# The night shift brain: functional network reorganization in sleep-deprived medical staff

## TL;DR

This study shows how sleep deprivation affects brain networks in medical staff, leading to cognitive impairments and suggesting ways to prevent these effects.

## Contribution

The study identifies specific brain network changes in sleep-deprived medical staff and links them to cognitive performance.

## Key findings

- Sleep deprivation increases clustering coefficients and alters key brain regions like the right superior medial frontal gyrus.
- Cognitive performance declines correlate with changes in brain network topology, especially in older medical staff.
- Enhanced functional connectivity in frontal/temporal regions was observed under sleep deprivation.

## Abstract

Medical staff frequently experience sleep deprivation, impacting both their health and patient care quality. Understanding brain network changes under sleep deprivation can guide preventive strategies. This study aims to determine how total sleep deprivation (TSD) alters brain network topology in medical professionals.

Using graph-theory analysis of resting-state fMRI data from 36 medical staff, we assessed global and local brain network properties following TSD and normal sleep (rested wakefulness, RW), examining topological changes and their correlation with cognitive performance.

Small-world properties were present in both conditions, but the TSD condition showed higher clustering coefficients (p = 0.044). Key nodal changes included increased degree centrality in the right superior medial frontal gyrus (p = 0.0006) and decreased nodal efficiency in the left fusiform gyrus (p = 0.0004). Using the right superior medial frontal gyrus as ROI, enhanced functional connectivity (zFC) was observed in multiple bilateral frontal/temporal regions (peak t > 4.5). These topological changes correlated with cognitive deficits: reduced Digit Symbol Test (DST) scores (p < 0.001), prolonged Number Connection Test-A (NCT-A) and Line Tracing Test (LTT) completion times (p < 0.05), while increased clustering coefficients (Cp) positively correlated with NCT-A/SDT performance changes (r = 0.341–0.411, p < 0.05). And older staff exhibited greater vulnerability in global network efficiency and path length (r = −0.352, r = 0.390, p < 0.05).

By identifying key brain network nodes affected by TSD, this study provides insights into neural adaptations under TSD, offering an evidence-based framework for developing both therapeutic interventions and preventive strategies to mitigate cognitive and health impacts in high-risk populations.