Parametric Analysis of fMRI Data Using Linear Systems Methods

doi:10.1006/nimg.1997.0278

NeuroImage

Volume 6, Issue 2, August 1997, Pages 93-103

https://doi.org/10.1006/nimg.1997.0278 Get rights and content

Abstract

Using a model of the functional MRI (fMRI) impulse response based on published data, we have demonstrated that the form of the fMRI response to stimuli of freely varied timing can be modeled well by convolution of the impulse response with the behavioral stimulus. The amplitudes of the responses as a function of parametrically varied behavioral conditions are fitted well using a piecewise linear approximation. Use of the combined model, in conjunction with correlation analysis, results in an increase in sensitivity for the MRI study. This approach, based on the well-established methods of linear systems analysis, also allows a quantitative comparison of the response amplitudes across subjects to a broad range of behavioral conditions. Fit parameters, derived from the amplitude data, are relatively insensitive to a variety of MRI-related artifacts and yield results that are compared readily across subjects.

References (47)

S. Bookheimer
Functional MRI applications in clinical epilepsy
Neuroimage
(1996)
C. Büchel et al.
Nonlinear regression in parametric activation studies
Neuroimage
(1996)
M.S. Cohen et al.
Ultra-fast imaging
Magn. Reson. Imaging
(1991)
K. Friston et al.
Analysis of fMRI time series revisited
Neuroimage
(1995)
C. Price et al.
Regional response differences within the human auditory cortex when listening to words
Neurosci. Lett.
(1992)
J. VanMeter et al.
Parametric analysis of functional neuroimages: Application to a variable-rate motor taks
Neuroimage
(1995)
P.A. Bandettini et al.
Processing strategies for time-course data sets in functional MRI of the human brain
Magn. Reson. Med.
(1993)
J.W. Belliveau et al.
Functional mapping of the human visual cortex by magnetic resonance imaging
Science
(1991)
S. Bookheimer et al.
Functional MRI
Epilepsy, A Comprehensive Textbook
(1997)
S. Y. Bookheimer, M. S. Cohen, M. Dapretto, I. Fried, A. Shewmon, K. Black, et al. 1995, Functional MRI in surgical...

G.M. Boynton et al.

Linear systems analysis of functional magnetic resonance imaging in human V1

J. Neurosci.

(1996)

R. Buxton et al.

A model for the coupling between cerebral blood flow and oxygen metabolism during neural stimulation

J. Cereb. Blood Flow Metab.

(1997)

M.S. Cohen

Rapid MRI and functional applications

M.S. Cohen et al.

Parametric analysis of functional MRI data: A physiologically relevant transform

Cerebral Blood Flow and Metabolism

(1995)

M. S. Cohen, M. F. Green, 1995, Where the voices come from: Imaging of schizophrenic auditory hallucinations, Society...

M. S. Cohen, D. A. Kelley, M. L. Rohan, P. A. Roemer, 1996a, An MR instrument optimized for intracranial neuroimaging,...

M.S. Cohen et al.

Changes in cortical activity during mental rotation: A mapping study using functional magnetic resonance imaging

Brain

(1996)

R.T. Constable et al.

An ROC approach for evaluating functional brain MR imaging and postprocessing protocols

Magn. Reson. Med.

(1995)

B.H. Dobkin et al.

Functional magnetic resonance imaging to study brain adaptations during rehabilitation of upper extremity function after hemiplegic stroke

J. Neuro. Rehab.

(1996)

S.A. Engel et al.

fMRI of human visual cortex

Nature

(1994)

F. Farzaneh et al.

Analysis of T2 limitations and off-resonance effects on spatial resolution and artifacts in echo-planar imaging

Magn. Reson. Med.

(1990)

C.R. Fisel et al.

MR contrast due to microscopically heterogeneous magnetic susceptibility: Numerical simulations and applications to cerebral physiology

Magn. Reson. Med.

(1991)

K. Friston et al.

Analysis of functional MRI time series

Hum. Brain Mapping

(1994)

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Regular ArticleParametric Analysis of fMRI Data Using Linear Systems Methods

Abstract

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Regular Article
Parametric Analysis of fMRI Data Using Linear Systems Methods