Alfonso Nieto-Castanon

arXiv2022

Preparing fMRI Data for Statistical Analysis

This chapter describes several procedures used to prepare fMRI data for statistical analyses. It includes the description of common preprocessing steps, such as spatial realignment, coregistration, and spatial normalization, aimed at the spatial alignment of all fMRI data within- and between- subjects, as well as several denoising procedures aimed at minimizing the impact of common noise sources, including physiological and residual subject motion effects, on the BOLD signal time series. The chapter ends with a description of quality control procedures recommended for detecting potential problems in the fMRI data and evaluating its suitability for subsequent statistical analyses.

q-bio.QM↗

arXiv2022

Brain-wide connectome inferences using functional connectivity MultiVariate Pattern Analyses (fc-MVPA)

Current functional Magnetic Resonance Imaging technology is able to resolve billions of individual functional connections characterizing the human connectome. Classical statistical inferential procedures attempting to make valid inferences across this many measures from a reduced set of observations and from a limited number of subjects can be severely underpowered for any but the largest effect sizes. This manuscript discusses fc-MVPA (functional connectivity Multivariate Pattern Analysis), a novel application of multivariate pattern analysis techniques in the context of brain-wide connectome inferences. The theory behind fc-MVPA is presented, and several of its key concepts are illustrated through examples from a publicly available resting state dataset, including an example analysis evaluating gender differences across the entire functional connectome. Last, Monte Carlo simulations are used to demonstrated this method's validity and sensitivity. In addition to offering powerful whole-brain inferences, fc-MVPA also provides a meaningful characterization of the heterogeneity in functional connectivity across subjects.

q-bio.QM↗