Evidence map›Paper›PMID 41757508›Full record

SynthesisSleep2026

Domain-specific circadian rescue following sleep deprivation.

Bowen Guo, Kaikai Yan, Yao Deng, Weiwei Zhao, Xing Chen, Chenye Xue, Ya Chai, Peng Quan, Namni Goel, Mathias Basner and 2 more

Abstract readMeta-Analysis
In one paragraph

Synthesis in Sleep, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Bowen GuoCenter for Magnetic Resonance Imaging Research and Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China.ORCID 0009-0002-0015-9284
Kaikai YanCenter for Magnetic Resonance Imaging Research and Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China.
Yao DengCenter for Functional Neuroimaging, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States.
Weiwei ZhaoCenter for Magnetic Resonance Imaging Research and Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China.
Xing ChenCenter for Magnetic Resonance Imaging Research and Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China.
Chenye XueCenter for Functional Neuroimaging, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States.
Ya ChaiCenter for Functional Neuroimaging, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States.
Peng QuanCenter for Functional Neuroimaging, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States.
Namni GoelBiological Rhythms Research Laboratory, Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, IL, United States.ORCID 0000-0003-2602-1996
Mathias BasnerUnit for Experimental Psychiatry, Division of Sleep and Chronobiology, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States.ORCID 0000-0002-8453-0812
Tianxin MaoCenter for Magnetic Resonance Imaging Research and Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China.ORCID 0000-0002-7038-0358
Hengyi RaoCenter for Magnetic Resonance Imaging Research and Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China.ORCID 0000-0003-2735-2500

Funding

Neural mechanisms underlying the antidepressant effects of sleep deprivationR01MH107571 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI GEHRMAN, PHILIP RICHARD, RAO, HENGYI · 2016 to 2019
$2.6M
Neural basis for differential vulnerability to sleep deprivationR01HL102119 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI RAO, HENGYI · 2010 to 2014
$2.3M
National Natural Science Foundation of China 32200889National Natural Science Foundation of China 32441108NHLBI NIH HHS R01 HL102119NIH HHS R01-HL102119NIH HHS R01-MH107571NIMH NIH HHS R01 MH107571Shanghai International Studies University Research Projects 2021114002Shanghai International Studies University Research Projects 2025DSYL054
6 · The paper itself

Abstract

STUDY

objectivesCircadian rhythms regulate sleep-wake cycles and modulate cognitive functions over a 24-h period. Following sleep loss, certain cognitive performance partially rebounds in the early evening, a phenomenon known as circadian rescue. Yet, the magnitude and domain specificity of circadian rescue remain poorly understood. Here, we integrate experimental and meta-analytic approaches to differential contributions of circadian and homeostatic processes to cognitive rescue following sleep deprivation.

methodsIn study 1, 54 healthy adults remained awake for 35 consecutive hours while repeatedly completing the Psychomotor Vigilance Task, the Digit Symbol Substitution Test, and the Karolinska Sleepiness Scale. Performance dynamics were modeled using the two-process framework of sleep regulation. In study 2, a meta-analysis of published data contextualized these findings across protocols.

resultsResults reveal domain-specific circadian recovery rates of 33.0%-52.1% for Psychomotor Vigilance Task, 45.7% for Digit Symbol Substitution Test, and 23.5% for Karolinska Sleepiness Scale, indicating that subjective sleepiness is predominantly driven by homeostatic load, whereas objective cognitive performance retains significant circadian modulation under conditions of acute homeostatic pressure.

conclusionsThese findings clarify how circadian and homeostatic drives interact to shape cognitive task performance and subjective sleepiness outcomes under sleep loss, with practical implications for optimizing performance in fatigue-prone environments.

Indexed as

Circadian RhythmCognitionSleep DeprivationAdultFemaleHomeostasisHumansMalePsychomotor PerformanceSleepinessWakefulnessYoung Adultcircadian rescuecognitive performancehomeostaticsleep deprivationtwo-process model

Identifiers

PMID41757508
PMCPMC13357512

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.