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Updated: Apr 18, 2026
Optogenetic Manipulation of Neural Circuits During Monitoring Sleep/wakefulness States in Mice
Published on: June 19, 2019
Neural-molecular signatures of insomnia: Insights from signed differential mapping and gene expression analysis
Ruisi Wang1, Yuwen He2, Andriy Myachykov3
1Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, China.
Background:
Insomnia disorder (ID) exhibits considerable heterogeneity in neuroimaging findings across studies, and whether functional brain alterations are consistent across resting and task states remains unclear. This study aimed to identify neural dysfunction across states in ID and explore its transcriptomic correlates.
Methods:
We conducted coordinate-based meta-analyses on 29 whole-brain fMRI studies (22 resting-state, 7 task-based; 2,579 participants: 1,305 ID, 1,274 healthy controls). Conjunction and contrast analyses were performed to disentangle state-common from state-specific alterations. Functional decoding via BrainMap characterized the behavioral profile of convergent regions. Meta-analytic spatial patterns were subsequently correlated with transcriptomic data using partial least squares regression.
Results:
The main meta-analysis revealed three convergent network disruptions: salience network hyperactivity, default mode network hypoactivity, and executive circuit dysfunction. State-specific analyses showed that resting-state alterations specifically involved left insula and fusiform gyrus hyperactivity, while task-based alterations implicated anterior cingulate hypoactivity and left inferior temporal gyrus hyperactivity. Critically, conjunction analysis identified state-common hypoactivation of the rIFG as the sole convergent abnormality across both brain states, which functional decoding linked to inhibitory control, working memory, action observation, and attention. Transcriptomic analysis revealed that this spatial pattern of functional alterations was significantly associated with synaptic signaling genes (positive loading) and carbohydrate metabolism and mitochondrial function genes (negative loading).
Conclusions:
These findings delineate a state-common and state-specific neural signatures of ID, with state-common rIFG hypoactivation representing a robust neural substrate of executive dysfunction, and implicate synaptic and metabolic molecular pathways in the biological underpinnings of ID.
