Insights from Non-Human Primate Anatomy

Despite its importance as a model for human brain development and neurological disorders, the marmoset lacks a comprehensive MRI-histology-based parcellation and 3D atlas of brain areas. Here, we first generated a Subcortical Atlas of the Marmoset, called the SAM, from 251 delineated subcortical regions derived from the ex vivo high-resolution multimodal MRIs [1,2] and matched histology with multiple stains derived from the same brain specimen. Tracing and validating atlas-based brain regions is imperative for neurosurgical planning, anatomical tract tracer injections, deep brain stimulation probes navigation, functional imaging (fMRI) studies, and establishing brain structure-function relationships. 

One of the major challenges to carrying out in vivo neuroanatomical analyses is that regional cytoarchitectural variation is difficult to capture by MRI. As a result, parcellation of brain regions is often limited to a scale too coarse for the understanding of their functions. While this presents a challenge for many regions of the brain, the insula is comprised of distinct laminar cyto-archetypes that form the basis of highly integrative whole brain networks. These subregions have been linked to an astonishing number of functional roles, and may ultimately be targets for future development of interventions in physical and mental health ^1–4. In order to capture the heterogeneity of these subregions and networks, it is necessary to improve the specificity of neuroanatomical data and analyses using resolutions across disparate spatial scales and contrasting modalities from within the same subjects. We present our first integrated (Mic)ro to (Mac)ro Macaque brain dataset, here called MicMac (Fig. 1). MicMac is an extendable workflow, represented by a within-subject whole brain dataset that integrates aligned multi-parametric in vivo MRI, high resolution ex vivo MRI, and histology within a single, standardized template space. We then translate this workflow to perform a group level network analysis to identify network features in an in vivo cohort of n=16 middle to older aged macaques (7-20 years, 3M, 13F). 

Anatomical connectivity changes during evolution may underlie functional specialization of the human brain (Thiebaut de Schotten et al. 2022). Although chimpanzees (Pan troglodytes) are crucial as a comparative reference for investigating human brain evolution (Varki et al. 2005), comprehensive connectional analyses between human and chimpanzee brains have been limited due to lack of comparable cross-species brain atlases. Moreover, the underlying genetic association of human-specific brain connectivity also remains unclear. To address these questions, we built the Chimpanzee Brainnetome Atlas (ChimpBNA), investigated cross-species connectivity divergence and examined the associated genetic factors. 

Within the gyral white matter, three types of fibers converge: association, projection, and commissural. Currently, models of gyral white matter structural organization based on autoradiographic studies conducted in non-human primates (Schmahmann and Pandya, 2010; Dannhoff et al. 2023) describe their trajectory until the fibers arrive near the grey matter. However, there is little information characterizing their organization within the gyrus. The development of ultra-high field ex vivo MRI and advances in tractography algorithms, at the mesoscopic resolution, now allow us access to this intermingled fibers' organization. We sought to investigate the structural organization of white matter in the precentral gyrus (PrCG) of the macaque brain, using an ultra-high field 11.7T diffusion MRI dataset. 

Since Brodmann's seminal work [1], studies have aimed to divide the brain into spatially contiguous areas or parcels that are functionally or anatomically homogeneous. These parcellations have historically been based on histology, but recent works have derived them by combining neuroimaging with sophisticated algorithms [2,3]. However, current approaches are not generalizable and offer no insight into the generative mechanisms that may have shaped the regional organization of the brain.

Here, we draw on evidence that regional patterning in the brain is strongly shaped by geometrically constrained gradients of gene expression [4] to develop a novel parcellation approach using the eigenmodes of brain geometry [5]. We show that the resulting geometry-derived parcellations are more homogeneous across hundreds of diverse anatomical, functional, cellular, and molecular properties than many existing parcellations of human, non-human primate, and mouse brains. 

While global tractography offers enhanced accuracy and reliability compared to standard streamline tractography methods [1], it comes at the cost of substantial computational resources in terms of both time and memory. This approach simultaneously generates and optimizes the trajectories of virtual axonal white matter (WM) fibers (represented as connected spin-glasses), considering the local orientation distribution derived from diffusion MRI (dMRI) data. Leveraging this method on extensive datasets poses significant challenges, leading to the creation of ExaTract [2], a novel High-Performance Computing (HPC) global tractography approach.
In this study, we demonstrate the ability of ExaTract to reconstruct numerous brain connections and robustly identify deep WM bundles from a very high resolution (250μm) dataset in a reasonable time.