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Serial correlations in single-subject fMRI with sub-second TR.

Saskia Bollmann¹, Alexander M Puckett², Ross Cunnington³

¹Centre for Advanced Imaging, The University of Queensland, Brisbane QLD 4072, Australia.

|October 26, 2017

Summary

This summary is machine-generated.

Standard statistical models for fMRI data are inadequate for fast imaging sequences. Advanced noise modeling and pre-whitening are crucial for accurate single-subject analysis with simultaneous multislice imaging.

Keywords:

Autocorrelation Autoregressive model Physiological noise Simultaneous multislice (SMS)Variational Bayes fMRI analysis

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Published on: June 20, 2012

Area of Science:

Neuroimaging
Statistical analysis
Magnetic Resonance Imaging

Background:

Serial correlations in functional MRI (fMRI) data require careful consideration for valid statistical inference.
Underestimating parameter estimate variability due to serial correlations can lead to increased false-positive rates in fMRI analysis.
Traditional fMRI noise modeling often uses a first-order autoregressive (AR) process, with pre-whitening applied to remove these correlations.

Purpose of the Study:

To investigate the impact of sub-second temporal resolution from simultaneous multislice (SMS) imaging on fMRI noise structure.
To determine the optimal autoregressive (AR) model order for fMRI sequences with a repetition time (TR) under 600 ms.
To evaluate the adequacy of common noise models for fast fMRI acquisition.

Main Methods:

Fitting a higher-order AR model to fMRI time series data acquired with sub-second TRs.
Estimating the optimal AR model order for sequences with TR < 600 ms and whole-brain coverage.
Assessing the effectiveness of physiological noise modeling in conjunction with advanced pre-whitening schemes.

Main Results:

Physiological noise modeling effectively reduced the required AR model order in fMRI data.
Despite physiological noise modeling, remaining serial correlations necessitated a more advanced noise model.
First-order autoregressive (AR(1)) models were found to be inadequate for modeling serial correlations in fMRI data acquired with sub-second TRs.

Conclusions:

Commonly used noise models are insufficient for accurately modeling serial correlations in fMRI data acquired with sub-second TRs.
Advanced pre-whitening schemes combined with physiological noise modeling are essential for valid single-subject statistical inference in fast fMRI sequences.
Future research should focus on developing and implementing sophisticated noise models for high-temporal-resolution fMRI studies.