KEYWORDS: Image restoration, Data modeling, Functional magnetic resonance imaging, Algorithm development, Detection and tracking algorithms, Brain, Scanners, Hemodynamics, Performance modeling, Signal to noise ratio
The objective of fMRI data analysis is to detect the region of the brain that gets activated in response to a specific
stimulus presented to the subject. We develop a new algorithm for activation detection in event-related fMRI
data. We utilize a forward model for fMRI data acquisition which explicitly incorporates physiological noise,
scanner noise and the spatial blurring introduced by the scanner. After slice-by-slice image restoration procedure
that independently restores each data slice corresponding to each time index, we estimate the parameters of the
hemodynamic response function (HRF) model for each pixel of the restored data. In order to enforce spatial
regularity in our estimates, we model the prior distribution of the HRF parameters as a generalized Gaussian
Markov random field (GGMRF) model. We develop an algorithm to compute the maximum a posteriori (MAP)
estimates of the parameters. We then threshold the amplitude parameters to obtain the final activation map. We
illustrate our algorithm by comparing it with the widely used general linear model (GLM) method. In synthetic
data experiments, under the same probability of false alarm, the probability of correct detection for our method
is up to 15% higher than GLM. In real data experiments, through anatomical analysis and benchmark testing
using block paradigm results, we demonstrate that our algorithm produces fewer false alarms than GLM.
KEYWORDS: Functional magnetic resonance imaging, Independent component analysis, Principal component analysis, Data modeling, High dynamic range imaging, Statistical analysis, Hemodynamics, Autoregressive models, Data analysis, Image segmentation
We have previously developed a novel framework for the analysis of single-slice functional magnetic resonance imaging
(fMRI) data that identifies multi-pixel regions of activation through iterative segmentation-based optimization over
hemodynamic response (HDR) estimates, generated at the level of both individual pixels and regional groupings.
Through the addition of a correction for the disparate sampling times associated with multi-slice acquisitions in fMRI,
the algorithm has been extended to permit analysis of full volumetric data. Additional improvement in performance is
achieved through inclusion of an estimate of the covariance matrix of the fMRI data, previously assumed to be
proportional to the identity matrix across all regions. Simulations using synthetic activation embedded in autoregressive
noise reveal the proposed procedure to be more sensitive and selective than conventional fMRI analysis methods
(reference set: general linear model test, GLM; independent component analysis, ICA; principal component analysis,
PCA) in identification of active regions over the range of average contrast-to-noise ratios of 0.7 to 2.
This paper presents the application of the expectation-maximization/maximization of the posterior marginals
(EM/MPM) algorithm to signal detection for functional MRI (fMRI). On basis of assumptions for fMRI 3-D image
data, a novel analysis method is proposed and applied to synthetic data and human brain data. Synthetic data analysis is
conducted using two statistical noise models (white and autoregressive of order 1) and, for low contrast-to-noise ratio
(CNR) data, reveals better sensitivity and specificity for the new method than for the traditional General Linear Model
(GLM) approach. When applied to human brain data, functional activation regions are found to be consistent with those
obtained using the GLM approach.
KEYWORDS: Functional magnetic resonance imaging, Data modeling, High dynamic range imaging, Independent component analysis, Hemodynamics, Image segmentation, Statistical analysis, Principal component analysis, Data analysis, Image processing algorithms and systems
Analysis of functional magnetic resonance imaging (fMRI) data has been performed using both model-driven
(parametric) methods and data-driven methods. An advantage of model-driven methods is incorporation of prior
knowledge of spatial and temporal properties of the hemodynamic response (HDR). A novel analytical framework for
fMRI data has been developed that identifies multi-voxel regions of activation through iterative segmentation-based
optimization over HDR estimates for both individual voxels and regional groupings. Simulations using synthetic
activation embedded in autoregressive integrated moving average (ARIMA) noise reveal the proposed procedure to be
more sensitive and selective than conventional fMRI analysis methods (reference set: principle component analysis,
PCA; independent component analysis, ICA; k-means clustering, k=100; univariate t-test) in identification of active
regions over the range of average contrast-to-noise ratios of 0.5 to 4.0. Results of analysis of extant human data (for
which the average contrast-to-noise ratio is unknown) are further suggestive of greater statistical detection power.
Refinement of this new procedure is expected to reduce both false positive and negative rates, without resorting to
filtering that can reduce the effective spatial resolution.
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