bioRxivNeurosciencePreprint
Brain structure and activity predicting cognitive maturation in adolescence
Junda ZhuiD, Clément M. GariniD, Xue-Lian QiiD, Anna MachadoiD, Zhengyang WangiD, Suliann Ben HamediD, Terrence R. StanfordiD, Emilio SalinasiD, Christopher T. WhitlowiD, Adam W. AndersoniD, Xin ZhouiD, Finnegan J. CalabroiD, Beatríz LunaiD, Christos ConstantinidisiD
Abstract
Cognitive abilities of primates, including humans, continue to improve through adolescence 1,2. While a range of changes in brain structure and connectivity have been documented 3,4, how they affect neuronal activity that ultimately determines performance of cognitive functions remains unknown. Here, we conducted a multilevel longitudinal study of monkey adolescent neurocognitive development. The developmental trajectory of neural activity in the prefrontal cortex accounted remarkably well for working memory improvements. While complex aspects of activity changed progressively during adolescence, such as the rotation of stimulus representation in multidimensional neuronal space, which has been implicated in cognitive flexibility, even simpler attributes, such as the baseline firing rate in the period preceding a stimulus appearance had predictive power over behavior. Unexpectedly, decreases in brain volume and thickness, which are widely thought to underlie cognitive changes in humans 5 did not predict well the trajectory of neural activity or cognitive performance changes. Whole brain cortical volume in particular, exhibited an increase and reached a local maximum in late adolescence, at a time of rapid behavioral improvement. Maturation of long-distance white matter tracts linking the frontal lobe with areas of the association cortex and subcortical regions best predicted changes in neuronal activity and behavior. Our results provide evidence that optimization of neural activity depending on widely distributed circuitry effects cognitive development in adolescence.
- organism: non-human primate
- assay: electronic health records
- research focus: cell type annotation
- approach: statistical model
- Citations
- 4
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