Brainglance: visualizing group level MRI data at one glance

The vast majority of studies using functional magnetic resonance imaging (fMRI) are analysed 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 modelling 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

Predicting Intelligence from FMRI Data of the Human Brain

In recent years, the prediction of individual behaviour from the fMRI-based functional connectome has become a major focus of research. The motivation behind this research is to find generalizable neuromarkers of cognitive functions. However, insufficient prediction accuracies and long scan time requirements are still unsolved issues. Here we propose a new machine learning algorithm for predicting intelligence scores of healthy human subjects from resting state (rsfMRI) or task-based fMRI (tfMRI). In a cohort of 390 unrelated test subjects of the Human Connectome Project, we found correlations between the observed and the predicted general intelligence of more than 50~percent in tfMRI, and of around 59~percent when results from two tasks are combined. Surprisingly, we found that the tfMRI data were significantly more predictive of intelligence than rsfMRI even though they were acquired at much shorter scan times (approximately 10~minutes versus 1~hour). Existing methods that we investigated in a benchmark comparison underperformed on tfMRI data and produced prediction accuracies well below our results. Our proposed algorithm differs from existing methods in that it achieves dimensionality reduction via ensemble learning and partial least squares regression rather than via brain parcellations or ICA decompositions. In addition, it introduces Ricci-Forman curvature as a novel type of edge weight. Reference: G. Lohmann, E. Lacosse, T. Ethofer, V.J. Kumar, K. Scheffler, J. Jost, Predicting intelligence from fMRI data of the human brain in a few minutes of scan time, biorxiv (2021), doi: https://doi.org/10.1101/2021.03.18.435935

Predicting intelligence from fMRI data of the human brain in a few minutes of scan time

A number of recent studies have investigated machine learning techniques for predicting individual behaviour from fMRI. Even though encouraging results have been obtained, excessive scan times – especially in resting state fMRI – are a limiting factor. Here we present a new machine learning algorithm for predicting individual behaviour of healthy human subjects using both resting state (rsfMRI) as well as task-based fMRI (tfMRI). It achieves dimensionality reduction via ensemble learning and partial least squares regression rather than via brain parcellations or ICA decompositions. In addition, it introduces Ricci-Forman curvature as a novel type of edge weight. As a proof of concept, we focus on predicting fluid, crystallized and general intelligence scores. In a cohort of 390 unrelated test subjects of the Human Connectome Project, we found correlations between the observed and the predicted general intelligence of more than 50 percent in tfMRI, and of around 59 percent (R2 ≈ 0.29) when results from two tasks are combined. We compare these results against a benchmark of existing methods that produced correlations below 50 percent in both rsfMRI and tfMRI. We conclude that with novel machine learning techniques applied to tfMRI it is possible to obtain significantly better prediction accuracies at a fraction of the scan time

Neural Control of Discrete and Rhythmic Movements

Human dexterity exceeds that of modern robots, despite omnipresent noise and vastly slower neural processing. Several lines of research have accrued evidence that the complex biological system is hierarchically organized, with building blocks or motor primitives under only partial cortical supervision. This study continues research on the hypothesis that viable candidates for such motor primitives are discrete and rhythmic movements, defined as point and limit cycle attractors, generated in the neuromechanical network. The distinction between rhythmic and discrete primitives was supported by a previous neuroimaging study that showed strikingly different cortical and subcortical activation: while rhythmic movements were associated with mostly primary motor areas, contralateral M1 and ipsilateral cerebellum, discrete movements involved a significantly broader network of cortical areas, including bilateral parietal and prefrontal regions. Given the rapid advances in imaging technology and analysis, the present study aims to replicate and extend these results from more than 10 years ago. Using the same movements, we recorded fMRI data with simultaneous recording of behavior. Subjects performed flexions and extensions with their right dominant wrist during acquisition of whole-brain scans in a 3T scanner; kinematic data were acquired by a custom-made goniometric device. In Experiment 1, movements were self-paced, performed in continuously rhythmic fashion or as single flexions and extensions self-initiated at random intervals over the 36-sec run. Using a GLM analysis, results largely replicated those of the previous study: rhythmic movement primarily elicited contralateral motor cortical, supplemental motor cortical, and ipsilateral cerebellar activations, whereas the discrete condition implicated a broader network of parietal and prefrontal areas, in addition to primary motor areas. In order to probe whether self-initiated timing was responsible for the extensive activations, Experiment 2 compared rhythmic and discrete movements that were visually cued and the number of initiations and terminations were matched. Preliminary results were largely consistent with the earlier study, but also raised additional questions. To shed light on the functional meaning of the activated network, novel connectivity analyses will be conducted. In addition, more focused follow-up measurements will be done using 9.4T scanning that provide significant increases in spatial resolution. These studies will provide a first important replication of previous influential results, supporting that even non-visually guided discrete movements require an extensiveset of cortical areas, while continuous rhythmic movements are generated with significantly less cortical substrate, but possibly rely on lower brainstem activation. This different neurophysiological substrate underscores that these two movement types actas different building blocks at different levels of the neural axis

Inflated False Negative Rates Undermine Reproducibility In Task-Based fMRI

Reproducibility is generally regarded as a hallmark of scientific validity. It can be undermined by two very different factors, namely inflated false positive rates or inflated false negative rates. Here we investigate the role of the second factor, i.e. the degree to which true effects are not detected reliably. The availability of large public databases and also supercomputing allows us to tackle this problem quantitatively. Specifically, we estimated the reproducibility in task-based fMRI data over different samples randomly drawn from a large cohort of subjects obtained from the Human Connectome Project. We use the full cohort as a standard of reference to approximate true positive effects, and compute the fraction of those effects that was detected reliably using standard software packages at various smaller sample sizes. We found that with standard sample sizes this fraction was less than 25 percent. We conclude that inflated false negative rates are a major factor that undermine reproducibility. We introduce a new statistical inference algorithm based on a novel test statistic and show that it improves reproducibility without inflating false positive rates

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)

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 protocols with a voxel size down to 650 μm isotropic were performed at 9.4 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

An Active-Learning Approach to Fostering Understanding of Research Methods in Large Classes

The current investigation tested the effectiveness of an online student research project designed to supplement traditional methods (e.g., lectures, discussions, and assigned readings) of teaching research methods in a large-enrollment Introduction to Psychology course. Over the course of the semester, students completed seven assignments, each representing a stage of the research process. Students formed hypotheses, tested their hypotheses using data from the class, interpreted their results, generated future directions, created PowerPoint slides summarizing their projects, and presented their results in a poster session. We found support for the hypothesis that the research methods intervention would lead to better performance on a research methods quiz compared to students in a nonintervention section taught by the same instructor. This intervention demonstrated that it is feasible to use project-oriented active-learning techniques to foster understanding of research methods in large classes. © 2017, © The Author(s) 2017