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Biology subjects

Perez-Guevara, M.

Publications and source records attributed to Perez-Guevara, M..

2 recordsLinked to original sources

An fMRI study of composition in noun and verb phrases

How do the language areas of the human brain combine multiple words into meaningful phrases and sentences remains ill-understood. Here, to address this question, we determined the response profile of temporal and inferior frontal language areas to the composition of up to four words into phrases. We tested whether brain activity increases with the number of merged words, and whether this profile differs for noun and verb phrases. To this aim, we used fMRI to quantify the brain responses to individual noun and verb phrases of varying length and to tightly matched word lists. Increasing phrase length was associated to an increase in activation in all regions of the temporo-frontal language network. The effect was more pronounced for phrases built around verbs than for phrases built around nouns, suggesting that verbs involve a more complex syntactic tree structure than nouns. Even with word lists, several regions, notably the inferior frontal gyrus (IFG) pars triangularis and opercularis and the posterior superior temporal sulcus showed clear increases in activity with the length of sequences, although the words could not be merged into phrases. By contrast, other regions (IFG pars orbitalis, anterior temporal lobe, temporo-parietal junction) did not react to scrambled word lists. Those different functional response profiles inform theories of how composition is implemented in the human brain.

neuroscience↗

Biolearn, an open-source library for biomarkers of aging

Aging biomarkers are essential for understanding and quantifying the aging process and developing targeted longevity interventions. However, validation of these tools has been hindered by the lack of standardized approaches for cross-population validation, disparate biomarker designs, and inconsistencies in dataset structures. To address these challenges, we developed Biolearn, an open-source library that provides a unified framework for the curation, harmonization, and systematic evaluation of aging biomarkers. Leveraging Biolearn, we conducted a comprehensive evaluation of various aging biomarkers across multiple datasets. Our systematic approach involved three key steps: (1) harmonizing existing and novel aging biomarkers in standardized formats; (2) unifying public datasets to ensure coherent structuring and formatting; and (3) applying computational methodologies to assess the harmonized biomarkers against the unified datasets. This evaluation yielded valuable insights into the performance, robustness, and generalizability of aging biomarkers across different populations and datasets. The Biolearn python library, which forms the foundation of this systematic evaluation, is freely available at https://Bio-Learn.github.io. Our work establishes a unified framework for the curation and evaluation of aging biomarkers, paving the way for more efficient and effective clinical validation and application in the field of longevity research.

bioinformatics↗