Search

Traditionally, in neuroimaging, model-free analyses are used to find significant differences between brain states via signal detection theory. Depending on the a priori assumptions about the underlying data, different spatio-temporal features can be analysed. Alternatively, model-based techniques infer features from the data and compare significance from model parameters. However, to assess transitions from one brain state to another remains a challenge in current paradigms. Here, we introduce a “Dynamic Sensitivity Analysis” framework that quantifies transitions between brain states in terms of stimulation ability to rebalance spatio-temporal brain activity towards a target state such as healthy brain dynamics. In practice, it means building a whole-brain model fitted to the spatio-temporal description of brain dynamics, and applying systematic stimulations in-silico to assess the optimal strategy to drive brain dynamics towards a target state. Further, we show how Dynamic Sensitivity Analysis extends to various brain stimulation paradigms, ultimately contributing to improving the efficacy of personalised clinical interventions.

Existing whole-brain models are generally tailored to the modelling of a particular data modality (e.g., fMRI or MEG/EEG). We propose that despite the differing aspects of neural activity each modality captures, they originate from shared network dynamics. Building on the universal principles of self-organising delay-coupled nonlinear systems, we aim to link distinct features of brain activity - captured across modalities - to the dynamics unfolding on a macroscopic structural connectome.To jointly predict connectivity, spatiotemporal and transient features of distinct signal modalities, we consider two large-scale models - the Stuart Landau and Wilson and Cowan models - which generate short-lived 40 Hz oscillations with varying levels of realism. To this end, we measure features of functional connectivity and metastable oscillatory modes (MOMs) in fMRI and MEG signals - and compare them against simulated data.We show that both models can represent MEG functional connectivity (FC), functional connectivity dynamics (FCD) and generate MOMs to a comparable degree. This is achieved by adjusting the global coupling and mean conduction time delay and, in the WC model, through the inclusion of balance between excitation and inhibition. For both models, the omission of delays dramatically decreased the performance. For fMRI, the SL model performed worse for FCD and MOMs, highlighting the importance of balanced dynamics for the emergence of spatiotemporal and transient patterns of ultra-slow dynamics. Notably, optimal working points varied across modalities and no model was able to achieve a correlation with empirical FC higher than 0.4 across modalities for the same set of parameters. Nonetheless, both displayed the emergence of FC patterns that extended beyond the constraints of the anatomical structure. Finally, we show that both models can generate MOMs with empirical-like properties such as size (number of brain regions engaging in a mode) and duration (continuous time interval during which a mode appears).Our results demonstrate the emergence of static and dynamic properties of neural activity at different timescales from networks of delay-coupled oscillators at 40 Hz. Given the higher dependence of simulated FC on the underlying structural connectivity, we suggest that mesoscale heterogeneities in neural circuitry may be critical for the emergence of parallel cross-modal functional networks and should be accounted for in future modelling endeavours.

The mechanisms underlying transient brain rhythms and weekly stable synchronization of distant brain areas and their link with neural activity is still a matter of debate. Here, the authors use a brain network model to study spatio-temporal synchronization dynamics of brain regions and find that there is an optimal regime where spatially and spectrally resolved metastable oscillatory modes, similar to human magnetoencephalography data, emerge.

In Parkinson’s disease (PD), beta frequency oscillations are synchronised across the cortico-basal-ganglia circuit. The authors show in human participants that high beta frequencies propagate from the cortex to the basal ganglia via the hyperdirect pathway, indicating a pathophysiological role for this pathway in PD.

Synchronization of neural oscillations is thought to facilitate communication in the brain. Neurodegenerative pathologies such as Parkinson's disease (PD) can result in synaptic reorganization of the motor circuit, leading to altered neuronal dynamics and impaired neural communication. Treatments for PD aim to restore network function via pharmacological means such as dopamine replacement, or by suppressing pathological oscillations with deep brain stimulation. We tested the hypothesis that brain stimulation can operate beyond a simple "reversible lesion" effect to augment network communication. Specifically, we examined the modulation of beta band (14-30 Hz) activity, a known biomarker of motor deficits and potential control signal for stimulation in Parkinson's. To do this we setup a neural mass model of population activity within the cortico-basal ganglia-thalamic (CBGT) circuit with parameters that were constrained to yield spectral features comparable to those in experimental Parkinsonism. We modulated the connectivity of two major pathways known to be disrupted in PD and constructed statistical summaries of the spectra and functional connectivity of the resulting spontaneous activity. These were then used to assess the network-wide outcomes of closed-loop stimulation delivered to motor cortex and phase locked to subthalamic beta activity. Our results demonstrate that the spatial pattern of beta synchrony is dependent upon the strength of inputs to the STN. Precisely timed stimulation has the capacity to recover network states, with stimulation phase inducing activity with distinct spectral and spatial properties. These results provide a theoretical basis for the design of the next-generation brain stimulators that aim to restore neural communication in disease.

Beamforming is a popular method for functional source reconstruction using magnetoencephalography (MEG) and electroencephalography (EEG) data. Beamformers, which were first proposed for MEG more than two decades ago, have since been applied in hundreds of studies, demonstrating that they are a versatile and robust tool for neuroscience. However, certain characteristics of beamformers remain somewhat elusive and there currently does not exist a unified documentation of the mathematical underpinnings and computational subtleties of beamformers as implemented in the most widely used academic open source software packages for MEG analysis (Brainstorm, FieldTrip, MNE, and SPM). Here, we provide such documentation that aims at providing the mathematical background of beamforming and unifying the terminology. Beamformer implementations are compared across toolboxes and pitfalls of beamforming analyses are discussed. Specifically, we provide details on handling rank deficient covariance matrices, prewhitening, the rank reduction of forward fields, and on the combination of heterogeneous sensor types, such as magnetometers and gradiometers. The overall aim of this paper is to contribute to contemporary efforts towards higher levels of computational transparency in functional neuroimaging.

Abstract We demonstrate the first use of Optically Pumped Magnetoencephalography (OP‐MEG) in an epilepsy patient with unrestricted head movement. Current clinical MEG uses a traditional SQUID system, where sensors are cryogenically cooled and housed in a helmet in which the patient’s head is fixed. Here, we use a different type of sensor (OPM), which operates at room temperature and can be placed directly on the patient’s scalp, permitting free head movement. We performed OP‐MEG recording in a patient with refractory focal epilepsy. OP‐MEG‐identified analogous interictal activity to scalp EEG, and source localized this activity to an appropriate brain region.

This paper describes and validates a novel framework using the Approximate Bayesian Computation (ABC) algorithm for parameter estimation and model selection in models of mesoscale brain network activity. We provide a proof of principle, first pass validation of this framework using a set of neural mass models of the cortico-basal ganglia thalamic circuit inverted upon spectral features from experimental, in vivo recordings. This optimization scheme relaxes an assumption of fixed-form posteriors (i.e. the Laplace approximation) taken in previous approaches to inverse modelling of spectral features. This enables the exploration of model dynamics beyond that approximated from local linearity assumptions and so fit to explicit, numerical solutions of the underlying non-linear system of equations. In this first paper, we establish a face validation of the optimization procedures in terms of: (i) the ability to approximate posterior densities over parameters that are plausible given the known causes of the data; (ii) the ability of the model comparison procedures to yield posterior model probabilities that can identify the model structure known to generate the data; and (iii) the robustness of these procedures to local minima in the face of different starting conditions. Finally, as an illustrative application we show (iv) that model comparison can yield plausible conclusions given the known neurobiology of the cortico-basal ganglia-thalamic circuit in Parkinsonism. These results lay the groundwork for future studies utilizing highly nonlinear or brittle models that can explain time dependant dynamics, such as oscillatory bursts, in terms of the underlying neural circuits.

Background: Intracranial electroencephalography (iEEG) recordings are used for clinical evaluation prior to surgical resection of the focus of epileptic seizures and also provide a window into normal brain function. A major difficulty with interpreting iEEG results at the group level is inconsistent placement of electrodes between subjects making it difficult to select contacts that correspond to the same functional areas. Recent work using time delay embedded hidden Markov model (HMM) applied to magnetoencephalography (MEG) resting data revealed a distinct set of brain states with each state engaging a specific set of cortical regions. Here we use a rare group dataset with simultaneously acquired resting iEEG and MEG to test whether there is correspondence between HMM states and iEEG power changes that would allow classifying iEEG contacts into functional clusters. Methods: Simultaneous MEG-iEEG recordings were performed at rest on 11 patients with epilepsy whose intracranial electrodes were implanted for pre-surgical evaluation. Pre-processed MEG sensor data was projected to source space. Time delay embedded HMM was then applied to MEG time series. At the same time, iEEG time series were analyzed with time-frequency decomposition to obtain spectral power changes with time. To relate MEG and iEEG results, correlations were computed between HMM probability time courses of state activation and iEEG power time course from the mid contact pair for each electrode in equally spaced frequency bins and presented as correlation spectra for the respective states and iEEG channels. Association of iEEG electrodes with HMM states based on significant correlations was compared to that based on the distance to peaks in subject-specific state topographies. Results: Five HMM states were inferred from MEG. Two of them corresponded to the left and the right temporal activations and had a spectral signature primarily in the theta/alpha frequency band. All the electrodes had significant correlations with at least one of the states (p

We recently described a dynamic causal model of a COVID-19 outbreak within a single region. Here, we combine several instantiations of this (epidemic) model to create a (pandemic) model of viral spread among regions. Our focus is on a second wave of new cases that may result from loss of immunity—and the exchange of people between regions—and how mortality rates can be ameliorated under different strategic responses. In particular, we consider hard or soft social distancing strategies predicated on national (Federal) or regional (State) estimates of the prevalence of infection in the population. The modelling is demonstrated using timeseries of new cases and deaths from the United States to estimate the parameters of a factorial (compartmental) epidemiological model of each State and, crucially, coupling between States. Using Bayesian model reduction, we identify the effective connectivity between States that best explains the initial phases of the outbreak in the United States. Using the ensuing posterior parameter estimates, we then evaluate the likely outcomes of different policies in terms of mortality, working days lost due to lockdown and demands upon critical care. The provisional results of this modelling suggest that social distancing and loss of immunity are the two key factors that underwrite a return to endemic equilibrium.

Deep brain stimulation (DBS) is an effective treatment method for a range of neurological and psychiatric disorders. It involves implantation of stimulating electrodes in a precisely guided fashion into subcortical structures and, at a later stage, chronic stimulation of these structures with an implantable pulse generator. While the DBS surgery makes it possible to both record brain activity and stimulate parts of the brain that are difficult to reach with non-invasive techniques, electroencephalography (EEG) and magnetoencephalography (MEG) provide complementary information from other brain areas, which can be used to characterize brain networks targeted through DBS. This requires, however, the careful consideration of different types of artifacts in the data acquisition and the subsequent analyses. Here, we review both the technical issues associated with EEG/MEG recordings in DBS patients and the experimental findings to date. One major line of research is simultaneous recording of local field potentials (LFPs) from DBS targets and EEG/MEG. These studies revealed a set of cortico-subcortical coherent networks functioning at distinguishable physiological frequencies. Specific network responses were linked to clinical state, task or stimulation parameters. Another experimental approach is mapping of DBS-targeted networks in chronically implanted patients by recording EEG/MEG responses during stimulation. One can track responses evoked by single stimulation pulses or bursts as well as brain state shifts caused by DBS. These studies have the potential to provide biomarkers for network responses that can be adapted to guide stereotactic implantation or optimization of stimulation parameters. This is especially important for diseases where the clinical effect of DBS is delayed or develops slowly over time. The same biomarkers could also potentially be utilized for the online control of DBS network effects in the new generation of closed-loop stimulators that are currently entering clinical use. Through future studies, the use of network biomarkers may facilitate the integration of circuit physiology into clinical decision making.

This study offers a novel and efficient measure based on a higher order version of autocorrelative signal memory that can identify nonlinearities in a single time series. The suggested method was applied to simultaneously recorded subthalamic nucleus (STN) local field potentials (LFP) and magnetoencephalography (MEG) from fourteen Parkinson's Disease (PD) patients who underwent surgery for deep brain stimulation. Recordings were obtained during rest for both OFF and ON dopaminergic medication states. We analyzed the bilateral LFP channels that had the maximum beta power in the OFF state and the cortical sources that had the maximum coherence with the selected LFP channels in the alpha band. Our findings revealed the inherent nonlinearity in the PD data as subcortical high beta(20–30 Hz) band and cortical alpha (8–12 Hz) band activities. While the former was discernible without medication (p=0.015), the latter was induced upon the dopaminergic medication (p

In sensorimotor integration, the brain needs to decide how its predictions should accommodate novel evidence by ‘gating’ sensory data depending on the current context. Here, we examined the oscillatory correlates of this process by recording magnetoencephalography (MEG) data during a new task requiring action under intersensory conflict. We used virtual reality to decouple visual (virtual) and proprioceptive (real) hand postures during a task in which the phase of grasping movements tracked a target (in either modality). Thus, we rendered visual information either task-relevant or a (to-be-ignored) distractor. Under visuo-proprioceptive incongruence, occipital beta power decreased (relative to congruence) when vision was task-relevant but increased when it had to be ignored. Dynamic causal modeling (DCM) revealed that this interaction was best explained by diametrical, task-dependent changes in visual gain. These results suggest a crucial role for beta oscillations in the contextual gating (i.e., gain or precision control) of visual vs proprioceptive action feedback, depending on current behavioral demands.

Parkinson's disease dementia (PDD) and dementia with Lewy bodies (DLB) are two related diseases which can be difficult to distinguish. There is no objective biomarker which can reliably differentiate between them. The synergistic combination of electrophysiological and neuroimaging approaches is a powerful method for interrogation of functional brain networks in vivo. We recorded bilateral local field potentials (LFPs) from the nucleus basalis of Meynert (NBM) and the internal globus pallidus (GPi) with simultaneous cortical magnetoencephalography (MEG) in six PDD and five DLB patients undergoing surgery for deep brain stimulation (DBS) to look for differences in underlying resting-state network pathophysiology. In both patient groups we observed spectral peaks in the theta (2–8 Hz) band in both the NBM and the GPi. Furthermore, both the NBM and the GPi exhibited similar spatial and spectral patterns of coupling with the cortex in the two disease states. Specifically, we report two distinct coherent networks between the NBM/GPi and cortical regions: (1) a theta band (2–8 Hz) network linking the NBM/GPi to temporal cortical regions, and (2) a beta band (13–22 Hz) network coupling the NBM/GPi to sensorimotor areas. We also found differences between the two disease groups: oscillatory power in the low beta (13–22Hz) band was significantly higher in the globus pallidus in PDD patients compared to DLB, and coherence in the high beta (22–35Hz) band between the globus pallidus and lateral sensorimotor cortex was significantly higher in DLB patients compared to PDD. Overall, our findings reveal coherent networks of the NBM/GPi region that are common to both DLB and PDD. Although the neurophysiological differences between the two conditions in this study are confounded by systematic differences in DBS lead trajectories and motor symptom severity, they lend support to the hypothesis that DLB and PDD, though closely related, are distinguishable from a neurophysiological perspective.

There has been considerable debate and concern as to whether there is a replication crisis in the scientific literature. A likely cause of poor replication is the multiple comparisons problem. An important way in which this problem can manifest in the M/EEG context is through post hoc tailoring of analysis windows (a.k.a. regions-of-interest, ROIs) to landmarks in the collected data. Post hoc tailoring of ROIs is used because it allows researchers to adapt to inter-experiment variability and discover novel differences that fall outside of windows defined by prior precedent, thereby reducing Type II errors. However, this approach can dramatically inflate Type I error rates. One way to avoid this problem is to tailor windows according to a contrast that is orthogonal (strictly parametrically orthogonal) to the contrast being tested. A key approach of this kind is to identify windows on a fully flattened average. On the basis of simulations, this approach has been argued to be safe for post hoc tailoring of analysis windows under many conditions. Here, we present further simulations and mathematical proofs to show exactly why the Fully Flattened Average approach is unbiased, providing a formal grounding to the approach, clarifying the limits of its applicability and resolving published misconceptions about the method. We also provide a statistical power analysis, which shows that, in specific contexts, the fully flattened average approach provides higher statistical power than Fieldtrip cluster inference. This suggests that the Fully Flattened Average approach will enable researchers to identify more effects from their data without incurring an inflation of the false positive rate.

Deep brain stimulation (DBS) can be a very efficient treatment option for movement disorders and psychiatric diseases. To better understand DBS mechanisms, brain activity can be recorded using magnetoencephalography (MEG) with the stimulator turned on. However, DBS produces large artefacts compromising MEG data quality due to both the applied current and the movement of wires connecting the stimulator with the electrode. To filter out these artefacts, several methods to suppress the DBS artefact have been proposed in the literature. A comparative study evaluating each method’s effectiveness, however, is missing so far.In this study, we evaluate the performance of four artefact rejection methods on MEG data from phantom recordings with DBS acquired with an Elekta Neuromag and a CTF system: (i) Hampel-filter, (ii) spectral signal space projection (S3P), (iii) independent component analysis with mutual information (ICA-MI), and (iv) temporal signal space separation (tSSS). In the sensor space, the largest increase in signal-to-noise (SNR) ratio was achieved by ICA-MI, while the best correspondence in terms of source activations was obtained by tSSS. LCMV beamforming alone was not sufficient to suppress the DBS-induced artefacts.

This technical report addresses a pressing issue in the trajectory of the coronavirus outbreak; namely, the rate at which effective immunity is lost following the first wave of the pandemic. This is a crucial epidemiological parameter that speaks to both the consequences of relaxing lockdown and the propensity for a second wave of infections. Using a dynamic causal model of reported cases and deaths from multiple countries, we evaluated the evidence models of progressively longer periods of immunity. The results speak to an effective population immunity of about three months that, under the model, defers any second wave for approximately six months in most countries. This may have implications for the window of opportunity for tracking and tracing, as well as for developing vaccination programmes, and other therapeutic interventions.

Background: ‘Non-parametric directionality’ (NPD) is a novel method for estimation of directed functional connectivity (dFC) in neural data. The method has previously been verified in its ability to recover causal interactions in simulated spiking networks in Halliday et al. (2015). Methods: This work presents a validation of NPD in continuous neural recordings (e.g. local field potentials). Specifically, we use autoregressive models to simulate time delayed correlations between neural signals. We then test for the accurate recovery of networks in the face of several confounds typically encountered in empirical data. We examine the effects of NPD under varying: a) signal-to-noise ratios, b) asymmetries in signal strength, c) instantaneous mixing, d) common drive, e) data length, and f) parallel/convergent signal routing. We also apply NPD to data from a patient who underwent simultaneous magnetoencephalography and deep brain recording. Results: We demonstrate that NPD can accurately recover directed functional connectivity from simulations with known patterns of connectivity. The performance of the NPD measure is compared with non-parametric estimators of Granger causality (NPG), a well-established methodology for model-free estimation of dFC. A series of simulations investigating synthetically imposed confounds demonstrate that NPD provides estimates of connectivity that are equivalent to NPG, albeit with an increased sensitivity to data length. However, we provide evidence that: i) NPD is less sensitive than NPG to degradation by noise; ii) NPD is more robust to the generation of false positive identification of connectivity resulting from SNR asymmetries; iii) NPD is more robust to corruption via moderate amounts of instantaneous signal mixing. Conclusions: The results in this paper highlight that to be practically applied to neural data, connectivity metrics should not only be accurate in their recovery of causal networks but also resistant to the confounding effects often encountered in experimental recordings of multimodal data. Taken together, these findings position NPD at the state-of-the-art with respect to the estimation of directed functional connectivity in neuroimaging.

This technical note presents a dynamic causal modelling (DCM) procedure for evaluating different models of neurovascular coupling in the human brain – using combined electromagnetic (M/EEG) and functional magnetic resonance imaging (fMRI) data. This procedure compares the evidence for biologically informed models of neurovascular coupling using Bayesian model comparison. First, fMRI data are used to localise regionally specific neuronal responses. The coordinates of these responses are then used as the location priors in a DCM of electrophysiological responses elicited by the same paradigm. The ensuing estimates of model parameters are then used to generate neuronal drive functions, which model pre- or post-synaptic activity for each experimental condition. These functions form the input to a model of neurovascular coupling, whose parameters are estimated from the fMRI data. Crucially, this enables one to evaluate different models of neurovascular coupling, using Bayesian model comparison – asking, for example, whether instantaneous or delayed, pre- or post-synaptic signals mediate haemodynamic responses. We provide an illustrative application of the procedure using a single-subject auditory fMRI and MEG dataset. The code and exemplar data accompanying this technical note are available through the statistical parametric mapping (SPM) software.

Beamformers are applied for estimating spatiotemporal characteristics of neuronal sources underlying measured MEG/EEG signals. Several MEG analysis toolboxes include an implementation of a linearly constrained minimum-variance (LCMV) beamformer. However, differences in implementations and in their results complicate the selection and application of beamformers and may hinder their wider adoption in research and clinical use. Additionally, combinations of different MEG sensor types (such as magnetometers and planar gradiometers) and application of preprocessing methods for interference suppression, such as signal space separation (SSS), can affect the results in different ways for different implementations. So far, a systematic evaluation of the different implementations has not been performed. Here, we compared the localization performance of the LCMV beamformer pipelines in four widely used open-source toolboxes (MNE-Python, FieldTrip, DAiSS (SPM12), and Brainstorm) using datasets both with and without SSS interference suppression.We analyzed MEG data that were i) simulated, ii) recorded from a static and moving phantom, and iii) recorded from a healthy volunteer receiving auditory, visual, and somatosensory stimulation. We also investigated the effects of SSS and the combination of the magnetometer and gradiometer signals. We quantified how localization error and point-spread volume vary with the signal-to-noise ratio (SNR) in all four toolboxes.When applied carefully to MEG data with a typical SNR (3–15 ​dB), all four toolboxes localized the sources reliably; however, they differed in their sensitivity to preprocessing parameters. As expected, localizations were highly unreliable at very low SNR, but we found high localization error also at very high SNRs for the first three toolboxes while Brainstorm showed greater robustness but with lower spatial resolution. We also found that the SNR improvement offered by SSS led to more accurate localization.

This technical report describes a dynamic causal model of the spread of coronavirus through a population. The model is based upon ensemble or population dynamics that generate outcomes, like new cases and deaths over time. The purpose of this model is to quantify the uncertainty that attends predictions of relevant outcomes. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Technically, this model leverages state-of-the-art variational (Bayesian) model inversion and comparison procedures, originally developed to characterise the responses of neuronal ensembles to perturbations. Here, this modelling is applied to epidemiological populations—to illustrate the kind of inferences that are supported and how the model per se can be optimised given timeseries data. Although the purpose of this paper is to describe a modelling protocol, the results illustrate some interesting perspectives on the current pandemic; for example, the nonlinear effects of herd immunity that speak to a self-organised mitigation process.

This paper asks whether integrating multimodal EEG and fMRI data offers a better characterisation of functional brain architectures than either modality alone. This evaluation rests upon a dynamic causal model that generates both EEG and fMRI data from the same neuronal dynamics. We introduce the use of Bayesian fusion to provide informative (empirical) neuronal priors – derived from dynamic causal modelling (DCM) of EEG data – for subsequent DCM of fMRI data. To illustrate this procedure, we generated synthetic EEG and fMRI timeseries for a mismatch negativity (or auditory oddball) paradigm, using biologically plausible model parameters (i.e., posterior expectations from a DCM of empirical, open access, EEG data). Using model inversion, we found that Bayesian fusion provided a substantial improvement in marginal likelihood or model evidence, indicating a more efficient estimation of model parameters, in relation to inverting fMRI data alone. We quantified the benefits of multimodal fusion with the information gain pertaining to neuronal and haemodynamic parameters – as measured by the Kullback-Leibler divergence between their prior and posterior densities. Remarkably, this analysis suggested that EEG data can improve estimates of haemodynamic parameters; thereby furnishing proof-of-principle that Bayesian fusion of EEG and fMRI is necessary to resolve conditional dependencies between neuronal and haemodynamic estimators. These results suggest that Bayesian fusion may offer a useful approach that exploits the complementary temporal (EEG) and spatial (fMRI) precision of different data modalities. We envisage the procedure could be applied to any multimodal dataset that can be explained by a DCM with a common neuronal parameterisation.

Parkinson's disease (PD) is a movement disorder caused by dopaminergic neurodegeneration. Levodopa (L-dopa) is an effective medication for alleviating motor symptoms in PD that has been shown previously to reduce subcortical beta (13–30 Hz) oscillations. How L-dopa influences oscillations in the motor cortex is unclear. In this study, 21 PD patients were recorded with magnetoencephalography (MEG) in L-dopa ON and OFF states. Oscillatory components of resting-state power spectra were compared between the two states and the significant effect was localized using beamforming. Unified Parkinson's Disease Rating Scale (UPDRS) III akinesia and rigidity sub-scores for the most affected hemibody were correlated with source power values for the contralateral hemisphere. An L-dopa-induced power increase was found over the central sensors significant in the 18–30 Hz range (F(1,20) > 14.8, PFWE corr

The subthalamic nucleus (STN) is a common target for deep brain stimulation (DBS) treatment in Parkinson’s disease (PD) but much less frequently targeted for other disorders. Here we report the results of simultaneous local field potential (LFP) recordings and magnetoencephalography (MEG) in a single patient who was implanted bilaterally in the STN for the treatment of dystonia induced by chorea-acanthocytosis. Consistent with the previous results in PD, the dystonia patient showed significant subthalamo-cortical coherence in the high beta band (28–35 Hz) on both sides localized to the mesial sensorimotor areas. In addition, on the right side, significant coherence was found in the theta-alpha band (4–12 Hz) that localized to the medial prefrontal cortex with the peak in the anterior cingulate gyrus. Comparison of STN power spectra with a previously reported PD cohort showed increased power in the theta and alpha bands and decreased power in the low beta band in dystonia which is consistent with most of the previous studies. The present report extends the range of disorders for which cortico-subthalamic oscillatory connectivity has been characterized. Our results strengthen the evidence that at least some of the subthalamo-cortical oscillatory coherent networks are a feature of the healthy brain, although we do not rule out that coherence magnitude could be affected by disease.

We describe the steps involved in analysis of multi-modal, multi-subject human neuroimaging data using the SPM12 free and open source software (https://www.fil.ion.ucl.ac.uk/spm/) and a publically-available dataset organized according to the Brain Imaging Data Structure (BIDS) format (https://openneuro.org/datasets/ds000117/). The dataset contains electroencephalographic (EEG), magnetoencephalographic (MEG), and functional and structural magnetic resonance imaging (MRI) data from 16 subjects who undertook multiple runs of a simple task performed on a large number of famous, unfamiliar and scrambled faces. We demonstrate: (1) batching and scripting of preprocessing of multiple runs/subjects of combined MEG and EEG data, (2) creation of trial-averaged evoked responses, (3) source-reconstruction of the power (induced and evoked) across trials within a time-frequency window around the “N/M170” evoked component, using structural MRI for forward modeling and simultaneous inversion (fusion) of MEG and EEG data, (4) group-based optimisation of spatial priors during M/EEG source reconstruction using fMRI data on the same paradigm, and (5) statistical mapping across subjects of cortical source power increases for faces vs. scrambled faces.

Rett syndrome is a devastating neurodevelopmental disorder, primarily caused by mutations of methyl CpG-binding protein 2 (MeCP2). Although the genetic cause of disease was identified over a decade ago, a significant gap still remains in both our clinical and scientific understanding of its pathogenesis. Neurons are known to be primary players in pathology, with their dysfunction being the key in Rett syndrome. While studies in mice have demonstrated a clear causative – and potential therapeutic – role for neurons in Rett syndrome, recent work has suggested that other tissues also contribute significantly to progression of the disease. Indeed, Rett syndrome is known to present with several common peripheral pathologies, such as osteopenia, scoliosis, gastrointestinal problems including nutritional defects, and general growth deficit. Mouse models assessing the potential role of non-neuronal cell types have confirmed both roles in disease and potential therapeutic targets. A new picture is emerging in which neurons both initiate and drive pathology, while dysfunction of other cell types and peripheral tissues exacerbate disease, possibly amplifying further neurologic problems, and ultimately result in a positive feedback loop of progressively worsening symptoms. Here, we review what is known about neuronal and non-neuronal cell types, and discuss how this new, integrative understanding of the disease may allow for additional clinical and scientific pathways for treating and understanding Rett syndrome.

In this paper we investigated the dopaminergic modulation of neuronal interactions occurring in the subthalamic nucleus (STN) during Parkinson’s disease (PD). We utilized linear measures of local and long range synchrony such as power and coherence, as well as Detrended Fluctuation Analysis for Phase Synchrony (DFA-PS)- a recently developed non-linear method that computes the extent of long tailed autocorrelations present in the phase interactions between two coupled signals. Through analysis of local field potentials (LFPs) taken from the STN we seek to determine changes in the neurodynamics that may underpin the pathophysiology of PD in a group of 12 patients who had undergone surgery for deep brain stimulation. We demonstrate up modulation of alpha-theta (5-12 Hz) band power in response to L-DOPA treatment, whilst low beta band power (15-20 Hz) band-power is suppressed. We also find evidence for significant local connectivity within the region surrounding STN although there was no evidence for modulation via administration of L-DOPA. Further to this we present evidence for a positive correlation between the phase ordering of bilateral STN interactions and the severity of bradykinetic and rigidity symptoms in PD. Although the ability of non-linear measures to predict clinical state did not exceed standard measures such as beta power, these measures may help identify the connections which play a role in pathological dynamics.

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.

Cortico-basal ganglia-thalamocortical circuits are severely disrupted by the dopamine depletion of Parkinson's disease (PD), leading to pathologically exaggerated beta oscillations. Abnormal rhythms, found in several circuit nodes are correlated with movement impairments but their neural basis remains unclear. Here, we used dynamic causal modelling (DCM) and the 6-hydroxydopamine-lesioned rat model of PD to examine the effective connectivity underlying these spectral abnormalities. We acquired auto-spectral and cross-spectral measures of beta oscillations (10-35 Hz) from local field potential recordings made simultaneously in the frontal cortex, striatum, external globus pallidus (GPe) and subthalamic nucleus (STN), and used these data to optimise neurobiologically plausible models. Chronic dopamine depletion reorganised the cortico-basal ganglia-thalamocortical circuit, with increased effective connectivity in the pathway from cortex to STN and decreased connectivity from STN to GPe. Moreover, a contribution analysis of the Parkinsonian circuit distinguished between pathogenic and compensatory processes and revealed how effective connectivity along the indirect pathway acquired a strategic importance that underpins beta oscillations. In modelling excessive beta synchrony in PD, these findings provide a novel perspective on how altered connectivity in basal ganglia-thalamocortical circuits reflects a balance between pathogenesis and compensation, and predicts potential new therapeutic targets to overcome dysfunctional oscillations.

Macrophages are versatile immune cells that can detect a variety of pathogen-associated molecular patterns through their Toll-like receptors (TLRs). In response to microbial challenge, the TLR-stimulated macrophage undergoes an activation program controlled by a dynamically inducible transcriptional regulatory network. Mapping a complex mammalian transcriptional network poses significant challenges and requires the integration of multiple experimental data types. In this work, we inferred a transcriptional network underlying TLR-stimulated murine macrophage activation. Microarray-based expression profiling and transcription factor binding site motif scanning were used to infer a network of associations between transcription factor genes and clusters of co-expressed target genes. The time-lagged correlation was used to analyze temporal expression data in order to identify potential causal influences in the network. A novel statistical test was developed to assess the significance of the time-lagged correlation. Several associations in the resulting inferred network were validated using targeted ChIP-on-chip experiments. The network incorporates known regulators and gives insight into the transcriptional control of macrophage activation. Our analysis identified a novel regulator (TGIF1) that may have a role in macrophage activation.