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Computational reconstruction of evolutionary selection in human brain networks
Lukasz Piszczek1, Clara Fazzari1,2, Sophia Ulonska3
1Department of Neuronal Cell Biology, Center for Brain Research, Medical University of Vienna, Vienna, Austria.
Frontiers in Neuroinformatics
|February 11, 2026
Summary
We developed a novel workflow to map human brain evolution using genomic and fMRI data. This approach reveals evolutionary patterns in socio-affective traits, offering insights into human brain development.
Area of Science:
- Neuroscience
- Evolutionary Biology
- Genomics
Background:
- Integrating genomic and brain data presents challenges for exploring brain evolution.
- Developing versatile strategies for multimodal dataset mining is crucial for novel neuroscientific insights.
Purpose of the Study:
- To optimize an integrated workflow for mapping multigenic evolutionary traits in the human brain.
- To explore cognitive, cellular, and molecular levels of brain evolution.
Main Methods:
- Fused evolutionary genetic data with functional magnetic resonance imaging (fMRI) databases.
- Employed a Genetic Algorithm for Generalized Biclustering (GABi) to mine gene sets under evolutionary selection correlated with fMRI networks.
Main Results:
- Identified evolutionary patterns across cognitive traits, brain cell types, and molecular mechanisms.
- Highlighted adaptive selection peaks in social interaction and theory of mind networks in human ancestry.
- Found enrichment of Gene Ontology terms related to cell signaling and neuronal morphology.
Conclusions:
- Demonstrated an integrated workflow for molecular to systems-level brain exploration.
- Provided new perspectives on the evolutionary history of human socio-affective functions.
- The workflow can be adapted for studying mental disorders and other phylogenies.
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