DOI: 10.1101/497180Dec 14, 2018Paper

Analysing linear multivariate pattern transformations in neuroimaging data

BioRxiv : the Preprint Server for Biology
Alessio BastiOlaf Hauk

Abstract

Most connectivity metrics in neuroimaging research reduce multivariate activity patterns in regions-of-interests (ROIs) to one dimension, which leads to a loss of information. Importantly, it prevents us from investigating the transformations between patterns in different ROIs. Here, we applied linear estimation theory in order to robustly estimate the linear transformations between multivariate fMRI patterns with a cross-validated ridge regression approach. We derived three functional connectivity metrics that describe different features of these voxel-by-voxel mappings: goodness-of-fit, sparsity and pattern deformation. The goodness-of-fit describes the degree to which the patterns in an input region can be described as a linear transformation of patterns in an output region. The sparsity metric, which relies on a Monte Carlo procedure, was introduced in order to test whether the transformation mostly consists of one-to-one mappings between voxels in different regions. Furthermore, we defined a metric for pattern deformation, i.e. the degree to which the transformation rotates or rescales the input patterns. As a proof of concept, we applied these metrics to an event-related fMRI data set consisting of four subjects that has ...Continue Reading

Related Concepts

Research
Temporal Lobe
Transformation, Genetic
Visual Cortex
Research Subject
Visual Cortex Injury
Parahippocampal Gyrus
FMRI
Patterns
Neuroimaging

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