BigBrainWarp: Toolbox for integration of BigBrain 3D histology with multimodal neuroimaging

Abstract

Neuroimaging stands to benefit from emerging ultrahigh-resolution histological atlases of the human brain; the first of which is “BigBrain”. Ongoing research aims to characterise regional differentiation of cytoarchitecture with BigBrain and to optimise registration of BigBrain with standard neuroimaging templates. Together, this work paves the way for multi-scale investigations of brain organisation. However, working with BigBrain can present new challenges for neuroimagers, including dealing with cellular resolution neuroanatomy and complex transformation procedures. To simplify workflows and support adoption of best practices, we developed BigBrainWarp, a toolbox for integration of BigBrain with multimodal neuroimaging. The primary BigBrainWarp function wraps multiple state-of-the-art deformation matrices into one line of code, allowing users to easily map data between BigBrain and standard MRI spaces. Additionally, the toolbox contains ready-to-use cytoarchitectural features to improve accessibility of histological information. The present article discusses recent contributions to BigBrain-MRI integration and demonstrates the utility of BigBrainWarp for further investigations.

Local and distant cortical responses to single pulse intracranial stimulation in the human brain are differentially modulated by specific stimulation parameters

“…In the preceding results, we tested our hypotheses pooling all data regardless of brain region. Increasing literature, however, as well as inferences from known cytoarchitectural and connectivity differences would suggest that the responses to stimulation differ in different brain regions [24,29,35,36,48,61,[93][94][95][96]. We explored this question by specifically comparing responses when stimulation was in lateral frontal lobe, cingulate, and lateral temporal lobe (as we had the most coverage of these J o u r n a l P r e -p r o o f 34 regions; Fig.…”

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

“…Indeed, prior studies have used these datasets to relate task meta-analyses, genetic expression, and histology to markers of morphology (Valk et al, 2020;Wagstyl et al, 2020;Whitaker et al, 2016), function (Benkarim et al, 2021;Krienen et al, 2016;Margulies et al, 2016;Paquola et al, 2019), and structural connectivity (Romme et al, 2017;Vos de Wael et al, 2021). Though numerous packages exist to enable these analyses Paquola et al, 2021;Salo et al, 2020;Yarkoni et al, 2011), using and integrating these requires expertise and proficiency in their respective programming languages.…”

“…The resting-state module contextualizes neuroimaging findings using canonical networks (Yeo et al, 2011) and functional gradients (Margulies et al, 2016). The histological submodule fetches cell-body-staining intensity profiles from the BigBrain atlas (Amunts et al, 2013) at 50 different depths to extract cytoarchitectural axes (Paquola et al, 2019(Paquola et al, , 2021Vos de Wael et al, 2020).…”

Impact of stimulation location relative to grey and white matter on single pulse electrical stimulation responses in the human brain

“…In the preceding results, we tested our hypotheses pooling all data regardless of brain region. Increasing literature, however, as well as inferences from known cytoarchitectural and connectivity differences would suggest that the rules for stimulation differ in different brain regions [24,28,51,60,[84][85][86][87]. We explored this question by specifically comparing responses when stimulation was in lateral prefrontal lobe, cingulate, and lateral temporal lobe (as we had the most coverage of these regions; Fig.…”

“…We posit there could be an effect of being closer to the axon hillocks of large pyramidal cells in cortical layers 4-6 and that the grey-white boundary is a convergence point for multiple output neurons or is a site with a higher concentration of excitatory versus inhibitory contributions which could explain the peak local responses [83]. To test these ideas, modelling in combination with cytoarchitectonic maps are likely necessary [84,85] in addition to sampling of neural data on microscale levels [14,92,95].…”