Continuity, Divergence, and the Evolution of Brain Language Pathways

Recently, the assumption of evolutionary continuity between humans and non-human primates has been used to bolster the hypothesis that human language is mediated especially by the ventral extreme capsule pathway that mediates auditory object recognition in macaques. Here, we argue for the importance of evolutionary divergence in understanding brain language evolution. We present new comparative data reinforcing our previous conclusion that the dorsal arcuate fasciculus pathway was more significantly modified than the ventral extreme capsule pathway in human evolution. Twenty-six adult human and twenty-six adult chimpanzees were imaged with diffusion-weighted MRI and probabilistic tractography was used to track and compare the dorsal and ventral language pathways. Based on these and other data, we argue that the arcuate fasciculus is likely to be the pathway most essential for higher-order aspects of human language such as syntax and lexical–semantics

Symmetry in Human Evolution, from Biology to Behaviours

Our knowledge of human evolution has made particular progress recently, due to the discovery of new fossils, the use of new methods and multidisciplinary approaches. Moreover, studies on the departure from symmetry, including variations in fluctuating or directional asymmetries, have contributed to the expansion of this knowledge. This Special Issue brings together articles that deal with symmetry and human evolution. The notion of symmetry is addressed, including whether to reconstruct deformed fossil specimens, study biological variations within hominins or compare them with extant primates, address the shape of the brain or seek possible relationships between biological and behavioural data

The individuality of shape asymmetries of the human cerebral cortex

Asymmetries of the cerebral cortex are found across diverse phyla and are particularly pronounced in humans, with important implications for brain function and disease. However, many prior studies have confounded asymmetries due to size with those due to shape. Here, we introduce a novel approach to characterize asymmetries of the whole cortical shape, independent of size, across different spatial frequencies using magnetic resonance imaging data in three independent datasets. We find that cortical shape asymmetry is highly individualized and robust, akin to a cortical fingerprint, and identifies individuals more accurately than size-based descriptors, such as cortical thickness and surface area, or measures of inter-regional functional coupling of brain activity. Individual identifiability is optimal at coarse spatial scales (~37 mm wavelength), and shape asymmetries show scale-specific associations with sex and cognition, but not handedness. While unihemispheric cortical shape shows significant heritability at coarse scales (~65 mm wavelength), shape asymmetries are determined primarily by subject-specific environmental effects. Thus, coarse-scale shape asymmetries are highly personalized, sexually dimorphic, linked to individual differences in cognition, and are primarily driven by stochastic environmental influences

BrainPrint: A discriminative characterization of brain morphology

We introduce BrainPrint, a compact and discriminative representation of brain morphology. BrainPrint captures shape information of an ensemble of cortical and subcortical structures by solving the eigenvalue problem of the 2D and 3D Laplace–Beltrami operator on triangular (boundary) and tetrahedral (volumetric) meshes. This discriminative characterization enables new ways to study the similarity between brains; the focus can either be on a specific brain structure of interest or on the overall brain similarity. We highlight four applications for BrainPrint in this article: (i) subject identification, (ii) age and sex prediction, (iii) brain asymmetry analysis, and (iv) potential genetic influences on brain morphology. The properties of BrainPrint require the derivation of new algorithms to account for the heterogeneous mix of brain structures with varying discriminative power. We conduct experiments on three datasets, including over 3000 MRI scans from the ADNI database, 436 MRI scans from the OASIS dataset, and 236 MRI scans from the VETSA twin study. All processing steps for obtaining the compact representation are fully automated, making this processing framework particularly attractive for handling large datasets.National Cancer Institute (U.S.) (1K25-CA181632-01)Athinoula A. Martinos Center for Biomedical Imaging (P41-RR014075)Athinoula A. Martinos Center for Biomedical Imaging (P41-EB015896)National Alliance for Medical Image Computing (U.S.) (U54-EB005149)Neuroimaging Analysis Center (U.S.) (P41-EB015902)National Center for Research Resources (U.S.) (U24 RR021382)National Institute of Biomedical Imaging and Bioengineering (U.S.) (5P41EB015896-15)National Institute of Biomedical Imaging and Bioengineering (U.S.) (R01EB006758)National Institute on Aging (AG022381)National Institute on Aging (5R01AG008122-22)National Institute on Aging (AG018344)National Institute on Aging (AG018386)National Center for Complementary and Alternative Medicine (U.S.) (RC1 AT005728-01)National Institute of Neurological Diseases and Stroke (U.S.) (R01 NS052585-01)National Institute of Neurological Diseases and Stroke (U.S.) (1R21NS072652-01)National Institute of Neurological Diseases and Stroke (U.S.) (1R01NS070963)National Institute of Neurological Diseases and Stroke (U.S.) (R01NS083534)National Institutes of Health (U.S.) ((5U01-MH093765

BrainPrint: A discriminative characterization of brain morphology

We introduce BrainPrint, a compact and discriminative representation of brain morphology. BrainPrint captures shape information of an ensemble of cortical and subcortical structures by solving the eigenvalue problem of the 2D and 3D Laplace–Beltrami operator on triangular (boundary) and tetrahedral (volumetric) meshes. This discriminative characterization enables new ways to study the similarity between brains; the focus can either be on a specific brain structure of interest or on the overall brain similarity. We highlight four applications for BrainPrint in this article: (i) subject identification, (ii) age and sex prediction, (iii) brain asymmetry analysis, and (iv) potential genetic influences on brain morphology. The properties of BrainPrint require the derivation of new algorithms to account for the heterogeneous mix of brain structures with varying discriminative power. We conduct experiments on three datasets, including over 3000 MRI scans from the ADNI database, 436 MRI scans from the OASIS dataset, and 236 MRI scans from the VETSA twin study. All processing steps for obtaining the compact representation are fully automated, making this processing framework particularly attractive for handling large datasets.National Cancer Institute (U.S.) (1K25-CA181632-01)Athinoula A. Martinos Center for Biomedical Imaging (P41-RR014075)Athinoula A. Martinos Center for Biomedical Imaging (P41-EB015896)National Alliance for Medical Image Computing (U.S.) (U54-EB005149)Neuroimaging Analysis Center (U.S.) (P41-EB015902)National Center for Research Resources (U.S.) (U24 RR021382)National Institute of Biomedical Imaging and Bioengineering (U.S.) (5P41EB015896-15)National Institute of Biomedical Imaging and Bioengineering (U.S.) (R01EB006758)National Institute on Aging (AG022381)National Institute on Aging (5R01AG008122-22)National Institute on Aging (AG018344)National Institute on Aging (AG018386)National Center for Complementary and Alternative Medicine (U.S.) (RC1 AT005728-01)National Institute of Neurological Diseases and Stroke (U.S.) (R01 NS052585-01)National Institute of Neurological Diseases and Stroke (U.S.) (1R21NS072652-01)National Institute of Neurological Diseases and Stroke (U.S.) (1R01NS070963)National Institute of Neurological Diseases and Stroke (U.S.) (R01NS083534)National Institutes of Health (U.S.) ((5U01-MH093765

What do brain endocasts tell us? A comparative analysis of the accuracy of sulcal identification by experts and perspectives in palaeoanthropology

Palaeoneurology is a complex field as the object of study, the brain, does not fossilize. Studies rely therefore on the (brain) endocranial cast (often named endocast), the only available and reliable proxy for brain shape, size and details of surface. However, researchers debate whether or not specific marks found on endocasts correspond reliably to particular sulci and/or gyri of the brain that were imprinted in the braincase. The aim of this study is to measure the accuracy of sulcal identification through an experiment that reproduces the conditions that palaeoneurologists face when working with hominin endocasts. We asked 14 experts to manually identify well-known foldings in a proxy endocast that was obtained from an MRI of an actual in vivo Homo sapiens head. We observe clear differences in the results when comparing the non-corrected labels (the original labels proposed by each expert) with the corrected labels. This result illustrates that trying to reconstruct a sulcus following the very general known shape/position in the literature or from a mean specimen may induce a bias when looking at an endocast and trying to follow the marks observed there. We also observe that the identification of sulci appears to be better in the lower part of the endocast compared to the upper part. The results concerning specific anatomical traits have implications for highly debated topics in palaeoanthropology. Endocranial description of fossil specimens should in the future consider the variation in position and shape of sulci in addition to using models of mean brain shape. Moreover, it is clear from this study that researchers can perceive sulcal imprints with reasonably high accuracy, but their correct identification and labelling remains a challenge, particularly when dealing with extinct species for which we lack direct knowledge of the brain

Identifying animal complex cognition requires natural complexity

The search for human cognitive uniqueness often relied on low ecological tests with subjects experiencing unnatural ontogeny. Recently, neuroscience demonstrated the significance of a rich environment on the development of brain structures and cognitive abilities. This stresses the importance to consider the prior knowledge that subjects bring in any experiment. Second, recent developments in multivariate statistics control precisely for a number of factors and their interactions. Making controls in natural observations equivalent and sometimes superior to captive experimental studies without the drawbacks of the latter methods. Thus, we can now investigate complex cognition by accounting for many different factors, as required when solving tasks in nature. Combining both progresses allow us to move towards an “experience-specific cognition”, recognizing that cognition vary extensively in nature as individuals adapt to the precise challenges they experience in life. Such cognitive specialization makes cross-species comparisons more complex, while potentially identifying human cognitive uniqueness

The nonhuman primate neuroimaging and neuroanatomy project

Multi-modal neuroimaging projects such as the Human Connectome Project (HCP) and UK Biobank are advancing our understanding of human brain architecture, function, connectivity, and their variability across individuals using high-quality non-invasive data from many subjects. Such efforts depend upon the accuracy of non-invasive brain imaging measures. However, ‘ground truth’ validation of connectivity using invasive tracers is not feasible in humans. Studies using nonhuman primates (NHPs) enable comparisons between invasive and non-invasive measures, including exploration of how “functional connectivity” from fMRI and “tractographic connectivity” from diffusion MRI compare with long-distance connections measured using tract tracing. Our NonHuman Primate Neuroimaging & Neuroanatomy Project (NHP_NNP) is an international effort (6 laboratories in 5 countries) to: (i) acquire and analyze high-quality multi-modal brain imaging data of macaque and marmoset monkeys using protocols and methods adapted from the HCP; (ii) acquire quantitative invasive tract-tracing data for cortical and subcortical projections to cortical areas; and (iii) map the distributions of different brain cell types with immunocytochemical stains to better define brain areal boundaries. We are acquiring high-resolution structural, functional, and diffusion MRI data together with behavioral measures from over 100 individual macaques and marmosets in order to generate non-invasive measures of brain architecture such as myelin and cortical thickness maps, as well as functional and diffusion tractography-based connectomes. We are using classical and next-generation anatomical tracers to generate quantitative connectivity maps based on brain-wide counting of labeled cortical and subcortical neurons, providing ground truth measures of connectivity. Advanced statistical modeling techniques address the consistency of both kinds of data across individuals, allowing comparison of tracer-based and non-invasive MRI-based connectivity measures. We aim to develop improved cortical and subcortical areal atlases by combining histological and imaging methods. Finally, we are collecting genetic and sociality-associated behavioral data in all animals in an effort to understand how genetic variation shapes the connectome and behavior