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Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

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Is First-Order Vector Autoregressive Model Optimal for fMRI Data?

Chee-Ming Ting¹, Abd-Krim Seghouane², Muhammad Usman Khalid³

¹Center for Biomedical Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia cmting@utm.my.

Neural Computation

|July 11, 2015

Summary

This summary is machine-generated.

Selecting the optimal order for vector autoregressive (VAR) models in fMRI data is crucial. This study shows bias-corrected criteria, especially Kullback

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Area of Science:

Neuroscience
Computational Neuroscience
Data Science

Background:

Previous studies often assumed a vector autoregressive (VAR) model order of one for fMRI data, which may not be optimal.
Classical information criteria (IC) like AIC are biased and unsuitable for high-dimensional fMRI data with small sample sizes.
Varying data dimensions, subjects, tasks, and experimental designs necessitate robust model order selection.

Purpose of the Study:

To comprehensively evaluate optimal VAR model orders for fMRI data across different experimental designs and brain networks.
To assess the validity of the order-one hypothesis for fMRI VAR models.
To compare the performance of classical versus bias-corrected information criteria for VAR model selection in fMRI.

Main Methods:

Evaluated multiple model selection criteria, including Kullback's IC (KIC) and bias-corrected versions (AICc, KICc), on resting-state, event-related, and block-design fMRI data.
Utilized varying time series dimensions from distinct functional brain networks.
Conducted simulations to assess small-sample selection performance.

Main Results:

Bias-corrected criteria (AICc, KICc) demonstrated superior small-sample performance over classical criteria, reducing overfitting.
Kullback's IC corrected for bias (KICc) performed best overall in simulations.
Real fMRI data analysis revealed that VAR orders greater than one were selected for small to moderate dimensions (e.g., default mode network, motor networks), while low orders near one were chosen for large dimensions (full-brain networks).

Conclusions:

The order-one hypothesis for fMRI VAR models is not universally valid and depends on network size and data characteristics.
Bias-corrected information criteria, particularly KICc, are recommended for accurate VAR model order selection in fMRI, especially with limited sample sizes.
Optimal VAR orders vary significantly across different brain networks and experimental paradigms.