Analysing linear multivariate pattern transformations in neuroimaging data

Autoři: Alessio Basti aff001;  Marieke Mur aff002;  Nikolaus Kriegeskorte aff003;  Vittorio Pizzella aff001;  Laura Marzetti aff001;  Olaf Hauk aff002
Působiště autorů: Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Italy aff001;  MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, England, United Kingdom aff002;  Department of Psychology, Department of Neuroscience, Department of Electrical Engineering, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, United States of America aff003;  Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Italy aff004
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article


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 used 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 been used in previous studies. We focused on the transformations from early visual cortex (EVC) to inferior temporal cortex (ITC), fusiform face area (FFA) and parahippocampal place area (PPA). Our results suggest that the estimated linear mappings explain a significant amount of response variance in the three output ROIs. The transformation from EVC to ITC shows the highest goodness-of-fit, and those from EVC to FFA and PPA show the expected preference for faces and places as well as animate and inanimate objects, respectively. The pattern transformations are sparse, but sparsity is lower than would have been expected for one-to-one mappings, thus suggesting the presence of one-to-few voxel mappings. The mappings are also characterised by different levels of pattern deformations, thus indicating that the transformations differentially amplify or dampen certain dimensions of the input patterns. While our results are only based on a small number of subjects, they show that our pattern transformation metrics can describe novel aspects of multivariate functional connectivity in neuroimaging data.

Klíčová slova:

Curve fitting – Deformation – Functional magnetic resonance imaging – Neuroimaging – Normal distribution – Permutation – Topographic maps – Vision


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2019 Číslo 10
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