Functional localization and effective connectivity of cortical theta and alpha oscillatory activity during an attention task

Objectives: The aim of this paper is to investigate cortical electric neuronal activity as an indicator of brain function, in a mental arithmetic task that requires sustained attention, as compared to the resting state condition. The two questions of interest are the cortical localization of different oscillatory activities, and the directional effective flow of oscillatory activity between regions of interest, in the task condition compared to resting state. In particular, theta and alpha activity are of interest here, due to their important role in attention processing. Methods: We adapted mental arithmetic as an attention ask in this study. Eyes closed 61-channel EEG was recorded in 14 participants during resting and in a mental arithmetic task (“serial sevens subtraction”). Functional localization and connectivity analyses were based on cortical signals of electric neuronal activity estimated with sLORETA (standardized low resolution electromagnetic tomography). Functional localization was based on the comparison of the cortical distributions of the generators of oscillatory activity between task and resting conditions. Assessment of effective connectivity was based on the iCoh (isolated effective coherence) method, which provides an appropriate frequency decomposition of the directional flow of oscillatory activity between brain regions. Nine regions of interest comprising nodes from the dorsal and ventral attention networks were selected for the connectivity analysis. Results: Cortical spectral density distribution comparing task minus rest showed significant activity increase in medial prefrontal areas and decreased activity in left parietal lobe for the theta band, and decreased activity in parietal-occipital regions for the alpha1 band. At a global level, connections among right hemispheric nodes were predominantly decreased during the task condition, while connections among left hemispheric nodes were predominantly increased. At more detailed level, decreased flow from right inferior frontal gyrus to anterior cingulate cortex for theta, and low and high alpha oscillations, and increased feedback (bidirectional flow) between left superior temporal gyrus and left inferior frontal gyrus, were observed during the arithmetic task. Conclusions: Task related medial prefrontal increase in theta oscillations possibly corresponds to frontal midline theta, while parietal decreased alpha1 activity indicates the active role of this region in the numerical task. Task related decrease of intracortical right hemispheric connectivity support the notion that these nodes need to disengage from one another in order to not interfere with the ongoing numerical processing. The bidirectional feedback between left frontal-temporal-parietal regions in the arithmetic task is very likely to be related to attention network working memory function. Significance: The methods of analysis and the results presented here will hopefully contribute to clarify the roles of the different EEG oscillations during sustained attention, both in terms of their functional localization and in terms of how they integrate brain function by supporting information flow between different cortical regions. The methodology presented here might be clinically relevant in evaluating abnormal attention function

A Comprehensive Review of Magnetoencephalography (MEG) Studies for Brain Functionality in Healthy Aging and Alzheimer's Disease (AD)

Neural oscillations were established with their association with neurophysiological activities and the altered rhythmic patterns are believed to be linked directly to the progression of cognitive decline. Magnetoencephalography (MEG) is a non-invasive technique to record such neuronal activity due to excellent temporal and fair amount of spatial resolution. Single channel, connectivity as well as brain network analysis using MEG data in resting state and task-based experiments were analyzed from existing literature. Single channel analysis studies reported a less complex, more regular and predictable oscillations in Alzheimer's disease (AD) primarily in the left parietal, temporal and occipital regions. Investigations on both functional connectivity (FC) and effective (EC) connectivity analysis demonstrated a loss of connectivity in AD compared to healthy control (HC) subjects found in higher frequency bands. It has been reported from multiplex network of MEG study in AD in the affected regions of hippocampus, posterior default mode network (DMN) and occipital areas, however, conclusions cannot be drawn due to limited availability of clinical literature. Potential utilization of high spatial resolution in MEG likely to provide information related to in-depth brain functioning and underlying factors responsible for changes in neuronal waves in AD. This review is a comprehensive report to investigate diagnostic biomarkers for AD may be identified by from MEG data. It is also important to note that MEG data can also be utilized for the same pursuit in combination with other imaging modalities

Magnetoencephalography

This is a practical book on MEG that covers a wide range of topics. The book begins with a series of reviews on the use of MEG for clinical applications, the study of cognitive functions in various diseases, and one chapter focusing specifically on studies of memory with MEG. There are sections with chapters that describe source localization issues, the use of beamformers and dipole source methods, as well as phase-based analyses, and a step-by-step guide to using dipoles for epilepsy spike analyses. The book ends with a section describing new innovations in MEG systems, namely an on-line real-time MEG data acquisition system, novel applications for MEG research, and a proposal for a helium re-circulation system. With such breadth of topics, there will be a chapter that is of interest to every MEG researcher or clinician

Spatiotemporal organisation of human sensorimotor beta burst activity

Beta oscillations in human sensorimotor cortex are hallmark signatures of healthy and pathological movement. In single trials, beta oscillations include bursts of intermittent, transient periods of high-power activity. These burst events have been linked to a range of sensory and motor processes, but their precise spatial, spectral, and temporal structure remains unclear. Specifically, a role for beta burst activity in information coding and communication suggests spatiotemporal patterns, or travelling wave activity, along specific anatomical gradients. We here show in human magnetoencephalography recordings that burst activity in sensorimotor cortex occurs in planar spatiotemporal wave-like patterns that dominate along two axes either parallel or perpendicular to the central sulcus. Moreover, we find that the two propagation directions are characterised by distinct anatomical and physiological features. Finally, our results suggest that sensorimotor beta bursts occurring before and after a movement can be distinguished by their anatomical, spectral and spatiotemporal characteristics, indicating distinct functional roles

Intra- and Inter-Frequency Brain Network Structure in Health and Schizophrenia

Peer reviewe

Building Mental Experiences: From Scenes to Events

Mental events are central to everyday cognition, be it our continuous perception of the world, recalling autobiographical memories, or imagining the future. Little is known about the fine-grained temporal dynamics of these processes. Given the apparent predominance of scene imagery across cognition, in this thesis I used magnetoencephalography to investigate whether and how activity in the hippocampus and ventromedial prefrontal cortex (vmPFC) supports the mental construction of scenes and the events to which they give rise. In the first experiment, participants gradually imagined scenes and also closely matched non-scene arrays; this allowed me to assess whether any brain regions showed preferential responses to scene imagery. The anterior hippocampus and vmPFC were particularly engaged by the construction of scene imagery, with the vmPFC driving hippocampal activity. In the second experiment, I found that certain objects – those that were space-defining – preferentially engaged the vmPFC and superior temporal gyrus during scene construction, providing insight into how objects affect the creation of scene representations. The third experiment involved boundary extension during scene perception, permitting me to examine how single scenes might be prepared for inclusion into events. I observed changes in evoked responses just 12.5-58 ms after scene onset over fronto-temporal sensors, with again the vmPFC exerting a driving influence on other brain regions, including the hippocampus. In the final experiment, participants watched brief movies of events built from a series of scenes or non-scene patterns. A difference in evoked responses between the two event types emerged during the first frame of the movies, the primary source of which was shown to be the hippocampus. The enduring theme of the results across experiments was scene-specific engagement of the hippocampus and vmPFC, with the latter being the driving influence. Overall, this thesis provides insights into the neural dynamics of how scenes are built, made ready for inclusion into unfolding mental episodes, and then linked to produce our seamless experience of the world

Dynamics of large-scale electrophysiological networks: a technical review

For several years it has been argued that neural synchronisation is crucial for cognition. The idea that synchronised temporal patterns between different neural groups carries information above and beyond the isolated activity of these groups has inspired a shift in focus in the field of functional neuroimaging. Specifically, investigation into the activation elicited within certain regions by some stimulus or task has, in part, given way to analysis of patterns of co-activation or functional connectivity between distal regions. Recently, the functional connectivity community has been looking beyond the assumptions of stationarity that earlier work was based on, and has introduced methods to incorporate temporal dynamics into the analysis of connectivity. In particular, non-invasive electrophysiological data (magnetoencephalography / electroencephalography (MEG/EEG)), which provides direct measurement of whole-brain activity and rich temporal information, offers an exceptional window into such (potentially fast) brain dynamics. In this review, we discuss challenges, solutions, and a collection of analysis tools that have been developed in recent years to facilitate the investigation of dynamic functional connectivity using these imaging modalities. Further, we discuss the applications of these approaches in the study of cognition and neuropsychiatric disorders. Finally, we review some existing developments that, by using realistic computational models, pursue a deeper understanding of the underlying causes of non-stationary connectivity

Novel measure of olfactory bulb function in health and disease

Present neuroimaging techniques are capable of recording the neural activity from all over the brain but the olfactory bulb (OB). The OB is the first olfactory processing stage of the central nervous system and the site of insult in several neurological disorders, particularly Parkinson’s disease (PD). It has been suggested that the OB has a pivotal role in the olfactory system anal-ogous to primary visual cortex (V1) and thalamus in the visual system. However, due to the existing technical limitations, there has not been any non-invasive technique that can reliably measure the OB function in humans, consequently limiting its functional recording to one in-tracranial study dating back to the 60s. Initially in Study I, a non-invasive method of measuring the function of human OB is devel-oped, so-called electrobulbogram (EBG). In line with previous animal literature as well as the only intracranial study in human OB, it was demonstrated that gamma oscillations on the EBG electrodes occurred shortly after the odor onset. Subsequently, applying source recon-struction analysis provided evidence that observed oscillations were localized to the OB. Ad-ditionally, the OB recording with the EBG method showed a test-retest reliability comparable with visual event related potentials. Notably, the detected gamma oscillations were demon-strated to be insensitive to habituation, the OB’s marked characteristic which has previously been demonstrated in rodents. Last, but not least, assessing the EBG response in an individual who did not have the bilateral OB indicated that the lack of OB results in disappearance of gamma oscillations in the EBG electrodes. Given that Study I determined the possibility of reliably measuring the function of the OB using the EBG, in Study II, I assessed the functional role of OB’s oscillations in the pro-cessing of the odor valence. Odor valence has been suggested to be linked to approach–avoidance responses and therefore, processing of odor valence is thought to be one of the core aspects of odor processing in the olfactory system. Consequently, using combined EBG and EEG recording, OB activity was reconstructed on the source level during processing of odors with different valences. Gamma and beta oscillations were found to be related to va-lence perception in the human OB. Moreover, the early beta oscillations were associated with negative but not positive odors, where these beta oscillations can be linked to preparatory neural responses in the motor cortex. Subsequently, in a separate experiment, negative odors were demonstrated to trigger a whole-body motor avoidance response in the time window overlapping with the valence processes in the OB. These negative odor-elicited motor re-sponses were measured by a force plate as a leaning backward motion. Altogether, the results from Study II indicated that the human OB processes odor valence sequentially in the gamma and beta frequency bands, where the early processing of negative odors in the OB might be facilitating rapid approach-avoidance behaviors. To further evaluate the functional role of the OB in odor processing, in Study III, OB’s communication with its immediate recipient, namely piriform cortex (PC), was assessed. These two areas are critical nodes of the olfactory system which communicate with each other through neural oscillations. The activity of the OB and the PC were reconstructed using a combination of EBG, EEG, and source reconstruction techniques. Subsequently, the cross spectrogram of the OB and the PC was assessed as a measure of functional connectivity where temporal evolution from fast to slow oscillations in the OB–PC connectivity was found during the one second odor processing. Furthermore, the spectrally resolved Granger causal-ity analysis suggested that the afferent connection form the OB to the PC occurred in the gamma and beta bands whereas the efferent connection from the PC to the OB was concen-trated in the theta and delta bands. Notably, odor identity could be deciphered from the low gamma oscillatory pattern in the OB–PC connectivity as early as 100ms after the odor onset. Hence, findings from this study elucidate on our understanding of the bidirectional infor-mation flow in the human olfactory system. Olfactory dysfunction, due to neurodegeneration in the OB, commonly appears several years earlier than the occurrence of the PD-related characteristic motor symptoms. Consequently, a functional measure of the OB may serve as a potential early biomarker of PD. In Study IV, OB function was assessed in PD to answer whether the EBG method can be used to dissociate individuals with a PD diagnosis from healthy age-matched controls. The spectrogram of the EBG signals indicated that there were different values in gamma, beta, and theta for PDs compared with healthy controls. Specifically, six components were found in the EBG re-sponse during early and late time points which together dissociate PDs from controls with a 90% sensitivity and a 100% specificity. Furthermore, these components were linked to med-ication, disease duration and severity, as well as clinical odor identification performance. Overall, these findings support the notion that EBG has a diagnostic value and can be further developed to serve as an early biomarker for PD. In the last study, Study V, the prevalence of COVID-19 was determined using odor intensity ratings as an indication of olfactory dysfunction. Using a large sample data (n = 2440) from a Swedish population, odor intensity ratings of common household items over time were found to be closely associated with prevalence prediction of COVID-19 in the Stockholm region over the same time-period (r = -.83). Impairment in odor intensity rating was further correlated with the number of reported COVID-19 symptoms. Relatedly, individuals who progressed from having no symptoms to having at least one symptom had a marked decline in their odor intensity ratings. The results from this study, given the relatively large sample size, provided a concrete basis for the future studies to further assess the potential association between the deficits in the OB function and olfactory dysfunction in COVID-19. In conclusion, our proposed method for non-invasive measurement of the OB function was shown to provide a reliable recording with a potential as a diagnostic tool for PD. Combining EBG and EEG allowed for reconstruction of the OB signal at the source level, where specific oscillations were found to be critical for odor valence processing and rapid avoidance re-sponse. Moreover, oscillations in different frequency bands were found to be critical for the OB reciprocal communications and transfer of odor identity information to higher order ol-factory subsystems. Finally, COVID-19 was found to be associated with a decline in olfactory acuity which might originate from damage to the patient’s OB. In conclusion, the results from the studies within this thesis provide a new perspective on the functional role of oscillations in the human OB

Revealing Distinct Neural Signatures in Magnetoencephalography with Hidden Markov Models

Magnetoencephalography (MEG) is a functional neuroimaging method which measures the magnetic fields produced by neural communication in the brain. Specifically, the fields induced by dendritic current flow in assemblies of pyramidal neurons. Because these magnetic fields are generated directly by brain electrophysiology, and are mostly unperturbed by the skull, MEG data are rich in spatial and temporal information. This thesis is chiefly concerned with interpreting these data in a way that produces useful results whilst minimising bias. Hidden Markov modelling (HMM) is a robust statistical method which has been applied to fields as diverse as speech recognition and financial market prediction. It parses data into a number of ‘hidden states’, each with their own unique characteristics, in an unsupervised way. Because it is data-driven, it can create a model unique to each participant’s brain activity and specific to each task. In addition, the HMM framework itself is flexible so it can be applied to both sensor and source-space data and can be applied to multiple channels (multivariate) or to a single time course (univariate). Choice of an observation model allows states to be characterised by amplitude, spatial, or spectral content depending on the research question. The aim of this thesis is to apply hidden Markov modelling (HMM) to whole head MEG data to identify repeated patterns of transient neural activity occurring throughout the brain. Once these patterns were identified, the interaction between these short ‘bursts’ of activity across the cortex was established which provided a unique measure of functional connectivity. Three studies were undertaken: The role of transient spectral bursts in MEG functional connectivity: In recent years, the smoothly varying neural oscillations often studied in MEG (such as those trial-averaged responses in the traditional neurophysiological (such as alpha/beta) frequency bands) have been shown to be made up of single-trial high-amplitude ‘bursts’ of activity. These bursts can be observed in the beta frequency band and are therefore often referred to as beta bursts. In this study, a novel time-delay embedded HMM was used to identif bursts in broadband data based on their spectral content for MEG data from 66 healthy adult participants. The burst amplitude, duration and frequency of occurrence were characterised across the cortex in resting state data, and in a motor task the classic movement-related beta desynchronisation and post movement beta rebound were shown to be made up of changes in burst occurrence. A novel functional connectivity metric was then introduced based on the coincidence of bursts from distal brain regions, allowing the known beta band functional connectome to be reproduced. Bursts coincident across spatially separate brain regions were also shown to correspond to periods of heightened coherence, lending evidence to the communication by coherence (Fries 2005, 2015) hypothesis. Post-stimulus responses across the cortex: During a motor task, both primary (during stimulation) and post stimulus responses (PSR) can be observed. These are well characterised in the literature, but little is known about their functional significance. The PSR in particular is modified in a range of seemingly unrelated neurological conditions with variable symptoms, such as schizophrenia (Robson et al. 2016), autism spectrum disorder (Gaetz et al. 2020) and multiple sclerosis (Barratt et al. 2017), indicating that the PSR is a fundamental neurophysiological process, the disturbance of which has implications on both healthy and pathological brain function. This work therefore tested the hypothesis that the PSR is present across the cortex. MEG data were acquired and analysed from two experiments with 15 healthy adult volunteers each – the first was a right-hand grip task with visual feedback, the second involved passive left visual field stimulation. Both experiments varied stimulus duration (2s, 5s and 10s) with a 30s rest-period between trials to allow characterisation of the full PSR. A univariate 3-state time-delay-embedded hidden Markov model (HMM) was used to characterise the spatial distributions of the primary and PSR across the cortex for both tasks. Results showed that for both tasks, the primary response state was more bilateral over the sensorimotor or visual areas (depending on task) where the PSR state was more unilateral and confined to the contralateral sensorimotor or visual areas (again, dependant on task). A state coincidence metric was then used to investigate the integration of the primary and PSR states across brain regions as a measure of task-related functional connectivity. Hidden Markov modelling of the interictal brain: Epilepsy is a highly heterogeneous disease with variations in the temporal morphology and localisation of epileptiform activity across patients. Unsupervised machine learning techniques like the HMM allow us to take into account this variability and ensure that every model is tailored to each individual. In this work, a multivariate time-delay embedded HMM was used to identify brain states based on their spatial and spectral properties in sensor-level MEG data acquired as part of standard clinical care for patients at the Children’s Hospital of Philadelphia. State allocations were used together with a linearly constrained minimum variance (LCMV) beamformer to produce a 3D map of state variance, hence localising probable epileptogenic foci. Clinical MEG epilepsy data are routinely analysed by excess kurtosis mapping (EKM) and so the performance of the HMM was assessed against this for three patient groups, each with increasingly complex epilepsy manifestation (10 patients in total). The difference in localization of epileptogenic foci for the two methods was 7 ± 2mm (mean ± SD over all 10 patients); and 94 ± 13% of EKM temporal markers were matched by an HMM state visit. It is therefore clear that this method localizes epileptogenic areas in agreement with EKM and in patients with more than one focus the HMM provides additional information about the relationship between them

Cortical mechanisms for tinnitus in humans /

PhD ThesisThis work sought to characterise neurochemical and neurophysiological processes underlying tinnitus in humans. The first study involved invasive brain recordings from a neurosurgical patient, along with experimental manipulation of his tinnitus, to map the cortical system underlying his tinnitus. Widespread tinnitus-linked changes in low- and high-frequency oscillations were observed, along with inter-regional and cross-frequency patterns of communication. The second and third studies compared tinnitus patients to controls matched for age, sex and hearing loss, measuring auditory cortex spontaneous oscillations (with magnetoencephalography) and neurochemical concentrations (with magnetic resonance spectroscopy) respectively. Unlike in previous studies not controlled for hearing loss, there were no group differences in oscillatory activity attributable to tinnitus. However, there was a significant correlation between gamma oscillations (>30Hz) and hearing loss in the tinnitus group, and between delta oscillations (1-4Hz) and perceived tinnitus loudness. In the neurochemical study, tinnitus patients had significantly reduced GABA concentrations compared to matched controls, and within this group there was a positive correlation between choline concentration (potentially linked to acetylcholine and/or neuronal plasticity) and both hearing loss, and subjective tinnitus intensity and distress. In light of present and previous findings, tinnitus may be best explained by a predictive coding model of perception, which was tested in the final experiment. This directly controlled the three main quantities comprising predictive coding models, and found that delta/theta/alpha oscillations (1-12Hz) encoded the precision of predictions, beta oscillations (12-30Hz) encoded changes to predictions, and gamma oscillations represented surprise (unexpectedness of stimuli based on predictions). The work concludes with a predictive coding model of tinnitus that builds upon the present findings and settles unresolved paradoxes in the literature. In this, precursor processes (in varying combinations) synergise to increase the precision associated with spontaneous activity in the auditory pathway to the point where it overrides higher predictions of ‘silence’.Medical Research Council Wellcome Trust and the National Institutes of Healt