Sleep

The impact of sleep deprivation on dynamic functional connectivity of the brain: Based on alertness task performance.

TL;DR

Sleep deprivation increased time spent in a 'maladaptive compensatory state' of globally strengthened brain synchronization, with dynamic functional connectivity metrics—particularly within the dorsal attention network—predicting psychomotor vigilance task lapses.

Key Findings

Sleep deprivation caused participants to spend more time in a maladaptive compensatory brain state characterized by globally strengthened synchronization.

  • Forty healthy adults underwent 30-hour sleep deprivation with resting-state fMRI collected at baseline and post-SD
  • Two recurrent whole-brain dynamic functional connectivity states were identified using sliding windows and k-means clustering
  • State (i) was an 'economical state' with sparse, weaker global coupling; State (ii) was a 'maladaptive compensatory state' with globally strengthened synchronization
  • SD increased both the fraction of windows and mean dwell time (MDT) spent in the maladaptive compensatory state

PVT lapses correlated positively with time spent in the maladaptive brain state and negatively with time spent in the economical brain state.

  • PVT (psychomotor vigilance task) lapses correlated positively with the maladaptive compensatory state's MDT and fraction of windows
  • PVT lapses correlated negatively with the economical state's MDT and fraction of windows
  • These correlations were assessed across participants at both baseline and post-SD measurement points
  • PVT performance served as the primary behavioral measure of alertness impairment

An interpretable predictive model using CARS-PLSR identified connections within the dorsal attention network (DAN) as key predictors of PVT lapses.

  • The model used competitive adaptive reweighted sampling partial least-squares regression (CARS-PLSR)
  • The model was described as providing a 'sparse, testable feature set'
  • Connections within the dorsal attention network were highlighted as key predictors of behavioral impairment
  • The model was built to predict PVT lapses from dynamic functional connectivity features

Sleep deprivation impaired mood and cognition in healthy adults undergoing 30 hours of total sleep deprivation.

  • Sample consisted of 40 healthy adults
  • Participants completed mood assessments, resting-state fMRI, and PVT tests at both baseline and post-SD timepoints
  • The SD protocol involved 30 hours of sleep deprivation
  • Both mood and cognitive (alertness) outcomes were assessed

The study's findings link behavioral impairment from sleep deprivation to altered brain-state dynamics and support early risk stratification.

  • The authors propose the identified dFC features can 'support early risk stratification and intervention for SD-related cognitive decline'
  • The predictive model was described as 'interpretable', suggesting translational potential
  • The feature set was characterized as 'sparse' and 'testable', indicating practical utility for clinical or operational monitoring
  • Dynamic rather than static functional connectivity measures were used to capture temporal brain-state transitions

What This Means

This research suggests that when people are sleep deprived, their brains shift into an inefficient operating mode. Using brain scans (fMRI) taken before and after 30 hours without sleep in 40 healthy adults, researchers identified two distinct patterns of brain activity: an 'economical' state where brain regions communicate selectively, and a 'maladaptive compensatory' state where brain regions become overly synchronized across the whole brain. After sleep deprivation, participants spent more time in this maladaptive state, and the more time they spent there, the worse they performed on a test of sustained attention called the psychomotor vigilance task (PVT), which measures how quickly and consistently people can respond to simple signals. The researchers also built a statistical model to predict how many attention lapses a person would have based on their brain connectivity patterns. This model identified a specific brain network—the dorsal attention network, which is involved in directing focused attention—as particularly important for predicting performance decline after sleep loss. Notably, the model was designed to be interpretable and use a small number of features, making it potentially practical for real-world use. This research suggests that measuring how the brain dynamically switches between activity states, rather than just looking at average brain connectivity, may provide a more sensitive way to detect the cognitive effects of sleep deprivation. The findings could eventually help identify individuals at highest risk for dangerous performance lapses in safety-critical jobs such as medicine, transportation, or military settings, and may point toward specific brain network targets for future interventions aimed at countering the effects of sleep loss.

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Citation

Ouyang A, Lin X, Zhang T, Li J, Li C, Wang L, et al.. (2026). The impact of sleep deprivation on dynamic functional connectivity of the brain: Based on alertness task performance.. Brain research bulletin. https://doi.org/10.1016/j.brainresbull.2025.111712