ORIGINAL RESEARCH article
Front. Neuroinform.
This article is part of the Research TopicMultimodal Brain Data Integration and Computational ModelingView all 6 articles
Computational reconstruction of evolutionary selection in human brain networks
Provisionally accepted
- 1Department of Neuronal Cell Biology, Center for Brain Research, Medical University of Vienna, Vienna, Austria
- 2Department of Biology, University of Rome Tor Vergata, Rome, Italy
- 3Biomedical Image Informatics, VRVis GmbH, Vienna, Austria
- 4Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna, Austria
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
The accumulation of genomic and brain data opens new opportunities for resource-friendly, data-driven brain exploration. A key challenge is to develop versatile and easy-access strategies that integrate and mine multimodal datasets for novel neuroscientific insights. Here, we optimized an integrated workflow for mapping multigenic evolutionary traits in the human brain space across cognitive, cellular, and molecular levels. At the input, it fuses an evolutionary genetic dataset with searchable synthetic functional magnetic resonance imaging (fMRI) databases, pre-clustered into concise psychological domains for improved interpretability. At its core, a Genetic Algorithm for Generalized Biclustering (GABi) mines gene sets under evolutionary selection, along with a high expression correlation to fMRI networks. As an output, this study identified evolutionary patterns across cognitive traits, brain cell types, and molecular mechanisms. Focusing on socio-affective traits, the algorithm highlighted peaks in adaptive selection in networks for social interaction (language) and social concepts (theory of mind) in hominid, early hominin, and anatomically modern human (AMH) ancestry. These traits emerge from a broad spectrum of excitatory (glutamatergic) and inhibitory (GABAergic) neuronal and non-neuronal cell types. Moreover, the associated Gene Ontology (GO) terms were enriched for cell signaling, synaptic organization, and neuronal morphology. Together, we present an integrated workflow for molecular-to-systems-level exploration of the brain and provide new perspectives on human socio-affective history. This approach can be adapted to screen for functional traits in the context of mental disorders or in the brains of other phylogenies in a similar manner.
Keywords: adaptive evolution, Archaic humans, brain evolution, computational neuroarchaeology, evolutionary genetics, social brain evolution
Received: 05 May 2025; Accepted: 11 Dec 2025.
Copyright: © 2025 Piszczek, Fazzari, Ulonska, Bühler and Haubensak. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Wulf Haubensak
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.