The role of auxiliary parameters in evaluating voxel-wise encoding models for 3T and 7T BOLD fMRI data

In neuroimaging, voxel-wise encoding models are a popular tool to predict brain activity elicited by a stimulus. To evaluate the accuracy of these predictions across multiple voxels, one can choose between multiple quality metrics. However, each quality metric requires specifying auxiliary parameters such as the number and selection criteria of voxels, whose influence on model validation is unknown. In this study, we systematically vary these parameters and observe their effects on three common quality metrics of voxel-wise encoding models in two open datasets of 3- and 7-Tesla BOLD fMRI activity elicited by musical stimuli. We show that such auxiliary parameters not only exert substantial influence on model validation, but also differ in how they affect each quality metric. Finally, we give several recommendations for validating voxel-wise encoding models that may limit variability due to different numbers of voxels, voxel selection criteria, and magnetic field strengths.

X-RAY CT Reconstruction by using Spatially Non Homogeneous ICD Optimization

Recent applications of conventional iterative coordinate descent (ICD) algorithms to multislice helical CT reconstructions have shown that conventional ICD can greatly improve image quality by increasing resolution as well as reducing noise and some artifacts. However, high computational cost and long reconstruction times remain as a barrier to the use of conventional algorithm in the practical applications. Among the various iterative methods that have been studied for conventional, ICD has been found to have relatively low overall computational requirements due to its fast convergence. This paper presents a fast model-based iterative reconstruction algorithm using spatially nonhomogeneous ICD (NH-ICD) optimization. The NH-ICD algorithm speeds up convergence by focusing computation where it is most needed. The NH-ICD algorithm has a mechanism that adaptively selects voxels for update. First, a voxel selection criterion VSC determines the voxels in greatest need of update. Then a voxel selection algorithm VSA selects the order of successive voxel updates based upon the need for repeated updates of some locations, while retaining characteristics for global convergence. In order to speed up each voxel update, we also propose a fast 3-D optimization algorithm that uses a quadratic substitute function to upper bound the local 3-D objective function, so that a closed form solution can be obtained rather than using a computationally expensive line search algorithm. The experimental results show that the proposed method accelerates the reconstructions by roughly a factor of three on average for typical 3-D multislice geometries.

Reliability-Based Voxel Selection

While functional magnetic resonance imaging (fMRI) studies typically measure responses across the whole brain, not all regions are likely to be informative for a given study. Which voxels should be considered? Here we propose a method for voxel selection based on the reliability of the data. This method isolates voxels that respond consistently across imaging runs while maximizing the reliability of multi-voxel patterns across the selected voxels. We estimate that it is suitable for designs with at least 15 conditions. In two example datasets, we found that this proposed method defines a smaller set of voxels than another common method, activity-based voxel selection. Broadly, this method eliminates the need to define regions or statistical thresholds a priori and puts the focus on data reliability as the first step in analyzing fMRI data.

The effect of acquisition resolution on orientation decoding from V1: comparison of 3T and 7T

Previously published results indicate that the accuracy of decoding visual orientation from 7 Tesla fMRI data of V1 peaks at spatial acquisition resolutions that are routinely accessible with more conventional 3 Tesla scanners. This study directly compares the decoding performance between a 3 Tesla and a 7 Tesla dataset that were acquired using the same stimulation paradigm by applying an identical analysis procedure. The results indicate that decoding models built on 3 Tesla data are comparatively impaired. Moreover, we found no evidence for a strong coupling of BOLD signal change magnitude or temporal signal to noise ratio (tSNR) with decoding performance. Direct enhancement of tSNR via multiband fMRI acquisition at the same resolution did not translate into improved decoding performance. Additional voxel selection can boost 3 Tesla decoding performance to the 7 Tesla level only at a 3 mm acquisition resolution. In both datasets the BOLD signal available for orientation decoding is spatially broadband, but, consistent with the size of the BOLD point-spread-function, decoding models at 3 Tesla utilize spatially coarser image components.

A decomposition model and voxel selection framework for fMRI analysis to predict neural response of visual stimuli

This paper presents an fMRI signal analysis methodology using geometric mean curve decomposition (GMCD) and mutual information-based voxel selection framework. Previously, the fMRI signal analysis has been conducted using empirical mean curve decomposition (EMCD) model and voxel selection on raw fMRI signal. The erstwhile methodology loses frequency component, while the latter methodology suffers from signal redundancy. Both challenges are addressed by our methodology in which the frequency component is considered by decomposing the raw fMRI signal using geometric mean rather than arithmetic mean and the voxels are selected from EMCD signal using GMCD components, rather than raw fMRI signal. The proposed methodologies are adopted for predicting the neural response. Experimentations are conducted in the openly available fMRI data of six subjects, and comparisons are made with existing decomposition models and voxel selection frameworks. Subsequently, the effect of degree of selected voxels and the selection constraints are analyzed. The comparative results and the analysis demonstrate the superiority and the reliability of the proposed methodology.