Voxel-level functional connectivity using spatial regularization

NeuroImage
Christopher BaldassanoLi Fei-Fei

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...Continue Reading

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Citations

Jun 15, 2014·Magnetic Resonance Imaging·Xiaomu Song, Nan-kuei Chen
Oct 17, 2015·Journal of Neuroscience Methods·Xiaomu SongNan-kuei Chen
Mar 20, 2013·NeuroImage·Christopher BaldassanoLi Fei-Fei
Aug 5, 2015·Visual Neuroscience·Jonathan Winawer, Nathan Witthoft
Jun 1, 2016·Neuropsychologia·Fatma UyarMarlene Behrmann
Jun 14, 2017·Human Brain Mapping·Kun-Han LuZhongming Liu
Mar 23, 2017·IEEE Transactions on Medical Imaging·Ali-Reza Mohammadi-NejadHamid Soltanian-Zadeh
Nov 21, 2017·PLoS Computational Biology·Stefano AnzellottiRebecca Saxe
Jul 31, 2018·Annual Review of Vision Science·Bevil R Conway

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