•We obtained invasive subthalamic nucleus recordings in 33 Parkinson’s disease patients.•Phase–amplitude coupling between beta band and high-frequency oscillations correlates with severity of motor impairments.•Parkinsonian pathophysiology is more closely linked with low-beta band frequencies.
High-amplitude beta band oscillations within the subthalamic nucleus are frequently associated with Parkinson’s disease but it is unclear how they might lead to motor impairments. Here we investigate a likely pathological coupling between the phase of beta band oscillations and the amplitude of high-frequency oscillations around 300 Hz.
We analysed an extensive data set comprising resting-state recordings obtained from deep brain stimulation electrodes in 33 patients before and/or after taking dopaminergic medication. We correlated mean values of spectral power and phase–amplitude coupling with severity of hemibody bradykinesia/rigidity. In addition, we used simultaneously recorded magnetoencephalography to look at functional interactions between the subthalamic nucleus and ipsilateral motor cortex.
Beta band power and phase–amplitude coupling within the subthalamic nucleus correlated positively with severity of motor impairment. This effect was more pronounced within the low-beta range, whilst coherence between subthalamic nucleus and motor cortex was dominant in the high-beta range.
We speculate that the beta band might impede pro-kinetic high-frequency activity patterns when phase–amplitude coupling is prominent. Furthermore, results provide evidence for a functional subdivision of the beta band into low and high frequencies.
Our findings contribute to the interpretation of oscillatory activity within the cortico-basal ganglia circuit.
DBS, deep brain stimulation; HFO, high-frequency oscillations; LFP, local field potential; MEG, magnetoencephalography; PAC, phase–amplitude coupling; STN, subthalamic nucleus; UPDRS, Unified Parkinson’s Disease Rating Scale; Parkinson’s disease; Subthalamic nucleus; Cross-frequency coupling; Beta oscillations; Motor system; Local field potentials
•Setup for MEG and intracranial recordings during Deep Brain Stimulation is described.•Phantom experiment showed correct recovery of oscillatory sources despite artefacts.•The method is applied to real data from a patient with Parkinson's Disease.•Cortico-subthalamic coherence profiles on and off stimulation were comparable.
Deep Brain Stimulation (DBS) is an effective treatment for several neurological and psychiatric disorders. In order to gain insights into the therapeutic mechanisms of DBS and to advance future therapies a better understanding of the effects of DBS on large-scale brain networks is required.
In this paper, we describe an experimental protocol and analysis pipeline for simultaneously performing DBS and intracranial local field potential (LFP) recordings at a target brain region during concurrent magnetoencephalography (MEG) measurement. Firstly we describe a phantom setup that allowed us to precisely characterise the MEG artefacts that occurred during DBS at clinical settings.
Using the phantom recordings we demonstrate that with MEG beamforming it is possible to recover oscillatory activity synchronised to a reference channel, despite the presence of high amplitude artefacts evoked by DBS. Finally, we highlight the applicability of these methods by illustrating in a single patient with Parkinson's disease (PD), that changes in cortical-subthalamic nucleus coupling can be induced by DBS.
Comparison with existing approaches
To our knowledge this paper provides the first technical description of a recording and analysis pipeline for combining simultaneous cortical recordings using MEG, with intracranial LFP recordings of a target brain nucleus during DBS.
Magnetoencephalography (MEG); Local Field Potential (LFP); Deep Brain Stimulation (DBS); Parkinson's disease
This technical note describes some Bayesian procedures for the analysis of group studies that use nonlinear models at the first (within-subject) level – e.g., dynamic causal models – and linear models at subsequent (between-subject) levels. Its focus is on using Bayesian model reduction to finesse the inversion of multiple models of a single dataset or a single (hierarchical or empirical Bayes) model of multiple datasets. These applications of Bayesian model reduction allow one to consider parametric random effects and make inferences about group effects very efficiently (in a few seconds). We provide the relatively straightforward theoretical background to these procedures and illustrate their application using a worked example. This example uses a simulated mismatch negativity study of schizophrenia. We illustrate the robustness of Bayesian model reduction to violations of the (commonly used) Laplace assumption in dynamic causal modelling and show how its recursive application can facilitate both classical and Bayesian inference about group differences. Finally, we consider the application of these empirical Bayesian procedures to classification and prediction.
•We describe a novel scheme for inverting non-linear models (e.g. DCMs) within subjects and linear models at the group level•We demonstrate this scheme is more robust to violations of the (commonly used) Laplace assumption than the standard approach•We validate the approach using a simulated mismatch negativity study of schizophrenia•We demonstrate the application of this scheme to classification and prediction of group membership
Empirical Bayes; Random effects; Fixed effects; Dynamic causal modelling; Classification; Bayesian model reduction; Hierarchical modelling
Immune responses are tightly regulated to ensure efficient pathogen clearance while avoiding tissue damage. Here we report that SET domain bifurcated 2 (Setdb2) was the only protein lysine methyltransferase induced during influenza virus infection. Setdb2 expression depended on type-I interferon signaling and it repressed the expression of the neutrophil attractant Cxcl1 and other NF-κB target genes. This coincided with Setdb2 occupancy at the Cxcl1 promoter, which in the absence of Setdb2 displayed reduced H3K9 tri-methylation. Setdb2 hypomorphic gene-trap mice exhibited increased neutrophil infiltration in sterile lung inflammation and were less sensitive to bacterial superinfection upon influenza virus infection. This suggests that a Setdb2-mediated regulatory crosstalk between the type-I interferon and NF-κB pathways represents an important mechanism for virus-induced susceptibility to bacterial superinfection.
The nucleus accumbens is thought to contribute to action selection by integrating behaviorally relevant information from multiple regions, including prefrontal cortex. Studies in rodents suggest that information flow to the nucleus accumbens may be regulated via task-dependent oscillatory coupling between regions. During instrumental behavior, local field potentials (LFP) in the rat nucleus accumbens and prefrontal cortex are coupled at delta frequencies (Gruber AJ, Hussain RJ, O'Donnell P. PLoS One 4: e5062, 2009), possibly mediating suppression of afferent input from other areas and thereby supporting cortical control (Calhoon GG, O'Donnell P. Neuron 78: 181–190, 2013). In this report, we demonstrate low-frequency cortico-accumbens coupling in humans, both at rest and during a decision-making task. We recorded LFP from the nucleus accumbens in six epilepsy patients who underwent implantation of deep brain stimulation electrodes. All patients showed significant coherence and phase-synchronization between LFP and surface EEG at delta and low theta frequencies. Although the direction of this coupling as indexed by Granger causality varied between subjects in the resting-state data, all patients showed a cortical drive of the nucleus accumbens during action selection in a decision-making task. In three patients this was accompanied by a significant coherence increase over baseline. Our results suggest that low-frequency cortico-accumbens coupling represents a highly conserved regulatory mechanism for action selection.
nucleus accumbens; action selection; local field potentials; synchronization; deep brain stimulation
•A brief treatment of dynamic coordination in terms of predictive coding.•Understanding synchronous message passing in terms of hierarchical predictive coding.•Characterising cortical gain control with the dynamic causal modelling of neural fields.•Characterising pathophysiological oscillations with dynamic causal modelling of neural masses.
This review surveys recent trends in the use of local field potentials—and their non-invasive counterparts—to address the principles of functional brain architectures. In particular, we treat oscillations as the (observable) signature of context-sensitive changes in synaptic efficacy that underlie coordinated dynamics and message-passing in the brain. This rich source of information is now being exploited by various procedures—like dynamic causal modelling—to test hypotheses about neuronal circuits in health and disease. Furthermore, the roles played by neuromodulatory mechanisms can be addressed directly through their effects on oscillatory phenomena. These neuromodulatory or gain control processes are central to many theories of normal brain function (e.g. attention) and the pathophysiology of several neuropsychiatric conditions (e.g. Parkinson's disease).
We demonstrate that interferon (IFN)-β-1b induces an alternative-start transcript containing the C-terminal TLDc domain of nuclear receptor coactivator protein 7 (NCOA7), a member of the OXR family of oxidation resistance proteins. IFN-β-1b induces NCOA7-AS (alternative start) expression in peripheral blood mononuclear cells (PBMCs) obtained from healthy individuals and multiple sclerosis patients and human fetal brain cells, astrocytoma, neuroblastoma, and fibrosarcoma cells. NCOA7-AS is a previously undocumented IFN-β-inducible gene that contains only the last 5 exons of full-length NCOA7 plus a unique first exon (exon 10a) that is not found in longer forms of NCOA7. This exon encodes a domain closely related to an important class of bacterial aldo-keto oxido-reductase proteins that play a critical role in regulating redox activity. We demonstrate that NCOA7-AS is induced by IFN and LPS, but not by oxidative stress and exhibits, independently, oxidation resistance activity. We further demonstrate that induction of NCOA7-AS by IFN is dependent on IFN-receptor activation, the Janus kinase-signal transducers and activators of transcription (JAK-STAT) signaling pathway, and a canonical IFN-stimulated response element regulatory sequence upstream of exon 10a. We describe a new role for IFN-βs involving a mechanism of action that leads to an increase in resistance to inflammation-mediated oxidative stress.
This technical note considers a simple but important methodological issue in estimating effective connectivity; namely, how do we integrate measurements from multiple subjects to infer functional brain architectures that are conserved over subjects. We offer a solution to this problem that rests on a generalization of random effects analyses to Bayesian inference about nonlinear models of electrophysiological time-series data. Specifically, we present an empirical Bayesian scheme for group or hierarchical models, in the setting of dynamic causal modeling (DCM). Recent developments in approximate Bayesian inference for hierarchical models enable the efficient estimation of group effects in DCM studies of multiple trials, sessions, or subjects. This approach estimates second (e.g., between-subject) level parameters based on posterior estimates from the first (e.g., within-subject) level. Here, we use empirical priors from the second level to iteratively optimize posterior densities over parameters at the first level. The motivation for this iterative application is to finesse the local minima problem inherent in the (first level) inversion of nonlinear and ill-posed models. Effectively, the empirical priors shrink the first level parameter estimates toward the global maximum, to provide more robust and efficient estimates of within (and between-subject) effects. This paper describes the inversion scheme using a worked example based upon simulated electrophysiological responses. In a subsequent paper, we will assess its robustness and reproducibility using an empirical example.
empirical Bayes; random effects; fixed effects; dynamic causal modeling; Bayesian model reduction; hierarchical modeling
This technical note addresses some key reproducibility issues in the dynamic causal modelling of group studies of event related potentials. Specifically, we address the reproducibility of Bayesian model comparison (and inferences about model parameters) from three important perspectives namely: (i) reproducibility with independent data (obtained by averaging over odd and even trials); (ii) reproducibility over formally distinct models (namely, classic ERP and canonical microcircuit or CMC models); and (iii) reproducibility over inversion schemes (inversion of the grand average and estimation of group effects using empirical Bayes). Our hope was to illustrate the degree of reproducibility one can expect from DCM when analysing different data, under different models with different analyses.
empirical Bayes; random effects; fixed effects; dynamic causal modelling; Bayesian model reduction; reproducibility
Beamforming is a spatial filtering based source reconstruction method for EEG and MEG that allows the estimation of neuronal activity at a particular location within the brain. The computation of the location specific filter depends solely on an estimate of the data covariance matrix and on the forward model. Increasing the number of M/EEG sensors, increases the quantity of data required for accurate covariance matrix estimation. Often however we have a prior hypothesis about the site of, or the signal of interest. Here we show how this prior specification, in combination with optimal estimations of data dimensionality, can give enhanced beamformer performance for relatively short data segments. Specifically we show how temporal (Bayesian Principal Component Analysis) and spatial (lead field projection) methods can be combined to produce improvements in source estimation over and above employing the approaches individually.
•This paper concerns optimising beamformer analysis for anatomical ROIs.•Channel reduction is performed using an ROI projection and Bayesian PCA.•This improves covariance matrix estimation for a given data length.•The proposed approach results in improvements in source estimation.
Beamforming; Regions of interest; Bayesian PCA
This technical paper offers a critical re-evaluation of (spectral) Granger causality measures in the analysis of biological timeseries. Using realistic (neural mass) models of coupled neuronal dynamics, we evaluate the robustness of parametric and nonparametric Granger causality. Starting from a broad class of generative (state-space) models of neuronal dynamics, we show how their Volterra kernels prescribe the second-order statistics of their response to random fluctuations; characterised in terms of cross-spectral density, cross-covariance, autoregressive coefficients and directed transfer functions. These quantities in turn specify Granger causality — providing a direct (analytic) link between the parameters of a generative model and the expected Granger causality. We use this link to show that Granger causality measures based upon autoregressive models can become unreliable when the underlying dynamics is dominated by slow (unstable) modes — as quantified by the principal Lyapunov exponent. However, nonparametric measures based on causal spectral factors are robust to dynamical instability. We then demonstrate how both parametric and nonparametric spectral causality measures can become unreliable in the presence of measurement noise. Finally, we show that this problem can be finessed by deriving spectral causality measures from Volterra kernels, estimated using dynamic causal modelling.
•This paper describes the evaluation of expected Granger causality measures.•It uses these measures to quantify problems with dynamical instability and noise.•These problems are resolved by basing Granger measures on DCM estimates.
Granger causality; Dynamic causal modelling; Effective connectivity; Functional connectivity; Dynamics; Cross spectra; Neurophysiology
This paper tests the hypothesis that patients with schizophrenia have a deficit in selectively attending to predictable events. We used dynamic causal modeling (DCM) of electrophysiological responses – to predictable and unpredictable visual targets – to quantify the effective connectivity within and between cortical sources in the visual hierarchy in 25 schizophrenia patients and 25 age-matched controls. We found evidence for marked differences between normal subjects and schizophrenia patients in the strength of extrinsic backward connections from higher hierarchical levels to lower levels within the visual system. In addition, we show that not only do schizophrenia subjects have abnormal connectivity but also that they fail to adjust or optimize this connectivity when events can be predicted. Thus, the differential intrinsic recurrent connectivity observed during processing of predictable versus unpredictable targets was markedly attenuated in schizophrenia patients compared with controls, suggesting a failure to modulate the sensitivity of neurons responsible for passing sensory information of prediction errors up the visual cortical hierarchy. The findings support the proposed role of abnormal connectivity in the neuropathology and pathophysiology of schizophrenia.
Dynamic causal modeling; Schizophrenia; EEG; Prediction; Connectivity
The type I interferon (IFN) response protects cells from viral infection by inducing hundreds of interferon-stimulated genes (ISGs), some of which encode direct antiviral effectors1–3. Recent screening studies have begun to catalogue ISGs with antiviral activity against several RNA and DNA viruses4–13. However, antiviral ISG specificity across multiple distinct classes of viruses remains largely unexplored. Here we used an ectopic expression assay to screen a library of more than 350 human ISGs for effects on 14 viruses representing 7 families and 11 genera. We show that 47 genes inhibit one or more viruses, and 25 genes enhance virus infectivity. Comparative analysis reveals that the screened ISGs target positive-sense single-stranded RNA viruses more effectively than negative-sense single-stranded RNA viruses. Gene clustering highlights the cytosolic DNA sensor cyclic GMP-AMP synthase (cGAS, also known as MB21D1) as a gene whose expression also broadly inhibits several RNA viruses. In vitro, lentiviral delivery of enzymatically active cGAS triggers a STING-dependent, IRF3-mediated antiviral program that functions independently of canonical IFN/STAT1 signalling. In vivo, genetic ablation of murine cGAS reveals its requirement in the antiviral response to two DNA viruses, and an unappreciated contribution to the innate control of an RNA virus. These studies uncover new paradigms for the preferential specificity of IFN-mediated antiviral pathways spanning several virus families.
The integration of auditory feedback with vocal motor output is important for the control of voice fundamental frequency (F0). We used a pitch-shift paradigm where subjects respond to an alteration, or shift, of voice pitch auditory feedback with a reflexive change in F0. We presented varying magnitudes of pitch shifted auditory feedback to subjects during vocalization and passive listening and measured event related potentials (ERP’s) to the feedback shifts. Shifts were delivered at +100 and +400 cents (200 ms duration). The ERP data were modeled with Dynamic Causal Modeling (DCM) techniques where the effective connectivity between the superior temporal gyrus (STG), inferior frontal gyrus and premotor areas were tested. We compared three main factors; the effect of intrinsic STG connectivity, STG modulation across hemispheres and the specific effect of hemisphere. A Bayesian model selection procedure was used to make inference about model families. Results suggest that both intrinsic STG and left to right STG connections are important in the identification of self-voice error and sensory motor integration. We identified differences in left to right STG connections between 100 cent and 400 cent shift conditions suggesting that self and non-self voice error are processed differently in the left and right hemisphere. These results also highlight the potential of DCM modeling of ERP responses to characterize specific network properties of forward models of voice control.
Vocalization; Auditory feedback; ERP; DCM; Audio-vocal integration; Pitch shift
Movement is accompanied by changes in the degree to which neurons in corticobasal ganglia loops synchronize their activity within discrete frequency ranges. Although two principal frequency bands—beta (15–30 Hz) and gamma (60–90 Hz)—have been implicated in motor control, the precise functional correlates of their activities remain unclear. Local field potential (LFP) recordings in humans with Parkinson's disease undergoing surgery for deep brain stimulation to the subthalamic nucleus (STN) indicate that spectral changes both anticipate movement and occur perimovement. The extent to which such changes are modulated by cognitive factors involved in making a correct response seems critical in characterizing the functional associations of these oscillations. Accordingly, by recording LFP activity from the STN in parkinsonian patients, we demonstrate that perimovement beta and gamma reactivity is modulated by task complexity in a dopamine-dependent manner, despite the dynamics of the movement remaining unchanged. In contrast, spectral changes occurring in anticipation of future movement were limited to the beta band and, although modulated by dopaminergic therapy, were not modulated by task complexity. Our findings suggest two dopamine-dependent processes indexed by spectral changes in the STN: (1) an anticipatory activity reflected in the beta band that signals the likelihood of future action but does not proactively change with the cognitive demands of the potential response, and (2) perimovement activity that involves reciprocal beta and gamma band changes and is not exclusively related to explicit motor processing. Rather perimovement activity can also vary with, and may reflect, the cognitive complexity of the task.
Antiviral responses must be tightly regulated to rapidly defend against infection while minimizing inflammatory damage. Type 1 interferons (IFN-I) are crucial mediators of antiviral responses1 and their transcription is regulated by a variety of transcription factors2; principal amongst these is the family of interferon regulatory factors (IRFs)3. The IRF gene regulatory networks are complex and contain multiple feedback loops. The tools of systems biology are well suited to elucidate the complex interactions that give rise to precise coordination of the interferon response. Here we have used an unbiased systems approach to predict that a member of the forkhead family of transcription factors, FOXO3, is a negative regulator of a subset of antiviral genes. This prediction was validated using macrophages isolated from Foxo3-null mice. Genome-wide location analysis combined with gene deletion studies identified the Irf7 gene as a critical target of FOXO3. FOXO3 was identified as a negative regulator of Irf7 transcription and we have further demonstrated that FOXO3, IRF7 and IFN-I form a coherent feed-forward regulatory circuit. Our data suggest that the FOXO3-IRF7 regulatory circuit represents a novel mechanism for establishing the requisite set points in the interferon pathway that balances the beneficial effects and deleterious sequelae of the antiviral response.
Functional neurosurgical techniques provide a unique opportunity to explore patterns of interaction between the cerebral cortex and basal ganglia in patients with Parkinson's disease (PD). Previous work using simultaneous magnetoencephalographic (MEG) and local field potential (LFP) recordings from the region of the subthalamic nucleus (STNr) has characterised resting patterns of connectivity in the alpha and beta frequency bands and their modulation by dopaminergic medication. Recently we have also characterised the effect of movement on patterns of gamma band coherence between the STNr and cortical sites. Here we specifically investigate how the prominent coherence between the STNr and temporal cortex in the alpha band is modulated by movement both on and off dopaminergic medication in patients following the insertion of Deep Brain Stimulation (DBS) electrodes. We show that movement is associated with a suppression of local alpha power in the temporal cortex and STNr that begins about 2 s prior to a self-paced movement and is independent of dopaminergic status. In contrast, the peak reduction in coherence between these sites occurs after movement onset and is more marked in the on than in the off dopaminergic medication state. The difference in alpha band coherence on and off medication was found to correlate with the drug related improvement in clinical parameters. Overall, the movement-related behaviour of activities in the alpha band in patients with PD serves to highlight the role of dopamine in modulating large-scale, interregional synchronisation.
► We studied subthalamo-cortical coherence in the alpha band in Parkinson's patients. ► A decrease in coherence occurred with movement facilitated by dopamine. ► This effect correlated with clinical improvement.
Oscillations; Human; Intracranial recordings
Functional neurosurgery has afforded the opportunity to assess interactions between populations of neurons in the human cerebral cortex and basal ganglia in patients with Parkinson’s disease (PD). Interactions occur over a wide range of frequencies, and the functional significance of those above 30 Hz is particularly unclear. Do they improve movement and, if so, in what way? We acquired simultaneously magnetoencephalography (MEG) and direct recordings from the subthalamic nucleus (STN) in 17 PD patients. We examined the effect of synchronous and sequential finger movements and of the dopamine prodrug levodopa on induced power in the contralateral primary motor cortex (M1) and STN and on the coherence between the two structures. We observed discrete peaks in M1 and STN power over 60-90 Hz and 300-400 Hz. All these power peaks increased with movement and levodopa treatment. Only STN activity over 60-90 Hz was coherent with activity in M1. Directionality analysis showed that STN gamma activity at 60-90 Hz tended to drive gamma activity in M1. The effects of levodopa on both local and distant synchronisation over 60-90 Hz correlated with the degree of improvement in bradykinesia-rigidity, as did local STN activity at 300-400 Hz. Despite this, there were no effects of movement type, nor interactions between movement type and levodopa in the STN, nor in the coherence between STN and M1. We conclude that synchronisation over 60-90 Hz in the basal ganglia cortical network is prokinetic, but likely through a modulatory effect rather than any involvement in explicit motor processing.
Oscillatory activity in the beta frequency band has been shown to be modulated during the preparation and execution of voluntary movements at both cortical and subcortical levels. The exaggeration of beta activity in the basal ganglia of patients with Parkinson's disease has heightened interest in this phenomenon. However, the precise function, if any, subserved by modulations in beta activity remains unclear. Here we test the hypothesis that beta reactivity can be dissociated from processing of specific actions and can index the salience of cues with respect to future behavior in a way that might help prospectively prioritize resources. To this end we used an experimental paradigm designed to dissociate salient warning cues from processing of specific motor or cognitive actions. We recorded local field potential activity from the subthalamic nucleus of humans undergoing functional neurosurgery for the treatment of Parkinson's disease, while the same patients were on or off the dopamine prodrug levodopa. In this way we demonstrate that beta reactivity is indeed dependent on the salience of cues with respect to future motor and cognitive action and is promoted by dopamine. The loss of normal beta encoding of saliency may underlie some of the motor and cognitive features of basal ganglia disorders such as Parkinson's disease.
Magnetoencephalographic (MEG) recordings are a rich source of information about the neural dynamics underlying cognitive processes in the brain, with excellent temporal and good spatial resolution. In recent years there have been considerable advances in MEG hardware developments and methods. Sophisticated analysis techniques are now routinely applied and continuously improved, leading to fascinating insights into the intricate dynamics of neural processes. However, the rapidly increasing level of complexity of the different steps in a MEG study make it difficult for novices, and sometimes even for experts, to stay aware of possible limitations and caveats. Furthermore, the complexity of MEG data acquisition and data analysis requires special attention when describing MEG studies in publications, in order to facilitate interpretation and reproduction of the results. This manuscript aims at making recommendations for a number of important data acquisition and data analysis steps and suggests details that should be specified in manuscripts reporting MEG studies. These recommendations will hopefully serve as guidelines that help to strengthen the position of the MEG research community within the field of neuroscience, and may foster discussion in order to further enhance the quality and impact of MEG research.
Magnetoencephalography; MEG; Acquisition; Analysis; Connectivity; Source localization; Guidelines; Recommendations; Reproducible research; Spectral analysis
In Kilner et al. [Kilner, J.M., Kiebel, S.J., Friston, K.J., 2005. Applications of random field theory to electrophysiology. Neurosci. Lett. 374, 174–178.] we described a fairly general analysis of induced responses—in electromagnetic brain signals—using the summary statistic approach and statistical parametric mapping. This involves localising induced responses—in peristimulus time and frequency—by testing for effects in time–frequency images that summarise the response of each subject to each trial type. Conventionally, these time–frequency summaries are estimated using post‐hoc averaging of epoched data. However, post‐hoc averaging of this sort fails when the induced responses overlap or when there are multiple response components that have variable timing within each trial (for example stimulus and response components associated with different reaction times). In these situations, it is advantageous to estimate response components using a convolution model of the sort that is standard in the analysis of fMRI time series. In this paper, we describe one such approach, based upon ordinary least squares deconvolution of induced responses to input functions encoding the onset of different components within each trial. There are a number of fundamental advantages to this approach: for example; (i) one can disambiguate induced responses to stimulus onsets and variably timed responses; (ii) one can test for the modulation of induced responses—over peristimulus time and frequency—by parametric experimental factors and (iii) one can gracefully handle confounds—such as slow drifts in power—by including them in the model. In what follows, we consider optimal forms for convolution models of induced responses, in terms of impulse response basis function sets and illustrate the utility of deconvolution estimators using simulated and real MEG data.
► We propose a new approach to analysis of induced responses in M/EEG. ► The General Linear Model is used to model continuous power as in fMRI 1st-level. ► The results can be presented as conventional time–frequency images. ► Our method is better for experiments with variable timing and overlapping events.
EEG; MEG; ERSP; Induced responses; Time–frequency analysis; General linear model; Convolution; Statistical parametric mapping
Stroke results in reorganization of residual brain networks. The functional role of brain regions within these networks remains unclear, particularly those in the contralesional hemisphere. We studied 25 stroke patients with a range of motor impairment and 23 healthy age-matched controls using magnetoencephalography (MEG) and electromyography (EMG) to measure oscillatory signals from the brain and affected muscles simultaneously during a simple isometric hand grip, from which cortico-muscular coherence (CMC) was calculated. Peaks of cortico-muscular coherence in both the beta and gamma bands were found in the contralateral sensorimotor cortex in all healthy controls, but were more widespread in stroke patients, including some peaks found in the contralesional hemisphere (7 patients for beta coherence and 5 for gamma coherence). Neither the coherence value nor the distance of the coherence peak from the mean of controls correlated with impairment. Peak CMC in the contralesional hemisphere was found not only in some highly impaired patients, but also in some patients with good functional recovery. Our results provide evidence that a wide range of cortical brain regions, including some in the contralesional hemisphere, may have influence over EMG activity in the affected muscles after stroke thereby supporting functional recovery.
► We examined cortico-muscular coherence location in stroke patients and controls. ► The location of peak coherence was more widely distributed in stroke patients. ► In some patients, peak coherence was found in the contralesional hemisphere. ► The location of coherence in patients did not correlate with impairment. ► Contralesional hemisphere can support functional motor recovery after stroke.
CMC, cortico-muscular coherence; MEG, magnetoencephalography; EMG, electromyography; fMRI, functional magnetic resonance imaging; TMS, transcranial magnetic stimulation; M1, primary motor cortex; PCA, principal component analysis; MVC, maximum voluntary contraction; DICS, dynamic imaging of coherent sources; Magnetoencephalography; Cortico-muscular coherence; Stroke recovery; Motor; Brain
A multimodal neuroimaging study of virtual spatial navigation extends the role of the hippocampal theta rhythm to human memory and self-directed learning.
The hippocampus is crucial for episodic or declarative memory and the theta rhythm has been implicated in mnemonic processing, but the functional contribution of theta to memory remains the subject of intense speculation. Recent evidence suggests that the hippocampus might function as a network hub for volitional learning. In contrast to human experiments, electrophysiological recordings in the hippocampus of behaving rodents are dominated by theta oscillations reflecting volitional movement, which has been linked to spatial exploration and encoding. This literature makes the surprising cross-species prediction that the human hippocampal theta rhythm supports memory by coordinating exploratory movements in the service of self-directed learning. We examined the links between theta, spatial exploration, and memory encoding by designing an interactive human spatial navigation paradigm combined with multimodal neuroimaging. We used both non-invasive whole-head Magnetoencephalography (MEG) to look at theta oscillations and Functional Magnetic Resonance Imaging (fMRI) to look at brain regions associated with volitional movement and learning. We found that theta power increases during the self-initiation of virtual movement, additionally correlating with subsequent memory performance and environmental familiarity. Performance-related hippocampal theta increases were observed during a static pre-navigation retrieval phase, where planning for subsequent navigation occurred. Furthermore, periods of the task showing movement-related theta increases showed decreased fMRI activity in the parahippocampus and increased activity in the hippocampus and other brain regions that strikingly overlap with the previously observed volitional learning network (the reverse pattern was seen for stationary periods). These fMRI changes also correlated with participant's performance. Our findings suggest that the human hippocampal theta rhythm supports memory by coordinating exploratory movements in the service of self-directed learning. These findings directly extend the role of the hippocampus in spatial exploration in rodents to human memory and self-directed learning.
Neural activity both within and across brain regions can oscillate in different frequency ranges (such as alpha, gamma, and theta frequencies), and these different ranges are associated with distinct functions. In behaving rodents, for example, theta rhythms (4–12 Hz) in the hippocampus are prominent during the initiation of movement and have been linked to spatial exploration. Recent evidence in humans, however, suggests that the human hippocampus is involved in guiding self-directed learning. This suggests that the human hippocampal theta rhythm supports memory by coordinating exploratory movements in the service of self-directed learning. In this study, we tested whether there is a human analogue for the movement-initiation-related theta rhythm found in the rodent hippocampus by using a virtual navigation paradigm, combined with non-invasive recordings and functional imaging techniques. Our recordings showed that, indeed, theta power increases are linked to movement initiation. We also examined the relationship to memory encoding, and we found that hippocampal theta oscillations related to pre-retrieval planning predicted memory performance. Imaging results revealed that periods of the task showing movement-related theta also showed increased activity in the hippocampus, as well as other brain regions associated with self-directed learning. These findings directly extend the role of the hippocampal theta rhythm in rodent spatial exploration to human memory and self-directed learning.
Statistical parametric mapping (SPM) locates significant clusters based on a ratio of signal to noise (a ‘contrast’ of the parameters divided by its standard error) meaning that very low noise regions, for example outside the brain, can attain artefactually high statistical values. Similarly, the commonly applied preprocessing step of Gaussian spatial smoothing can shift the peak statistical significance away from the peak of the contrast and towards regions of lower variance. These problems have previously been identified in positron emission tomography (PET) (Reimold et al., 2006) and voxel-based morphometry (VBM) (Acosta-Cabronero et al., 2008), but can also appear in functional magnetic resonance imaging (fMRI) studies. Additionally, for source-reconstructed magneto- and electro-encephalography (M/EEG), the problems are particularly severe because sparsity-favouring priors constrain meaningfully large signal and variance to a small set of compactly supported regions within the brain. (Acosta-Cabronero et al., 2008) suggested adding noise to background voxels (the ‘haircut’), effectively increasing their noise variance, but at the cost of contaminating neighbouring regions with the added noise once smoothed. Following theory and simulations, we propose to modify – directly and solely – the noise variance estimate, and investigate this solution on real imaging data from a range of modalities.
► Statistical parametric mapping judges significance with a signal-to-noise ratio. ► Low noise, e.g. outside the brain, can yield artefactually high statistical values. ► Spatial smoothing can shift peaks substantially towards regions of low variance. ► Source-reconstructed M/EEG data exhibits the problem particularly severely. ► The problem can be addressed by modifying the noise variance estimate.
EEG, electroencephalography; fMRI, functional magnetic resonance imaging; FWHM, full-width at half-maximum; GM, grey matter; MEG, magnetoencephalography; MIP, maximum intensity projection; MNI, Montreal Neurological Institute; ResMS, residual mean squares; SPM, statistical parametric mapping; PET, positron emission tomography; VBM, voxel-based morphometry; SPM; Low variance; VBM; MEG; EEG; Source reconstruction
Optimal decision-making is guided by evaluating the outcomes of previous decisions. Prediction errors are theoretical teaching signals which integrate two features of an outcome: its inherent value and prior expectation of its occurrence. To uncover the magnetic signature of prediction errors in the human brain we acquired magnetoencephalographic (MEG) data while participants performed a gambling task. Our primary objective was to use formal criteria, based upon an axiomatic model (Caplin and Dean, 2008a), to determine the presence and timing profile of MEG signals that express prediction errors. We report analyses at the sensor level, implemented in SPM8, time locked to outcome onset. We identified, for the first time, a MEG signature of prediction error, which emerged approximately 320 ms after an outcome and expressed as an interaction between outcome valence and probability. This signal followed earlier, separate signals for outcome valence and probability, which emerged approximately 200 ms after an outcome. Strikingly, the time course of the prediction error signal, as well as the early valence signal, resembled the Feedback-Related Negativity (FRN). In simultaneously acquired EEG data we obtained a robust FRN, but the win and loss signals that comprised this difference wave did not comply with the axiomatic model. Our findings motivate an explicit examination of the critical issue of timing embodied in computational models of prediction errors as seen in human electrophysiological data.
► We identified, for the first time, an MEG signature of a human prediction error. ► The waveform resembled the Feedback-Related Negativity (FRN) signal in EEG. ► MEG effects of probability and valence were emerged before the prediction error signals, 200 ms after the outcome. ► The EEG data revealed classic FRN which was modulated by probability.
Decision-making; Prediction error; Reward; MEG; Feedback-related negativity; Error-related negativity