Biology subjects

van Mourik, T.

Publications and source records attributed to van Mourik, T..

7 recordsLinked to original sources

bioRxiv2022

Inducing a mental context for associative memory formation with real-time fMRI neurofeedback

Memory, one of the hallmarks of human cognition, can be modified when humans voluntarily modulate single neuron or neural population activity using neurofeedback. However, it is currently unknown whether memory is facilitated or impaired after such neural perturbation. In this study, participants memorized objects while we trained them with abstract neurofeedback to modulate their brain activity patterns in the ventral visual stream to represent either faces or houses. The neurofeedback created an implicit face or house context in the brain while memorizing the objects. The results revealed that participants created associations between each memorized object and its implicit context solely due to the neurofeedback manipulation. Our findings shed light onto how memory formation can be influenced by synthetic memory tags with neurofeedback and advance our understanding of mnemonic processing.

neuroscience↗

bioRxiv2021

Investigation of layer specific BOLD in the human visual cortex during visual attention

Directing spatial attention towards a particular stimulus location enhances cortical responses at corresponding regions in cortex. How attention modulates the laminar response profile within the attended region, however, remains unclear. In this paper, we use high field (7T) fMRI to investigate the effects of attention on laminar activity profiles in areas V1-V3; both when a stimulus was presented to the observer, and in the absence of visual stimulation. Replicating previous findings, we find robust increases in the overall BOLD response for attended regions in cortex, both with and without visual stimulation. When analyzing the BOLD response across the individual layers in visual cortex, we observed no evidence for laminar-specific differentiation with attention. We offer several potential explanations for these results, including theoretical, methodological and technical reasons. Additionally, we provide all data and pipelines openly, in order to promote analytic consistency across layer-specific studies, improve reproducibility, and decrease the false positive rate as a result of analytical flexibility.

neuroscience↗

bioRxiv2020

Relating neural oscillations to laminar fMRI connectivity

Laminar fMRI holds the potential to study connectivity at the laminar level in humans. Here we analyze simultaneously recorded EEG and high resolution fMRI data to investigate how EEG power modulations, induced by a task with an attentional component, relate to changes in fMRI laminar connectivity between and within brain regions. Our results indicate that our task induced decrease in beta power relates to an increase in deep-to-deep layer coupling between regions and to an increase in deep/middle-to-superficial layer connectivity within brain regions. The attention-related alpha power decrease predominantly relates to reduced connectivity between deep and superficial layers within brain regions, since, unlike beta power, alpha power was found to be positively correlated to connectivity. We observed no strong relation between laminar connectivity and gamma band oscillations. These results indicate that especially beta band, and to a lesser extent alpha band oscillations relate to laminar specific fMRI connectivity. These differential effects for the alpha and beta bands suggest a complex picture of possibly co-occurring neural processes that can differentially affect laminar connectivity.

neuroscience↗

bioRxiv2019

Laminar Specific fMRI Reveals Directed Interactions in Distributed Networks During Language Processing

Laminar resolution, functional magnetic resonance imaging (lfMRI) is a noninvasive technique with the potential to distinguish top-down and bottom-up signal contributions on the basis of laminar specific interactions between distal regions. Hitherto, lfMRI could not be demonstrated for either whole-brain distributed networks or for complex cognitive tasks. We show that lfMRI can reveal whole-brain directed networks during word reading. We identify distinct, language critical regions based on their association with the top-down signal stream and establish lfMRI for the noninvasive assessment of directed connectivity during task performance.

neuroscience↗

bioRxiv2018

Laminar signal extraction over extended cortical areas by means of a spatial GLM

There is converging evidence that distinct neuronal processes leave distinguishable footprints in the laminar BOLD response. However, even though the achievable spatial resolution in functional MRI has much improved over the years, it is still challenging to separate signals arising from different cortical layers. In this work, we propose a new method to extract laminar signals. We use a spatial General Linear Model in combination with the equivolume principle of cortical layers to unmix laminar signals instead of interpolating through and integrating over a cortical area: thus reducing partial volume effects. Not only do we provide a mathematical framework for extracting laminar signals with a spatial GLM, we also illustrate that the best case scenarios of existing methods can be seen as special cases within the same framework. By means of simulation, we show that this approach has a sharper point spread function, providing better signal localisation. We further assess the partial volume contamination in cortical profiles from high resolution human ex vivo and in vivo structural data, and provide a full account of the benefits and potential caveats. We eschew here any attempt to validate the spatial GLM on the basis of fMRI data as a generally accepted ground-truth pattern of laminar activation does not currently exist. This approach is flexible in terms of the number of layers and their respective thickness, and naturally integrates spatial regularisation along the cortex, while preserving laminar specificity. Care must be taken, however, as this procedure of unmixing is susceptible to sources of noise in the data or inaccuracies in the laminar segmentation.

neuroscience↗

bioRxiv2018

Improved cortical boundary registration for locally distorted fMRI scans

With continuing advances in MRI techniques and the emergence of higher static field strengths, submillimetre spatial resolution is now possible in human functional imaging experiments. This has opened up the way for more specific types of analysis, for example investigation of the cortical layers of the brain. With this increased specificity, it is important to correct for the geometrical distortions that are inherent to echo planar imaging (EPI). Inconveniently, higher field strength also increases these distortions. The resulting displacements can easily amount to several millimetres and as such pose a serious problem for laminar analysis. We here present a method, Recursive Boundary Registration (RBR), that corrects distortions between an anatomical and an EPI volume. By recursively applying Boundary Based Registration (BBR) on progressively smaller subregions of the brain we generate an accurate whole-brain registration, based on the grey-white matter contrast. Explicit care is taken that the deformation does not break the topology of the cortical surface, which is an important requirement for several of the most common subsequent steps in laminar analysis. We show that RBR obtains submillimetre accuracy with respect to a manually distorted gold standard, and apply it to a set of human in vivo scans to show a clear increase in spacial specificity. RBR further automates the process of non-linear distortion correction. This is an important step towards routine human laminar fMRI. We provide the code for the RBR algorithm, as well as a variety of functions to better investigate registration performance in a public GitHub repository, https://github.com/TimVanMourik/OpenFmriAnalysis, under the GPL 3.0 license.

neuroscience↗

bioRxiv2017

Porcupine: a visual pipeline tool for neuroimaging analysis

The field of neuroimaging is rapidly adopting a more reproducible approach to data acquisition and analysis. Data structures and formats are being standardised and data analyses are getting more automated. However, as data analysis becomes more complicated, researchers often have to write longer analysis scripts, spanning different tools across multiple programming languages. This makes it more difficult to share or recreate code, reducing the reproducibility of the analysis. We present a tool, Porcupine, that constructs ones analysis visually and automatically produces analysis code. The graphical representation improves understanding of the performed analysis, while retaining the flexibility of modifying the produced code manually to custom needs. Not only does Porcupine produce the analysis code, it also creates a shareable environment for running the code, in the form of a Docker image. Together, this forms a reproducible way of constructing, visualising and sharing ones analysis. Currently, Porcupine links to Nipype functionalities, which in turn accesses most standard neuroimaging analysis tools. With Porcupine, we bridge the gap between a conceptual and an implementational level of analysis and thus create reproducible and shareable science. We give the researcher a better oversight of their pipeline, both while developing and communicating their work. This will reduce the threshold at which less expert users can generate reusable pipelines. We provide a wide range of examples and documentation, as well as installer files for all platforms on our website: https://timvanmourik.github.io/Porcupine. Porcupine is free, open source, andreleased under the GNU General Public License v3.0.\n\nAuthor SummaryThe neuroimaging community is fervently debating that its reproducibility and transparency should be improved, but it is a challenging problem as to how to accomplish this. We here propose a tool, Porcupine, to aid in this process by more easily creating shareable workflows for analysing neuroimaging data. The conceptual understanding of a pipeline is improved by means of the graphical interface, and it automatically produces the code to perform the analysis and to create a sharing environment. This retains full flexibility to modify the script afterwards but in principle produces readily executable code for an end-to-end analysis. Porcupine currently links to all Nipype functionality, but is designed to be extendable to other workflow packages in neuroimaging and beyond. Porcupine is free and is released under the GNU General Public License.

neuroscience↗