Structure-function relationships underlying calculation: a combined diffusion tensor imaging and fMRI study

Both neuropsychological and functional neuroimaging studies have identified brain regions that are critical for the neurocognitive processes related to the calculation of arithmetic problems. In particular, the left angular gyrus (lAG) has been repeatedly implicated in arithmetic problem solving and found to be most activated during the retrieval of arithmetic facts. While significant progress has been made in determining the functional role of specific grey matter areas underlying calculation, very little is known about the relationship between these activated regions and their underlying white matter structures. In this study, we collected both diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) data while participants performed a mental arithmetic task. Fractional anisotropy (FA) values were extracted from predefined, hypothesis-driven, white matter regions and correlated with fMRI activation values, which were extracted from anatomically defined grey matter regions. Results indicated structure-function relationships on multiple levels. Specifically, a link between the integrity of the left superior corona radiata (SCR) and neural activity in the lAG during calculation was observed, which was found to be particularly strong for problems that have a high probability of being solved via the retrieval of arithmetic facts (problems with a relatively small problem size). The findings reported provide a link between functional activation and structural integrity of grey and white matter regions in the left temporoparietal cortex, thereby contributing to our understanding of the role of both the function and structure of this brain region in calculation.

Minimizing macrovessel signal in cerebral perfusion imaging using independent component analysis

The pronounced susceptibility effect of macrovessels in MR bolus-tracking studies induces spots of artificially high blood flow and volume in perfusion parameter images. These high-intensity regions impede the detection of perfusion changes and lead to elevated perfusion parameters in adjacent tissues. The purpose of this work was to explore postprocessing methods to reduce the influence of macrovessel signal in dynamic MRI. After data reduction was performed with the use of a principal component analysis (PCA), an independent component analysis (ICA) was applied to separate signal components of different compartments. Based on this decomposition, the dynamic time series were reconstructed with minimized contributions of macrovessel signal and noise. The influence of the temporal resolution and signal-to-noise ratio (SNR) of the source data were investigated by means of a simulation study. A region-of-interest (ROI)-based analysis of corrected and uncorrected in vivo data demonstrated that the influence of arteries and veins was reduced at least by 50%, while gray matter (GM) and white matter (WM) tissues were nearly unaffected by the correction process. Hemodynamic parameter images of the cerebral blood volume (CBV), cerebral blood flow (CBF), and mean transit time (MTT) were calculated from corrected and uncorrected scans. The corrected parameter images showed a clearly reduced macrovessel signal and an improved perceptibility of microvascular perfusion changes compared to the uncorrected ones.