Establishing a Core Domain Set for early-phase clinical trials of electrical stimulation interventions for tinnitus in adults: protocol for an online Delphi study

BACKGROUND

Tinnitus is the awareness of a sound in the ear or head in the absence of an external source. It affects around 10-15% of people and current treatment options are limited. Experimental treatments include various forms of electrical stimulation of the brain. Currently, there is no consensus on the outcomes that should be measured when investigating the efficacy of this type of intervention for tinnitus. This study seeks to address this by establishing a Core Domain Set: a common standard of what specific tinnitus-related complaints are critical and important to assess in all clinical trials of electrical stimulation-based interventions for tinnitus.

METHODS

A two-round online survey will be conducted, followed by a stakeholder consensus meeting to identify a Core Domain Set. Participants will belong to one of two stakeholder groups: healthcare users with lived experience of tinnitus, and professionals with relevant clinical, commercial, or research experience.

DISCUSSION

This study will establish a Core Domain Set for the evaluation of electrical stimulation-based interventions for tinnitus via an e-Delphi study. The resulting Core Domain Set will act as a minimum standard for reporting in future clinical trials of electrical stimulation interventions for tinnitus. Standardisation will facilitate comparability of research findings.

Mapping Interictal activity in epilepsy using a hidden Markov model: A magnetoencephalography study

Epilepsy is a highly heterogeneous neurological disorder with variable etiology, manifestation, and response to treatment. It is imperative that new models of epileptiform brain activity account for this variability, to identify individual needs and allow clinicians to curate personalized care. Here, we use a hidden Markov model (HMM) to create a unique statistical model of interictal brain activity for 10 pediatric patients. We use magnetoencephalography (MEG) data acquired as part of standard clinical care for patients at the Children's Hospital of Philadelphia. These data are routinely analyzed using excess kurtosis mapping (EKM); however, as cases become more complex (extreme multifocal and/or polymorphic activity), they become harder to interpret with EKM. We assessed the performance of the HMM against EKM for three patient groups, with increasingly complicated presentation. The difference in localization of epileptogenic foci for the two methods was 7 ± 2 mm (mean ± SD over all 10 patients); and 94% ± 13% of EKM temporal markers were matched by an HMM state visit. The HMM localizes epileptogenic areas (in agreement with EKM) and provides additional information about the relationship between those areas. A key advantage over current methods is that the HMM is a data-driven model, so the output is tuned to each individual. Finally, the model output is intuitive, allowing a user (clinician) to review the result and manually select the HMM epileptiform state, offering multiple advantages over previous methods and allowing for broader implementation of MEG epileptiform analysis in surgical decision-making for patients with intractable epilepsy.

Determining the Effects of Transcranial Direct Current Stimulation on Tinnitus, Depression, and Anxiety: A Systematic Review

(1) Background: Tinnitus is the awareness of a sound in the absence of an external source. It affects around 10–15% of people, a significant proportion of whom also experience symptoms such as depression or anxiety that negatively affect their quality of life. Transcranial direct current stimulation (tDCS) is a technique involving constant low-intensity direct current delivered via scalp electrodes. It is a potential treatment option for tinnitus, as well as tinnitus-related conditions such as depression and anxiety. This systematic review estimates the effects of tDCS on outcomes relevant to tinnitus. In addition, it sheds light on the relationship between stimulation parameters and the effect of tDCS on these outcomes; (2) Methods: Exhaustive searches of electronic databases were conducted. Randomised controlled trials were included if they reported at least one of the following outcomes: tinnitus symptom severity, anxiety, or depression. Where available, data on quality of life, adverse effects, and neurophysiological changes were also reviewed. GRADE was used to assess the certainty in the estimate; (3) Results: Meta-analyses revealed a statistically significant reduction in tinnitus (moderate certainty) and depression (low certainty)-but not anxiety-following active tDCS compared to sham control. Network meta-analyses revealed potential optimal stimulation parameters; (4) Conclusions: The evidence synthesised in this review suggests tDCS has the potential to reduce symptom severity in tinnitus and depression. It further narrows down the number of potentially optimal stimulation parameters.

Mild traumatic brain injury impairs the coordination of intrinsic and motor-related neural dynamics

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MTBI is poorly understood and lacks objective diagnostic and prognostic tools.

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Abnormal neural oscillations are found in subjects with a history of mTBI.

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We identify transient bursts in MEG data using a Hidden Markov Model.

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We explain a deficit in beta connectivity and power in terms of transient bursts.

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Data-driven feature selection identifies symptom-relevant functional connections.

Mild traumatic brain injury (mTBI) poses a considerable burden on healthcare systems. Whilst most patients recover quickly, a significant number suffer from sequelae that are not accompanied by measurable structural damage. Understanding the neural underpinnings of these debilitating effects and developing a means to detect injury, would address an important unmet clinical need. It could inform interventions and help predict prognosis. Magnetoencephalography (MEG) affords excellent sensitivity in probing neural function and presents significant promise for assessing mTBI, with abnormal neural oscillations being a potential specific biomarker. However, growing evidence suggests that neural dynamics are (at least in part) driven by transient, pan-spectral bursting and in this paper, we employ this model to investigate mTBI. We applied a Hidden Markov Model to MEG data recorded during resting state and a motor task and show that previous findings of diminished intrinsic beta amplitude in individuals with mTBI are largely due to the reduced beta band spectral content of bursts, and that diminished beta connectivity results from a loss in the temporal coincidence of burst states. In a motor task, mTBI results in diminished burst amplitude, altered modulation of burst probability during movement, and a loss in connectivity in motor networks. These results suggest that, mechanistically, mTBI disrupts the structural framework underlying neural synchrony, which impairs network function. Whilst the damage may be too subtle for structural imaging to see, the functional consequences are detectable and persist after injury. Our work shows that mTBI impairs the dynamic coordination of neural network activity and proposes a potent new method for understanding mTBI.

Magnetoencephalography abnormalities in adult mild traumatic brain injury: A systematic review

BACKGROUND

The global incidence of traumatic brain injuries is rising, with at least 80% being classified as mild. These mild injuries are not visible on routine clinical imaging. The potential clinical role of a specific imaging biomarker be it diagnostic, prognostic or directing and monitoring progress of personalised treatment and rehabilitation has driven the exploration of several new neuroimaging modalities. This systematic review examined the evidence for magnetoencephalography (MEG) to provide an imaging biomarker in mild traumatic brain injury (mTBI).

METHODS

Our review was prospectively registered on PROSPERO: CRD42019151387. We searched EMBASE, MEDLINE, trial registers, PsycINFO, Cochrane Library and conference abstracts and identified 37 papers describing MEG changes in mTBI eligible for inclusion. Since meta-analysis was not possible, based on the heterogeneity of reported outcomes, we provide a narrative synthesis of results.

RESULTS

The two most promising MEG biomarkers are excess resting state low frequency power, and widespread connectivity changes in all frequency bands. These may represent biomarkers with potential for diagnostic application, which reflect time sensitive changes, or may be capable of offering clinically relevant prognostic information. In addition, the rich data that MEG produces are well-suited to new methods of machine learning analysis, which is now being actively explored.

INTERPRETATION

MEG reveals several promising biomarkers, in the absence of structural abnormalities demonstrable with either computerised tomography or magnetic resonance imaging. This review has not identified sufficient evidence to support routine clinical use of MEG in mTBI currently. However, verifying MEG's potential would help meet an urgent clinical need within civilian, sports and military medicine.

Determining the effects of transcranial direct current stimulation on tinnitus and tinnitus-related outcomes: protocol for a systematic review

INTRODUCTION

Tinnitus is the awareness of a sound in the ear or head in the absence of an external source. It affects around 10%-15% of people. About 20% of people with tinnitus also experience symptoms such as depression or anxiety that negatively affect their life. Transcranial direct current stimulation (tDCS) is a technique involving constant low-intensity direct current delivered via electrodes on the head. It is postulated to modulate (suppress or enhance) neural activity in the region between electrodes. As such, it represents a potential treatment option for tinnitus, as well as comorbid depression or anxiety. This systematic review will estimate the effects of tDCS on outcomes relevant to tinnitus. In addition, it will determine whether there is any relationship between stimulation parameters (electrode montage, current intensity, and length and frequency of stimulation sessions) and the effect of tDCS on these outcomes.

METHODS AND ANALYSIS

Electronic searches for peer-reviewed journal articles will be performed in the Cochrane Register of Studies online (the Cochrane Ear, Nose and Throat Disorders Group Register and CENTRAL, current issue), PubMed, EMBASE, CINAHL, LILACS, KoreaMed, IndMed, PakMediNet, CNKI, AMED, PsycINFO, Web of Science, ClinicalTrials.gov, ICTRP and Google Scholar using the following search terms: transcranial Direct Current Stimulation OR tDCS AND tinnitus OR depression OR anxiety OR quality of life OR adverse effects OR neurophys*.Searches were not limited by date. Methods are reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P). Randomised controlled trials will be included if they report at least one of the following outcomes: tinnitus symptom severity, anxiety or depression as measured by relevant validated instruments. Where available, data on quality of life, adverse effects and neurophysiological changes will also be reviewed. In addition to an analysis of the effect of each parameter, an analysis will be performed to uncover any interactions between parameters. Where appropriate, meta-analyses will be performed.

ETHICS AND DISSEMINATION

This systematic review will make use of secondary data only. As no data will be obtained from participants directly, ethical approval has not been sought. No other ethical issues are foreseen. Findings will be submitted for peer-reviewed publication and presented at academic conferences. The results of this review will inform future research.

PROSPERO REGISTRATION NUMBER

CRD42020185567.

Motor-related oscillatory activity in schizophrenia according to phase of illness and clinical symptom severity

Magnetoencephalography (MEG) measures magnetic fields generated by synchronised neural current flow and provides direct inference on brain electrophysiology and connectivity, with high spatial and temporal resolution. The movement-related beta decrease (MRBD) and the post-movement beta rebound (PMBR) are well-characterised effects in magnetoencephalography (MEG), with the latter having been shown to relate to long-range network integrity. Our previous work has shown that the PMBR is diminished (relative to controls) in a group of schizophrenia patients. However, little is known about how this effect might differ in patients at different stages of illness and degrees of clinical severity. Here, we extend our previous findings showing that the MEG derived PMBR abnormality in schizophrenia exists in 29 recent-onset and 35 established cases (i.e., chronic patients), compared to 42 control cases. In established cases, PMBR is negatively correlated with severity of disorganization symptoms. Further, using a hidden Markov model analysis, we show that transient pan-spectral oscillatory "bursts", which underlie the PMBR, differ between healthy controls and patients. Results corroborate that PMBR is associated with disorganization of mental activity in schizophrenia.

The role of transient spectral 'bursts' in functional connectivity: A magnetoencephalography study

Neural oscillations dominate electrophysiological measures of macroscopic brain activity and fluctuations in these rhythms offer an insightful window on cortical excitation, inhibition, and connectivity. However, in recent years the 'classical' picture of smoothly varying oscillations has been challenged by the idea that many 'oscillations' may actually be formed from the recurrence of punctate high-amplitude bursts in activity, whose spectral composition intersects the traditionally defined frequency ranges (e.g. alpha/beta band). This finding offers a new interpretation of measurable brain activity, however neither the methodological means to detect bursts, nor their link to other findings (e.g. connectivity) have been settled. Here, we use a new approach to detect bursts in magnetoencephalography (MEG) data. We show that a time-delay embedded Hidden Markov Model (HMM) can be used to delineate single-region bursts which are in agreement with existing techniques. However, unlike existing techniques, the HMM looks for specific spectral patterns in timecourse data. We characterise the distribution of burst duration, frequency of occurrence and amplitude across the cortex in resting state MEG data. During a motor task we show how the movement related beta decrease and post movement beta rebound are driven by changes in burst occurrence. Finally, we show that the beta band functional connectome can be derived using a simple measure of burst overlap, and that coincident bursts in separate regions correspond to a period of heightened coherence. In summary, this paper offers a new methodology for burst identification and connectivity analysis which will be important for future investigations of neural oscillations.

Attenuated Post-Movement Beta Rebound Associated With Schizotypal Features in Healthy People

INTRODUCTION

Schizophrenia and schizotypal personality disorder (SPD) lie on a single spectrum of mental illness and converging evidence suggests similarities in the etiology of the 2 conditions. However, schizotypy is a heterogeneous facet of personality in the healthy population and so may be seen as a bridge between health and mental illness. Neural evidence for such a continuity would have implications for the characterization and treatment of schizophrenia. Based on our previous work identifying a relationship between symptomology in schizophrenia and abnormal movement-induced electrophysiological response (the post-movement beta rebound [PMBR]), we predicted that if subclinical schizotypy arises from similar neural mechanisms to schizophrenia, schizotypy in healthy individuals would be associated with reduced PMBR.

METHODS

One-hundred sixteen participants completed a visuomotor task while their neural activity was recorded by magnetoencephalography. Partial correlations were computed between a measure of PMBR extracted from left primary motor cortex and scores on the Schizotypal Personality Questionnaire (SPQ), a self-report measure of schizotypal personality. Correlations between PMBR and SPQ factor scores measuring cognitive-perceptual, interpersonal and disorganization dimensions of schizotypy were also computed. Effects of site, age, and sex were controlled for.

RESULTS

We found a significant negative correlation between total SPQ score and PMBR. This was most strongly mediated by variance shared between interpersonal and disorganization factor scores.

CONCLUSION

These findings indicate a continuum of neural deficit between schizotypy and schizophrenia, with diminution of PMBR, previously reported in schizophrenia, also measurable in individuals with schizotypal features, particularly disorganization and impaired interpersonal relations.

Changes in electrophysiological markers of cognitive control after administration of galantamine

The healthy brain is able to maintain a stable balance between bottom-up sensory processing and top-down cognitive control. The neurotransmitter acetylcholine plays a substantial role in this. Disruption of this balance could contribute to symptoms occurring in psychosis, including subtle disruption of motor control and aberrant appropriation of salience to external stimuli; however the pathological mechanisms are poorly understood. On account of the role beta oscillations play in mediating cognitive control, investigation of beta oscillations is potentially informative about such mechanisms. Here, we used magnetoencephalography to investigate the effect of the acetylcholinesterase-inhibitor, galantamine, on beta oscillations within the sensorimotor region during both a sensorimotor task and a relevance-modulation task in healthy participants, employing a double blind randomized placebo controlled cross-over design. In the galantamine condition, we found a significant reduction in the post-movement beta rebound in the case of executed movements and also in a planned but not executed movement. In the latter case, the effect was significantly greater following task-relevant compared with irrelevant stimuli. The results suggest that the action of galantamine reduces the influence of top-down cognitive processing relative to bottom-up perceptual processing in a manner resembling changes previously reported in schizophrenia.

Altered temporal stability in dynamic neural networks underlies connectivity changes in neurodevelopment

Network connectivity is an integral feature of human brain function, and characterising its maturational trajectory is a critical step towards understanding healthy and atypical neurodevelopment. Here, we used magnetoencephalography (MEG) to investigate both stationary (i.e. time averaged) and rapidly modulating (dynamic) electrophysiological connectivity, in participants aged from mid-childhood to early adulthood (youngest participant 9 years old; oldest participant 25 years old). Stationary functional connectivity (measured via inter-regional coordination of neural oscillations) increased with age in the alpha and beta frequency bands, particularly in bilateral parietal and temporo-parietal connections. Our dynamic analysis (also applied to alpha/beta oscillations) revealed the spatiotemporal signatures of 8 dynamic networks; these modulate on a ∼100 ms time scale, and temporal stability in attentional networks was found to increase with age. Significant overlap was found between age-modulated dynamic networks and inter-regional oscillatory coordination, implying that altered network dynamics underlie age related changes in functional connectivity. Our results provide novel insights into brain network electrophysiology, and lay a foundation for future work in childhood disorders.

The auditory evoked-gamma response and its relation with the N1m

This study explored the patterns of oscillatory activity that underpin the N1m auditory evoked response. Evoked gamma activity is a small and relatively rarely-reported component of the auditory evoked response, and the objective of this work was to determine how this component relates to the larger and more prolonged changes in lower frequency bands. An event-related beamformer analysis of MEG data from monaural click stimulation was used to reconstruct volumetric images and virtual electrode time series. Group analysis of localisations showed that activity in the gamma band originated from a source that was more medial than those for activity in the theta-to-beta band, and virtual-electrode analysis showed that the source of the gamma activity could be statistically dissociated from the lower-frequency response. These findings are in accordance with separate functional roles for the activity in each frequency band, and provide evidence that the oscillatory activity that underpins the auditory evoked response may contain important information about the physiological basis of the macroscopic signals recorded by MEG in response to auditory stimulation.

Optimising experimental design for MEG resting state functional connectivity measurement

The study of functional connectivity using magnetoencephalography (MEG) is an expanding area of neuroimaging, and adds an extra dimension to the more common assessments made using fMRI. The importance of such metrics is growing, with recent demonstrations of their utility in clinical research, however previous reports suggest that whilst group level resting state connectivity is robust, single session recordings lack repeatability. Such robustness is critical if MEG measures in individual subjects are to prove clinically valuable. In the present paper, we test how practical aspects of experimental design affect the intra-subject repeatability of MEG findings; specifically we assess the effect of co-registration method and data recording duration. We show that the use of a foam head-cast, which is known to improve co-registration accuracy, increased significantly the between session repeatability of both beamformer reconstruction and connectivity estimation. We also show that recording duration is a critical parameter, with large improvements in repeatability apparent when using ten minute, compared to five minute recordings. Further analyses suggest that the origin of this latter effect is not underpinned by technical aspects of source reconstruction, but rather by a genuine effect of brain state; short recordings are simply inefficient at capturing the canonical MEG network in a single subject. Our results provide important insights on experimental design and will prove valuable for future MEG connectivity studies.

Measurement of dynamic task related functional networks using MEG

The characterisation of dynamic electrophysiological brain networks, which form and dissolve in order to support ongoing cognitive function, is one of the most important goals in neuroscience. Here, we introduce a method for measuring such networks in the human brain using magnetoencephalography (MEG). Previous network analyses look for brain regions that share a common temporal profile of activity. Here distinctly, we exploit the high spatio-temporal resolution of MEG to measure the temporal evolution of connectivity between pairs of parcellated brain regions. We then use an ICA based procedure to identify networks of connections whose temporal dynamics covary. We validate our method using MEG data recorded during a finger movement task, identifying a transient network of connections linking somatosensory and primary motor regions, which modulates during the task. Next, we use our method to image the networks which support cognition during a Sternberg working memory task. We generate a novel neuroscientific picture of cognitive processing, showing the formation and dissolution of multiple networks which relate to semantic processing, pattern recognition and language as well as vision and movement. Our method tracks the dynamics of functional connectivity in the brain on a timescale commensurate to the task they are undertaking.

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A method is developed to track dynamic electrophysiological networks using MEG.

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Method based on ICA applied to timecourses measuring evolution of connectivity.

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Method allows a unique picture of transient networks that support cognition.

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Method validated in MEG data recorded during a Sternberg working memory task.

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Sensory networks observed include visual and sensorimotor.

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Cognitive networks relate to semantic processing, pattern recognition and language.

A multi-layer network approach to MEG connectivity analysis

Recent years have shown the critical importance of inter-regional neural network connectivity in supporting healthy brain function. Such connectivity is measurable using neuroimaging techniques such as MEG, however the richness of the electrophysiological signal makes gaining a complete picture challenging. Specifically, connectivity can be calculated as statistical interdependencies between neural oscillations within a large range of different frequency bands. Further, connectivity can be computed between frequency bands. This pan-spectral network hierarchy likely helps to mediate simultaneous formation of multiple brain networks, which support ongoing task demand. However, to date it has been largely overlooked, with many electrophysiological functional connectivity studies treating individual frequency bands in isolation. Here, we combine oscillatory envelope based functional connectivity metrics with a multi-layer network framework in order to derive a more complete picture of connectivity within and between frequencies. We test this methodology using MEG data recorded during a visuomotor task, highlighting simultaneous and transient formation of motor networks in the beta band, visual networks in the gamma band and a beta to gamma interaction. Having tested our method, we use it to demonstrate differences in occipital alpha band connectivity in patients with schizophrenia compared to healthy controls. We further show that these connectivity differences are predictive of the severity of persistent symptoms of the disease, highlighting their clinical relevance. Our findings demonstrate the unique potential of MEG to characterise neural network formation and dissolution. Further, we add weight to the argument that dysconnectivity is a core feature of the neuropathology underlying schizophrenia.

Predicting haemodynamic networks using electrophysiology: The role of non-linear and cross-frequency interactions

Understanding the electrophysiological basis of resting state networks (RSNs) in the human brain is a critical step towards elucidating how inter-areal connectivity supports healthy brain function. In recent years, the relationship between RSNs (typically measured using haemodynamic signals) and electrophysiology has been explored using functional Magnetic Resonance Imaging (fMRI) and magnetoencephalography (MEG). Significant progress has been made, with similar spatial structure observable in both modalities. However, there is a pressing need to understand this relationship beyond simple visual similarity of RSN patterns. Here, we introduce a mathematical model to predict fMRI-based RSNs using MEG. Our unique model, based upon a multivariate Taylor series, incorporates both phase and amplitude based MEG connectivity metrics, as well as linear and non-linear interactions within and between neural oscillations measured in multiple frequency bands. We show that including non-linear interactions, multiple frequency bands and cross-frequency terms significantly improves fMRI network prediction. This shows that fMRI connectivity is not only the result of direct electrophysiological connections, but is also driven by the overlap of connectivity profiles between separate regions. Our results indicate that a complete understanding of the electrophysiological basis of RSNs goes beyond simple frequency-specific analysis, and further exploration of non-linear and cross-frequency interactions will shed new light on distributed network connectivity, and its perturbation in pathology.

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We introduce a mathematical model to predict fMRI-based RSNs using MEG.

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Our model is based on a multi-variate Taylor series expansion.

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The electrophysiological basis of RSNs goes beyond frequency-band specific analysis.

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RSNs result 1) from multiple frequency bands and cross-frequency coupling.

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RSNs result 2) from direct and shared electrophysiological connectivity.