving important scientific problems
    • Diverse scientific applications
scientific collaborators
Scientific Collaborators
  • Susan Bassett – fMRI, Alzheimer’s
  • Danny Reich – DTI, DCE-MRI, MS
  • Brian Schwartz – lead exposure, VBM, DTI, white matter imaging
  • Stewart Mostofsky – fMRI, rsfcMRI, Autism, ADHD, Turrets
  • Naresh Punjabi – EEG, sleep, sleep diseases
  • Dzung Pham / PilouBazin – Cortical shape, thickness, lesion detection, MS
  • Dean Wong – PET, fMRI substance abuse
  • Susan Resnick –BLSA
  • Jerry Prince – BLSA, ADNI
  • Jim Pekar, Peter Van Zijl – 7T MRI, fMRI, rsfcMRI preprocessing, scanner physics
  • Christos Davatzikos- RAVENS
  • Susumu Mori – DTI, tractography
  • Dana Boatman – ECOG, EEG, epilepsy
  • Graham Redgrave – fMRI, DTI, Huntington’s, anorexia/bulimia
  • Tudor Badea, Bruno Jednyak – Neuron classification, morphometry, 3D structure and shape
  • Tom Glass – Gizmos
  • Merck – EEG, neuroimaging
  • Pfizer – imaging biomarkers?
longitudinal functional principal component analysis lfpca
Longitudinal Functional Principal Component Analysis (LFPCA)
  • I=1000, J=4, D=100: 15’
  • I=1000, J=8, D=200: 70’

Greven, Crainiceanu, Caffo, Reich, 2010. LFPCA, EJS, to appear

a simple regression formula
A simple regression formula
  • Data compression via longitudinal PCA
  • MoM estimators of covariance matrices, smoothing
  • Need: all covariance operators
  • Solution: regress Yij(d)Yik(d’) on 1, Tik, Tij, TikTij, djk
functional regression
Functional regression
  • No paper on longitudinal functional regression
  • No paper published with this data structure
  • Longitudinal extensions are not “simple”
  • Technical details are hard without the correct “recipe” for known and published “ingredients”
  • No available method that scales up

Goldsmith, Feder, Crainiceanu, Caffo, Reich, 2010. PFR, JCGS, to appear

Goldsmith, Crainiceanu, Caffo, Reich, 2010. LPFR, to appear?

slide15
PVD

Yi = P ViD + Ei

  • P is T*A
  • D is B*F
  • Vi is A*B
  • A << T, B << F
slide16

Singular Value Decomposition (SVD) summarizes variance

One subject

Time

Subject-specific Data

Frequency.

Frequency

Diagonal

Matrix

Eigenvariates

Eigenfrequencies

slide17

Default PVD

(Start here)

Eigenvariates

SVD

Subject-specific Data

Eigenfrequencies

Low rank approximation

SVD

Population decomposition

Stacked across subjects

Projecting original data onto population bases

...

Subject-specific Data

Caffo BS, Crainiceanu CM, Verduzco G, Joel SE, Mostofsky SH, Bassett SS, Pekar JJ. Two-Stage decompositions for the analysis of functional connectivity for fMRI with application to Alzheimer’s disease risk. NeuroImage (In Press).

slide19

Currently:

    • Deploying PVD to the 1000 Functional Connectomes Project
    • http://www.nitrc.org/projects/fcon_1000/
    • Comparing rsfcMRI in stroke versus normal subjects
main message backed by 100tb of data
Main message, backed by 100Tb of data
  • Eventually, good tech makes into observational and clinical trials
  • Longitudinal/Multilevel FDA is the natural next step in FDA
  • Data is changing the way we do business: availability, size, complexity
  • Likely: funding will be based much more on relevance than on technical ability
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