Depthgram: Visualizing outliers in high-dimensional functional data with application to fMRI data exploration Articles uri icon

publication date

  • May 2022

issue

  • 11

volume

  • 41

International Standard Serial Number (ISSN)

  • 0277-6715

Electronic International Standard Serial Number (EISSN)

  • 1097-0258

abstract

  • (copyright) 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.Functional magnetic resonance imaging (fMRI) is a non-invasive technique that facilitates the study of brain activity by measuring changes in blood flow. Brain activity signals can be recorded during the alternate performance of given tasks, that is, task fMRI (tfMRI), or during resting-state, that is, resting-state fMRI (rsfMRI), as a measure of baseline brain activity. This contributes to the understanding of how the human brain is organized in functionally distinct subdivisions. fMRI experiments from high-resolution scans provide hundred of thousands of longitudinal signals for each individual, corresponding to brain activity measurements over each voxel of the brain along the duration of the experiment. In this context, we propose novel visualization techniques for high-dimensional functional data relying on depth-based notions that enable computationally efficient 2-dim representations of fMRI data, which elucidate sample composition, outlier presence, and individual variability. We believe that this previous step is crucial to any inferential approach willing to identify neuroscientific patterns across individuals, tasks, and brain regions. We present the proposed technique via an extensive simulation study, and demonstrate its application on a motor and language tfMRI experiment.

keywords

  • data visualization; dimensionality reduction; fmri; functional depth; multidimensional outliers