An altered balance of integrated and segregated brain activity is a marker of cognitive deficits following sleep deprivation
Bibliographic record
Abstract
Abstract Sleep deprivation (SD) leads to impairments in cognitive function. Here, we tested the hypothesis that cognitive changes in the sleep-deprived brain can be explained by information processing within and between large-scale cortical networks. We acquired functional magnetic resonance imaging (fMRI) scans of 20 healthy volunteers during attention and executive tasks following a regular night of sleep, a night of sleep deprivation, and a recovery nap containing non-rapid eye movement (NREM) sleep. Overall, sleep deprivation was associated with increased cortex-wide functional integration, driven by a rise of integration within cortical networks. The ratio of within vs between network integration in the cortex increased further in the recovery nap, suggesting that prolonged wakefulness drives the cortex toward a state resembling sleep. This balance of integration and segregation in the sleep-deprived state was tightly associated with deficits in cognitive performance. This was a distinct and better marker of cognitive impairment than conventional indicators of homeostatic sleep pressure, as well as the pronounced thalamo-cortical connectivity changes that occurs towards falling asleep. Importantly, restoration of the balance between segregation and integration of cortical activity was also related to performance recovery after the nap, demonstrating a bi-directional effect. These results demonstrate that intra- and inter-individual differences in cortical network integration and segregation during task performance may play a critical role in vulnerability to cognitive impairment in the sleep deprived state. Significance Statement Sleep deprivation has significant negative consequences for cognitive function. Understanding how changes in brain activity underpin changes in cognition is important not only to discover why performance declines following extended periods of wakefulness, but also for answering the fundamental question of why we require regular and recurrent sleep for optimal performance. Finding neural correlates that predict performance following sleep deprivation also has the potential to understand which individuals are particularly vulnerable to sleep deprivation, and what aspects of brain function may protect them from these negative consequences on performance. Finally, understanding how perturbations to regular (well-rested) brain functioning affect cognitive performance, will provide important insight into how underlying principles of information processing in the brain may support cognition generally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".