Physiological changes in sleep that affect fMRI inference

Brain-wide functional connectivity artifactually inflates throughout functional magnetic resonance imaging scans

Functional magnetic resonance imaging (fMRI) is a central tool for investigating human brain function, organization and disease. Here, we show that fMRI-based estimates of functional brain connectivity artifactually inflate at spatially heterogeneous rates during resting-state and task-based scans. This produces false positive connection strength changes and spatial distortion of brain connectivity maps. We demonstrate that this artefact is driven by temporal inflation of the non-neuronal, systemic low-frequency oscillation (sLFO) blood flow signal during fMRI scanning and is not addressed by standard denoising procedures. We provide evidence that sLFO inflation reflects perturbations in cerebral blood flow by respiration and heart rate changes that accompany diminishing arousal during scanning, although the mechanisms of this pathway are uncertain. Finally, we show that adding a specialized sLFO denoising procedure to fMRI processing pipelines mitigates the artifactual inflation of functional connectivity, enhancing the validity and within-scan reliability of fMRI findings.

Modulate the impact of the drowsiness on the resting state functional connectivity

This research explores different methodologies to modulate the effects of drowsiness on functional connectivity (FC) during resting-state functional magnetic resonance imaging (RS-fMRI). The study utilized a cohort of students (MRi-Share) and classified individuals into drowsy, alert, and mixed/undetermined states based on observed respiratory oscillations. We analyzed the FC group difference between drowsy and alert individuals after five different processing methods: the reference method, two based on physiological and a global signal regression of the BOLD time series signal, and two based on Gaussian standardizations of the FC distribution. According to the reference method, drowsy individuals exhibit higher cortico-cortical FC than alert individuals. First, we demonstrated that each method reduced the differences between drowsy and alert states. The second result is that the global signal regression was quantitively the most effective, minimizing significant FC differences to only 3.3% of the total FCs. However, one should consider the risks of overcorrection often associated with this methodology. Therefore, choosing a less aggressive form of regression, such as the physiological method or Gaussian-based approaches, might be a more cautious approach. Third and last, using the Gaussian-based methods, cortico-subcortical and intra-default mode network (DMN) FCs were significantly greater in alert than drowsy subjects. These findings bear resemblance to the anticipated patterns during the onset of sleep, where the cortex isolates itself to assist in transitioning into deeper slow wave sleep phases, simultaneously disconnecting the DMN.

An autonomic mode of brain activity

The relevance of interactions between autonomic and central nervous systems remains unclear for human brain function and health, particularly when both systems are challenged under sleep deprivation (SD). We measured brain activity (with fMRI), pulse and respiratory signals, and baseline brain amyloid beta burden (with PET) in healthy participants. We found that SD relative to rested wakefulness (RW) resulted in a significant increase in synchronized low frequency (LF, < 0.1 Hz) activity in an autonomically-related network (AN), including dorsal attention, visual, and sensorimotor regions, which we previously found to have consistent temporal coupling with LF pulse signal changes (regulated by sympathetic tone). SD resulted in a significant phase coherence between the LF component of the pulse signal and a medial network with peak effects in the midbrain reticular formation, and between LF component of the respiratory variations (regulated by respiratory motor output) and a cerebellar network. The LF power of AN during SD was significantly and independently correlated with pulse-medial network and respiratory-cerebellar network phase coherences (total adjusted R2 = 0.78). Higher LF power of AN during SD (but not RW) was associated with lower amyloid beta burden (Cohen's d = 0.8). In sum, SD triggered an autonomic mode of synchronized brain activity that was associated with distinct autonomic-central interactions. Findings highlight the direct relevance of global cortical synchronization to brain clearance mechanisms.

Understanding the Need for Sleep to Improve Cognition

The restorative function of sleep is shaped by its duration, timing, continuity, subjective quality, and efficiency. Current sleep recommendations specify only nocturnal duration and have been largely derived from sleep self-reports that can be imprecise and miss relevant details. Sleep duration, preferred timing, and ability to withstand sleep deprivation are heritable traits whose expression may change with age and affect the optimal sleep prescription for an individual. Prevailing societal norms and circumstances related to work and relationships interact to influence sleep opportunity and quality. The value of allocating time for sleep is revealed by the impact of its restriction on behavior, functional brain imaging, sleep macrostructure, and late-life cognition. Augmentation of sleep slow oscillations and spindles have been proposed for enhancing sleep quality, but they inconsistently achieve their goal. Crafting bespoke sleep recommendations could benefit from large-scale, longitudinal collection of objective sleep data integrated with behavioral and self-reported data.

An orderly sequence of autonomic and neural events at transient arousal changes

Resting-state functional magnetic resonance imaging (rsfMRI) allows the study of functional brain connectivity based on spatially structured variations in neuronal activity. Proper evaluation of connectivity requires removal of non-neural contributions to the fMRI signal, in particular hemodynamic changes associated with autonomic variability. Regression analysis based on autonomic indicator signals has been used for this purpose, but may be inadequate if neuronal and autonomic activities covary. To investigate this potential co-variation, we performed rsfMRI experiments while concurrently acquiring electroencephalography (EEG) and autonomic indicator signals, including heart rate, respiratory depth, and peripheral vascular tone. We identified a recurrent and systematic spatiotemporal pattern of fMRI (named as fMRI cascade), which features brief signal reductions in salience and default-mode networks and the thalamus, followed by a biphasic global change with a sensory-motor dominance. This fMRI cascade, which was mostly observed during eyes-closed condition, was accompanied by large EEG and autonomic changes indicative of arousal modulations. Importantly, the removal of the fMRI cascade dynamics from rsfMRI diminished its correlations with various signals. These results suggest that the rsfMRI correlations with various physiological and neural signals are not independent but arise, at least partly, from the fMRI cascades and associated neural and physiological changes at arousal modulations.

Neural underpinning of a respiration-associated resting-state fMRI network

Respiration can induce motion and CO2 fluctuation during resting-state fMRI (rsfMRI) scans, which will lead to non-neural artifacts in the rsfMRI signal. In the meantime, as a crucial physiologic process, respiration can directly drive neural activity change in the brain, and may thereby modulate the rsfMRI signal. Nonetheless, this potential neural component in the respiration-fMRI relationship is largely unexplored. To elucidate this issue, here we simultaneously recorded the electrophysiology, rsfMRI, and respiration signals in rats. Our data show that respiration is indeed associated with neural activity changes, evidenced by a phase-locking relationship between slow respiration variations and the gamma-band power of the electrophysiological signal recorded in the anterior cingulate cortex. Intriguingly, slow respiration variations are also linked to a characteristic rsfMRI network, which is mediated by gamma-band neural activity. In addition, this respiration-related brain network disappears when brain-wide neural activity is silenced at an isoelectrical state, while the respiration is maintained, further confirming the necessary role of neural activity in this network. Taken together, this study identifies a respiration-related brain network underpinned by neural activity, which represents a novel component in the respiration-rsfMRI relationship that is distinct from respiration-related rsfMRI artifacts. It opens a new avenue for investigating the interactions between respiration, neural activity, and resting-state brain networks in both healthy and diseased conditions.

A temporal sequence of thalamic activity unfolds at transitions in behavioral arousal state

Awakening from sleep reflects a profound transformation in neural activity and behavior. The thalamus is a key controller of arousal state, but whether its diverse nuclei exhibit coordinated or distinct activity at transitions in behavioral arousal state is unknown. Using fast fMRI at ultra-high field (7 Tesla), we measured sub-second activity across thalamocortical networks and within nine thalamic nuclei to delineate these dynamics during spontaneous transitions in behavioral arousal state. We discovered a stereotyped sequence of activity across thalamic nuclei and cingulate cortex that preceded behavioral arousal after a period of inactivity, followed by widespread deactivation. These thalamic dynamics were linked to whether participants subsequently fell back into unresponsiveness, with unified thalamic activation reflecting maintenance of behavior. These results provide an outline of the complex interactions across thalamocortical circuits that orchestrate behavioral arousal state transitions, and additionally, demonstrate that fast fMRI can resolve sub-second subcortical dynamics in the human brain.

Autonomic arousals contribute to brain fluid pulsations during sleep

During sleep, slow waves of neuro-electrical activity engulf the human brain and aid in the consolidation of memories. Recent research suggests that these slow waves may also promote brain health by facilitating the removal of metabolic waste, possibly by orchestrating the pulsatile flow of cerebro-spinal fluid (CSF) through local neural control over vascular tone. To investigate the role of slow waves in the generation of CSF pulsations, we analyzed functional MRI data obtained across the full sleep-wake cycle and during a respiratory task during wakefulness. This revealed a novel generating mechanism that relies on the autonomic regulation of cerebral vascular tone without requiring slow electrocortical activity or even sleep. Therefore, the role of CSF pulsations in brain waste clearance may, in part, depend on proper autoregulatory control of cerebral blood flow.

State-related neural influences on fMRI connectivity estimation

The spatiotemporal structure of functional magnetic resonance imaging (fMRI) signals has provided a valuable window into the network underpinnings of human brain function and dysfunction. Although some cross-regional temporal correlation patterns (functional connectivity; FC) exhibit a high degree of stability across individuals and species, there is growing acknowledgment that measures of FC can exhibit marked changes over a range of temporal scales. Further, FC can covary with experimental task demands and ongoing neural processes linked to arousal, consciousness and perception, cognitive and affective state, and brain-body interactions. The increased recognition that such interrelated neural processes modulate FC measurements has raised both challenges and new opportunities in using FC to investigate brain function. Here, we review recent advances in the quantification of neural effects that shape fMRI FC and discuss the broad implications of these findings in the design and analysis of fMRI studies. We also discuss how a more complete understanding of the neural factors that shape FC measurements can resolve apparent inconsistencies in the literature and lead to more interpretable conclusions from fMRI studies.

Decoding the brain state-dependent relationship between pupil dynamics and resting state fMRI signal fluctuation

Pupil dynamics serve as a physiological indicator of cognitive processes and arousal states of the brain across a diverse range of behavioral experiments. Pupil diameter changes reflect brain state fluctuations driven by neuromodulatory systems. Resting-state fMRI (rs-fMRI) has been used to identify global patterns of neuronal correlation with pupil diameter changes; however, the linkage between distinct brain state-dependent activation patterns of neuromodulatory nuclei with pupil dynamics remains to be explored. Here, we identified four clusters of trials with unique activity patterns related to pupil diameter changes in anesthetized rat brains. Going beyond the typical rs-fMRI correlation analysis with pupil dynamics, we decomposed spatiotemporal patterns of rs-fMRI with principal component analysis (PCA) and characterized the cluster-specific pupil-fMRI relationships by optimizing the PCA component weighting via decoding methods. This work shows that pupil dynamics are tightly coupled with different neuromodulatory centers in different trials, presenting a novel PCA-based decoding method to study the brain state-dependent pupil-fMRI relationship.

fMRI-based detection of alertness predicts behavioral response variability

Levels of alertness are closely linked with human behavior and cognition. However, while functional magnetic resonance imaging (fMRI) allows for investigating whole-brain dynamics during behavior and task engagement, concurrent measures of alertness (such as EEG or pupillometry) are often unavailable. Here, we extract a continuous, time-resolved marker of alertness from fMRI data alone. We demonstrate that this fMRI alertness marker, calculated in a short pre-stimulus interval, captures trial-to-trial behavioral responses to incoming sensory stimuli. In addition, we find that the prediction of both EEG and behavioral responses during the task may be accomplished using only a small fraction of fMRI voxels. Furthermore, we observe that accounting for alertness appears to increase the statistical detection of task-activated brain areas. These findings have broad implications for augmenting a large body of existing datasets with information about ongoing arousal states, enriching fMRI studies of neural variability in health and disease.

Respiratory, cardiac, EEG, BOLD signals and functional connectivity over multiple microsleep episodes

Falling asleep is common in fMRI studies. By using long eyelid closures to detect microsleep onset, we showed that the onset and termination of short sleep episodes invokes a systematic sequence of BOLD signal changes that are large, widespread, and consistent across different microsleep durations. The signal changes are intimately intertwined with shifts in respiration and heart rate, indicating that autonomic contributions are integral to the brain physiology evaluated using fMRI and cannot be simply treated as nuisance signals. Additionally, resting state functional connectivity (RSFC) was altered in accord with the frequency of falling asleep and in a manner that global signal regression does not eliminate. Our findings point to the need to develop a consensus among neuroscientists using fMRI on how to deal with microsleep intrusions. SIGNIFICANCE STATEMENT: Sleep, breathing and cardiac action are influenced by common brainstem nuclei. We show that falling asleep and awakening are associated with a sequence of BOLD signal changes that are large, widespread and consistent across varied durations of sleep onset and awakening. These signal changes follow closely those associated with deceleration and acceleration of respiration and heart rate, calling into question the separation of the latter signals as 'noise' when the frequency of falling asleep, which is commonplace in RSFC studies, correlates with the extent of RSFC perturbation. Autonomic and central nervous system contributions to BOLD signal have to be jointly considered when interpreting fMRI and RSFC studies.

Cortical and subcortical hemodynamic changes during sleep slow waves in human light sleep

EEG slow waves, the hallmarks of NREM sleep are thought to be crucial for the regulation of several important processes, including learning, sensory disconnection and the removal of brain metabolic wastes. Animal research indicates that slow waves may involve complex interactions within and between cortical and subcortical structures. Conventional EEG in humans, however, has a low spatial resolution and is unable to accurately describe changes in the activity of subcortical and deep cortical structures. To overcome these limitations, here we took advantage of simultaneous EEG-fMRI recordings to map cortical and subcortical hemodynamic (BOLD) fluctuations time-locked to slow waves of light sleep. Recordings were performed in twenty healthy adults during an afternoon nap. Slow waves were associated with BOLD-signal increases in the posterior brainstem and in portions of thalamus and cerebellum characterized by preferential functional connectivity with limbic and somatomotor areas, respectively. At the cortical level, significant BOLD-signal decreases were instead found in several areas, including insula and somatomotor cortex. Specifically, a slow signal increase preceded slow-wave onset and was followed by a delayed, stronger signal decrease. Similar hemodynamic changes were found to occur at different delays across most cortical brain areas, mirroring the propagation of electrophysiological slow waves, from centro-frontal to inferior temporo-occipital cortices. Finally, we found that the amplitude of electrophysiological slow waves was positively related to the magnitude and inversely related to the delay of cortical and subcortical BOLD-signal changes. These regional patterns of brain activity are consistent with theoretical accounts of the functions of sleep slow waves.

Multimodal EEG-fMRI: advancing insight into large-scale human brain dynamics

Advances in the acquisition and analysis of functional magnetic resonance imaging (fMRI) data are revealing increasingly rich spatiotemporal structure across the human brain. Nonetheless, uncertainty surrounding the origins of fMRI hemodynamic signals, and in the link between large-scale fMRI patterns and ongoing functional states, presently limits the neurobiological conclusions one can draw from fMRI alone. Electroencephalography (EEG) provides complementary information about neural electrical activity and state change, and simultaneously acquiring EEG together with fMRI presents unique opportunities for studying large-scale brain activity and gaining more information from fMRI itself. Here, we discuss recent progress in the use of concurrent EEG-fMRI to enrich the investigation of neural and physiological states and clarify the origins of fMRI hemodynamic signals. Throughout, we outline perspectives on future directions and open challenges.

Reconstruction of respiratory variation signals from fMRI data

Functional MRI signals can be heavily influenced by systemic physiological processes in addition to local neural activity. For example, widespread hemodynamic fluctuations across the brain have been found to correlate with natural, low-frequency variations in the depth and rate of breathing over time. Acquiring peripheral measures of respiration during fMRI scanning not only allows for modeling such effects in fMRI analysis, but also provides valuable information for interrogating brain-body physiology. However, physiological recordings are frequently unavailable or have insufficient quality. Here, we propose a computational technique for reconstructing continuous low-frequency respiration volume (RV) fluctuations from fMRI data alone. We evaluate the performance of this approach across different fMRI preprocessing strategies. Further, we demonstrate that the predicted RV signals can account for similar patterns of temporal variation in resting-state fMRI data compared to measured RV fluctuations. These findings indicate that fluctuations in respiration volume can be extracted from fMRI alone, in the common scenario of missing or corrupted respiration recordings. The results have implications for enriching a large volume of existing fMRI datasets through retrospective addition of respiratory variations information.