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. 2024 Jan 8:17:1299087.
doi: 10.3389/fnint.2023.1299087. eCollection 2023.

Brain structure and function: a multidisciplinary pipeline to study hominoid brain evolution

Angela D Friederici #  1 , Roman M Wittig #  2   3   4 , Alfred Anwander  1 , Cornelius Eichner  1 , Tobias Gräßle  5 , Carsten Jäger  6   7 , Evgeniya Kirilina  6 , Ilona Lipp  6 , Ariane Düx  5   8 , Luke J Edwards  6 , Cédric Girard-Buttoz  2   3 , Anna Jauch  6 , Kathrin S Kopp  9 , Michael Paquette  1 , Kerrin J Pine  6 , Steve Unwin  10 , Daniel B M Haun  9 , Fabian H Leendertz  5   8 , Richard McElreath  11 , Markus Morawski  7 , Philipp Gunz  12 , Nikolaus Weiskopf #  6   13 , Catherine Crockford #  2   3   4 ; EBC Consortium
Collaborators, Affiliations

Brain structure and function: a multidisciplinary pipeline to study hominoid brain evolution

Angela D Friederici et al. Front Integr Neurosci. .

Abstract

To decipher the evolution of the hominoid brain and its functions, it is essential to conduct comparative studies in primates, including our closest living relatives. However, strong ethical concerns preclude in vivo neuroimaging of great apes. We propose a responsible and multidisciplinary alternative approach that links behavior to brain anatomy in non-human primates from diverse ecological backgrounds. The brains of primates observed in the wild or in captivity are extracted and fixed shortly after natural death, and then studied using advanced MRI neuroimaging and histology to reveal macro- and microstructures. By linking detailed neuroanatomy with observed behavior within and across primate species, our approach provides new perspectives on brain evolution. Combined with endocranial brain imprints extracted from computed tomographic scans of the skulls these data provide a framework for decoding evolutionary changes in hominin fossils. This approach is poised to become a key resource for investigating the evolution and functional differentiation of hominoid brains.

Keywords: behavior; histology; hominoid fossil; non-human primates; structural MRI.

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Conflict of interest statement

The Max Planck Institute for Human Cognitive and Brain Sciences has an institutional research agreement with Siemens Healthcare. NW holds a patent on acquisition of MRI data during spoiler gradients (US 10,401,453 B2). NW was a speaker at an event organized by Siemens Healthcare and was reimbursed for the travel expenses. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The boxes in this figure represent the different habitat sites of the animals (green flat boxes) and the different units of experts working in the project from in vivo behavior (turquoise box) before brain extraction (blue box) to post mortem macro-, micro-structural and histological analyses of the brain (dark blue to purple boxes) and to analysis of the endocast (dark purple box). The strong blue arrows indicate the transition of the brains after extraction to the respective brain analysis units in a sequential order and the animals’ skulls to the hominoid fossil unit. Green lines indicate possible correlations of in vivo behavioral data with post mortem brain analysis data. Purple lines indicate correlations between different brain structural analyses. Dark purple lines to the hominoid fossil unit (dark purple box) indicate correlations between gyral structure of the brain and the skull shape. The statistical unit (red box) provides methods to allow analyses of predictions under the condition of missing data points in the respective measurements which are based on input from the behavioral unit and from the brain analysis units, indicated by red lines.
Figure 2
Figure 2
Non-human primate brain network consisting of ape and monkey field sites (unmarked Capuchins at Taboga, Costa Rica) and sanctuaries, as well as European zoos caring for great apes (color coded for species and ecology). The brains represent a subset of the collection of chimpanzee brains (sorted by age and site) which were collected between 2019 and 2022. The complete set of brains collected are listed in Table 1.
Figure 3
Figure 3
Bigram flexibility and ordering (N = 817 bigrams and 58 unique bigrams). (A) the bigram combinatorial network with the 10 single units that were combined with one other unit, depicted as circled nodes. Color gradients (hot-to-cold) represent the number of times a certain unit is found in the bigram set. Note that the single units (NV) and (PR) that never occurred in bigrams in this dataset are not shown here. This network was also used for the calculation of the Betweenness Centrality among the units. The size of the directional edges (arrows) expresses the number of times the specific bigram is found in the sample (thick-to-thin). (B) The number of different single units with which each single unit forms a bigram in the sample as first unit (left) or second unit in the bigram (right). Adapted from Girard-Buttoz et al. (2022a).
Figure 4
Figure 4
Example diffusion MRI reconstruction and tracking results. (A) The unprecedented data quality with ultra-high resolution allows for mapping the structural connectivity of the chimpanzee brain (wild, female, 6 years) with high precision. The images shows slices of the color coded fractional anisotropy computed from the measurement (color coding of main diffusion direction: red—left right, green—superior inferior, blue – anterior posterior). (B) The high-resolution dMRI data enables tractography on fine spatial scales in the same chimpanzee brain. Three respective tract reconstructions are depicted: Inferior Fronto-Occipital Fasciculus, IFOF (green); Corticospinal tract (blue); Cingulum (turquoise). The three depicted tracts were selected as examples for this illustration due to their well-known morphology in humans.
Figure 5
Figure 5
Microstructural mapping of a chimpanzee brain using quantitative MRI. (A) The quantitative MRI parameters R1, R2*, MTsat and PD acquired at 7 T and ultra-high resolution are shown for the same axial slice of one post mortem chimpanzee brain (wild, male, 45 years.). All parameters show exquisite contrast between gray and white matter and are sensitive biomarkers of brain tissue microstructure including brain myelination and brain iron content. (B) The high resolution multimodal maps allow for comprehensive characterization of brain anatomy resolving smallest neuroanatomical features including (i) layers within the cortex (arrow indicates the highly myelinated Stria of Gennari in the primary visual cortex in the lunate sulcus), (ii) thin gray matter structures such as the claustrum, (iii) substructures within the hippocampus and (iv) iron accumulation in subcortical structures. (C) Whole brain quantitative MRI maps allow for mapping of myeloarchitecture across the entire brain facilitating quantitative comparison of brain organization across species. R1 sampled at middle cortical depth and mapped across the neocortex for (bottom panel) one post mortem chimpanzee brain (captive, female, 44 years.) reflect different myelination of brain areas (highly myelinated motor, sensory and auditory cortex indicated with arrows) and can be compared with the same metric obtained in (right panel) humans in vivo (Kirilina et al., 2020). Note that absolute R1 values are larger due to fixation of the post mortem brain tissue compared to in vivo.
Figure 6
Figure 6
Histological investigation of great ape brain samples. (A–D) Myelination from macro- to ultrastructure in a chimpanzee brain. (A) Section of one hemisphere illustrating myelin distribution (Gallyas silver stain). (B) Cortical myelination profile (Gallyas). (C) Cortical myelinated fibers in detail (Gallyas). (D) Myelinated axons in electron microscopy. Scalebars: (A) 10 mm, (B) 1 mm, (C) 50 μm, (D) 5 μm. (E–H) Histological validation of human’s Broca areal homolog in a bonobo brain. (E) T2*-weighted MR image acquired at 50 μm isotropic resolution. Low intracortical myelination in areas BA 44 and 45 results in higher image intensity. (F) Corresponding Nissl stained section. (G) Border between Brodmann area 44 and 45 homologs (dashed orange line) in (i) Nissl stain for cytoarchitecture, (ii) Aggrecan stain for neuronal extracellular matrix and (iii) Gallyas silver stain for myelin. (H) Border between Brodmann areas 45 and 9 homologs (dashed orange line) in (i) Nissl, (ii) Aggrecan and (iii) Gallyas. Scalebars: (E,F) 10 mm, (G) 1 mm, (H) 500 μm.
Figure 7
Figure 7
Computed tomographic (CT) and MRI scans of a chimpanzee. (A) Skull fragments are scanned using microCT and realigned virtually based on anatomical criteria (B). (C) Segmentation of the interior braincase yields an endocranial imprint (blue). The skull is virtually cut in the midsagittal plane. (D) Impressions created by blood vessels, cranial sutures, gyri and sulci are clearly visible on the virtual endocast. (E) We visualized the shape index of the surface as a gray-level gradient to facilitate the identification of sulcal impressions, and then manually color-coded brain features corresponding between endocast and brain. (F) MRI brain scan of the same chimpanzee individual, analyzed by the Diffusion MRI Unit. Corresponding features on the endocranial imprint in (E) are shown in the same color.
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Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was funded by the Max Planck Society under the inter-institutional funds of the president of the Max Planck Society for the Hominoid Brain Connectomics (now Evolution of Brain Connectivity: EBC) Project. NW has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007–2013)/ERC grant agreement No 616905; from the European Union’s Horizon 2020 Research and Innovation Programme under the grant agreement no. 681094; from the BMBF (01EW1711A&B) in the framework of ERA-NET NEURON. NW has received funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—project no. 347592254 (WE 5046/4–2 and KI 1337/2–2). This project has received funding from the Federal Ministry of Education and Research (BMBF) under support code 01ED2210.

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