Microstructures of Learning: Novel methods and approaches for assessing structural and functional changes underlying knowledge acquisition in the brain

Horne, Merle; Lindgren, Magnus; Nilsson, Markus; Roll, Mikael; Shtyrov, Yury; Ståhlberg, Freddy; Topgaard, Daniel

doi:10.3389/978-2-88919-480-3

Cited by 1 publication

(1 citation statement)

References 139 publications

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“…In tractometry, the streamlines are used to identify white-matter pathways in voxel-based images from which quantitative values are extracted, averaged and analysed. This can be done for specific tracts in the fashion of region of interest (ROI) analyses to increase the statistical power and the anatomical specificity of the results (Jones and Nilsson 2015), or across the entire white matter using analysis methods based on automated tractography clustering techniques (Siless et al 2020;Zhang, Wu, Ning, et al 2018). In a connectomic analysis, the brain's structural connectivity is modelled as networks of grey matter regions (nodes) interconnected by the streamlines (edges) that are analysed as graph-theory objects in terms of connectivity strength or other topological properties (Fornito, Zalesky, and Breakspear 2015).…”

Section: Introductionmentioning

confidence: 99%

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

Laguna

Beyh

Requejo

et al. 2022

Preprint

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Most neuroimaging modalities use regular grids of voxels to represent the three-dimensional space occupied by the brain. However, the topological uniformity of the 3D voxel grid does not realistically reflect relevant properties of the brain's anatomy such as its heterogeneity and the complexity of its structural connectivity. In contrast, tractography reconstructions based on diffusion MRI can provide compelling characterisation of the brain's structural connectivity that reflects the local and global organisation of the neuronal fibres. In this work we introduce hypervoxels as a new analysis framework that combine the anatomical and connectivity information provided by tractography streamlines with the spatial encoding capabilities of multidimensional voxels. We provide a detailed description and implementation of the framework and demonstrate its benefits by carrying out comparative voxel and hypervoxel analyses on neuroimaging data acquired to investigate the neuroanatomical substrate of motor neurone disease (MND). The hypervoxel analyses outperformed the corresponding voxel analyses in their ability to detect effects of interest in brain regions known to be affected in MND, as demonstrated by the widespread number of significant results, their statistical significance and their anatomical specificity. We conclude that the anatomical and topological information provided by the hypervoxel framework can greatly improve the sensitivity and anatomical specificity of neuroimaging analyses through its application to any method or imaging modality based on traditional voxels.

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Section: Introductionmentioning

confidence: 99%