Razlighi, Q. | Search bioRxiv

bioRxiv2020

fMRI-guided white matter connectivity in fluid and crystallized cognitive abilities in healthy adults

This study examined within-subject differences among three fluid abilities that decline with age: reasoning, episodic memory and processing speed, compared with vocabulary, a crystallized ability that is maintained with age. The data were obtained from the Reference Ability Neural Network (RANN) study from which 221 participants had complete behavioral data for all 12 cognitive tasks, three per ability, along with fMRI and diffusion weighted imaging data. We used fMRI task activation to guide white matter tractography, and generated mean percent signal change in the regions associated with the processing of each ability along with diffusion tensor imaging measures, fractional anisotropy (FA) and mean diffusivity (MD), for each cognitive ability. Qualitatively brain regions associated with vocabulary were more localized and lateralized to the left hemisphere whereas the fluid abilities were associated with brain activations that were more distributed across the brain and bilaterally situated. Using continuous age, we observed smaller correlations between MD and age for white matter tracts connecting brain regions associated with the vocabulary ability than that for the fluid abilities, suggesting that vocabulary white matter tracts were better maintained with age. Furthermore, after multiple comparisons correction, the mean percent signal change for the episodic memory showed positive associations with behavioral performance, and the associations between MD and percent signal change differed by age such that, when divided into three age groups to further explore this interaction, only the oldest age group show a significant negative correlation between the two brain measures. Overall, the vocabulary ability may be better maintained with age due to the more localized brain regions involved, which places smaller reliance on long distance white matter tracts for signal transduction. These results support the hypothesis that functional activation and white matter structures underlying the vocabulary ability contribute to the abilitys greater resistance against aging.

neuroscience↗

bioRxiv2019

Attenuation of Motion Artifacts in fMRI using Discrete Reconstruction of Irregular fMRI Trajectories (DRIFT)

Numerous studies reported motion as the most detrimental source of noise and artifacts in functional magnetic resonance imaging (fMRI). Different approaches have been proposed and used to attenuate the effect of motion on fMRI data, including both prospective and retrospective (post-processing) techniques. However, each type of motion (e.g. translation versus rotation or in-plane versus out-of-plane) has a distinct effect on the MR signal, which is not fully understood nor appropriately modeled in the field. In addition, effects of the same motion can be substantially different depending on when it occurs during the pulse sequence (e.g. RF excitation, gradient encoding, or k-space read-out). Thus, each distinct kind of motion and the time of its occurrence may require a unique approach to be optimally corrected. Therefore, we start with an investigation of the effects of different motions on the MR signal based on the Bloch equation. We then simulate their unique effects with a comprehensive fMRI simulator. Our results indicate that current motion correction methods fail to completely address the motion problem. Retrospective techniques such as spatial realignment can correct for between-volume misalignment, but fail to address within volume contamination and spin-history artifacts. Because of the steady state nature of the fMRI acquisition, spin-history artifacts arising from over/under excitation during slice-selection causes the motion artifacts to contaminate MR signal even after cessation of motion, which makes it challenging to be corrected retrospectively. Prospective motion correction has been proposed to prevent spin-history artifacts, but fails to address motion artifacts during k-space readout. In this article, we propose a novel method to remove these artifacts: Discrete reconstruction of irregular fMRI trajectory (DRIFT). Our method calculates the exact displacement of k-space recording due to motion at each dwell time and retrospectively corrects each slice of the fMRI volume using an inverse nonuniform Fourier transform. We evaluate our proposed methods using simulated data as well as fMRI data collected from a rotating phantom inside a 3T Siemens Prisma scanner. We conclude that a hybrid approach with both prospective and retrospective components are essentially required for optimal removal of motion artifacts from the fMRI data.

neuroscience↗