Integration of Simultaneous Resting-State EEG, fMRI, and Eye Tracker Methods to Determine and Verify EEG Vigilance Measure

Resting-state functional magnetic resonance imaging (rsfMRI) has been widely used for studying the (presumably) awake and alert human brain. Although rsfMRI scans are typically collected while individuals are instructed to focus their eyes on a fixation cross, objective and verified experimental measures to quantify degree of alertness (e.g., vigilance) are not readily available. Concurrent electroencephalography and fMRI (EEG-fMRI) measurements are also widely used to study human brain with high spatial/temporal resolution. EEG is the modality extensively used for estimating vigilance during eyes-closed resting state. On the other hand, pupil size measured using an eye-tracker device could provide an indirect index of vigilance. In this study, we investigated whether simultaneous multimodal EEG-fMRI combined with eye-tracker measurements can be used to determine EEG signal feature associated with pupil size changes (e.g., vigilance measure) in healthy human subjects (n=10) during brain rest with eyes open. We found that EEG frontal and occipital beta power (FOBP) correlates with pupil size changes, an indirect index for locus coeruleus activity implicated in vigilance regulation (r=0.306, p<0.001). Moreover, FOBP also correlated with heart rate (r=0.255, p<0.001), as well as several brain regions in the anti-correlated network, including the bilateral insula and inferior parietal lobule. These results support the conclusion that FOBP is an objective measure of vigilance in healthy human subjects

Physiological Mechanisms and Significance of Intracranial B Waves.

Objective Recently published studies have described slow spontaneous cerebral blood flow (CBF) and cerebrospinal fluid (CSF) oscillations measured by magnetic resonance imaging (MRI) as potential drivers of brain glymphatic flow, with a similar frequency as intracranial B-waves. Aiming to establish the relationship between these waveforms, we performed additional analysis of frequency and waveform parameters, of our previously published transcranial Doppler (TCD) and intracranial pressure (ICP) recordings of intracranial B waves, to compare to published MRI frequency measurements of CBF and CSF slow oscillations. Patients and Methods We analyzed digital recordings of B waves in 29 patients with head injury, including middle cerebral artery (MCA) flow velocity (FV), ICP, end tidal CO2, and arterial blood pressure (ABP). A subset of these recordings demonstrated high B wave activity and was further analyzed for parameters including frequency, interaction, and waveform distribution curve features. These measures were compared to published similar measurements of spontaneous CBF and CSF fluctuations evaluated using MRI. Results In patients with at least 10% amplitude B wave activity, the MCA blood flow velocity oscillations comprising the B waves, had a maximum amplitude at 0.0245 Hz, and time derivative a maximum amplitude at 0.035 Hz. The frequency range of the B waves was between 0.6-2.3 cycles per min (0.011-0.038 Hz), which is in the same range as MRI measured CBF slow oscillations, reported in human volunteers. Waveform asymmetry in MCA velocity and ICP cycles during B waves, was also similar to published MRI measured CBF slow oscillations. Cross-correlation analysis showed equivalent time derivatives of FV vs. ICP in B waves, compared to MRI measured CBF slow oscillations vs. CSF flow fluctuations. Conclusions The TCD and ICP recordings of intracranial B waves show a similar frequency range as CBF and CSF flow oscillations measured using MRI, and share other unique morphological wave features. These findings strongly suggest a common physiological mechanism underlying the two classes of phenomena. The slow blood flow and volume oscillations causing intracranial B waves appear to be part of a cascade that may provide a significant driving force for compartmentalized CSF movement and facilitate glymphatic flow

Dynamic Change of Awareness during Meditation Techniques: Neural and Physiological Correlates

Recent fndings illustrate how changes in consciousness accommodated by neural correlates and plasticity of the brain advance a model of perceptual change as a function of meditative practice. During the mindbody response neural correlates of changing awareness illustrate how the autonomic nervous system shifts from a sympathetic dominant to a parasympathetic dominant state. Expansion of awareness during the practice of meditation techniques can be linked to the Default Mode Network (DMN), a network of brain regions that is active when the one is not focused on the outside world and the brain is restful yet awake (Chen et al., 2008). A model is presented illustrating the dynamic mindbody response before and after mindfulness meditation, and connections are made with prefrontal cortex activity, the cardiac and respiratory center, the thalamus and amygdala, the DMN and cortical function connectivity. The default status of the DMN changes corresponding to autonomic modulation resulting from meditation practice

Neural Basis of Functional Connectivity MRI

The brain is hierarchically organized across a range of scales. While studies based on electrophysiology and anatomy have been fruitful on the micron to millimeter scale, findings based on functional connectivity MRI (fcMRI) suggest that a higher level of brain organization has been largely overlooked. These findings show that the brain is organized into networks, and each network extends across multiple brain areas. This large-scale, across-area brain organization is functionally relevant and stable across subjects, primate species, and levels of consciousness. This dissertation addresses the neural origin of MRI functional connectivity. fcMRI relies on temporal correlation in at-rest blood oxygen level dependent (BOLD) fluctuations. Thus, understanding the neural origin of at-rest BOLD correlation is of critical significance. By shedding light on the origin of the large-scale brain organization captured by fcMRI, it will guide the design and interpretation of fcMRI studies. Prior investigations of the neural basis of BOLD have not addressed the at-rest BOLD correlation, and they have been focusing on task-related BOLD. At-rest BOLD correlation captured by fcMRI likely reflects a distinct physiological process that is different from that of task-related BOLD, since these two kinds of BOLD dynamics are different in their temporal scale, spatial spread, energy consumption, and their dependence on consciousness. To address this issue, we develop a system to simultaneously record oxygen and electrophysiology in at-rest, awake monkeys. We demonstrate that our oxygen measurement, oxygen polarography, captures the same physiological phenomenon as BOLD by showing that task-related polarographic oxygen responses and at-rest polarographic oxygen correlation are similar to those of BOLD. These results validate the use of oxygen polarography as a surrogate for BOLD to address the neural origin of MRI functional connectivity. Next, we show that at-rest oxygen correlation reflects at-rest correlation in electrophysiological signals, especially spiking activity of neurons. Using causality analysis, we show that oxygen is driven by slow changes in raw local field potential levels (slow LFP), and slow LFP itself is driven by spiking activity. These results provide critical support to the idea that oxygen correlation reflects neural activity, and pose significant challenges to the traditional view of neurohemodynamic coupling. In addition, we find that at-rest correlation does not originate from criticality, which has been the dominant hypothesis in the field. Instead, we show that at-rest correlation likely reflects a specific and potentially localized oscillatory process. We suggest that this oscillatory process could be a result of the delayed negative feedback loop between slow LFP and spiking activity. Thus, we conclude that at-rest BOLD correlation captured by fcMRI is driven by at-rest slow LFP correlation, which is itself driven by spiking activity correlation. The at-rest spiking activity correlation, itself, is likely driven by an oscillatory process. Future studies combining recording with interventional approaches, like pharmacological manipulation and microstimulation, will help to elucidate the circuitry underlying the oscillatory process and its potential functional role

Serial Correlations in Single-Subject fMRI with Sub-Second TR

When performing statistical analysis of single-subject fMRI data, serial correlations need to be taken into account to allow for valid inference. Otherwise, the variability in the parameter estimates might be under-estimated resulting in increased false-positive rates. Serial correlations in fMRI data are commonly characterized in terms of a first-order autoregressive (AR) process and then removed via pre-whitening. The required noise model for the pre-whitening depends on a number of parameters, particularly the repetition time (TR). Here we investigate how the sub-second temporal resolution provided by simultaneous multislice (SMS) imaging changes the noise structure in fMRI time series. We fit a higher-order AR model and then estimate the optimal AR model order for a sequence with a TR of less than 600 ms providing whole brain coverage. We show that physiological noise modelling successfully reduces the required AR model order, but remaining serial correlations necessitate an advanced noise model. We conclude that commonly used noise models, such as the AR(1) model, are inadequate for modelling serial correlations in fMRI using sub-second TRs. Rather, physiological noise modelling in combination with advanced pre-whitening schemes enable valid inference in single-subject analysis using fast fMRI sequences

Disambiguating the role of blood flow and global signal with partial information decomposition

Global signal (GS) is an ubiquitous construct in resting state functional magnetic resonance imaging (rs-fMRI), associated to nuisance, but containing by definition most of the neuronal signal. Global signal regression (GSR) effectively removes the impact of physiological noise and other artifacts, but at the same time it alters correlational patterns in unpredicted ways. Performing GSR taking into account the underlying physiology (mainly the blood arrival time) has been proven to be beneficial. From these observations we aimed to: 1) characterize the effect of GSR on network-level functional connectivity in a large dataset; 2) assess the complementary role of global signal and vessels; and 3) use the framework of partial information decomposition to further look into the joint dynamics of the global signal and vessels, and their respective influence on the dynamics of cortical areas. We observe that GSR affects intrinsic connectivity networks in the connectome in a non-uniform way. Furthermore, by estimating the predictive information of blood flow and the global signal using partial information decomposition, we observe that both signals are present in different amounts across intrinsic connectivity networks. Simulations showed that differences in blood arrival time can largely explain this phenomenon, while using hemodynamic and calcium mouse recordings we were able to confirm the presence of vascular effects, as calcium recordings lack hemodynamic information. With these results we confirm network-specific effects of GSR and the importance of taking blood flow into account for improving de-noising methods. Additionally, and beyond the mere issue of data denoising, we quantify the diverse and complementary effect of global and vessel BOLD signals on the dynamics of cortical areas

Oxygen Polarography in the Awake Macaque: Bridging BOLD fMRI and Electrophysiology

Blood oxygen level dependent (BOLD) fMRI is the predominant method for evaluating human brain activity. This technique identifies brain activity by measuring blood oxygen changes associated with neural activity. Although clearly related, the nature of the relationship between BOLD fMRI identified brain activity and electrophysiologically measured neural activity remains unclear. Direct comparison of BOLD fMRI and electrophysiology has been severely limited by the technical challenges of combining the two techniques. Microelectrode electrophysiology in non-human primates is an excellent model for studying neural activity related to high order brain function similar to that commonly studied with BOLD fMRI in humans, i.e. attention, working memory, engagement. This thesis discusses the development of, validation of, and first results obtained using a new multi-site oxygen polarographic recording system in the awake macaques as a surrogate for BOLD fMRI. Oxygen polarography measures tissue oxygen which is coupled to blood oxygen. This tool offers higher resolution than BOLD fMRI and can be more readily combined with electrophysiology. Using this new tool we evaluated local field potential and oxygen responses to an engaging visual stimulus in two distinct brain systems. In area V3, a key region in the visual system and representative of stimulus driven sensory cortex, we show increased tissue oxygen and local field potential power in response to visual stimulus. In area 23 of the posterior cingulate cortex (PCC), a hub of the default-mode network we show decreased oxygen and local field potential in response to the same stimulus. The default-mode network is a set of brain regions identified in humans whose BOLD fMRI activity is higher at rest than during external engagement, arguing that they sub-serve a function that is engaged as the default-mode in humans. Our results provide new evidence of default-mode network activity in the macaque similar to that seen in humans, provide evidence that the BOLD identified default-mode suppression reflects neural suppression and overall support a strong relationship between neural activity and BOLD fMRI. However, we also note that the LFP responses in both regions show substantial nuances that cannot be seen in the oxygen response and suggest response complexity that is invisible with fMRI. Further the nature of the relationship between LFP and oxygen differs between regions. Our multi-site technique also allows us to evaluate inter-regional interaction of ongoing oxygen fluctuations. Inter-regional correlation of BOLD fMRI fluctuations is commonly used as an index of functional connectivity and has provided new insight into behaviorally relevant aspects of the brains organization and its disruption in disease. Here we demonstrate that we can measure the same inter-regional correlation using oxygen polarography. We utilize the increased resolution of our technique to investigate the frequency structure of the signals driving the correlation and find that inter-regional correlation of oxygen fluctuations appears to depend on a rhythmic mechanism operating at ~0.06 Hz

FMRI spectral signatures of sleep

Sleep can be distinguished from wake by changes in brain electrical activity, typically assessed using electroencephalography (EEG). The hallmark of nonrapid-eye-movement (NREM) sleep is the shift from high-frequency, low-amplitude wake EEG to low-frequency, high-amplitude sleep EEG dominated by spindles and slow waves. Here we identified signatures of sleep in brain hemodynamic activity, using simultaneous functional MRI (fMRI) and EEG. We found that, at the transition from wake to sleep, fMRI blood oxygen level–dependent (BOLD) activity evolved from a mixed-frequency pattern to one dominated by two distinct oscillations: a low-frequency (0.1 Hz) prominent in deep sleep and correlated with the occurrence of slow waves. The two oscillations were both detectable across the brain but exhibited distinct spatiotemporal patterns. During the falling-asleep process, the low-frequency oscillation first appeared in the thalamus, then the posterior cortex, and lastly the frontal cortex, while the high-frequency oscillation first appeared in the midbrain, then the frontal cortex, and lastly the posterior cortex. During the waking-up process, both oscillations disappeared first from the thalamus, then the frontal cortex, and lastly the posterior cortex. The BOLD oscillations provide local signatures of spindle and slow wave activity. They may be employed to monitor the regional occurrence of sleep or wakefulness, track which regions are the first to fall asleep or wake up at the wake–sleep transitions, and investigate local homeostatic sleep processes

Neural Correlates of Spontaneous BOLD Fluctuations: A Simultaneous LFP-fMRI Investigation In The Non-human Primate

Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to explore functional connectivity (FC) between brain regions across neurological and psychiatric diseases. However, the neural basis of spontaneous low frequency blood-oxygen level dependent (BOLD) fluctuations is poorly understood. Here, we acquired rs-fMRI data in macaque monkeys together with simultaneous recordings of local field potentials (LFPs) in prefrontal cortex area 9/46d. We first evaluated the correlation between LFPs (1-100 Hz) and BOLD signals and found unique frequency power correlates of positive and negative FC. Anti-correlation of high and low power envelopes indicated that ongoing cross-frequency interactions are a neural correlate of FC. On the other hand, seed-based analysis of the BOLD signal from the vicinity of electrode revealed the same spatial topology when using the power envelopes of high frequency bands of LFPs in the regression analysis. Variations of the canonical hemodynamic response function (HRF) in distinct cortical areas were also investigated to find the optimal HRF that can best fit in model analysis and estimate the BOLD response. While we found the optimal HRF that yields the highest correlation, the HRF shape was consistent within subjects and between brain regions. Our results suggest that intrinsic connectivity networks may be specifically driven by unique LFP profiles and these profiles contribute differently to BOLD FC. This study provides insight into the neural correlates of spontaneous BOLD FC at rest