@article {70121,
	title = {Brainwide hemodynamics predict EEG neural rhythms across sleep and wakefulness in humans.},
	journal = {PLoS Comput Biol},
	volume = {21},
	year = {2025},
	month = {2025 Sep 19},
	pages = {e1013497},
	abstract = {<p>The brain exhibits rich oscillatory dynamics that play critical roles in vigilance and cognition, such as the neural rhythms that define sleep. These rhythms continuously fluctuate, signaling major changes in vigilance, but the widespread brain dynamics underlying these oscillations are difficult to investigate. Using simultaneous EEG and fast fMRI in humans who fell asleep inside the scanner, we developed a machine learning approach to investigate which fMRI regions and networks predict fluctuations in neural rhythms. We demonstrated that the rise and fall of alpha (8-12 Hz) and delta (1-4 Hz) power-two canonical EEG bands critically involved with cognition and vigilance-can be predicted from fMRI data in subjects that were not present in the training set. This approach also identified predictive information in individual brain regions across the cortex and subcortex. Finally, we developed an approach to identify shared and unique predictive information, and found that information about alpha rhythms was highly separable in two networks linked to arousal and visual systems. Conversely, delta rhythms were diffusely represented on a large spatial scale primarily across the cortex. These results demonstrate that EEG rhythms can be predicted from fMRI data, identify large-scale network patterns that underlie alpha and delta rhythms, and establish a novel framework for investigating multimodal brain dynamics.</p>
},
	keywords = {Adult, Brain, Brain Mapping, Computational Biology, Electroencephalography, Female, Hemodynamics, Humans, Machine Learning, Magnetic Resonance Imaging, Male, Sleep, Wakefulness, Young Adult},
	issn = {1553-7358},
	doi = {10.1371/journal.pcbi.1013497},
	url = {https://doi.org/10.1371/journal.pcbi.1013497},
	author = {Jacob, Leandro P L and Bailes, Sydney M and Williams, Stephanie D and Stringer, Carsen and Lewis, Laura D}
}
@article {70157,
	title = {Functional MRI signals as fast as 1Hz are coupled to brain states and predict spontaneous neural activity},
	journal = {bioRxiv},
	year = {2025},
	month = {2025 Oct 14},
	abstract = {<p>fMRI signals were traditionally seen as slow and sampled in the order of seconds, but recent technological advances have enabled much faster sampling rates. We hypothesized that high-frequency fMRI signals can capture spontaneous neural activity that index brain states. Using fast fMRI (TR=378ms) and simultaneous EEG in 27 humans drifting between sleep and wakefulness, we found that fMRI spectral power increased during NREM sleep (compared to wakefulness) across several frequency ranges as fast as 1Hz. This fast fMRI power was correlated with canonical arousal-linked EEG rhythms (alpha and delta), with spatiotemporal correlation patterns for each rhythm reflecting a combination of shared arousal dynamics and rhythm-specific neural signatures. Using machine learning, we found that alpha and delta EEG rhythms can be decoded from fast fMRI signals, in subjects held-out from the training set, showing that fMRI as fast as 0.9Hz (alpha) and 0.7Hz (delta) contains reliable neurally-coupled information that generalizes across individuals. Finally, we demonstrate that this fast fMRI acquisition allows for EEG rhythms to be decoded from 3.8s windows of fMRI data. These results reveal that high-frequency fMRI signals are coupled to dynamically varying brain states, and that fast fMRI sampling allows for more temporally precise quantification of spontaneous neural activity than previously thought possible.</p>
},
	doi = {10.1101/2025.10.13.681720},
	url = {https://www.biorxiv.org/content/early/2025/10/14/2025.10.13.681720},
	author = {Jacob, Leandro P. L. and Bailes, Sydney M and Stringer, Carsen and Polimeni, Jonathan R and Lewis, Laura D}
}
