Jumping over baselines with new methods to predict activation maps from resting-state fMRI.

Cognitive fMRI research primarily relies on task-averaged responses over many subjects to describe general principles of brain function. Nonetheless, there exists a large variability between subjects that is also reflected in spontaneous brain activity as measured by resting state fMRI (rsfMRI). Leveraging this fact, several recent studies have therefore aimed at predicting task activation from rsfMRI using various machine learning methods within a growing literature on 'connectome fingerprinting'. In reviewing these results, we found lack of an evaluation against robust baselines that reliably supports a novelty of predictions for this task. On closer examination to reported methods, we found most underperform against trivial baseline model performances based on massive group averaging when whole-cortex prediction is considered. Here we present a modification to published methods that remedies this problem to large extent. Our proposed modification is based on a single-vertex approach that replaces commonly used brain parcellations. We further provide a summary of this model evaluation by characterizing empirical properties of where prediction for this task appears possible, explaining why some predictions largely fail for certain targets. Finally, with these empirical observations we investigate whether individual prediction scores explain individual behavioral differences in a task.

Impact of prospective motion correction, distortion correction methods and large vein bias on the spatial accuracy of cortical laminar fMRI at 9.4 Tesla.

Functional imaging with sub-millimeter spatial resolution is a basic requirement for assessing functional MRI (fMRI) responses across different cortical depths and is used extensively in the emerging field of laminar fMRI. Such studies seek to investigate the detailed functional organization of the brain and may develop to a new powerful tool for human neuroscience. However, several studies have shown that measurement of laminar fMRI responses can be biased by the image acquisition and data processing strategies. In this work, measurements with three different gradient-echo EPI BOLD fMRI protocols with a voxel size down to 650 â€‹µm isotropic were performed at 9.4 â€‹T. We estimated how prospective motion correction can help to improve spatial accuracy by reducing the number of spatial resampling steps in postprocessing. In addition, we demonstrate key requirements for accurate geometric distortion correction to ensure that distortion correction maps are properly aligned to the functional data and that strong variations of distortions near large veins can lead to signal overlays which cannot be corrected for during postprocessing. Furthermore, this study illustrates the spatial extent of bias induced by pial and other larger veins in laminar BOLD experiments. Since these issues under investigation affect studies performed with more conventional spatial resolutions, the methods applied in this work may also help to improve the understanding of the BOLD signal more broadly.

Brainglance: Visualizing Group Level MRI Data at One Glance.

The vast majority of studies using functional magnetic resonance imaging (fMRI) are analyzed on the group level. Standard group-level analyses, however, come with severe drawbacks: First, they assume functional homogeneity within the group, building on the idea that we use our brains in similar ways. Second, group-level analyses require spatial warping and substantial smoothing to accommodate for anatomical variability across subjects. Such procedures massively distort the underlying fMRI data, which hampers the spatial specificity. Taken together, group statistics capture the effective overlap, rendering the modeling of individual deviations impossible - a major source of false positivity and negativity. The alternative analysis approach is to leave the data in the native subject space, but this makes comparison across individuals difficult. Here, we propose a new framework for visualizing group-level information, better preserving the information of individual subjects. Our proposal is to limit the use of invasive data procedures such as spatial smoothing and warping and rather extract regional information from the individuals. This information is then visualized for all subjects and brain areas at one glance - hence we term the method brainglance. Additionally, our method incorporates a means for clustering individuals to further identify common traits. We showcase our method on two publicly available data sets and discuss our findings.

LISA improves statistical analysis for fMRI.

One of the principal goals in functional magnetic resonance imaging (fMRI) is the detection of local activation in the human brain. However, lack of statistical power and inflated false positive rates have recently been identified as major problems in this regard. Here, we propose a non-parametric and threshold-free framework called LISA to address this demand. It uses a non-linear filter for incorporating spatial context without sacrificing spatial precision. Multiple comparison correction is achieved by controlling the false discovery rate in the filtered maps. Compared to widely used other methods, it shows a boost in statistical power and allows to find small activation areas that have previously evaded detection. The spatial sensitivity of LISA makes it especially suitable for the analysis of high-resolution fMRI data acquired at ultrahigh field (≥7 Tesla).