Biology subjects

Lewis, L. D.

Publications and source records attributed to Lewis, L. D..

At least 19 recordsLinked to original sources

bioRxiv2026

Spontaneous Hemodynamic Oscillations during Sleep Coordinate Directional CSF Flow in the Subarachnoid Space and Ventricles

During sleep, cerebrospinal fluid (CSF) flow is enhanced and thought to facilitate clearance of metabolic waste from the brain. Human imaging has shown that sleep is associated with large waves of bulk CSF flow in the fourth ventricle; however, the CSF flow dynamics around brain tissue are unknown. Here, we investigated how CSF flow is organized across the subarachnoid space, a key site both for fluid exchange across cortical surface and for transport out of the cranium. We used an MRI technology at 7 Tesla that simultaneously measures cortical hemodynamics and slow subarachnoid CSF flow and applied this during sleep. We found that spontaneous infraslow hemodynamic oscillations were coupled to spatially structured, directional CSF flow throughout both ventricular and subarachnoid compartments. Hemodynamic oscillations and fourth-ventricle bulk flow were both associated with brain-wide CSF flow at different phases, indicating that they reflect a single coordinated CSF movement cycle. Compared to cardiac pulsations, a canonical driver of CSF movement, hemodynamic-driven CSF flow showed a distinct directional pattern and was smaller in instantaneous amplitude but produced substantially greater cumulative displacement and thus greater potential for transport within an oscillation period. Together, these findings show that hemodynamic oscillations during sleep are coupled to temporally coordinated and directional CSF flow throughout the ventricles and subarachnoid space, suggesting that sleep-dependent neuronal activity drives fluid movement across the human brain in a distinct spatial pattern.

neuroscience↗

bioRxiv2026

Quantification of cardiac-locked brainstem velocity at high resolution based on retrospectively-gated DENSE MRI at 7T

PurposeThe motion of the brain tissue within the skull is thought to be induced by cardiac pulsations and other hemodynamic processes and may influence CSF flow, but its precise drivers and downstream effects are unclear. Understanding these phenomena requires an accurate and precise method of tissue motion quantification that can be extended to investigate multiple potential drivers of tissue motion in vivo. MethodsHere, a version of the Displacement ENcoding with Stimulated Echoes (DENSE) pulse sequence was implemented to measure cardiac-locked velocity responses in the pons and midbrain in eight healthy volunteers. The method featured retrospective cardiac gating, a single mixing time, and measured multiple voxel sizes. ResultsA previously undescribed double-peak pattern of cardiac-locked longitudinally directed brainstem velocity was identified that appears to reflect both nonrigid and rigid motion components. This pattern was only visible when estimating the cardiac response using absolute time after systole instead of the percentage of the cardiac cycle. Measurements were performed in a custom-built slow-flow phantom, and repeat sessions were acquired in two volunteers to assess accuracy and precision. Despite potential increased influence of CSF motion with larger voxel sizes, no voxel-size-dependent bias was found in the velocity estimations. Lack of voxel size bias was attributed to the complexities of partial-volume effects between CSF and tissue in phase-valued data that were evaluated using numerical simulations. ConclusionIn sum, a method to measure brain tissue motion with high spatiotemporal precision is presented that can be extended to applications beyond measuring cardiac-locked motion.

neuroscience↗

bioRxiv2026

Neural activity drives directional subarachnoid cerebrospinal fluid flow in the human brain

Neural activity is a potential driver of cerebrospinal fluid (CSF) dynamics and brain waste clearance, yet how neural activity shapes localized CSF flow in the subarachnoid space remains poorly understood in humans. Here we introduce an MRI framework that enables concurrent mapping of ultra-slow subarachnoid CSF flow at velocity scales of tens of micrometers-per-second together with hemodynamic activity measured by simultaneous functional MRI in the human brain. Using this framework, we found that neural activity drives spatiotemporally coherent, directional CSF flow in the subarachnoid space near sites of neural activation in awake humans. These flow responses were highly spatially specific, shaped by local cortical and subarachnoid anatomy, while exhibiting oscillatory temporal dynamics modulated by the stimulus cycle and strongly coupled to hemodynamic responses. These findings establish a link between neural activity, vascular dynamics, and localized subarachnoid CSF flow, and provide a framework for investigating neural-activity-driven CSF transport in humans.

neuroscience↗

bioRxiv2026

Mapping subarachnoid cerebrospinal fluid circulation in the human brain

The subarachnoid space is critical to cerebrospinal fluid (CSF) circulation and waste clearance, yet its flow organization in humans remains poorly characterized due to the lack of methods for direct, quantitative measurement of ultra-slow flow. Here we introduce slow-flow-sensitized phase-contrast imaging (SOPHI), which enables noninvasive quantification of ultra-slow flow velocities (e.g., [~]100 m/s) and directionality at high spatiotemporal resolution, allowing whole-brain mapping of complex CSF circulation in the human subarachnoid space. We found brain-wide, spatiotemporally coherent flow patterns characterized by strong cardiac-driven oscillations. Flow dynamics were closely coupled with vascular anatomy, exhibiting higher velocities and earlier responses in subarachnoid spaces near major arteries and a spatiotemporal propagative pattern to distal spaces. We further identified localized flow pathways associated with potential CSF efflux in ventricles and key subarachnoid regions. Together, SOPHI reveals a previously undercharacterized, highly organized CSF flow system, providing a macroscopic view of human CSF circulation and a framework for investigating brain fluid transport.

bioengineering↗

bioRxiv2026

A hypothalamic-brainstem activity sequence underlies arousal fluctuations during daytime drowsiness

During monotonous tasks, struggling with drowsiness leads to dynamic fluctuations in vigilance and task performance. Whether these moment-to-moment fluctuations are regulated by sleep-wake control circuitry remains unknown. Here, we used high-resolution 7T fMRI optimized for subcortical imaging to resolve activity across the human arousal regulatory system, including the hypothalamus, brainstem nuclei, basal forebrain, and thalamus, during a sustained attention task as subjects spontaneously experienced drowsiness. We identified a coordinated activity pattern locked to behavioral failures: widespread transient suppression during arousal drops and reactivation during recovery across all arousal-promoting regions, with opposite responses in the sleep-promoting hypothalamic preoptic area. These changes unfolded over [~]10-15 s in a 30 consistent sequence, led by the hypothalamic nuclei. Furthermore, suppression of brainstem nuclei preceded cortical infraslow (<0.05 Hz) oscillations, implicating a role in global neurovascular dynamics. These findings provide a temporally resolved, network-level portrait of human subcortical dynamics during drowsiness and identify a hypothalamic-led, brainstem-mediated activity sequence underlying behavioral arousal fluctuations and global cortical hemodynamics.

neuroscience↗

bioRxiv2026

Cortex-specific inversion of visual responses during sleep

During sleep, we functionally disengage from our external environment. Our eyes close, profoundly reducing visual input to the brain. However, some light passes through the eyelid, and luminance changes are perceived even through closed eyes during wakefulness. Although the relay of sensory information is thought to be gated by the thalamus during sleep, sensory information can still reach the cortex. To elucidate how visual inputs are modulated at each stage of thalamic and cortical processing during sleep, we used simultaneous EEG-fMRI while presenting luminance-modulated visual stimuli to sleeping humans. We discovered that responses to light remained intact in the visual thalamus during N1 and N2 sleep. However, stimulus-evoked responses in early visual cortex were profoundly suppressed, exhibiting an inverted pattern in which high-intensity visual stimulation evoked visual cortical deactivation. These findings suggest a cortical mechanism where inhibitory circuits regulate stimulus-driven deactivation in visual cortex, facilitating sensory isolation during early stages of sleep.

neuroscience↗

bioRxiv2025

Effects of different slow paced breathing regimes on cerebrospinal fluid (CSF) oscillations

Cerebrospinal fluid (CSF) transport is crucial for clearing waste from the brain and is largely driven by blood vessel oscillatory dynamics. During non-REM sleep, large oscillations occur in the infraslow range around 0.02-0.03 Hz, where noradrenergically driven vasomotion, blood volume and CSF flow are tightly coupled. Whether an infraslow oscillation induced by respiration can engage comparable dynamics during wakefulness is unknown. Utilizing a fast neuroimaging technique, we investigated the effects of four different slow paced respiratory conditions on CSF oscillations in 35 healthy adults (aged 18-30): paced breathing at 0.09-0.11 Hz and 0.05-0.07 Hz, and paced sighing at 0.05-0.07 Hz and 0.02-0.03 Hz. All four conditions increased respiratory volume, low-frequency respiratory power, CSF oscillatory power and BOLD global signal amplitude relative to rest, and CSF power did not differ among them. However, the infraslow slow sighing produced significantly higher CSF-BOLD coupling than all the other conditions, together with the largest global signal amplitude, an index associated with reduced arousal. This effect of infraslow sighing was not linked with respiratory power, as paced breathing at 0.05-0.07 Hz had the greatest respiratory power yet the weakest CSF-BOLD coupling, falling below rest. Cardiac spectral power was also lower during infraslow sighing than during any other breathing manipulation. Stimulating oscillatory dynamics via respiration during wakefulness therefore may recruit neurovascular-CSF coupling due to the same infraslow-specific dynamics that are believed to drive sleep-dependent waste clearance. Our study provides evidence that slow paced breathing increases CSF oscillations during wakefulness. Furthermore, we found that sighing at the frequency of the infraslow band linked to glymphatic clearance increases the coupling between blood and CSF dynamics.

neuroscience↗

bioRxiv2025

Functional MRI signals as fast as 1Hz are coupled to brain states and predict spontaneous neural activity

Technological advances have enabled fMRI acquisition with high temporal resolution, enabling brainwide imaging in just a few hundreds of milliseconds. However, the relationship between fast hemodynamic signals and spontaneous neural activity in the resting state is not yet well understood, limiting our ability to infer neural processes from these fast data. We hypothesized that high-frequency fMRI signals are linked to spontaneous neural activity tied to vigilance states, and that these high-frequency signals could be used to infer the dynamic variations in neuronal activity indexed by EEG neural rhythms. Using fast fMRI (TR=378 ms) and simultaneous EEG in 27 humans drifting between sleep and wakefulness, we found that fMRI power increased during NREM sleep (compared to wakefulness) in frequencies up to 1.3 Hz. High-frequency fMRI power was also correlated to canonical arousal-linked EEG rhythms (alpha and delta), with spatiotemporal cross-correlation patterns reflecting both shared arousal dynamics and rhythm-specific signatures. Using machine learning, we found that EEG alpha and delta power can be decoded from high-frequency fMRI signals in subjects held-out from the training set, showing that the high-frequency components of fMRI signals contain neurally-coupled information robust enough to generalize across individuals. These results reveal that high-frequency fMRI signals are coupled to dynamically varying brain states, and that fast fMRI allows for temporally precise quantification of spontaneous neural activity. Significance StatementFunctional MRI enables non-invasive brainwide neuroimaging in humans, but classically had limited temporal resolution. Recently, studies demonstrated that fast fMRI sampling can detect stimulus-evoked neural activity at subsecond timescales. However, it was unknown whether spontaneous neural activity is represented in fast fMRI signals. Using fMRI and simultaneous EEG in subjects who fell asleep, we found that EEG neural activity associated with sleep and wakefulness is represented in high-frequency fMRI signals. The results reveal that fast fMRI sampling can be used to investigate the spontaneous neural activity underlying brain states with high temporal precision.

neuroscience↗

bioRxiv2025

Modeling dynamic inflow effects in fMRI to quantify cerebrospinal fluid flow

Cerebrospinal fluid (CSF) flow in the brain is tightly regulated and essential for brain health, and imaging techniques are needed to quantitatively establish the properties of this flow system. Flow-sensitive fMRI has recently emerged as a tool to measure large scale CSF flow dynamics with high sensitivity and temporal resolution; however, the measured signal is not quantitative. Here, we developed a dynamic model to simulate fMRI inflow signals based on time-varying flow velocities. We validated the model in both human and phantom data, and used it to identify important properties of the fMRI inflow signal that inform how the signal should be interpreted. Additionally, we developed a physics-based deep learning framework to invert the model, which enables direct estimation of velocity using fMRI inflow data. This work allows new quantitative information to be obtained from fMRI, which will enable neuroimaging researchers to take advantage of the high sensitivity, high temporal resolution, and wide availability of fMRI to obtain flow signals that are physically interpretable.

neuroscience↗

bioRxiv2025

Quantifying brain-wide cerebrospinal fluid flow dynamics using slow-flow-sensitized phase-contrast MRI

Cerebrospinal fluid (CSF) flow is a key component of the brains waste clearance system. However, our understanding of CSF flow in the human brain, particularly within the brain-wide subarachnoid space (SAS), is limited due to a lack of non-invasive tools for measuring slow flow. Here, we propose a CSF flowmetry technique using phase-contrast MRI combined with a slow-flow-sensitized acquisition. It achieves high sensitivity in measuring slow CSF flow (e.g., 100 m/s), and enables quantitative measurement of the velocity and direction with whole-brain coverage, spanning from ventricles to SAS. Our proof-of-concept results demonstrate repeatable flow measurements and show that cardiac pulsation induces coherent CSF flow changes within the SAS. Our data also suggest that cardiac pulsation has a stronger driving effect on brain-wide CSF flow compared to respiration. This technique provides a valuable tool for investigating CSF dynamics and pathways to advance a holistic understanding of brain-wide CSF flow. TeaserA novel MR technique enables noninvasive, quantitative mapping of slow cerebrospinal fluid flow in the subarachnoid space across the human brain.

bioengineering↗

bioRxiv2025

The aging human brain exhibits reduced cerebrospinal fluid flow during sleep due to both neural and vascular factors

Aging reduces the quality and quantity of sleep, and greater sleep loss over the lifespan is predictive of neurodegeneration and cognitive decline. One mechanism by which sleep loss could contribute to impaired brain health is through disruption of cerebrospinal fluid (CSF) circulation. CSF is the primary waste transport system of the brain, and in young adults, CSF waves are largest during NREM sleep. However, whether sleep-dependent brain fluid physiology changes in aging is not known, due to the technical challenges of performing neuroimaging studies during sleep. We collected simultaneous fast fMRI and EEG data to measure large-scale CSF flow in healthy young and older adults and tested whether there were age-related changes to CSF dynamics during nighttime sleep. We found that sleep-dependent CSF flow was reduced in older adults, and this reduction was linked to impaired frontal EEG delta power and global hemodynamic oscillations during sleep. To identify mechanisms underlying reduced CSF flow, we used sensory and vasoactive stimuli to drive CSF flow in daytime task experiments, and found that both neural and cerebrovascular physiological changes contributed to the disruption of CSF flow during sleep. Finally, we found that this reduction in CSF flow was associated with gray matter atrophy in aging. Together, these results demonstrate that the aging human brain has reduced CSF flow during sleep, and identifies underlying neurovascular mechanisms that contribute to this age-related decline, suggesting targets for future interventions.

neuroscience↗

bioRxiv2024

Differentiating BOLD and non-BOLD signals in fMRI time series using cross-cortical depth delay patterns

Over the past two decades, rapid advancements in magnetic resonance technology have significantly enhanced the imaging resolution of functional Magnetic Resonance Imaging (fMRI), far surpassing its initial capabilities. Beyond mapping brain functional architecture at unprecedented scales, high-spatial-resolution acquisitions have also inspired and enabled several novel analytical strategies that can potentially improve the sensitivity and neuronal specificity of fMRI. With small voxels, one can sample from different levels of the vascular hierarchy within the cerebral cortex and resolve the temporal progression of hemodynamic changes from parenchymal to pial vessels. We propose that this characteristic pattern of temporal progression across cortical depths can aid in distinguishing neurogenic blood-oxygenation-level-dependent (BOLD) signals from typical nuisance factors arising from non-BOLD origins, such as head motion and pulsatility. In this study, we examine the feasibility of applying cross-cortical depth temporal lag (CortiLag) patterns to automatically categorize BOLD and non-BOLD signal components in modern-resolution BOLD-fMRI data. We construct an independent component analysis (ICA)-based framework for fMRI de-noising, analogous to previously proposed multi-echo (ME) ICA, except that here we explore the across-depth instead of across-echo dependence to distinguish BOLD and non-BOLD components. The efficacy of this framework is demonstrated using visual task data at three graded spatiotemporal resolutions (voxel sizes = 1.1, 1.5, and 2.0 mm isotropic at temporal intervals = 1700, 1120, and 928 ms). The proposed CortiLag-ICA framework leverages prior knowledge of the spatiotemporal properties of BOLD-fMRI and serves as an alternative to ME-ICA for cleaning moderate- and high-spatial-resolution fMRI data when multi-echo acquisitions are not available.

neuroscience↗

bioRxiv2024

Attentional failures after sleep deprivation represent moments of cerebrospinal fluid flow

Sleep deprivation rapidly disrupts cognitive function, and in the long term contributes to neurological disease. Why sleep deprivation has such profound effects on cognition is not well understood. Here, we use simultaneous fast fMRI-EEG to test how sleep deprivation modulates cognitive, neural, and fluid dynamics in the human brain. We demonstrate that after sleep deprivation, sleep-like pulsatile cerebrospinal fluid (CSF) flow events intrude into the awake state. CSF flow is coupled to attentional function, with high flow during attentional impairment. Furthermore, CSF flow is tightly orchestrated in a series of brain-body changes including broadband neuronal shifts, pupil constriction, and altered systemic physiology, pointing to a coupled system of fluid dynamics and neuromodulatory state. The timing of these dynamics is consistent with a vascular mechanism regulated by neuromodulatory state, in which CSF begins to flow outward when attention fails, and flow reverses when attention recovers. The attentional costs of sleep deprivation may thus reflect an irrepressible need for neuronal rest periods and widespread pulsatile fluid flow.

neuroscience↗

bioRxiv2024

Subcortical Hubs of Brain Networks Sustaining Human Consciousness

Neuromodulation of subcortical network hubs by pharmacologic, electrical, or ultrasonic stimulation is a promising therapeutic strategy for patients with disorders of consciousness (DoC). However, optimal subcortical targets for therapeutic stimulation are not well established. Here, we leveraged 7 Tesla resting-state functional MRI (rs-fMRI) data from 168 healthy subjects from the Human Connectome Project to map the subcortical connectivity of six canonical cortical networks that modulate higher-order cognition and function: the default mode, executive control, salience, dorsal attention, visual, and somatomotor networks. Based on spatiotemporally overlapped networks generated by the Nadam-Accelerated SCAlable and Robust (NASCAR) tensor decomposition method, our goal was to identify subcortical hubs that are functionally connected to multiple cortical networks. We found that the ventral tegmental area (VTA) in the midbrain and the central lateral (CL) and parafascicular (Pf) nuclei of the thalamus - regions that have historically been targeted by neuromodulatory therapies to restore consciousness - are subcortical hubs widely connected to multiple cortical networks. Further, we identified a subcortical hub in the pontomesencephalic tegmentum that overlapped with multiple reticular and extrareticular arousal nuclei and that encompassed a well-established "hot spot" for coma-causing brainstem lesions. Multiple hubs within the brainstem arousal nuclei and thalamic intralaminar nuclei were functionally connected to both the DMN and SN, emphasizing the importance of these cortical networks in integrative subcortico-cortical signaling. Additional subcortical connectivity hubs were observed within the caudate head, putamen, amygdala, hippocampus, and bed nucleus of the stria terminalis, regions classically associated with modulation of cognition, behavior, and sensorimotor function. Collectively, these results suggest that multiple subcortical hubs in the brainstem tegmentum, thalamus, basal ganglia, and medial temporal lobe modulate cortical function in the human brain. Our findings strengthen the evidence for targeting subcortical hubs in the ventral tegmental area, central lateral thalamus, and pontomesencephalic tegmentum to restore consciousness in patients with DoC. We release all subcortical connectivity maps to support ongoing efforts at therapeutic neuromodulation of consciousness.

neuroscience↗

bioRxiv2024

Neurovascular Impulse Response Function (IRF) during spontaneous activity differentially reflects intrinsic neuromodulation across cortical regions

Ascending neuromodulatory projections from deep brain nuclei generate internal brain states that differentially engage specific neuronal cell types. Because neurovascular coupling is cell-type specific and neuromodulatory transmitters have vasoactive properties, we hypothesized that the impulse response function (IRF) linking spontaneous neuronal activity with hemodynamics would depend on neuromodulation. To test this hypothesis, we used optical imaging to measure (1) release of neuromodulatory transmitters norepinephrine (NE) or acetylcholine (ACh), (2) Ca2+ activity of local cortical neurons, and (3) changes in hemoglobin concentration and oxygenation across the dorsal surface of cerebral cortex during spontaneous neuronal activity in awake mice. A canonical convolution model with a global, stationary IRF (i.e., the convolution kernel) describing evolution of total hemoglobin (HbT, reflective of dilation dynamics) with respect to Ca2+, resulted in a poor fit to the data. However, the HbT time-course was well predicted, pixel-by-pixel, by a weighted sum of Ca2+ and NE time-courses. Consistent with this result, modeling HbT as a weighted sum of stationary Ca2+ - and NE-specific IRFs (IRFCa2+ and IRFNE) convolved with the respective time-courses dramatically improved the fit compared to the global IRF. IRFCa2+ and IRFNE, estimated from the data, were positive and negative, respectively. In contrast to NE, ACh was largely redundant with Ca2+ and therefore did not improve HbT estimation. Because NE covaried with arousal, we observed instances of the diminished hemodynamic coherence between cortical regions during high arousal despite coherent behavior of the underlying neuronal Ca2+ activity. We conclude that while neurovascular coupling with respect to neuronal Ca2+ is a dynamic and seemingly complex phenomenon, hemodynamic fluctuations can be captured by a simple linear model with stationary IRFs with respect to the underlying dilatory and constrictive forces. In the current study, these forces were captured by the positive IRFCa2+ (dilation) and negative IRFNE (constriction). Without accounting for NE neuromodulation and the associated vasoconstriction, diminished hemodynamic coherence, commonly referred to as "functional (dys)connectivity" in BOLD fMRI studies, can be falsely interpreted as neuronal desynchronizations.

neuroscience↗

bioRxiv2024

The temporal specificity of BOLD fMRI is systematically related to anatomical and vascular features of the human brain

The ability to detect fast responses with functional MRI depends on the speed of hemodynamic responses to neural activity, because hemodynamic responses act as a temporal low-pass filter which blurs rapid changes. However, the shape and timing of hemodynamic responses are highly variable across the brain and across stimuli. This heterogeneity of responses implies that the temporal specificity of fMRI signals, or the ability of fMRI to preserve fast information, could also vary substantially across the cortex. In this work we investigated how local differences in hemodynamic response timing affect the temporal specificity of fMRI. We used ultra-high field (7T) fMRI at high spatiotemporal resolution, studying the primary visual cortex (V1) as a model area for investigation. We used visual stimuli oscillating at slow and fast frequencies to probe the temporal specificity of individual voxels. As expected, we identified substantial variability in temporal specificity, with some voxels preserving their responses to fast neural activity more effectively than others. We investigated which voxels had the highest temporal specificity, and tested whether voxel timing was related to anatomical and vascular features. We found that low temporal specificity is only weakly explained by the presence of large veins or cerebral cortical depth. Notably, however, temporal specificity depended strongly on a voxels position along the anterior-posterior anatomical axis of V1, with voxels within the calcarine sulcus being capable of preserving close to 25% of their amplitude as the frequency of stimulation increased from 0.05Hz to 0.20Hz, and voxels nearest to the occipital pole preserving less than 18%. These results indicate that detection biases in high-resolution fMRI will depend on the anatomical and vascular features of the area being imaged, and that these biases will differ depending on the timing of the underlying neuronal activity. While we attribute this variance primarily to hemodynamic effects, neuronal nonlinearities may also influence response timing. Importantly, this spatial heterogeneity of temporal specificity suggests that it could be exploited to achieve higher specificity in some locations, and that tailored data analysis strategies may help improve the detection and interpretation of fast fMRI responses.

neuroscience↗

bioRxiv2024

Distributed fMRI dynamics predict distinct EEG rhythms across sleep and wakefulness

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. Author summaryNeurons often fire in synchrony, generating rhythms that play major roles in brain functioning. These rhythms are hallmarks of different brain states of vigilance, such as sleep and wakefulness. Sleep disorders are extremely prevalent among adults, and studying neural rhythms associated with vigilance states is a key step towards understanding sleep disorders and how healthy sleep can be restored. Measuring how neural rhythms affect the brain, however, is difficult: the primary method used in humans, electroencephalography (EEG), can only measure neural activity close to the scalp. EEG can be combined with functional magnetic resonance imaging (fMRI), which is capable of measuring activity in deep brain regions, but fMRI data can be difficult to analyze, as it estimates neural activity indirectly by measuring changes in blood oxygenation. We developed an approach to analyze combined EEG-fMRI data using machine learning, and used it to investigate how fluctuations in neural rhythms across sleep and wakefulness are tied to changes in neural activity throughout the whole brain. Our results describe how different brain networks are coupled to alpha and delta rhythms, and provide a new approach for analyzing EEG-fMRI data that can be employed to investigate other neural rhythms necessary for healthy brain functioning.

neuroscience↗

bioRxiv2023

Differential cortical and subcortical visual processing with eyes shut

Closing our eyes largely shuts down our ability to see. That said, our eyelids still pass some light, allowing our visual system to coarsely process information about visual scenes, such as changes in luminance. However, the specific impact of eye closure on processing within the early visual system remains largely unknown. To understand how visual processing is modulated when eyes are shut, we used functional magnetic resonance imaging (fMRI) to measure responses to a flickering visual stimulus at high (100%) and low (10%) temporal contrasts, while participants viewed the stimuli with their eyes open or closed. Interestingly, we discovered that eye closure produced a qualitatively distinct pattern of effects across the visual thalamus and visual cortex. We found that with eyes open, low temporal contrast stimuli produced smaller responses, across the lateral geniculate nucleus (LGN), primary (V1) and extrastriate visual cortex (V2). However, with eyes closed, we discovered that the LGN and V1 maintained similar BOLD responses as the eyes open condition, despite the suppressed visual input through the eyelid. In contrast, V2 and V3 had strongly attenuated BOLD response when eyes were closed, regardless of temporal contrast. Our findings reveal a qualitative distinct pattern of visual processing when the eyes are closed - one that is not simply an overall attenuation, but rather reflects distinct responses across visual thalamocortical networks, wherein the earliest stages of processing preserves information about stimuli but is then gated off downstream in visual cortex. Significance statementWhen we close our eyes, not all information is blocked out. Coarse luminance information is still accessible for processing by the visual system, even when our eyes are closed. Using functional magnetic resonance imaging (fMRI), we examined whether eyelid closure plays a unique role in visual processing. We discovered that while the thalamus and primary visual cortex (V1) show equivalent luminance-dependent responses both when the eyes are open and closed, extrastriate cortex exhibited a qualitatively distinct pattern of responses. Specifically, eye closure attenuated luminance responses in extrastriate cortices, but responses were preserved in LGN and V1. This pattern suggests that during brain states where the eyes are closed, visual information is still accessible to the very earliest stages of visual processing, but that downstream visual processing areas appear to become blind to this information.

neuroscience↗