Biology subjects

Quinn, A. J.

Publications and source records attributed to Quinn, A. J..

29 records · Page 2Linked to original sources

bioRxiv2021

Human motor cortical gamma activity relates to GABAergic signalling and to behaviour

Gamma activity ({gamma}, >30 Hz) is universally demonstrated across brain regions and species. However, the physiological basis and functional role of {gamma} sub-bands (slow-{gamma}, mid-{gamma}, fast-{gamma}) have been predominantly studied in rodent hippocampus; {gamma} activity in the human neocortex is much less well understood. Here we combined neuroimaging and non-invasive brain stimulation to examine the properties of {gamma} activity sub-bands in the primary motor cortex (M1), and their relationship to both local GABAergic activity and to motor learning. In 33 healthy individuals, we quantified movement-related {gamma} activity in M1 using magnetoencephalography, assessed GABAergic signaling using transcranial magnetic stimulation (TMS), and estimated motor learning via a serial reaction time task. We characterised two distinct {gamma} sub-bands (slow-{gamma}, mid-{gamma}) which show movement-related increase in activity during unilateral index finger movements and are characterised by distinct temporal-spectral-spatial profiles. Bayesian correlation analysis revealed strong evidence for a positive relationship between slow-{gamma} ([~]30-60Hz) peak frequency and endogenous GABA signalling during movement preparation (as assessed using the TMS-metric short interval intracortical inhibition). There was also moderate evidence for a relationship between power of the movement-related mid-{gamma} activity (60-90Hz) and motor learning. These relationships were neurochemically- and frequency-specific. These data provide new insights into the neurophysiological basis and functional roles of {gamma} activity in human M1 and allow the development of a new theoretical framework for {gamma} activity in the human neocortex. Significance StatementGamma ({gamma}) activity is ubiquitous in the brain, yet our understanding of the mechanisms and function of {gamma} activity in the human neocortex, and particularly in the human motor cortex, is limited. Using a multimodal approach, we characterised two patterns of movement-related {gamma} activity in the human motor cortex (slow-{gamma} and mid-{gamma}), with different spatial, temporal and spectral properties. Slow-{gamma} peak frequency was correlated to local GABA-A activity, whereas mid-gamma power predicted performance in a subsequent motor learning task. Based on these findings and previous research, we propose a theoretical framework to explain how human motor cortical {gamma} activities may arise and their potential role in plasticity and motor learning, providing new hypotheses to be tested in future studies. Key PointsO_LIWe combined neuroimaging (i.e. MEG) and non-invasive brain stimulation (i.e. TMS) to examine the properties of {gamma} activity sub-bands in the primary motor cortex. C_LIO_LITwo distinct {gamma} sub-bands (slow-{gamma}, mid-{gamma}) show a movement-related increase in activity during finger movements and are characterised by distinct temporal-spectral-spatial profiles. C_LIO_LIWe found strong evidence for a positive relationship between slow-{gamma} ([~]30-60Hz) peak frequency and endogenous GABA signalling during movement preparation (as assessed using the TMS-metric short interval intracortical inhibition). C_LI

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bioRxiv2021

Reduced cortico-muscular beta coupling in Parkinsons disease predicts motor impairment

Long-range communication through the motor system is thought to be facilitated by phase coupling between neural activity in the 15-30 Hz beta range. During periods of sustained muscle contraction (grip), such coupling is manifest between motor cortex and the contralateral forearm muscles - measured as the cortico-muscular coherence (CMC). We examined alterations in CMC in individuals with Parkinsons disease (PD), while equating grip strength between individuals with PD (off their medication) and healthy control participants. We show a marked reduction in beta CMC in the PD group, even though the grip strength was comparable between the two groups. Moreover, the reduced CMC was related to motor symptoms, so that individuals with lower CMC also displayed worse motor symptoms. These findings highlight the CMC as a simple, effective, and clinically relevant neural marker of PD pathology, with the potential to aid monitoring of disease progression and the efficacy of novel treatments for PD.

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bioRxiv2021

Within-cycle instantaneous frequency profiles report oscillatory waveform dynamics.

Non-sinusoidal waveform is emerging as an important feature of neuronal oscillations. However, the role of single cycle shape dynamics in rapidly unfolding brain activity remains unclear. Here, we develop an analytical framework that isolates oscillatory signals from time-series using masked Empirical Mode Decomposition to quantify dynamical changes in the shape of individual cycles (along with amplitude, frequency and phase) using instantaneous frequency. We show how phase-alignment, a process of projecting cycles into a regularly sampled phase-grid space, makes it possible to compare cycles of different durations and shapes. Normalised shapes can then be constructed with high temporal detail whilst accounting for differences in both duration and amplitude. We find that the instantaneous frequency tracks non-sinusoidal shapes in both simulated and real data. Notably, in local field potential recordings of mouse hippocampal CA1, we find that theta oscillations have a stereotyped slow-descending slope in the cycle-wise average, yet exhibiting high variability on a cycle-by-cycle basis. We show how Principal Components Analysis allows identification of motifs of theta cycle waveform that have distinct associations to cycle amplitude, cycle duration and animal movement speed. By allowing investigation into oscillation shape at high temporal resolution, this analytical framework will open new lines of enquiry into how neuronal oscillations support moment-by-moment information processing and integration in brain networks.

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bioRxiv2021

Transient beta activity and connectivity during sustained motor behaviour

Neural oscillations are thought to play a central role in orchestrating activity states between distant neural populations. In humans, long-range neural connectivity has been particularly well characterised for 13-30 Hz beta activity which becomes phase coupled between the motor cortex and the contralateral muscle during isometric contraction. Based on this and related observations, beta activity and connectivity have been linked to sustaining stable cognitive and motor states - or the status quo - in the brain. Recently, however, beta activity has been shown to be short-lived, as opposed to sustained - though so far this has been reported for regional beta activity in tasks without sustained motor demands. Here, we measured magnetoencephalography (MEG) and electromyography (EMG) in 18 human participants performing an isometric-contraction (gripping) task designed to yield sustained behavioural output. If cortico-muscular beta connectivity is directly responsible for sustaining a stable motor state, then beta activity should be (or become) sustained in this context. In contrast, we found that beta activity and connectivity with the downstream muscle were transient, even when participants engaged in sustained gripping. Moreover, we found that sustained motor requirements did not prolong beta-event duration in comparison to rest. These findings suggest that long-range neural synchronisation may entail short bursts of frequency-specific connectivity, even when task demands - and behaviour - are sustained. HighlightsO_LITrial-average 13-30 Hz beta activity and connectivity with the muscle appear sustained during stable motor behaviour C_LIO_LISingle-trial beta activity and connectivity are short-lived, even when motor behaviour is sustained C_LIO_LISustained task demands do not prolong beta-event duration in comparison to resting state C_LI

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bioRxiv2020

Rhythmic modulation of visual perception by continuous rhythmic auditory stimulation

At any given moment our sensory systems receive multiple, often rhythmic, inputs from the environment. Processing of temporally structured events in one sensory modality can guide both behavioural and neural processing of events in other sensory modalities, but whether this occurs remains unclear. Here, we used human electroencephalography (EEG) to test the cross-modal influences of a continuous auditory frequency-modulated (FM) sound on visual perception and visual cortical activity. We report systematic fluctuations in perceptual discrimination of brief visual stimuli in line with the phase of the FM sound. We further show that this rhythmic modulation in visual perception is related to an accompanying rhythmic modulation of neural activity recorded over visual areas. Importantly, in our task, perceptual and neural visual modulations occurred without any abrupt and salient onsets in the energy of the auditory stimulation and without any rhythmic structure in the visual stimulus. As such, the results provide a critical validation for the existence and functional role of cross-modal entrainment and demonstrates its utility for organising the perception of multisensory stimulation in the natural environment. Significance StatementOur sensory environment is filled with rhythmic structures that are often multi-sensory in nature. Here we show that the alignment of neural activity to the phase of an auditory frequency-modulated sound has cross-modal consequences for vision: yielding systematic fluctuations in perceptual discrimination of brief visual stimuli that is mediated by accompanying rhythmic modulation of neural activity recorded over visual areas. These cross-modal effects on visual neural activity and perception occurred without any abrupt and salient onsets in the energy of the auditory stimulation and without any rhythmic structure in the visual stimulus. The current work shows that continuous auditory fluctuations in the natural environment can provide a pacing signal for neural activity and perception across the senses.

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bioRxiv2020

Delineating single subject oscillatory brain networks with Spatio-Spectral Eigenmodes

The spatial and spectral structure of oscillatory networks in the brain provide a readout of the underlying neuronal function. Within and between subject variability in these networks can be highly informative but also poses a considerable analytic challenge. Here, we describe a method that simultaneously estimate spectral and spatial network structure without assumptions about either feature distorting estimation of the other. This enables analyses exploring how variability in the frequency and spatial structure of oscillatory networks might vary both across the brain and across individuals. The method performs a modal decomposition of an autoregressive model to describe the oscillatory signals present within a time-series based on their peak frequency and damping time. Moreover, an alternate mathematical formulation for the system transfer function can be written in terms of these oscillatory modes; describing the spatial topography and network structure of each component. We define a set of Spatio-Spectral Eigenmodes (SSEs) from these parameters to provide a parsimonious description of oscillatory networks. Crucially, the SSEs preserve the rich between-subject variability and are constructed without pre-averaging within specified frequency bands or limiting analyses to single channels or regions. After validating the method on simulated data, we explore the structure of whole brain oscillatory networks in eyes-open resting state MEG data from the Human Connectome Project. We are able to show a wide variability in peak frequency and network structure of alpha oscillations and reveal a distinction between occipital high-frequency alpha and parietal low-frequency alpha. The frequency difference between occipital and parietal alpha components is present within individual participants but is partially masked by larger between subject variability; a 10Hz oscillation may represent the high-frequency occipital component in one participant and the low-frequency parietal component in another. This rich characterisation of individual neural phenotypes has the potential to enhance analyses into the relationship between neural dynamics and a persons behavioural, cognitive or clinical state

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bioRxiv2019

Emergence of neural dynamics within a co-ordinate space of large-scale neural hierarchies

When unoccupied by an explicit external task, humans engage in a wide range of different types of self-generated thinking. These are often unrelated to the immediate environment and have unique psychological features. Although contemporary perspectives on ongoing thought recognise the heterogeneity of these self-generated states, we lack both a clear understanding of how to classify the specific states, and how they can be mapped empirically. In the current study, we capitalise on advances in machine learning that allow continuous neural data to be divided into a set of distinct temporally re-occurring patterns, or states. We applied this technique to a large set of resting state data in which we also acquired retrospective descriptions of the participants experiences during the scan. We found that two of the identified states were predictive of patterns of thinking at rest. One state highlighted a pattern of neural activity commonly seen during demanding tasks, and the time individuals spent in this state was associated with descriptions of experience focused on problem solving in the future. A second state was associated with patterns of activity that are commonly seen under less demanding conditions, and the time spent in it was linked to reports of negative rumination. Finally, we found that these two neural states tended to fall at either end of a neural hierarchy that is thought to reflect the brains response to cognitive demands. Together, these results demonstrate that approaches which take advantage of time-varying changes in neural function can play an important role in understanding the repertoire of self-generated states. Moreover, they establish that important features of self-generated ongoing experience are related to variation along a similar vein to those seen when the brain responds to cognitive task demands.

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bioRxiv2018

Neural dynamics at rest associated with patterns of ongoing thought

Conscious experience is dynamic, and its fluidity is particularly marked when attention is not occupied by events in the external world and our minds are free to wander. Our study used measures of neural function, and advanced analyses techniques to examine how unconstrained neural state transitions relate to patterns of ongoing experience. Neural activity was recorded during wakeful rest using functional magnetic resonance imaging and Hidden Markov modelling identified recurrent patterns of brain activity constituting functional dynamic brain states. Individuals making more frequent transitions between states subsequently described experiences highlighting problem solving and lacking unpleasant intrusive features. Frequent switching between states also predicted better health and well-being as assessed by questionnaire. These data provide evidence that the fluidity with which individuals shift through dynamic neural states has an impact on the nature of ongoing thought, and suggest that greater flexibility at rest is an important indicator of a healthy mind.

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bioRxiv2018

Transient spectral events in resting state MEG predict individual time-frequency task responses

Even in response to apparently simple tasks such as hand moving, human brain activity shows remarkable inter-subject variability. Presumably, this variability reflects genuine behavioural or functional variability. Recently, spatial variability of resting-state features in fMRI - specifically connectivity - has been shown to explain (spatial) task-response variability. Such a link, however, is still missing for M/EEG data and its spectrally rich structure. At the same time, it has recently been shown that task responses in M/EEG can be well represented using transient spectral events bursting at fast time scales. Here, we show that individual differences in the spatio-spectral structure of M/EEG task responses, can, to a reasonable degree, be predicted from individual differences in transient spectral events identified at rest. In a MEG dataset of diverse task conditions (including motor responses, working memory and language comprehension tasks) and resting-state sessions for each subject (n = 89), we used Hidden-Markov-Modelling to identify transient spectral events as a feature set to learn the mapping of space-time-frequency content from rest to task. Resulting trial-averaged, subject-specific task-response predictions were then compared with the actual task responses in left-out subjects. All task conditions were predicted significantly above chance. Furthermore, we observed a systematic relationship between genetic similarity (e.g. unrelated subjects vs. twins) and predictability. These findings support the idea that subject-specific transient spectral events in resting-state neural activity are linked to, and predictive of, subject-specific trial-averaged task responses in a wide range of experimental conditions.

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bioRxiv2018

Motor learning shapes temporal activity in human sensorimotor cortex

Although neuroimaging techniques have provided vital insights into the anatomical regions involved in motor learning, the underlying changes in temporal dynamics are not well understood. Using magnetoencephalography and Hidden Markov Modelling to model the dynamics of neural oscillations on data-adaptive time-scales, we detected specific changes in movement-related sensorimotor {beta}-activity during practice of a self-paced sequential visuo-motor task. The behaviourally-relevant neural signature generalised to another motor task, emphasising the centrality of {beta}-activity in motor plasticity.

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bioRxiv2017

Spontaneous cortical activity transiently organises into frequency specific phase-coupling networks

Frequency-specific oscillations and phase-coupling of neuronal populations have been proposed as an essential mechanism for the coordination of activity between brain areas during cognitive tasks. To provide an effective substrate for cognitive function, we reasoned that ongoing functional brain networks should also be able to reorganise and coordinate in a similar manner. To test this hypothesis, we use a novel method for identifying repeating patterns of network dynamics, and show that resting networks in magnetoencephalography are well characterised by visits to short-lived transient brain states, with spatially distinct power and phase-coupling in specific frequency bands. Brain states were identified for sensory, motor networks and higher-order cognitive networks; the latter include a posterior higher-order cognitive network in the alpha range (8-12Hz) and an anterior cognitive network in the delta/theta range (1-7Hz). Both higher-order cognitive networks exhibit especially high power and coherence, and contain brain areas corresponding to posterior and anterior subdivisions of the default mode network. Our results show that large-scale cortical phase-coupling networks operate in very specific frequency bands, possibly reflecting functional specialisation at different intrinsic timescales.

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