Imaging NeuroscienceNeurosciencePeer-reviewed journal article
Multimodal state-dependent connectivity analysis of arousal and autonomic centers in the brainstem and basal forebrain
Haatef PourmotabbediD, Caroline G. Martin, Sarah E. GoodaleiD, Derek J. DossiD, Shiyu WangiD, Roza G. BayrakiD, Hakmook KangiD, Victoria L. MorganiD, Dario J. EnglotiD, Catie E. ChangiD
Abstract
Vigilance is a continuously altering state of cortical activation that influences cognition and behavior and is disrupted in multiple brain pathologies. Neuromodulatory nuclei in the brainstem and basal forebrain are implicated in arousal regulation and are key drivers of widespread neuronal activity and communication. However, it is unclear how their large-scale brain network architecture changes across dynamic variations in vigilance state (i.e., alertness and drowsiness). In this study, we leverage simultaneous EEG and 3T multi-echo functional magnetic resonance imaging (fMRI) to elucidate the vigilance-dependent connectivity of arousal regulation centers in the brainstem and basal forebrain. During states of low vigilance, most of the neuromodulatory nuclei investigated here exhibit a stronger global correlation pattern and greater connectivity to the thalamus, precuneus, and sensory and motor cortices. In a more alert state, the nuclei exhibit the strongest connectivity to the salience, default mode, and auditory networks. These vigilance-dependent correlation patterns persist even after applying multiple preprocessing strategies to reduce systemic vascular effects. To validate our findings, we analyze two large 3T and 7T fMRI datasets from the Human Connectome Project and demonstrate that the static and vigilance-dependent connectivity profiles of the arousal nuclei are reproducible across 3T multi-echo, 3T single-echo, and 7T single-echo fMRI modalities. Overall, this work provides novel insights into the role of neuromodulatory systems in vigilance-related brain activity.
- organism: human
- assay: electronic health records
- assay: imaging
- research focus: spatial domain detection
- approach: statistical model
- Citations
- 9
- FWCI
- 5.13
- Field percentile
- top 4%
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