Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Hypervoxels: a multidimensional framework for the representation and analysis of neuroimaging data

View ORCID ProfilePedro A. Luque Laguna, View ORCID ProfileAhmad Beyh, Francisco de S. Requejo, Richard Stones, View ORCID ProfileDerek K. Jones, View ORCID ProfileLaura. H. Goldstein, View ORCID ProfileMarco Catani, View ORCID ProfileSteve C.R. Williams, View ORCID ProfileFlavio Dell’Acqua
doi: https://doi.org/10.1101/2022.04.11.485553
Pedro A. Luque Laguna
1Natbrainlab, Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, United Kingdom
2Department of Neuroimaging, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, United Kingdom
5Cardiff University Brain Imaging Center, School of Psychology, Cardiff University, United Kingdom
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Pedro A. Luque Laguna
  • For correspondence: luquelagunap@cardiff.ac.uk
Ahmad Beyh
1Natbrainlab, Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, United Kingdom
4Laboratory of Neurobiology, University College London, United Kingdom
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Ahmad Beyh
Francisco de S. Requejo
1Natbrainlab, Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, United Kingdom
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Richard Stones
1Natbrainlab, Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, United Kingdom
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Derek K. Jones
5Cardiff University Brain Imaging Center, School of Psychology, Cardiff University, United Kingdom
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Derek K. Jones
Laura. H. Goldstein
3Department of Psychology, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, United Kingdom
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Laura. H. Goldstein
Marco Catani
1Natbrainlab, Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, United Kingdom
2Department of Neuroimaging, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, United Kingdom
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Marco Catani
Steve C.R. Williams
2Department of Neuroimaging, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, United Kingdom
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Steve C.R. Williams
Flavio Dell’Acqua
1Natbrainlab, Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, United Kingdom
2Department of Neuroimaging, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, United Kingdom
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Flavio Dell’Acqua
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Most neuroimaging modalities use regular grids of voxels to represent the three-dimensional space occupied by the brain. However, a regular 3D voxel grid does not reflect the anatomical and topological complexity represented by the brain’s white matter connections. In contrast, tractography reconstructions based on diffusion MRI provide a closer characterisation of the white matter pathways followed by the neuronal fibres interconnecting different brain regions. In this work, we introduce hypervoxels as a new methodological framework that combines the spatial encoding capabilities of multidimensional voxels with the anatomical and topological information found in tractography data. We provide a detailed description of the framework and evaluate the benefits of using hypervoxels by carrying out comparative voxel and hypervoxel cluster inference analyses on diffusion MRI data from a neuroimaging study on amyotrophic lateral sclerosis (ALS). Compared to the voxel analyses, the use of hypervoxels improved the detection of effects of interest in the data in terms of statistical significance levels and spatial distribution across white matter regions known to be affected in ALS. In these regions, the hypervoxel results also identified specific white matter pathways that resolve the anatomical ambiguity otherwise observed in the results from the voxel analyses. The observed increase in sensitivity and specificity can be explained by the superior ability of hypervoxel-based methods to represent and disentangle the anatomical overlap of white matter connections. Based on this premise, we expect that the use of hypervoxels should improve the analysis of neuroimaging data when the effects of interest under investigation are expected to be aligned along distinct but potentially overlapping white matter pathways.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted April 12, 2022.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Hypervoxels: a multidimensional framework for the representation and analysis of neuroimaging data
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Hypervoxels: a multidimensional framework for the representation and analysis of neuroimaging data
Pedro A. Luque Laguna, Ahmad Beyh, Francisco de S. Requejo, Richard Stones, Derek K. Jones, Laura. H. Goldstein, Marco Catani, Steve C.R. Williams, Flavio Dell’Acqua
bioRxiv 2022.04.11.485553; doi: https://doi.org/10.1101/2022.04.11.485553
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Hypervoxels: a multidimensional framework for the representation and analysis of neuroimaging data
Pedro A. Luque Laguna, Ahmad Beyh, Francisco de S. Requejo, Richard Stones, Derek K. Jones, Laura. H. Goldstein, Marco Catani, Steve C.R. Williams, Flavio Dell’Acqua
bioRxiv 2022.04.11.485553; doi: https://doi.org/10.1101/2022.04.11.485553

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (3609)
  • Biochemistry (7590)
  • Bioengineering (5533)
  • Bioinformatics (20833)
  • Biophysics (10347)
  • Cancer Biology (7998)
  • Cell Biology (11663)
  • Clinical Trials (138)
  • Developmental Biology (6619)
  • Ecology (10227)
  • Epidemiology (2065)
  • Evolutionary Biology (13648)
  • Genetics (9557)
  • Genomics (12860)
  • Immunology (7932)
  • Microbiology (19575)
  • Molecular Biology (7678)
  • Neuroscience (42193)
  • Paleontology (309)
  • Pathology (1259)
  • Pharmacology and Toxicology (2208)
  • Physiology (3272)
  • Plant Biology (7064)
  • Scientific Communication and Education (1295)
  • Synthetic Biology (1953)
  • Systems Biology (5435)
  • Zoology (1119)