BrainStat: a toolbox for brain-wide statistics and multimodal feature associations

Abstract

Analysis and interpretation of neuroimaging datasets has become a multidisciplinary endeavor, relying not only on statistical methods, but increasingly on associations with respect to other brain-derived features such as gene expression, histological data, and functional as well as cognitive architectures. Here we introduce BrainStat - a toolbox for (i) univariate and multivariate general linear models in volumetric and surface-based brain imaging datasets, and (ii) multidomain feature association of results with respect to spatial maps of post-mortem gene expression and histology, as well as task-based fMRI meta-analysis, canonical resting-state fMRI networks, and resting-state derived gradients across several common surface templates. The combination of statistics and feature associations into a turnkey toolbox offers to streamline analytical processes and accelerate cross-modal. The toolbox is implemented in both Python and MATLAB, two widely used programming languages in the neuroimaging and neuroinformatics communities. BrainStat is openly available and complemented by an expandable documentation.

Heritability of hippocampal functional and microstructural organisation

“…Subfield-isocortical FC measures were mapped using linear and mixed effects models in BrainStat and thresholded at t > 20 to indicate highest connections (https://github.com/MICA-MNI/BrainStat 54 ) ( Fig. 1B ).…”

Neurocognitive basis of model-based decision making and its metacontrol in childhood

“…Cortical thickness data were analyzed using the SurfStat toolbox for Matlab [https://www.math.mcgill.ca/keith/surfstat, ( de Waal et al, 2022 , Worsley et al, 2009 ). Cortex-wide linear models were used to assess the effects of age, sex, model-based decision-making, and metacontrol on thickness at each vertex.…”

Closing the mechanistic gap: the value of microarchitecture in understanding cognitive networks

“…The plots were produced with data from [93]. (B) Meta-analytic decoding [94,95] highlights the varied cognitive terms associated with each network. (C) Distributed regions of the cortex are defined as the MDN by task-related activation [96] or as the DMN based on resting-state functional connectivity (rsFC) [17].…”