Back

Young and old adult brains experience opposite effects of acute sleep restriction on the functional connectivity network

Neudorf, J.; Rokos, L.; Shen, K.; Kent, B.; McIntosh, A. R.

2025-07-31 neuroscience
10.1101/2025.07.25.666859 bioRxiv
Show abstract

Chronic, long-term sleep loss is detrimental to brain health and cognitive ability. However, older adults are affected differently by acute, short-term loss of sleep than young and middle-aged adults. Older adults are more resilient to the effects of acute sleep loss and, depending on the cognitive domain, may be completely unaffected while younger adults suffer. To elucidate the brain network responses to sleep loss underlying these cognitive differences between age groups, we investigated the static and dynamic functional connectivity effects of sleep restriction (sleep limited to 3 hours) and how these effects differ between younger adults (20-30 years) and older adults (65-75 years). We found a functional connectivity subnetwork that was primarily strengthened in younger adults after sleep restriction but weakened in older adults after sleep restriction. Similar crossover interactions were consistently observed in further analyses of functional connectivity degree, modularity, and dynamic functional connectivity state fractional occupancy. Our findings demonstrate that the effect of sleep restriction on older adults is fundamentally different from younger adults. These results most strongly support the compensation theory of aging, which predicts a fundamental shift in the effects of sleep loss, rather than a mere dampening of the sleep benefits experienced by younger adults.

Published in JoCN Forum · not in our set (fewer than 10 published preprints to learn from) · training set

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.