The methyltransferase Setdb2 mediates virus-induced susceptibility to bacterial superinfection.

Immune responses are tightly regulated to ensure efficient pathogen clearance while avoiding tissue damage. Here we report that Setdb2 was the only protein lysine methyltransferase induced during infection with influenza virus. Setdb2 expression depended on signaling via type I interferons, and Setdb2 repressed expression of the gene encoding the neutrophil attractant CXCL1 and other genes that are targets of the transcription factor NF-κB. This coincided with occupancy by Setdb2 at the Cxcl1 promoter, which in the absence of Setdb2 displayed diminished trimethylation of histone H3 Lys9 (H3K9me3). Mice with a hypomorphic gene-trap construct of Setdb2 exhibited increased infiltration of neutrophils during sterile lung inflammation and were less sensitive to bacterial superinfection after infection with influenza virus. This suggested that a Setdb2-mediated regulatory crosstalk between the type I interferons and NF-κB pathways represents an important mechanism for virus-induced susceptibility to bacterial superinfection.

The TREM2-APOE Pathway Drives the Transcriptional Phenotype of Dysfunctional Microglia in Neurodegenerative Diseases.

Microglia play a pivotal role in the maintenance of brain homeostasis but lose homeostatic function during neurodegenerative disorders. We identified a specific apolipoprotein E (APOE)-dependent molecular signature in microglia from models of amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS), and Alzheimer's disease (AD) and in microglia surrounding neuritic ß-amyloid (Aß)-plaques in the brains of people with AD. The APOE pathway mediated a switch from a homeostatic to a neurodegenerative microglia phenotype after phagocytosis of apoptotic neurons. TREM2 (triggering receptor expressed on myeloid cells 2) induced APOE signaling, and targeting the TREM2-APOE pathway restored the homeostatic signature of microglia in ALS and AD mouse models and prevented neuronal loss in an acute model of neurodegeneration. APOE-mediated neurodegenerative microglia had lost their tolerogenic function. Our work identifies the TREM2-APOE pathway as a major regulator of microglial functional phenotype in neurodegenerative diseases and serves as a novel target that could aid in the restoration of homeostatic microglia.

Spatiotemporal signal space separation for regions of interest: Application for extracting neuromagnetic responses evoked by deep brain stimulation.

Magnetoencephalography (MEG) recordings are often contaminated by interference that can exceed the amplitude of physiological brain activity by several orders of magnitude. Furthermore, the activity of interference sources may spatially extend (known as source leakage) into the activity of brain signals of interest, resulting in source estimation inaccuracies. This problem is particularly apparent when using MEG to interrogate the effects of brain stimulation on large-scale cortical networks. In this technical report, we develop a novel denoising approach for suppressing the leakage of interference source activity into the activity representing a brain region of interest. This approach leverages spatial and temporal domain projectors for signal arising from prespecified anatomical regions of interest. We apply this denoising approach to reconstruct simulated evoked response topographies to deep brain stimulation (DBS) in a phantom recording. We highlight the advantages of our approach compared to the benchmark-spatiotemporal signal space separation-and show that it can more accurately reveal brain stimulation-evoked response topographies. Finally, we apply our method to MEG recordings from a single patient with Parkinson's disease, to reveal early cortical-evoked responses to DBS of the subthalamic nucleus.

Methyl-CpG Binding Protein 2 Regulates Microglia and Macrophage Gene Expression in Response to Inflammatory Stimuli.

Mutations in MECP2, encoding the epigenetic regulator methyl-CpG-binding protein 2, are the predominant cause of Rett syndrome, a disease characterized by both neurological symptoms and systemic abnormalities. Microglial dysfunction is thought to contribute to disease pathogenesis, and here we found microglia become activated and subsequently lost with disease progression in Mecp2-null mice. Mecp2 was found to be expressed in peripheral macrophage and monocyte populations, several of which also became depleted in Mecp2-null mice. RNA-seq revealed increased expression of glucocorticoid- and hypoxia-induced transcripts in Mecp2-deficient microglia and peritoneal macrophages. Furthermore, Mecp2 was found to regulate inflammatory gene transcription in response to TNF stimulation. Postnatal re-expression of Mecp2 using Cx3cr1(creER) increased the lifespan of otherwise Mecp2-null mice. These data suggest that Mecp2 regulates microglia and macrophage responsiveness to environmental stimuli to promote homeostasis. Dysfunction of tissue-resident macrophages might contribute to the systemic pathologies observed in Rett syndrome.

Conflict Detection in a Sequential Decision Task Is Associated with Increased Cortico-Subthalamic Coherence and Prolonged Subthalamic Oscillatory Response in the ß Band.

Making accurate decisions often involves the integration of current and past evidence. Here, we examine the neural correlates of conflict and evidence integration during sequential decision-making. Female and male human patients implanted with deep-brain stimulation (DBS) electrodes and age-matched and gender-matched healthy controls performed an expanded judgment task, in which they were free to choose how many cues to sample. Behaviorally, we found that while patients sampled numerically more cues, they were less able to integrate evidence and showed suboptimal performance. Using recordings of magnetoencephalography (MEG) and local field potentials (LFPs; in patients) in the subthalamic nucleus (STN), we found that ß oscillations signaled conflict between cues within a sequence. Following cues that differed from previous cues, ß power in the STN and cortex first decreased and then increased. Importantly, the conflict signal in the STN outlasted the cortical one, carrying over to the next cue in the sequence. Furthermore, after a conflict, there was an increase in coherence between the dorsal premotor cortex and STN in the ß band. These results extend our understanding of cortico-subcortical dynamics of conflict processing, and do so in a context where evidence must be accumulated in discrete steps, much like in real life. Thus, the present work leads to a more nuanced picture of conflict monitoring systems in the brain and potential changes because of disease.SIGNIFICANCE STATEMENT Decision-making often involves the integration of multiple pieces of information over time to make accurate predictions. We simultaneously recorded whole-head magnetoencephalography (MEG) and local field potentials (LFPs) from the human subthalamic nucleus (STN) in a novel task which required integrating sequentially presented pieces of evidence. Our key finding is prolonged ß oscillations in the STN, with a concurrent increase in communication with frontal cortex, when presented with conflicting information. These neural effects reflect the behavioral profile of reduced tendency to respond after conflict, as well as relate to suboptimal cue integration in patients, which may be directly linked to clinically reported side-effects of deep-brain stimulation (DBS) such as impaired decision-making and impulsivity.

Multi-modal and multi-model interrogation of large-scale functional brain networks.

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.

Stimulating at the right time to recover network states in a model of the cortico-basal ganglia-thalamic circuit.

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.

Balance between competing spectral states in subthalamic nucleus is linked to motor impairment in Parkinson's disease.

Exaggerated local field potential bursts of activity at frequencies in the low beta band are a well-established phenomenon in the subthalamic nucleus of patients with Parkinson's disease. However, such activity is only moderately correlated with motor impairment. Here we test the hypothesis that beta bursts are just one of several dynamic states in the subthalamic nucleus local field potential in Parkinson's disease, and that together these different states predict motor impairment with high fidelity. Local field potentials were recorded in 32 patients (64 hemispheres) undergoing deep brain stimulation surgery targeting the subthalamic nucleus. Recordings were performed following overnight withdrawal of anti-parkinsonian medication, and after administration of levodopa. Local field potentials were analysed using hidden Markov modelling to identify transient spectral states with frequencies under 40 Hz. Findings in the low beta frequency band were similar to those previously reported; levodopa reduced occurrence rate and duration of low beta states, and the greater the reductions, the greater the improvement in motor impairment. However, additional local field potential states were distinguished in the theta, alpha and high beta bands, and these behaved in an opposite manner. They were increased in occurrence rate and duration by levodopa, and the greater the increases, the greater the improvement in motor impairment. In addition, levodopa favoured the transition of low beta states to other spectral states. When all local field potential states and corresponding features were considered in a multivariate model it was possible to predict 50% of the variance in patients' hemibody impairment OFF medication, and in the change in hemibody impairment following levodopa. This only improved slightly if signal amplitude or gamma band features were also included in the multivariate model. In addition, it compares with a prediction of only 16% of the variance when using beta bursts alone. We conclude that multiple spectral states in the subthalamic nucleus local field potential have a bearing on motor impairment, and that levodopa-induced shifts in the balance between these states can predict clinical change with high fidelity. This is important in suggesting that some states might be upregulated to improve parkinsonism and in suggesting how local field potential feedback can be made more informative in closed-loop deep brain stimulation systems.

Functional connectivity maps of theta/alpha and beta coherence within the subthalamic nucleus region.

The subthalamic nucleus (STN) is a primary target for deep brain stimulation in Parkinson's disease (PD). Although small in size, the STN is commonly partitioned into sensorimotor, cognitive/associative, and limbic subregions based on its structural connectivity profile to cortical areas. We investigated whether such a regional specialization is also supported by functional connectivity between local field potential recordings and simultaneous magnetoencephalography. Using a novel data set of 21 PD patients, we replicated previously reported cortico-STN coherence networks in the theta/alpha and beta frequency ranges, and looked for the spatial distribution of these networks within the STN region. Although theta/alpha and beta coherence peaks were both observed in on-medication recordings from electrode contacts at several locations within and around the STN, sites with theta/alpha coherence peaks were situated at significantly more inferior MNI coordinates than beta coherence peaks. Sites with only theta/alpha coherence peaks, i.e. without distinct beta coherence, were mostly located near the border of sensorimotor and cognitive/associative subregions as defined by a tractography-based atlas of the STN. Peak coherence values were largely unaltered by the medication state of the subject, however, theta/alpha peaks were more often identified in recordings obtained after administration of dopaminergic medication. Our findings suggest the existence of a frequency-specific topography of cortico-STN coherence within the STN, albeit with considerable spatial overlap between functional networks. Consequently, optimization of deep brain stimulation targeting might remain a trade-off between alleviating motor symptoms and avoiding adverse neuropsychiatric side effects.

A unified view on beamformers for M/EEG source reconstruction.

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.

Cortical connectivity of the nucleus basalis of Meynert in Parkinson's disease and Lewy body dementias.

Parkinson's disease dementia (PDD) and dementia with Lewy bodies (DLB) are related conditions that are associated with cholinergic system dysfunction. Dysfunction of the nucleus basalis of Meynert (NBM), a basal forebrain structure that provides the dominant source of cortical cholinergic innervation, has been implicated in the pathogenesis of both PDD and DLB. Here we leverage the temporal resolution of magnetoencephalography with the spatial resolution of MRI tractography to explore the intersection of functional and structural connectivity of the NBM in a unique cohort of PDD and DLB patients undergoing deep brain stimulation of this structure. We observe that NBM-cortical structural and functional connectivity correlate within spatially and spectrally segregated networks including: (i) a beta band network to supplementary motor area, where activity in this region was found to drive activity in the NBM; (ii) a delta/theta band network to medial temporal lobe structures encompassing the parahippocampal gyrus; and (iii) a delta/theta band network to visual areas including lingual gyrus. These findings reveal functional networks of the NBM that are likely to subserve important roles in motor control, memory and visual function, respectively. Furthermore, they motivate future studies aimed at disentangling network contribution to disease phenotype.

Spontaneous transient states of fronto-temporal and default-mode networks altered by suicide attempt in major depressive disorder.

Major depressive disorder (MDD) is associated with increased suicidality, and it's still challenging to identify suicide in clinical practice. Although suicide attempt (SA) is the most relevant precursor with multiple functional abnormalities reported from neuroimaging studies, little is known about how the spontaneous transient activated patterns organize and coordinate brain networks underlying SA. Thus, we obtained resting-state magnetoencephalography data for two MDD subgroups of 44 non-suicide patients and 34 suicide-attempted patients, together with 49 matched health-controls. For the source-space signals, Hidden Markov Model (HMM) helped to capture the sub-second dynamic activity via a hidden sequence of finite number of states. Temporal parameters and spectral activation were acquired for each state and then compared between groups. Here, HMM states characterized the spatiotemporal signatures of eight networks. The activity of suicide attempters switches more frequently into the fronto-temporal network, as the time spent occupancy of fronto-temporal state is increased and interval time is decreased compared with the non-suicide patients. Moreover, these changes are significantly correlated with Nurses' Global Assessment of Suicide Risk scores. Suicide attempters also exhibit increased state-wise activations in the theta band (4-8 Hz) in the posterior default mode network centered on posterior cingulate cortex, which can't be detected in the static spectral analysis. These alternations may disturb the time allocations of cognitive control regulations and cause inflexible decision making to SA. As the better sensitivity of dynamic study in reflecting SA diathesis than the static is validated, dynamic stability could serve as a potential neuronal marker for SA.

Unexpected role of interferon-γ in regulating neuronal connectivity and social behaviour.

Immune dysfunction is commonly associated with several neurological and mental disorders. Although the mechanisms by which peripheral immunity may influence neuronal function are largely unknown, recent findings implicate meningeal immunity influencing behaviour, such as spatial learning and memory. Here we show that meningeal immunity is also critical for social behaviour; mice deficient in adaptive immunity exhibit social deficits and hyper-connectivity of fronto-cortical brain regions. Associations between rodent transcriptomes from brain and cellular transcriptomes in response to T-cell-derived cytokines suggest a strong interaction between social behaviour and interferon-γ (IFN-γ)-driven responses. Concordantly, we demonstrate that inhibitory neurons respond to IFN-γ and increase GABAergic (γ-aminobutyric-acid) currents in projection neurons, suggesting that IFN-γ is a molecular link between meningeal immunity and neural circuits recruited for social behaviour. Meta-analysis of the transcriptomes of a range of organisms reveals that rodents, fish, and flies elevate IFN-γ/JAK-STAT-dependent gene signatures in a social context, suggesting that the IFN-γ signalling pathway could mediate a co-evolutionary link between social/aggregation behaviour and an efficient anti-pathogen response. This study implicates adaptive immune dysfunction, in particular IFN-γ, in disorders characterized by social dysfunction and suggests a co-evolutionary link between social behaviour and an anti-pathogen immune response driven by IFN-γ signalling.

Inference of brain networks with approximate Bayesian computation - assessing face validity with an example application in Parkinsonism.

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.

EEG and MEG primers for tracking DBS network effects.

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.

Dynamic analysis on simultaneous iEEG-MEG data via hidden Markov model.

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 < 0.05 uncorrected) and for 27/50 electrodes these survived within-subject FDR correction (q < 0.05). These correlations peaked in the theta/alpha band. There was a highly significant dependence between the association of states and electrodes based on functional correlations and that based on spatial proximity (p = 5.6e-6,χ2 test for independence). Despite the potentially atypical functional anatomy and physiological abnormalities related to epilepsy, HMM model estimated from the patient group was very similar to that estimated from healthy subjects. CONCLUSION: Epilepsy does not preclude HMM analysis of interictal data. The resulting group functional states are highly similar to those reported for healthy controls. Power changes recorded with iEEG correlate with HMM state time courses in the alpha-theta band and the presence of this correlation can be related to the spatial location of electrode contacts close to the individual peaks of the corresponding state topographies. Thus, the hypothesized relation between iEEG contacts and HMM states exists and HMM could be further explored as a method for identifying comparable iEEG channels across subjects for the purposes of group analysis.

The missing N1 or jittered P2: Electrophysiological correlates of pattern glare in the time and frequency domain.

Excessive sensitivity to certain visual stimuli (cortical hyperexcitability) is associated with a number of neurological disorders including migraine, epilepsy, multiple sclerosis, autism and possibly dyslexia. Others show disruptive sensitivity to visual stimuli with no other obvious pathology or symptom profile (visual stress) which can extend to discomfort and nausea. We used event-related potentials (ERPs) to explore the neural correlates of visual stress and headache proneness. We analysed ERPs in response to thick (0.37 cycles per degree [c/deg]), medium (3 c/deg) and thin (12 c/deg) gratings, using mass univariate analysis, considering three factors in the general population: headache proneness, visual stress and discomfort. We found relationships between ERP features and the headache and discomfort factors. Stimulus main effects were driven by the medium stimulus regardless of participant characteristics. Participants with high discomfort ratings had larger P1 components for the initial presentation of medium stimuli, suggesting initial cortical hyperexcitability that is later suppressed. The participants with high headache ratings showed atypical N1-P2 components for medium stripes relative to the other stimuli. This effect was present only after repeated stimulus presentation. These effects were also explored in the frequency domain, suggesting variations in intertrial theta band phase coherence. Our results suggest that discomfort and headache in response to striped stimuli are related to different neural processes; however, more exploration is needed to determine whether the results translate to a clinical migraine population.

Function of C/EBPdelta in a regulatory circuit that discriminates between transient and persistent TLR4-induced signals.

The innate immune system is like a double-edged sword: it is absolutely required for host defense against infection, but when uncontrolled, it can trigger a plethora of inflammatory diseases. Here we use systems-biology approaches to predict and confirm the existence of a gene-regulatory network involving dynamic interaction among the transcription factors NF-kappaB, C/EBPdelta and ATF3 that controls inflammatory responses. We mathematically modeled transcriptional regulation of the genes encoding interleukin 6 and C/EBPdelta and experimentally confirmed the prediction that the combination of an initiator (NF-kappaB), an amplifier (C/EBPdelta) and an attenuator (ATF3) forms a regulatory circuit that discriminates between transient and persistent Toll-like receptor 4-induced signals. Our results suggest a mechanism that enables the innate immune system to detect the duration of infection and to respond appropriately.

Breaking the circularity in circular analyses: Simulations and formal treatment of the flattened average approach.

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.

Sedation Modulates Frontotemporal Predictive Coding Circuits and the Double Surprise Acceleration Effect.

Two important theories in cognitive neuroscience are predictive coding (PC) and the global workspace (GW) theory. A key research task is to understand how these two theories relate to one another, and particularly, how the brain transitions from a predictive early state to the eventual engagement of a brain-scale state (the GW). To address this question, we present a source-localization of EEG responses evoked by the local-global task-an experimental paradigm that engages a predictive hierarchy, which encompasses the GW. The results of our source reconstruction suggest three phases of processing. The first phase involves the sensory (here auditory) regions of the superior temporal lobe and predicts sensory regularities over a short timeframe (as per the local effect). The third phase is brain-scale, involving inferior frontal, as well as inferior and superior parietal regions, consistent with a global neuronal workspace (GNW; as per the global effect). Crucially, our analysis suggests that there is an intermediate (second) phase, involving modulatory interactions between inferior frontal and superior temporal regions. Furthermore, sedation with propofol reduces modulatory interactions in the second phase. This selective effect is consistent with a PC explanation of sedation, with propofol acting on descending predictions of the precision of prediction errors; thereby constraining access to the GNW.