It’s trite knowledge that our brain function changes as we get older, but how it changes stays in a mysterious realm. Language function, interestingly enough, is commonly thought to remain relatively stable as we age. Billot et al. (2026) found that though neural networks supporting executive function was less consistent and connected in older adults than younger adults, activity in the language network was similar across ages.
How did they find this out?
The research team mapped network features using precision functional magnetic resonance imaging (fMRI) as participants completed two tasks; one was a reading task to test the language network and the other was a spatial working memory task to test broader executive function. A subset of participants also completed a resting state scan (to measure brain activity while not doing a task) and a passive listening task. Together, these different aspects of the experiment provided the data for analyses into language network or domain-specific activity and executive or domain-general activity.
▶️ Wait, what’s that again? Click here for a quick review on Executive Function and fMRI
Executive Function refers to the skills and processes we use to manage our cognitive resources and functioning to achieve different goals. If this sounds vague, it is because it is. It’s easier to think of executive function in terms of its examples, like:
- Creating plans
- Tracking time
- Reviewing concepts
- Remembering and manipulating information while doing another task
- Adapting thinking to new situations
For more general information on executive function, you may review:
https://cpa.ca/psychology-works-fact-sheet-executive-functioning
https://www.sciencedirect.com/topics/immunology-and-microbiology/executive-function.
Functional MRI (fMRI) is a specific type of imaging obtained through magnetic resonance imaging (MRI) scans by measuring changes in resonance of hydrogen atoms to capture changes in brain activity (i.e., measuring changes in brain function over time rather than just structural anatomy). A common approach is to measure the magnetic differences of hydrogen atoms over time in oxygen-rich blood flow to regions of the brain in different states of activity or rest. This is a measure known as the blood-oxygen level dependent (BOLD) signal, and is a common approach found in fMRI research (e.g., BOLD fMRI).
For more details on fMRI, you may review:
Glover, G. H. (2011). Overview of functional magnetic resonance imaging. Neurosurgery clinics of North America, 22(2), 133–vii. https://doi.org/10.1016/j.nec.2010.11.001
Also found here: https://pmc.ncbi.nlm.nih.gov/articles/PMC3073717/
Does this line up with other research?
Billot et al. (2026) note that some past research deviates from their findings and suggests instead that younger adults and older adults exhibit more marked differences in brain activity during language tasks. They explain that this may be due to prior studies potentially using tasks that mixed domain-specific and domain-general processes. Another factor is prior use of language production tasks rather than the comprehension tasks used in this study. Language production requires participants to actively construct and deliver meaning while comprehension requires participants to review and assess it; both are important and are aligned in adults, and may very well overlap and inform each other, but may still be distinctly defined through the brain activity they elicit. The present work from Billot et al. may indicate that language comprehension is relatively stable, or that language comprehension recruits more domain-specific networks than language production.
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Why the language network (or domain-specific networks) endures as we age is not yet well understood. It may be tied to the structural integrity of brain regions building language-specific networks, which may remain relatively stable or more receptive to compensatory neuroplasticity elsewhere in the brain. Though a pleasant result for the resilience of aging, the preservation of language networks remains a convoluted query subject to further review.
References
Billot, A., Jhingan, N., Varkanitsa, M., Wolna, A., Shain, C., Blank, I., Ryskin, R., Kiran, S., & Fedorenko, E. (2026). Preserved topography, lateralization, selectivity, and functional connectivity of the language network in older brains. Nature Communications, 17(1), Article 8573. https://doi.org/10.1038/s41467-026-76598-x
Also found here: https://www.nature.com/articles/s41467-026-76598-x
Ünal, E., & Papafragou, A. (2016). Production–comprehension asymmetries and the acquisition of evidential morphology. Journal of Memory and Language, 89, 179–199. https://doi.org/10.1016/j.jml.2015.12.001
Also found here: https://www.sciencedirect.com/science/article/abs/pii/S0749596X1500145X
Martin, C. D., Branzi, F. M., & Bar, M. (2018). Prediction is Production: The missing link between language production and comprehension. Scientific Reports, 8(1), Article 1079. https://doi.org/10.1038/s41598-018-19499-4
Also found here: https://www.nature.com/articles/s41598-018-19499-4
Jain, P., Akhter, A., & Banerjee, A. (2026). Frontal Vulnerability versus Temporal Resilience within the Core Language Network: Neuro-compensatory Mechanisms Underline Differential Language Aging Trajectories. The Journal of neuroscience : the official journal of the Society for Neuroscience, 46(2), e0839252025. https://doi.org/10.1523/JNEUROSCI.0839-25.2025
Also found here: https://pmc.ncbi.nlm.nih.gov/articles/PMC12809651/
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