The BigBrainWarp toolbox for integration of BigBrain 3D histology with multimodal neuroimaging

Abstract

Neuroimaging stands to benefit from emerging ultrahigh-resolution 3D histological atlases of the human brain; the first of which is 'BigBrain'. Here, we review recent methodological advances for the integration of BigBrain with multi-modal neuroimaging and introduce a toolbox, 'BigBrainWarp', that combines these developments. The aim of BigBrainWarp is to simplify workflows and support the adoption of best practices. This is accomplished with a simple wrapper function that allows users to easily map data between BigBrain and standard MRI spaces. The function automatically pulls specialised transformation procedures, based on ongoing research from a wide collaborative network of researchers. Additionally, the toolbox improves accessibility of histological information through dissemination of ready-to-use cytoarchitectural features. Finally, we demonstrate the utility of BigBrainWarp with three tutorials and discuss the potential of the toolbox to support multi-scale investigations of brain organisation.

Architecture of the Human Default Mode Network: Cytoarchitecture, Wiring and Signal Flow

“…The combination of post mortem microscopy and in vivo imaging depends upon precise mapping between atlases. The present work leveraged a specialised multi-modal surface matching procedure (Lewis et al, 2020;Paquola et al, 2021), which minimises the misregistration error of landmarks to approximately 4mm, on par with standard in vivo registrations. The issue of precise mapping of functional networks to the BigBrain is further complicated by subject-specificity of functional network topographies (Braga and Buckner, 2017;Kong et al, 2019;Seitzman et al, 2019) and the impossibility of defining subject-specific functional networks on BigBrain.…”

“…Additionally, surfacewise smoothing was performed at each depth independently and involved moving a 2-vertex FWHM Gaussian kernel across the surface mesh using SurfStat . The staining intensity profiles are made available in the BigBrainWarp toolbox [(https://github.com/caseypaquola/BigBrainWarp), (Paquola et al, 2021)].…”

Asymmetric Signaling Across the Hierarchy of Cytoarchitecture within the Human Connectome

“…Thus, functional images were processed using a top-performing preprocessing pipeline implemented using the eXtensible Connectivity Pipeline (XCP) Engine 83 , which includes tools from FSL 92,94 and AFNI 95 . This pipeline included (1) correction for distortions induced by magnetic field inhomogeneity using FSL's FUGUE utility, (2) removal of 4 initial volumes, (3) realignment of all volumes to a selected reference volume using FSL's MCFLIRT, (4) interpolation of intensity outliers in each voxel's time series using AFNI's 3dDespike utility, (5) demeaning and removal of any linear or quadratic trends, and (6) co-registration of functional data to the high-resolution structural image using boundarybased registration. Images were de-noised using a 36-parameter confound regression model that has been shown to minimize associations with motion artifact while retaining signals of interest in distinct sub-networks 83,96 .…”

“…This structural model suggests that the degree to which two regions share similar cytoarchitectural features predicts the distribution of their laminar projections. Critically, inter-regional similarity in cytoarchitecture varies gradually across the cortex, creating a sensory-fugal (S-F) axis 5,6 that predicts regions' profiles of extrinsic connectivity to the rest of the brain. This gradient positions contiguous visual and sensorimotor cortex at one end and distributed heteromodal association and paralimbic cortices at the other, and is correlated with other macroscopic gradients of brain structure and function [7][8][9][10][11][12][13][14][15] .…”

Micapipe: A Pipeline for Multimodal Neuroimaging and Connectome Analysis

“…Recent work has shown that the analysis of covariance patterns of intracortical microstructural profiles can generate new descriptions of large-scale network organization (Paquola et al, 2020; Royer et al, 2020). These networks appear to be primarily governed by systematic shifts in laminar differentiation and neuronal density, showing a principal organizational axes similar to those at the level of cytoarchitecture and intrinsic functional connectivity (Margulies et al, 2016; Paquola et al, 2021, 2019b). A further notable feature is the automated generation of cortico-cortical geodesic distance matrices, which indexes proximity between different regions on the folded cortical surface.…”