Abstract
Functional magnetic resonance imaging (fMRI) of the cortex is a powerful tool for neuroscience research, and its use has been extended into the brainstem and spinal cord as well. However, there are significant technical challenges with extrapolating the developments that have been achieved in the cortex to their use in the brainstem and spinal cord. Here, we develop a normalized coordinate system for the cervical spinal cord and brainstem, demonstrating a semiautomated method for spatially normalizing and coregistering fMRI data from these regions. fMRI data from 24 experiments in eight volunteers are normalized and combined to create the first anatomical reference volume, and based on this volume, we define a standardized region-of-interest (ROI) mask, as well as a map of 52 anatomical regions, which can be applied automatically to fMRI results. The normalization is demonstrated to have an accuracy of less than 2 mm in 93% of anatomical test points. The reverse of the normalization procedure is also demonstrated for automatic alignment of the standardized ROI mask and region-label map with fMRI data in its original (unnormalized) format. A reliable method for spatially normalizing fMRI data is essential for analyses of group data and for assessing the effects of spinal cord injury or disease on an individual basis by comparing with results from healthy subjects.
Highlights
Functional magnetic resonance imaging has become one of the most powerful tools for neuroscience research, and yet its use is limited to studies of the cortex, with relatively few exceptions [1,2,3,4]
This means that studies of distributed systems, such as those involved with pain, are limited in their scope and completeness until Functional magnetic resonance imaging (fMRI) can be applied in the entire central nervous system (CNS)
The efficacy of the normalization method across the cervical spinal cord and brainstem is shown in Fig. 2, as an averaged image across 24 experiments in the eight volunteers studied
Summary
Functional magnetic resonance imaging (fMRI) has become one of the most powerful tools for neuroscience research, and yet its use is limited to studies of the cortex, with relatively few exceptions [1,2,3,4]. This means that studies of distributed systems, such as those involved with pain, are limited in their scope and completeness until fMRI can be applied in the entire central nervous system (CNS). Despite the advances in fMRI techniques, there are fundamental problems in extrapolating the technological
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