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Journal Article Ppublish 5 citations

PubMed: 40206493 DOI: 10.52294/001c.87961

Acquisition and processing methods of whole-brain layer-fMRI VASO and BOLD: The Kenshu dataset.

Apert Neuro 3

Authors: Kenshu Koiso, Anna K Müller, Kazuaki Akamatsu, Sebastian Dresbach, Christopher J Wiggins, Omer Faruk Gulban, Rainer Goebel, Yoichi Miyawaki, Benedikt A Poser, Laurentius Huber

Affiliations

  • Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, NL.
  • Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan.
  • Imaging Core Facility, Forschungszentrum Jülich, Jülich, Germany.
  • Brain Innovation, Maastricht, NL.
  • Center for Neuroscience and Biomedical Engineering, The University of Electro-Communications, Tokyo, Japan.

Abstract

Cortical depth-dependent functional magnetic resonance image (fMRI), also known as layer-fMRI, has the potential to capture directional neural information flow of brain computations within and across large-scale cortical brain networks. E.g., layer-fMRI can differentiate feedforward and feedback cortical input in hierarchically organized brain networks. Recent advancements in 3D-EPI sampling approaches and MR contrast generation strategies have allowed proof-of-principle studies showing that layer-fMRI can provide sufficient data quality for capturing laminar changes in functional connectivity. These studies have however not shown how reliable the signal is and how repeatable the respective results are. It is especially unclear whether whole-brain layer-fMRI functional connectivity protocols are widely applicable across common neuroscience-driven analysis approaches. Moreover, there are no established preprocessing fMRI methods that are optimized to work for whole-brain layer-fMRI datasets. In this work, we aimed to serve the field of layer-fMRI and build tools for future routine whole-brain layer-fMRI in application-based neuroscience research. We have developed publicly available sequences, acquisition protocols, and processing pipelines for whole-brain layer-fMRI. These protocols are validated across 60 hours of scanning in nine participants. Specifically, we identified and exploited methodological advancements for maximizing tSNR efficiency and test-retest reliability. We are sharing an extensive multi-modal whole-brain layer-fMRI dataset (20 scan hours of movie-watching in a single participant) for the purpose of benchmarking future method developments: The Kenshu dataset. With this dataset, we are also exemplifying the usefulness of whole brain layer-fMRI for commonly applied analysis approaches in modern cognitive neuroscience fMRI studies. This includes connectivity analyses, representational similarity matrix estimations, general linear model analyses, principal component analysis clustering, etc. We believe that this work paves the road for future routine measurements of directional functional connectivity across the entire brain.

MeSH

Principal Component Analysis, Brain, Work, Neurosciences

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