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Reconstructing the Neanderthal brain using computational anatomy.

ABSTRACT: The present study attempted to reconstruct 3D brain shape of Neanderthals and early Homo sapiens based on computational neuroanatomy. We found that early Homo sapiens had relatively larger cerebellar hemispheres but a smaller occipital region in the cerebrum than Neanderthals long before the time that Neanderthals disappeared. Further, using behavioural and structural imaging data of living humans, the abilities such as cognitive flexibility, attention, the language processing, episodic and working memory capacity were positively correlated with size-adjusted cerebellar volume. As the cerebellar hemispheres are structured as a large array of uniform neural modules, a larger cerebellum may possess a larger capacity for cognitive information processing. Such a neuroanatomical difference in the cerebellum may have caused important differences in cognitive and social abilities between the two species and might have contributed to the replacement of Neanderthals by early Homo sapiens.

SUBMITTER: Kochiyama T

PROVIDER: S-EPMC5919901 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Reconstructing the Neanderthal brain using computational anatomy.

Kochiyama Takanori T Ogihara Naomichi N Tanabe Hiroki C HC Kondo Osamu O Amano Hideki H Hasegawa Kunihiro K Suzuki Hiromasa H Ponce de León Marcia S MS Zollikofer Christoph P E CPE Bastir Markus M Stringer Chris C Sadato Norihiro N Akazawa Takeru T

Scientific reports 20180426 1

The present study attempted to reconstruct 3D brain shape of Neanderthals and early Homo sapiens based on computational neuroanatomy. We found that early Homo sapiens had relatively larger cerebellar hemispheres but a smaller occipital region in the cerebrum than Neanderthals long before the time that Neanderthals disappeared. Further, using behavioural and structural imaging data of living humans, the abilities such as cognitive flexibility, attention, the language processing, episodic and work ...[more]

PMID: 29700382

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Project description:Linking structural neuroimaging data from multiple modalities to cognitive performance is an important challenge for cognitive neuroscience. In this study we examined the relationship between verbal fluency performance and neuroanatomy in 54 patients with frontotemporal degeneration (FTD) and 15 age-matched controls, all of whom had T1- and diffusion-weighted imaging. Our goal was to incorporate measures of both gray matter (voxel-based cortical thickness) and white matter (fractional anisotropy) into a single statistical model that relates to behavioral performance. We first used eigenanatomy to define data-driven regions of interest (DD-ROIs) for both gray matter and white matter. Eigenanatomy is a multivariate dimensionality reduction approach that identifies spatially smooth, unsigned principal components that explain the maximal amount of variance across subjects. We then used a statistical model selection procedure to see which of these DD-ROIs best modeled performance on verbal fluency tasks hypothesized to rely on distinct components of a large-scale neural network that support language: category fluency requires a semantic-guided search and is hypothesized to rely primarily on temporal cortices that support lexical-semantic representations; letter-guided fluency requires a strategic mental search and is hypothesized to require executive resources to support a more demanding search process, which depends on prefrontal cortex in addition to temporal network components that support lexical representations. We observed that both types of verbal fluency performance are best described by a network that includes a combination of gray matter and white matter. For category fluency, the identified regions included bilateral temporal cortex and a white matter region including left inferior longitudinal fasciculus and frontal-occipital fasciculus. For letter fluency, a left temporal lobe region was also selected, and also regions of frontal cortex. These results are consistent with our hypothesized neuroanatomical models of language processing and its breakdown in FTD. We conclude that clustering the data with eigenanatomy before performing linear regression is a promising tool for multimodal data analysis.

Dataset Information

Reconstructing the Neanderthal brain using computational anatomy.

Publications

Reconstructing the Neanderthal brain using computational anatomy.

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