<p>The analysis of
connectivity between parcellated regions of cortex provides insights into the
functional architecture of the brain at a systems level. However, there has
been less progress in the derivation of functional structures from voxel-wise
analyses at finer scales. We propose a novel method, called localized
topo-connectivity mapping with singular-value-decomposition-informed filtering
(or filtered LTM), to identify and characterize voxel-wise functional
structures in the human brain using resting-state fMRI data. Here we describe its mathematical background and provide a
proof-of-concept using simulated data that allow an intuitive interpretation of
the results of filtered LTM. The algorithm has also been applied to 7T fMRI
data as part of the Human Connectome Project to generate group-average LTM
images. Functional structures revealed by this approach agree moderately well
with anatomical structures identified by T<sub>1</sub>-weighted images and fractional
anisotropy maps derived from diffusion MRI. Moreover, the LTM images also
reveal subtle functional variations that are not apparent in the anatomical
structures. To assess the performance of LTM images, the subcortical region and
occipital white matter were separately parcellated. Statistical tests were performed to
demonstrate that the synchronies of fMRI signals in LTM-informed parcellations
are significantly larger than those of random parcellations. Overall, the
filtered LTM approach can serve as a tool to investigate the functional
organization of the brain at the scale of individual voxels as measured in
fMRI.</p>
Drug connectivity mapping and functional analysis reveal therapeutic small molecules that differentially modulate myelination
