Slow waves (0.5-4 Hz) are a key feature of non-rapid-eye-movement (NREM) sleep, traditionally believed to arise from neocortical circuits. However, growing evidence suggests that subcortical structures, particularly the thalamus, may play a crucial role in initiating and synchronizing slow waves. We tested the hypothesis that single slow waves may arise from distinct cortico-cortical and thalamo-cortical mechanisms using simultaneous EEG-fMRI in healthy adults. Spatial mapping based on thalamic fMRI responses revealed two types of slow waves. The first type (C1) characterized by an early thalamic fMRI-signal increase, corresponded to large, efficiently synchronized waves associated with sleep spindles and with markers of higher arousal and autonomic activation. The second type (C2) is marked by an initial negative fMRI response and corresponds to smaller slow waves potentially resulting from cortico-cortical synchronization. These waves occur more often during phases of stable NREM sleep. These findings highlight distinct slow-wave subtypes with different thalamic involvement and, potentially, synchronization mechanisms.

Functional magnetic resonance imaging (fMRI) is emerging as a useful technique for investigating brain activity during sleep, offering superior spatial resolution compared to electroencephalography (EEG). A prior pilot study conducted by our lab highlighted the potential of fMRI to elucidate the neural correlates of dreaming, arousal thresholds, and other sleep-related phenomena. To expand on these findings, we conducted an extensive all-night fMRI sleep study. Here, we describe quality control metrics obtained during data preprocessing. Healthy participants (n=58) were enrolled in a two-night fMRI-EEG sleep study protocol. 43 participants successfully completed both nights, while 15 were withdrawn during the first or second night. At 6-8 random intervals throughout the night, participants were woken by auditory stimuli, which increased in intensity until wake onset. Once awake, participants completed a dream questionnaire. Preprocessing of fMRI data was performed using the Analysis of Functional Neuroimages (AFNI) toolbox, including nonlinear alignment to a Tailarach template, data censoring to eliminate outlier time points and artifacts from excessive head motion, and regression of systemic confounds from autonomic variability. A total of 877 runs of fMRI data were recorded with a mean length of 43.4 min (SD=34.5 min). Average temporal signal-to-noise ratio in brain voxels across all runs was 48.14 (SD=4.99). Post-regression, average global correlation across voxels was 0.013 (SD=0.005), indicating effective suppression of systemic confounds. Average maximum within-run motion displacement was a modest 2.78 mm (SD=2.23 mm), and average fraction of time points censored due to motion or outliers was 8.84% (SD=8.88%). Average spatial correlation between each subject’s anatomical and functional scans was 0.837 (SD=0.021), attributable to differences in brain coverage and resolution. Average spatial correlation between each subject’s anatomical scan and the Tailarach template was 0.981 (SD=0.005). These preprocessing results indicate the successful collection and preprocessing of high-quality, low-noise data and validate the effectiveness of our all-night fMRI sleep study protocol. Our next step is to perform group analyses using the auditory arousal threshold and dream questionnaire data collected. This work was supported by the Intramural Research Programs of the NINDS, NIDCD, and NIMH.

Reports of sleep-specific brain activity patterns have been constrained by assessing brain function as it related to the conventional polysomnographic sleep stages. This limits the variety of sleep states and underlying activity patterns that one can discover. The current study used all-night functional MRI sleep data and defined sleep behaviorally with auditory arousal threshold (AAT) to characterize sleep depth better by searching for novel neural markers of sleep depth that are neuroanatomically localized and temporally unrelated to the conventional stages. Functional correlation values calculated in a four-min time window immediately before the determination of AAT were entered into a linear mixed effects model, allowing multiple arousals across the night per subject into the analysis, and compared to models with sleep stage to determine the unique relationships with AAT. These unique relationships were for thalamocerebellar correlations, the relationship between the right language network and the right "default-mode network dorsal medial prefrontal cortex subsystem," and the relationship between thalamus and ventral attention network. These novel neural markers of sleep depth would have remained undiscovered if the data were merely analyzed with the conventional sleep stages.

Vigilance naturally drifts over time, coinciding with marked changes in brain-wide functional magnetic resonance imaging (fMRI) signals. Though the precise origins of these hemodynamic changes are unclear, largely separate lines of research have linked different vigilance levels not only to changes in fMRI signal fluctuation amplitudes and functional connectivity, but also to significant variations in autonomic physiology. These findings raise the possibility that vigilance-related modulations in fMRI signals may arise in part from changes in autonomic physiology and their effects on cerebral hemodynamics. Here, using simultaneous recordings of fMRI, EEG-indexed vigilance, respiration, and pulse oximetry, we investigate how the relationship between autonomic and fMRI signals varies systematically as vigilance gradually drifts. Regression analyses indicated that the strength and extent of fMRI-autonomic covariation increased as vigilance diminished, during both resting state and an auditory vigilance task. Spatiotemporally, autonomic signals exhibited early positive correlations and delayed negative correlations with fMRI signals throughout much of the grey matter, accompanied by late positive correlations in the ventricles and periventricular white matter. Low-frequency EEG power fluctuations also demonstrated state-dependent associations with both fMRI and autonomic signals, with effects in fMRI that partially overlapped with those of peripheral autonomic variations. Functional connectivity between most brain networks strengthened as vigilance decreased, especially during resting-state scans, and removing autonomic variance from fMRI signals largely attenuated this effect. Together, these results demonstrate interactions between vigilance levels, autonomic physiology, and brain hemodynamics, showing that the physiological constituents of fMRI signals vary markedly over vigilance levels and brain regions. These findings contribute to knowledge of human brain physiology and toward the accurate parsing, analysis, and interpretation of fMRI data.

According to recent research, slow waves may increase metabolic waste clearance during sleep, possibly through cerebrospinal fluid (CSF) pulsatile movement. Pulsations can also be caused by direct pressure effects from the cardiac and respiratory cycles, such as deep breaths. To examine this possibility, we designed a cued deep breathing experiment, which was previously demonstrated to result in widespread fMRI global signal reductions. In this work, our findings point to an alternative pathway for the creation of CSF pulsations that relies on autonomic changes rather than sleep.

We report a fast quantitative CSF flow imaging method based on the spatial-temporal perturbation patterns formed at the transition bands in balanced steady-state-free-precession (SSFP) images when combined with a background magnetic field gradient. By modeling CSF flow as a combination of a pulsatile (AC) and a constant (DC) flow component across the cardiac cycle, a dictionary was generated based on Bloch simulations and used to translate the observed patterns to flow velocities. Monte-Carlo simulations were performed to evaluate the accuracy of the approach. CSF flow resulting from different physiological mechanisms was quantified in selected regions in 12 healthy subjects.

In addition to the well-established origin in cardiac and respiratory cycles, CSF pulsations may also result from the much slower variations in vascular tone associated with cerebral blood flow (CBF) autoregulation. Using a novel quantitative MRI approach, we measured CSF flow velocities and displaced volumes resulting from these three mechanisms in 12 healthy human controls. We found the autoregulatory effects to be a major factor, with associated CSF velocities comparable to and displaced volumes an order of magnitude larger than the cardio-respiratory effects. This may be an important mechanism underlying the CSF oscillations recently observed in resting-state fMRI.

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 cerebrospinal 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 waking respiratory task. 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.

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.

The 4th International Workshop on MRI Phase Contrast and QSM (2016, Graz, Austria) hosted the first QSM Challenge. A single‐orientation gradient recalled echo acquisition was provided, along with COSMOS and the χ33 STI component as ground truths. The submitted solutions differed more than expected depending on the error metric used for optimization and were generally over‐regularized. This raised (unanswered) questions about the ground truths and the metrics utilized.

fMRI relies on a localized cerebral blood flow (CBF) response to changes in cortical neuronal activity. An underappreciated aspect however is its sensitivity to contributions from autonomic physiology that may affect CBF through changes in vascular resistance and blood pressure. As is reviewed here, this is crucial to consider in fMRI studies of sleep, given the close linkage between the regulation of arousal state and autonomic physiology. Typical methods for separating these effects are based on the use of reference signals that may include physiological parameters such as heart rate and respiration; however, the use of time-invariant models may not be adequate due to the possibly changing relationship between reference and fMRI signals with arousal state. In addition, recent research indicates that additional physiological reference signals may be needed to accurately describe changes in systemic physiology, including sympathetic indicators such as finger skin vascular tone and blood pressure.

Literature reports contradicting results on the response of brain tumors to vascular stimuli measured in T2*-weighted MRI. Here, we analyzed the potential dependency of the MRI-response to (hypercapnic) hyperoxia on the order of the gas administration.

The interpretation of functional magnetic resonance imaging (fMRI) studies of brain activity is often hampered by the presence of brain-wide signal variations that may arise from a variety of neuronal and non-neuronal sources. Recent work suggests a contribution from the sympathetic vascular innervation, which may affect the fMRI signal through its putative and poorly understood role in cerebral blood flow (CBF) regulation. By analyzing fMRI and (electro-) physiological signals concurrently acquired during sleep, we found that widespread fMRI signal changes often co-occur with electroencephalography (EEG) K-complexes, signatures of sub-cortical arousal, and episodic drops in finger skin vascular tone; phenomena that have been associated with intermittent sympathetic activity. These findings support the notion that the extrinsic sympathetic innervation of the cerebral vasculature contributes to CBF regulation and the fMRI signal. Accounting for this mechanism could help separate systemic from local signal contributions and improve interpretation of fMRI studies.

Background: Previous functional magnetic resonance imaging (fMRI) sleep studies have been hampered by the difficulty of obtaining extended amounts of sleep in the sleep-adverse environment of the scanner and often have resorted to manipulations such as sleep depriving subjects before scanning. These manipulations limit the generalizability of the results. New method: The current study is a methodological validation of procedures aimed at obtaining all-night fMRI data in sleeping subjects with minimal exposure to experimentally induced sleep deprivation. Specifically, subjects slept in the scanner on two consecutive nights, allowing the first night to serve as an adaptation night. Results/comparison with existing method(s): Sleep scoring results from simultaneously acquired electroencephalography data on Night 2 indicate that subjects (n = 12) reached the full spectrum of sleep stages including slow-wave (M = 52.1 min, SD = 26.5 min) and rapid eye movement (REM, M = 45.2 min, SD = 27.9 min) sleep and exhibited a mean of 2.1 (SD = 1.1) nonREM-REM sleep cycles. Conclusions: It was found that by diligently applying fundamental principles and methodologies of sleep and neuroimaging science, performing all-night fMRI sleep studies is feasible. However, because the two nights of the study were performed consecutively, some sleep deprivation from Night 1 as a cause of the Night 2 results is likely, so consideration should be given to replicating the current study with a washout period. It is envisioned that other laboratories can adopt the core features of this protocol to obtain similar results.

To investigate a potential contribution of systemic physiology to recently reported BOLD fMRI signals in white matter, we compared photo-plethysmography (PPG) and whole-brain fMRI signals recorded simultaneously during long resting-state scans from an overnight sleep study. We found that intermittent drops in the amplitude of the PPG signal exhibited strong and widespread correlations with the fMRI signal, both in white matter (WM) and in gray matter (GM). The WM signal pattern resembled that seen in previous resting-state fMRI studies and closely tracked the location of medullary veins. Its temporal cross-correlation with the PPG amplitude was bipolar, with an early negative value. In GM, the correlation was consistently positive. Consistent with previous studies comparing physiological signals with fMRI, these findings point to a systemic vascular contribution to WM fMRI signals. The PPG drops are interpreted as systemic vasoconstrictive events, possibly related to intermittent increases in sympathetic tone related to fluctuations in arousal state. The counter-intuitive polarity of the WM signal is explained by long blood transit times in the medullary vasculature of WM, which cause blood oxygenation loss and a substantial timing mismatch between blood volume and blood oxygenation effects. A similar mechanism may explain previous findings of negative WM signals around large draining veins during both task-and resting-state fMRI.

Variations in sympathetic and parasympathetic activity during sleep can lead to fluctuations in heart rate and vascular tone, potentially confounding fMRI measurements of brain activity that rely on hemodynamic signals. To investigate this, photo-plethysmography (PPG) based measurements of peripheral vasoconstriction were correlated with fMRI signals acquired during an overnight sleep study. BOLD fMRI data was obtained at 3T with gradient-echo-EPI (flip-angle=900, repeatition-time=3s, echo-time=36ms, voxel-size=2.5x2.5x2mm3, matrix-size=96x70x50, acceleration-factor=2). EEG and ECG were acquired concurrently with the fMRI with a 64-channel recorder (61 EEG scalp, 2 electro-oculography, and 1 ECG electrodes), synchronized to the MRI system-clock. Heart rate (HR), vascular tone inferred from PPG signal amplitude (AMP), and respiratory volume (RV) were derived from the PPG and chest belt signals. Initial inspection of all data (n=16) showed, primarily during N1/N2 sleep, striking variations in AMP, consisting of intermittently occurring 10-15s drops accompanied by a biphasic change in HR. Based on this a subset of data was selected (~20% of the scans) that had a high incidence of AMP drops and fMRI without motion artifacts. AMP drops had a strong correlate in the fMRI signal in both grey-matter (GM) and white-matter (WM). The covariation of fMRI signal and PPG-AMP often exceeded that with the more conventional physiological measures of HR and RV. Voxel-vise correlations between PPG-AMP and fMRI signals showed a striking negative correlation in central WM and around medullary-veins at 0-lag, turning positive after about 3-6s. In GM, maximum positive correlation was observed at around 6s time-lags. Intermittent AMP drops are interpreted as systemic vasoconstrictive events, possibly relating to correlations between EEG measures of arousal and AMP drops, as previously observed during sleep (Ackner et al., 1957). The fMRI effects suggest that this vasoconstriction also involves the central-nervous-system vasculature. The lag-dependent characteristic of the WM signal can be explained by a temporal mismatch between blood-volume and blood-oxygenation effects originating from vascular transit delays. A similar mechanism may explain previous findings of WM fMRI signals during both task- and resting-state fMRI. Intramural Research Program of the NINDS.

PurposeTo assess the potential of a real-time field-control (FC) system for mitigating effects of spatiotemporal field fluctuations in quantitative susceptibility mapping (QSM) at 7T. MethodsMagnitude, phase, and QSM images of phantoms and healthy volunteers were acquired under standard conditions and under induced field perturbation (FP) (phantoms: periodic water-bottle displacement; volunteers: deep breathing and forearm movement) with and without FC, which continuously detects and minimizes magnetic-field variations. ResultsField control successfully eliminated FP-induced impairment of phantom image quality and deviations from a linear susceptibility increase for increasing gadolinium concentration in a Gd dilution series (y=320x - 0.60, R-2=0.93 for the scan with FP and FC versus y=259x - 0.54, R-2=0.78 for the scan with FP and no FC (slope literature value: 326ppm L/mol)). Similarly, in volunteers, FC allowed a recovery of a FP-induced loss of identifiable brain structures and reduced the relative change of mean susceptibilities and standard deviations (9353% to 34 +/- 46%) in all regions of interests with respect to the reference scan. ConclusionsReal-time FC improved the delineation of brain structures and the match of susceptibility values with reference values obtained without FP. Magn Reson Med 79:770-778, 2018. (c) 2017 International Society for Magnetic Resonance in Medicine.

Background and purpose: Quantitative susceptibility mapping has been previously used to differentiate lesions in patients with brain tumors. The aim of this work was to characterize the response of magnetic susceptibility differences in malignant brain tumors and surrounding edema to hyperoxic and hypercapnic respiratory challenges. Methods: Images of malignant brain tumor patients (2 glioblastoma multiforme, 2 anaplastic astrocytoma, 1 brain metastasis) with clinical MRI exams (contrast-enhanced T1w) were acquired at 3T. 3D multi-gradient-echo data sets were acquired while the patients inhaled medical-air (21% O2), oxygen (100% O2), and carbogen (95% O2, 5% CO2). Susceptibility maps were generated from real and imaginary data. Regions of interest were analyzed with respect to respiration-gas-induced susceptibility changes. Results: Contrast-enhancing tumor regions with high baseline magnetic susceptibility exhibited a marked susceptibility reduction under hyperoxic challenges, with a stronger effect (-0.040 to -0.100 ppm) under hypercapnia compared to hyperoxia ( -0.010 to -0.067 ppm). In contrast, regions attributed to necrotic tissue and to edema showed smaller changes of opposite sign, i.e. paramagnetic shift. There was a correlation between malignant tumor tissue magnetic susceptibility at baseline under normoxia and the corresponding susceptibility reduction under hypercapnia and to a lesser degree under hyperoxia. Conclusion: In this small cohort of analysis, quantification of susceptibility changes in response to respiratory challenges allowed a complementary, functional differentiation of tumorous sub-regions. Those changes, together with the correlations observed between baseline susceptibility under normoxia and susceptibility reduction with challenges, could prove helpful for a non-invasive characterization of local tumor microenvironment.

Fluctuations in blood-oxygenation level dependent (BOLD) signal and perfusion affect the quantification of changes in cerebral blood flow (CBF), coupled to neuronal activity, in arterial spin labeling (ASL). Subtraction methods for control and labeled MR images (i.e. pair-wise, surround subtraction, and subtraction of sinc-interpolated images), postulated to mitigate this interference in pseudo-continuous ASL (pCASL), were evaluated by comparison with quantitative 15O-water PET. At rest, a good agreement in the CBF values was found between PET and MRI for each of the subtraction methods. Stimulation of the visual system resulted in a regional CBF increase in the occipital lobe, which was detectable in both modalities. Bland–Altman analysis showed a systematic underestimation of the CBF values during activation in MRI. Evaluation of the relative CBF change induced by neuronal stimulation showed good inter-modality agreement for the three subtraction methods. Perfusion data obtained with each subtraction method followed the stimulation paradigm without significant differences in the correlation patterns or in the time lag between stimulation and perfusion response. Comparison to the gold standard confirmed the detectability of a neuronal stimulation pattern by pCASL. The results indicate that the combined use of background suppression and short TE reduces the BOLD-weighting in the pCASL signal.