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Voxel-level functional connectivity using spatial regularization.

Abstract：

:Discovering functional connectivity between and within brain regions is a key concern in neuroscience. Due to the noise inherent in fMRI data, it is challenging to characterize the properties of individual voxels, and current methods are unable to flexibly analyze voxel-level connectivity differences. We propose a new functional connectivity method which incorporates a spatial smoothness constraint using regularized optimization, enabling the discovery of voxel-level interactions between brain regions from the small datasets characteristic of fMRI experiments. We validate our method in two separate experiments, demonstrating that we can learn coherent connectivity maps that are consistent with known results. First, we examine the functional connectivity between early visual areas V1 and VP, confirming that this connectivity structure preserves retinotopic mapping. Then, we show that two category-selective regions in ventral cortex - the Parahippocampal Place Area (PPA) and the Fusiform Face Area (FFA) - exhibit an expected peripheral versus foveal bias in their connectivity with visual area hV4. These results show that our approach is powerful, widely applicable, and capable of uncovering complex connectivity patterns with only a small amount of input data.

journal_name

Neuroimage

journal_title

NeuroImage

authors

Baldassano C,Iordan MC,Beck DM,Fei-Fei L

doi

10.1016/j.neuroimage.2012.07.046

subject

Has Abstract

pub_date

2012-11-15 00:00:00

pages

1099-106

issue

3

eissn

1053-8119

issn

1095-9572

pii

S1053-8119(12)00775-6

journal_volume

63

pub_type

杂志文章

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