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

Measuring electrophysiological connectivity by power envelope correlation: a technical review on MEG methods

The human brain can be divided into multiple areas, each responsible for different aspects of behaviour. Healthy brain function relies upon efficient connectivity between these areas and, in recent years, neuroimaging has been revolutionised by an ability to estimate this connectivity. In this paper we discuss measurement of network connectivity using magnetoencephalography (MEG), a technique capable of imaging electrophysiological brain activity with good (~5mm) spatial resolution and excellent (~1ms) temporal resolution. The rich information content of MEG facilitates many disparate measures of connectivity between spatially separate regions and in this paper we discuss a single metric known as power envelope correlation. We review in detail the methodology required to measure power envelope correlation including i) projection of MEG data into source space, ii) removing confounds introduced by the MEG inverse problem and iii) estimation of connectivity itself. In this way, we aim to provide researchers with a description of the key steps required to assess envelope based functional networks, which are thought to represent an intrinsic mode of coupling in the human brain. We highlight the principal findings of the techniques discussed, and furthermore, we show evidence that this method can probe how the brain forms and dissolves multiple transient networks on a rapid timescale in order to support current processing demand. Overall, power envelope correlation offers a unique and verifiable means to gain novel insights into network coordination and is proving to be of significant value in elucidating the neural dynamics of the human connectome in health and disease

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

Ketamine effects on default mode network activity and vigilance: A randomized, placebo‐controlled crossover simultaneous fMRI/EEG study

In resting-state functional connectivity experiments, a steady state (of consciousness) is commonly supposed. However, recent research has shown that the resting state is a rather dynamic than a steady state. In particular, changes of vigilance appear to play a prominent role. Accordingly, it is critical to assess the state of vigilance when conducting pharmacodynamic studies with resting-state functional magnetic resonance imaging (fMRI) using drugs that are known to affect vigilance such as (subanesthetic) ketamine. In this study, we sought to clarify whether the previously described ketamine-induced prefrontal decrease of functional connectivity is related to diminished vigilance as assessed by electroencephalography (EEG). We conducted a randomized, double-blind, placebo-controlled crossover study with subanesthetic S-Ketamine in N = 24 healthy, young subjects by simultaneous acquisition of resting-state fMRI and EEG data. We conducted seed-based default mode network functional connectivity and EEG power spectrum analyses. After ketamine administration, decreased functional connectivity was found in medial prefrontal cortex whereas increased connectivities were observed in intraparietal cortices. In EEG, a shift of energy to slow (delta, theta) and fast (gamma) wave frequencies was seen in the ketamine condition. Frontal connectivity is negatively related to EEG gamma and theta activity while a positive relationship is found for parietal connectivity and EEG delta power. Our results suggest a direct relationship between ketamine-induced functional connectivity changes and the concomitant decrease of vigilance in EEG. The observed functional changes after ketamine administration may serve as surrogate end points and provide a neurophysiological framework, for example, for the antidepressant action of ketamine (trial name: 29JN1556, EudraCT Number: 2009-012399-28)

Measuring temporal, spectral and spatial changes in electrophysiological brain network connectivity

The topic of functional connectivity in neuroimaging is expanding rapidly and many studies now focus on coupling between spatially separate brain regions. These studies show that a relatively small number of large scale networks exist within the brain, and that healthy function of these networks is disrupted in many clinical populations. To date, the vast majority of studies probing connectivity employ techniques that compute time averaged correlation over several minutes, and between specific pre-defined brain locations. However, increasing evidence suggests that functional connectivity is non-stationary in time. Further, electrophysiological measurements show that connectivity is dependent on the frequency band of neural oscillations. It is also conceivable that networks exhibit a degree of spatial inhomogeneity, i.e. the large scale networks that we observe may result from the time average of multiple transiently synchronised sub-networks, each with their own spatial signature. This means that the next generation of neuroimaging tools to compute functional connectivity must account for spatial inhomogeneity, spectral non-uniformity and temporal non-stationarity. Here, we present a means to achieve this via application of windowed canonical correlation analysis (CCA) to source space projected MEG data. We describe the generation of time–frequency connectivity plots, showing the temporal and spectral distribution of coupling between brain regions. Moreover, CCA over voxels provides a means to assess spatial non-uniformity within short time–frequency windows. The feasibility of this technique is demonstrated in simulation and in a resting state MEG experiment where we elucidate multiple distinct spatio-temporal-spectral modes of covariation between the left and right sensorimotor areas

Individual Human Brain Areas Can Be Identified from Their Characteristic Spectral Activation Fingerprints

The human brain can be parcellated into diverse anatomical areas. We investigated whether rhythmic brain activity in these areas is characteristic and can be used for automatic classification. To this end, resting-state MEG data of 22 healthy adults was analysed. Power spectra of 1-s long data segments for atlas-defined brain areas were clustered into spectral profiles (“fingerprints”), using k-means and Gaussian mixture (GM) modelling. We demonstrate that individual areas can be identified from these spectral profiles with high accuracy. Our results suggest that each brain area engages in different spectral modes that are characteristic for individual areas. Clustering of brain areas according to similarity of spectral profiles reveals well-known brain networks. Furthermore, we demonstrate task-specific modulations of auditory spectral profiles during auditory processing. These findings have important implications for the classification of regional spectral activity and allow for novel approaches in neuroimaging and neurostimulation in health and disease

Increased functional connectivity within alpha and theta frequency bands in dysphoria: A resting-state EEG study

Background: The understanding of neurophysiological correlates underlying the risk of developing depression may have a significant impact on its early and objective identification. Research has identified abnormal resting-state electroencephalography (EEG) power and functional connectivity patterns in major depression. However, the entity of dysfunctional EEG dynamics in dysphoria is yet unknown. Methods: 32-channel EEG was recorded in 26 female individuals with dysphoria and in 38 age-matched, female healthy controls. EEG power spectra and alpha asymmetry in frontal and posterior channels were calculated in a 4-minute resting condition. An EEG functional connectivity analysis was conducted through phase locking values, particularly mean phase coherence. Results: While individuals with dysphoria did not differ from controls in EEG spectra and asymmetry, they exhibited dysfunctional brain connectivity. Particularly, in the theta band (4-8 Hz), participants with dysphoria showed increased connectivity between right frontal and central areas and right temporal and left occipital areas. Moreover, in the alpha band (8-12 Hz), dysphoria was associated with increased connectivity between right and left prefrontal cortex and between frontal and central-occipital areas bilaterally. Limitations: All participants belonged to the female gender and were relatively young. Mean phase coherence did not allow to compute the causal and directional relation between brain areas. Conclusions: An increased EEG functional connectivity in the theta and alpha bands characterizes dysphoria. These patterns may be associated with the excessive self-focus and ruminative thinking that typifies depressive symptoms. EEG connectivity patterns may represent a promising measure to identify individuals with a higher risk of developing depression

THE RELATIONSHIP BETWEEN THE DEFAULT MODE RESTING STATE NEURAL NETWORK, RESPIRATORY SINUS ARRHYTHMIA, AND SELF-FOCUSED COGNITION: AN EMPIRICAL ANALYSIS

Functional activity within the default mode resting state neural network (RSNN) and the resting respiratory sinus arrhythmia (RSA) may represent an integrated neural and peripheral cardiovascular index of the baseline, resting state in humans. Research also indicates that the integrated physiological baseline potentially formed by the default mode RSNN and resting RSA may be associated with self-focused cognition. We hypothesize that measures of default mode RSNN (namely functional connectivity strength), resting RSA, and self-focused cognition are, indeed, correlated and aim to demonstrate these relationships. Measures of default mode RSNN functional connectivity strength were derived using functional magnetic resonance imaging, measures of resting RSA were obtained via electrocardiogram, and self-focused cognition was assessed using survey methods. Although our results were largely unsupportive of our hypothesis, we present several possibly methodological confounds that may have impacted our findings, and we describe directions for future research

Data-driven MRI analysis reveals fitness-related functional change in default mode network and cognition following an exercise intervention

Previous research has indicated that cardiorespiratory fitness (CRF) is structurally and functionally neuroprotective in older adults. However, questions remain regarding the mechanistic role of CRF on cognitive and brain health. The purposes of this study were to investigate if higher pre-intervention CRF was associated with greater change in functional brain connectivity during an exercise intervention and to determine if the magnitude of change in connectivity was related to better post-intervention cognitive performance. The sample included low-active older adults (n = 139) who completed a 6-month exercise intervention and underwent neuropsychological testing, functional neuroimaging, and CRF testing before and after the intervention. A data-driven multi-voxel pattern analysis was performed on resting-state MRI scans to determine changes in whole-brain patterns of connectivity from pre- to post-intervention as a function of pre-intervention CRF. Results revealed a positive correlation between pre-intervention CRF and changes in functional connectivity in the precentral gyrus. Using the precentral gyrus as a seed, analyses indicated that CRF-related connectivity changes within the precentral gyrus were derived from increased correlation strength within clusters located in the Dorsal Attention Network (DAN) and increased anti-correlation strength within clusters located in the Default Mode Network (DMN). Exploratory analysis demonstrated that connectivity change between the precentral gyrus seed and DMN clusters were associated with improved post-intervention performance on perceptual speed tasks. These findings suggest that in a sample of low-active and mostly lower-fit older adults, even subtle individual differences in CRF may influence the relationship between functional connectivity and aspects of cognition following a 6-month exercise intervention.Center for Nutrition, Learning, and Memory at University of Illinois, Grant/Award Number: C4712National Institute on Aging, Grant/Award Number: R37 AG02566