Llfom a nonlinear hemodynamic response model
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LLFOM: A Nonlinear Hemodynamic Response Model. Bing Bai NEC Labs America Oct 2014. About who I am. Paul’s only student that got Ph.D in Computer Science Thus the least favorite one ( orz ) Worked with Paul on: Question answering fMRI image retrieval

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LLFOM: A Nonlinear Hemodynamic Response Model

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Llfom a nonlinear hemodynamic response model

LLFOM: A Nonlinear Hemodynamic Response Model

Bing Bai

NEC Labs America

Oct 2014


About who i am

About who I am

  • Paul’s only student that got Ph.D in Computer Science

    • Thus the least favorite one (orz)

    • Worked with Paul on:

      • Question answering

      • fMRI image retrieval

  • Currently researcher in NEC Labs America

    • Machine learning


Lagged limited first order model llfom

Lagged, Limited First Order Model (LLFOM)

  • A Nonlinear hemodynamic model used in fMRI study

  • A example of Paul’s many overlooked great ideas

    • A nice, novel idea

    • Published only in my thesis

  • A example of “Paul is a nice guy”

    • I could be still doing this right now, if he makes me


Active and inactive voxels

Active and Inactive voxels

  • The intensity change of some voxels are correlated with stimulus, they are considered to be “active”.

  • The unofficial goal of fMRI: detecting voxels activated by visual, audio, conscience, love … and whatever is interesting.


Generalized linear model glm

Generalized Linear Model (GLM)

  • How to get Design Matrix X?

    • Hypothesis:

      • A voxel is a linear time-invariant (LTI) system

      • The impulse response function is known as Hemodynamic Response Function (HRF)

    • If we convolve the HRF with the stimulus we will get a response time series, and we put it in the design matrix as a column.

  • Canonical HRF

    • An ad-hoc model


Lagged limited first order model llfom nonlinear model

Lagged, Limited First Order Model (LLFOM) nonlinear model

  • Earlier nonlinear hemodynamic models

    • Balloon model (Buxton et al. 1998)

      • A model with clear physiological explanations

      • Complicated

    • Volterra kernels (Friston et al. 2000).

      • Black box, no physiological explanations

      • Complicated

  • LLFOM model

    • With physiological explanation

    • Simple enough for large-scale processing


Lagged limited first order model llfom nonlinear model1

Lagged, Limited First Order Model (LLFOM) nonlinear model

  • The response is modeled with differential equation of 4 parameters ( ):

    • The first term is the positive response, proportional to the stimulus with a lag (τ), the the strength of the response, and limited by the capability of blood flow ( ). The second term is an exponential decay.

    • Can be regrouped as


Lagged limited first order model llfom nonlinear model2

Lagged, Limited First Order Model (LLFOM) nonlinear model

  • Model fitting:

    • is the constant component

    • Nonlinear optimization (BFGS-B)

    • Initial point in search (A=0.1, B=0.1, C=0.2)

    • Grid search for

    • (a) (b) (c) are ,

      and ,

      respectively.


Fmri retrieval based on glm

fMRI Retrieval Based on GLM

Condition 1

Condition 2


Results glm based features

Results: GLM-based Features


Concluding remarks

Concluding Remarks

  • Future work (what should have been done)

    • Smoothing across voxels

    • Analysis on the good performance on the pure Bayesian approach

  • I like to thank Paul for his guidance

    • On research

    • On many other things (morality, values, life, …)


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