Abstract
Identifying venous voxels in fMRI datasets is important to increase the specificity of fMRI analyses to microvasculature in the vicinity of the neural processes triggering the BOLD response. This is, however, difficult to achieve in particular in typical studies where magnitude images of BOLD EPI are the only data available. In this study, voxelwise functional connectivity graphs were computed on minimally preprocessed low TR (333 ms) multiband resting-state fMRI data, using both high positive and negative correlations to define edges between nodes (voxels). A high correlation threshold for binarization ensures that most edges in the resulting sparse graph reflect the high coherence of signals in medium to large veins. Graph clustering based on the optimization of modularity was then employed to identify clusters of coherent voxels in this graph, and all clusters of 50 or more voxels were then interpreted as corresponding to medium to large veins. Indeed, a comparison with SWI reveals that 75.6±5.9% of voxels within these large clusters overlap with veins visible in the SWI image or lie outside the brain parenchyma. Some of the remaining differences between the two modalities can be explained by imperfect alignment or geometric distortions between the two images. Overall, the graph clustering based method for identifying venous voxels has a high specificity as well as the additional advantages of being computed in the same voxel grid as the fMRI dataset itself and not needing any additional data beyond what is usually acquired (and exported) in standard fMRI experiments.
Highlights
Any interpretation of fMRI results as indirect measures of neuronal activation rests on the assumption that magnetization changes caused by changes in blood oxygenation are due to brain activity in the immediate vicinity
This observation can be quantified by comparing the mask gained from graph clustering with a binarized mask gained from segmenting the susceptibility weighted imaging (SWI) image, and the average proportion of voxels of the mask identified via graph clustering overlapping with veins in SWI is 0.67 ± 0.05
The results presented here show that graph based brain network analysis on a voxelwise basis can yield important insights on the origins of the underlying signals
Summary
Any interpretation of fMRI results as indirect measures of neuronal activation rests on the assumption that magnetization changes caused by changes in blood oxygenation are due to brain activity in the immediate vicinity. Several efforts have been made to reduce the influence of venous signals on fMRI measurements, and a number of effective ways for increasing the specificity of signals for the microvasculature have emerged from them. Specific corrections to eliminate venous signals based on phase images have been developed (Menon, 2002; Rowe and Logan, 2005; Curtis et al, 2014), but are rarely included in the sequences used by typical fMRI studies, and yield the risk of introducing errors through over-correction (Nencka and Rowe, 2007)
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