Alterations in resting-state control energy of limbic-to-subcortical networks in individuals with alcohol use disorder and comorbid sleep disturbances.
Comorbid sleep disturbance in AUD may reflect disrupted energetic regulation of limbic-subcortical state transitions, and network control energy provides a mechanistic framework for understanding sleep-brain interactions in addiction.
Abstract
Background
Alcohol use disorder (AUD) is frequently comorbid with sleep disturbances, yet the neural mechanisms through which sleep and AUD jointly shape brain organization remain unclear. Conventional resting-state analyses describe connectivity but not the energetic cost of transitions between large-scale network states.
Methods
Resting-state fMRI data from 93 individuals with AUD and 91 demographically matched healthy controls (HC) from the Human Connectome Project were analyzed. Sleep was assessed with the Pittsburgh Sleep Quality Index (PSQI). Static inter-network transition energies were computed across eight canonical networks using network control theory. Group × PSQI interactions were tested for directed transitions, with Bonferroni correction. Exploratory ROI analyses examined limbic-to-subcortical ROI pairs.
Results
Individuals with AUD showed greater sleep impairment than HC, particularly in sleep latency, efficiency, and nocturnal disturbances. A significant Group × PSQI interaction was observed for limbic-to-subcortical transition energy (Bonferroni-corrected p = 0.020). In HC, poorer sleep was associated with lower transition energy; this coupling was absent in AUD. Uncorrected effects converged on transitions targeting the subcortical network. Eleven ROI pairs survived correction, linking parahippocampal, amygdalar, and temporal regions to thalamic subnuclei.
Conclusion
Comorbid sleep disturbance in AUD may reflect disrupted energetic regulation of limbic-subcortical state transitions. Network control energy provides a mechanistic framework for understanding sleep-brain interactions in addiction.
Objective: Depressive symptoms exist on a continuum in the general population, yet the underlying neurobiological mechanisms, particularly the interplay between resting-state networks and task-evoked social cognitive responses, remain elusive. Methods: Leveraging the Human Connectome Project (HCP) dataset, we included 867 participants. With depression scores as the independent variable and age/sex as covariates, we systematically examined associations with sleep quality, negative emotions, sensory scores, gray matter volume (GMV), fractional amplitude of low-frequency fluctuations (fALFF), multi-seed resting-state functional connectivity (rsFC), as well as brain activation and behavioral performance during working memory, emotion recognition, social cognition, relational reasoning, language comprehension, and gambling tasks. The statistical threshold was set at voxel-level p < 0.001 (uncorrected) combined with cluster-level FWE correction at p < 0.05. Results: (1) Depression scores were positively correlated with sleep disturbances, negative emotions (anger/fear), and pain. (2) In resting-state, depression scores negatively correlated with ventral striatum (VS)–cerebellum/parahippocampal gyrus/fusiform rsFC, yet positively correlated with pregenual anterior cingulate cortex (preACC)–supplementary motor area (SMA) rsFC. (3) In task-fMRI, only the social task showed a positive association with task accuracy and regional activation in bilateral pre/postcentral gyri, superior temporal gyri, left middle frontal gyrus, and SMA/paracentral lobule. Conclusions: Elevated depression scores are linked to a pattern that may reflect relative decoupling between reward and perceptual systems, along with enhanced connectivity in cognitive control circuits. Socially, high scorers exhibit a pattern suggestive of compensatory hypervigilance, accompanied by enhanced behavioral performance. This study provides multidimensional evidence for the dimensional neural representation of depressive symptoms.
Fu-Feng Zheng, Song Zhang, Xiaoying Tang et al.· Brain Science· 0 citations
: Insomnia disorder is increasingly linked to abnormalities in resting-state functional connectivity (rsFC). We systematically synthesized rs-fMRI studies published from January 2020 to July 2025 to characterize disorder-related connectivity patterns and methodological limitations. Twenty-four study reports met the eligibility criteria; reported sample sizes summed to 1,229 participants with insomnia disorder and 1,077 healthy controls, although some cohorts may overlap across publications. Alterations involved the default mode, salience, executive-control, emotion, and sensorimotor systems and sleep–wake regulatory hubs, including the thalamus and hypothalamus. The overall pattern is compatible with hyperarousal, but both increased and decreased connectivity were reported; diagnostic definitions, sex, symptom severity and duration, medication exposure, acquisition conditions, and seed-based versus data-driven analyses may moderate direction and magnitude. A formal study-level appraisal with the JBI analytical cross-sectional checklist identified predominantly low risk or some concerns, most often related to confounding control. Because all included comparisons were cross-sectional, rsFC alterations cannot be interpreted as causes rather than consequences or correlates of insomnia. Methodological heterogeneity precluded quantitative pooling. Standardized phenotyping, medication control, acquisition and preprocessing, preregis-tered longitudinal studies, and external validation are required before connectivity markers can be used clinically
Ya-Nian Zhang, Yi-Wen Zhang, Cai-Li Shi et al.· Nature and Science of Sleep· 0 citations
Background: Opioid use disorder (OUD) is associated with a wide range of cognitive, affective, and motivational impairments, suggesting a disruption of large-scale brain systems that support diverse domains of functioning. Resting-state brain network segregation quantifies the degree of functional specialization within brain networks, is age-related, has been linked to brain glucose metabolism, and has been shown to be reduced in substance use disorders. We examined brain network segregation in individuals with OUD and non-OUD controls and tested associations with the duration of opioid use. Methods: Resting-state functional MRI data were collected from 149 individuals with OUD and 126 non-OUD controls. Functional connectivity was computed between brain regions assigned to functionally specialized networks supporting higher-order "association" or "sensorimotor" processes. For each network, segregation was quantified as the extent to which within-network connectivity exceeded between-network connectivity. Results: Individuals with OUD demonstrated lower segregation of the association and sensorimotor networks than non-OUD controls. Within the OUD group, more years of opioid use was associated with lower segregation of the association network, but not the sensorimotor network. Conclusions: OUD is characterized by overall lower resting-state brain network segregation. More years of opioid exposure was associated with lower association-network segregation, consistent with there being cumulative effects of chronic opioid use on large-scale brain organization, though causation could not be examined in this cross-sectional dataset. These findings identify altered network segregation as a potential neurobiological marker of OUD and suggest that restoration of brain network specialization is a measurable target of OUD treatment and potentially recovery.
Delaney McKinstry, Xinyi Li, A. Ramos-Rolón et al.· medRxiv· 0 citations
BACKGROUND
Schizophrenia is increasingly conceptualized as a disorder of large-scale brain network integration, yet how specific symptom phenotypes relate to reproducible resting-state functional connectivity (rsFC) signatures remains unclear.
METHODS
Resting-state fMRI data from 386 patients with schizophrenia and 212 healthy controls were analyzed to characterize large-scale functional connectivity patterns. Group-level connectivity differences were first identified (uncorrected P < 0.05, for exploratory feature selection), followed by within-patient analyses examining associations between altered connectivity and symptom subitems while adjusting for demographic factors. Multivariate models were then used to evaluate whether connectivity patterns showed systematic associations with individual symptom profiles.
RESULTS
Group comparisons revealed a dysconnectivity pattern characterized by reduced cross-network coupling between the visual network and higher-order systems, alongside selective increases in frontoparietal circuits. Within patients, connectivity alterations showed distinct association patterns across delusion, hallucination, and negative-symptom subitems. Multivariate analyses further indicated modest associations with several hallucination (e.g., H8, R²≈0.09) and delusion (e.g., D3, R²≈0.07) subitems, whereas associations with negative symptoms were minimal and showed limited generalization.
CONCLUSIONS
These findings support a hierarchical dysconnectivity profile centered on impaired perceptual-cognitive integration and suggest that specific positive-symptom phenotypes may be associated with partially consistent rsFC signatures. Overall effect sizes were modest, indicating that rsFC captures only a limited component of symptom variability.
CLINICAL TRIAL NUMBER
Not applicable.
Ting Yu, Shaokun Zhao, Yanli Li et al.· BMC Psychiatry· 0 citations
STUDY OBJECTIVES
Sleep deprivation is known to impair cognitive performance, but individuals differ in their ability to maintain function under sleep loss. This study examined the neural mechanisms that support such resilience by tracking changes in resting-state functional connectivity during prolonged wakefulness.
METHODS
Six resting-state fMRI sessions obtained during approximately the first 32 hours of a 39-hour total sleep deprivation protocol were analyzed in sixteen healthy adults, enabling unusually dense longitudinal sampling of brain network dynamics during prolonged wakefulness. Functional connectivity changes were analyzed using network-based statistics as the primary approach for analyzing pairwise functional connectivity, with nodal strength used as a secondary node-level follow-up analysis in relation to a behavioral resilience index derived from psychomotor vigilance performance.
RESULTS
Two NBS-defined subnetworks, referred to here as the thalamocortical and perceptual-memory subnetworks, showed significant interactions between time awake and resilience, indicating that the trajectory of functional connectivity across prolonged wakefulness differed as a function of behavioral resilience. Participants with higher resilience showed less negative connectivity trajectories within these networks, particularly in the thalamus, globus pallidus, and visual cortex. In contrast, participants with lower resilience exhibited progressive declines in connectivity.
CONCLUSION
The ability to withstand the cognitive effects of sleep deprivation appears to be associated with less negative connectivity trajectories within thalamocortical and perceptual-memory subnetworks. These patterns may represent state-dependent neural correlates of resilience during sleep deprivation and may be relevant to future fatigue-state monitoring.
Jungwon Cha, David C Negelspach, Alisa Huskey et al.· Sleep· 0 citations
Understanding functional connectivity alterations across manic, depressive, and remitted states of bipolar disorder (BD) remains an important challenge. In this cross-sectional study, we collected resting-state functional magnetic resonance imaging data from 117 BD patients, including manic BD patients (BipM: n = 38), depressive BD patients (BipD: n = 42), remitted BD patients (rBD: n = 37), and 35 healthy controls. We aimed to identify functional connectivity patterns associated with different BD episode states and shared alterations across groups, and to examine their normative reliability, heritability, and spatial correspondence with molecular and cellular brain maps. We identified shared altered connectivity patterns mainly involving ventral attention network-related regions, together with candidate state-associated patterns involving default-mode-related regions in BipM, frontoparietal/somatomotor/visual regions in BipD, and limbic-related regions in rBD. Using Human Connectome Project data, we further found that subsets of these selected connections showed normative test-retest reliability and heritability in healthy adults. Exploratory machine-learning analyses suggested that these patterns contained information related to BD episode-state classification and symptom variation, although the results require independent external validation. Spatial annotation analyses showed that the regional distribution of these connectivity patterns corresponded with normative neurotransmitter receptor, cell-type, and BD-related gene-expression maps. These findings suggest that BD episode states are associated with partially distinct and partially shared functional connectivity alterations. However, because the study was cross-sectional and single-center, and because the molecular analyses were based on normative spatial maps, the findings should be interpreted as exploratory state-associated network observations rather than causal mechanisms or validated clinical biomarkers.
Xiaobo Liu, Zhenqi Liu, Bin Wan et al.· Journal of Affective Disorde...· 0 citations
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