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Likelihood-based hypothesis tests for brain activation detection from MRI data disturbed by colored noise: a

A J den Dekker¹, D H J Poot, R Bos

¹Delft University of Technology, Delft Center for Systems and Control, 2828 CD Delft, The Netherlands. a.j.dendekker@tudelft.nl

IEEE Transactions on Medical Imaging

|February 4, 2009

Summary

This study introduces new statistical tests for functional magnetic resonance imaging (fMRI) data analysis that directly account for colored noise, improving detection rates for brain activity compared to standard methods.

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Area of Science:

Neuroimaging
Statistical analysis
Signal processing

Background:

Functional magnetic resonance imaging (fMRI) data often contains temporally colored noise.
Preprocessing steps like prewhitening are common but may not fully address noise issues.
Accurate functional activation detection is crucial for interpreting fMRI results.

Purpose of the Study:

To develop and evaluate likelihood-based hypothesis tests for fMRI data that directly incorporate colored noise.
To compare the performance of these new tests against the traditional General Linear Model (GLM) approach.
To investigate the impact of autoregressive (AR) model order selection on test performance.

Main Methods:

Modeled fMRI time series using a linear regression model with task, baseline, and drift regressors.
Modeled temporal noise structure using an autoregressive (AR) model, with order selected by Akaike's Information Criterion.
Developed three likelihood-based tests: Generalized Likelihood Ratio (GLR), Wald, and Rao tests.
Evaluated test performance using Monte Carlo simulations, focusing on detection and false alarm rates.

Main Results:

Theoretical asymptotic distributions for GLM, GLR, and Wald tests are unreliable for finite fMRI time series.
The proposed GLR test offers a statistically significant improvement in detection rate over GLM-based tests for a fixed false alarm rate.
Inadequate AR model order selection severely degrades performance; overmodeling has less impact.

Conclusions:

Likelihood-based tests offer a more robust approach to functional activation detection in fMRI by directly handling colored noise.
The GLR test demonstrates superior performance, suggesting its utility in real-world fMRI analyses.
Careful selection of the AR model order is critical for reliable fMRI data analysis.