fMRI alignment based on local functional connectivity patterns
2012
In functional neuroimaging studies, the inter-subject alignment of functional magnetic resonance imaging (fMRI) data is
a necessary precursor to improve functional consistency across subjects. Traditional structural MRI based registration
methods cannot achieve accurate inter-subject functional consistency in that functional units are not necessarily
consistently located relative to anatomical structures due to functional variability across subjects. Although spatial
smoothing commonly used in fMRI data preprocessing can reduce the inter-subject functional variability, it may blur the
functional signals and thus lose the fine-grained information. In this paper we propose a novel functional signal based
fMRI image registration method which aligns local functional connectivity patterns of different subjects to improve the
inter-subject functional consistency. Particularly, the functional connectivity is measured using Pearson correlation. For
each voxel of an fMRI image, its functional connectivity to every voxel in its local spatial neighborhood, referred to as
its local functional connectivity pattern, is characterized by a rotation and shift invariant representation. Based on this
representation, the spatial registration of two fMRI images is achieved by minimizing the difference between their
corresponding voxels' local functional connectivity patterns using a deformable image registration model. Experiment
results based on simulated fMRI data have demonstrated that the proposed method is more robust and reliable than the
existing fMRI image registration methods, including maximizing functional correlations and minimizing difference of
global connectivity matrices across different subjects. Experiment results based on real resting-state fMRI data have
further demonstrated that the proposed fMRI registration method can statistically significantly improve functional
consistency across subjects.
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