Resting-State EEG Functional Connectivity in Young Adults With Clinically Diagnosed Generalized Anxiety Disorder and Self-Reported Habitual Short Sleep: An Alpha-Band Network Analysis.
Young adults with clinically diagnosed GAD and self-reported habitual short sleep showed distributed alterations in resting-state alpha-band phase-based connectivity, with stronger weighted integration and clustering across network-level, nodal, and graph-theoretical analyses.
Key Findings
Results
Significant group differences in resting-state EEG functional connectivity were confined to the alpha-band weighted phase lag index (wPLI) network.
Functional connectivity was estimated using both weighted phase lag index (wPLI) and corrected amplitude envelope correlation (AEC-c) across delta, theta, alpha, and beta bands.
No significant group differences were found in delta, theta, or beta bands, nor in AEC-c measures.
The alpha-band wPLI network showed the only statistically significant group differences between GAD+HSS participants and healthy controls.
Results
Network-based statistics (NBS) identified a large connected component of 434 suprathreshold edges showing predominantly stronger connectivity in the GAD+HSS group.
NBS identified one connected component comprising 434 suprathreshold edges with FWER-corrected p = 0.0002.
The majority of edges within this component showed stronger connectivity in the GAD+HSS group compared to healthy controls.
The sample consisted of 74 college students: 37 with clinically diagnosed GAD and self-reported habitual short sleep (HSS), and 37 healthy controls.
Results
All 30 scalp nodes showed higher alpha-band node strength in the GAD+HSS group after FDR correction.
Higher node strength was observed across all 30 scalp nodes in the GAD+HSS group.
Results remained significant after FDR correction.
Elevated node strength was maintained after covariate adjustment for age and years of education.
This finding indicates a distributed, whole-network pattern of stronger nodal connectivity rather than focal regional effects.
Results
The GAD+HSS group showed altered graph-theoretical network properties indicating stronger weighted integration and clustering.
The GAD+HSS group showed higher global efficiency compared to healthy controls.
Characteristic path length was lower in the GAD+HSS group.
Weighted clustering coefficient was higher in the GAD+HSS group.
Transitivity did not differ significantly between groups.
These findings were directionally stable across graph density thresholds of 10%–30%.
Discussion
Habitual short sleep (HSS) in this study was defined by self-report rather than objective measurement, limiting interpretation of findings as biomarkers of objectively verified sleep loss.
HSS was defined as self-reported habitual weekday nocturnal sleep duration ≤ 6 hours/night for at least 1 month.
The authors explicitly state that 'these results should not be interpreted as biomarkers of objectively verified sleep loss.'
The independent contributions of anxiety and sleep status cannot be determined from this design, as the study lacked a GAD-only or HSS-only comparison group.
Sensitivity analyses accounted for age and years of education as covariates.
Methods
The study sample consisted exclusively of young adult college students with clinically diagnosed GAD combined with self-reported habitual short sleep.
74 college students were included: 37 in the GAD+HSS group and 37 healthy controls.
GAD was clinically diagnosed, distinguishing this sample from studies relying solely on self-report anxiety measures.
The combination of clinical GAD diagnosis with the HSS criterion means that the GAD+HSS group represented a specific comorbid phenotype rather than either condition alone.
The young adult/college student sample limits generalizability to other age groups or clinical populations.
What This Means
This research suggests that young adults who have been clinically diagnosed with generalized anxiety disorder (GAD) and who also report regularly sleeping six hours or less per night show measurable differences in how different regions of their brain communicate with each other at rest. Using electroencephalography (EEG), researchers recorded brain activity in 74 college students—half with GAD and short sleep, half healthy controls—and found that the anxiety/short-sleep group had stronger and more widespread brain network connections specifically in the alpha frequency band (brain waves associated with relaxed wakefulness and various cognitive functions). These differences appeared across all measured brain regions and were consistent across multiple ways of analyzing the data.
The pattern of differences pointed toward a brain network that was more highly integrated and clustered than in healthy controls, meaning signals were traveling more efficiently across the brain and local groups of brain regions were more tightly coordinated. These findings held up even after accounting for differences in age and education level between groups. However, the researchers caution that because sleep was measured only through self-report (not objective sleep monitoring), these results cannot be taken as evidence of what happens specifically due to sleep loss itself. Additionally, since all participants had both anxiety and short sleep, it is impossible from this study alone to separate how much each factor independently contributes to the observed brain differences.
This research matters because it provides detailed, network-level brain data for a common real-world combination of problems—anxiety and insufficient sleep—that frequently co-occur in college-aged populations. Understanding how the brain's connectivity patterns differ in this group could eventually help researchers develop better ways to identify or study these conditions, though much more work is needed before any clinical applications could be considered.
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Shi C, Wang M, Chen X, Gao J, Fu W, Guo Y, et al.. (2026). Resting-State EEG Functional Connectivity in Young Adults With Clinically Diagnosed Generalized Anxiety Disorder and Self-Reported Habitual Short Sleep: An Alpha-Band Network Analysis.. Brain and behavior. https://doi.org/10.1002/brb3.71685