Spatiotemporal Properties of Common Semantic Categories for Words and Pictures.

The timing of semantic processing during object recognition in the brain is a topic of ongoing discussion. One way of addressing this question is by applying multivariate pattern analysis to human electrophysiological responses to object images of different semantic categories. However, although multivariate pattern analysis can reveal whether neuronal activity patterns are distinct for different stimulus categories, concerns remain on whether low-level visual features also contribute to the classification results. To circumvent this issue, we applied a cross-decoding approach to magnetoencephalography data from stimuli from two different modalities: images and their corresponding written words. We employed items from three categories and presented them in a randomized order. We show that if the classifier is trained on words, pictures are classified between 150 and 430 msec after stimulus onset, and when training on pictures, words are classified between 225 and 430 msec. The topographical map, identified using a searchlight approach for cross-modal activation in both directions, showed left lateralization, confirming the involvement of linguistic representations. These results point to semantic activation of pictorial stimuli occurring at ∼150 msec, whereas for words, the semantic activation occurs at ∼230 msec.

Post-task responses following working memory and movement are driven by transient spectral bursts with similar characteristics.

The post-movement beta rebound has been studied extensively using magnetoencephalography (MEG) and is reliably modulated by various task parameters as well as illness. Our recent study showed that rebounds, which we generalise as "post-task responses" (PTRs), are a ubiquitous phenomenon in the brain, occurring across the cortex in theta, alpha, and beta bands. Currently, it is unknown whether PTRs following working memory are driven by transient bursts, which are moments of short-lived high amplitude activity, similar to those that drive the post-movement beta rebound. Here, we use three-state univariate hidden Markov models (HMMs), which can identify bursts without a priori knowledge of frequency content or response timings, to compare bursts that drive PTRs in working memory and visuomotor MEG datasets. Our results show that PTRs across working memory and visuomotor tasks are driven by pan-spectral transient bursts. These bursts have very similar spectral content variation over the cortex, correlating strongly between the two tasks in the alpha (R2 = .89) and beta (R2 = .53) bands. Bursts also have similar variation in duration over the cortex (e.g., long duration bursts occur in the motor cortex for both tasks), strongly correlating over cortical regions between tasks (R2 = .56), with a mean over all regions of around 300 ms in both datasets. Finally, we demonstrate the ability of HMMs to isolate signals of interest in MEG data, such that the HMM probability timecourse correlates more strongly with reaction times than frequency filtered power envelopes from the same brain regions. Overall, we show that induced PTRs across different tasks are driven by bursts with similar characteristics, which can be identified using HMMs. Given the similarity between bursts across tasks, we suggest that PTRs across the cortex may be driven by a common underlying neural phenomenon.

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

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

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.

Rhythmic Modulation of Visual Perception by Continuous Rhythmic Auditory Stimulation.

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

The relationship between frequency content and representational dynamics in the decoding of neurophysiological data.

Decoding of high temporal resolution, stimulus-evoked neurophysiological data is increasingly used to test theories about how the brain processes information. However, a fundamental relationship between the frequency spectra of the neural signal and the subsequent decoding accuracy timecourse is not widely recognised. We show that, in commonly used instantaneous signal decoding paradigms, each sinusoidal component of the evoked response is translated to double its original frequency in the subsequent decoding accuracy timecourses. We therefore recommend, where researchers use instantaneous signal decoding paradigms, that more aggressive low pass filtering is applied with a cut-off at one quarter of the sampling rate, to eliminate representational alias artefacts. However, this does not negate the accompanying interpretational challenges. We show that these can be resolved by decoding paradigms that utilise both a signal's instantaneous magnitude and its local gradient information as features for decoding. On a publicly available MEG dataset, this results in decoding accuracy metrics that are higher, more stable over time, and free of the technical and interpretational challenges previously characterised. We anticipate that a broader awareness of these fundamental relationships will enable stronger interpretations of decoding results by linking them more clearly to the underlying signal characteristics that drive them.

Mixtures of large-scale dynamic functional brain network modes.

Accurate temporal modelling of functional brain networks is essential in the quest for understanding how such networks facilitate cognition. Researchers are beginning to adopt time-varying analyses for electrophysiological data that capture highly dynamic processes on the order of milliseconds. Typically, these approaches, such as clustering of functional connectivity profiles and Hidden Markov Modelling (HMM), assume mutual exclusivity of networks over time. Whilst a powerful constraint, this assumption may be compromising the ability of these approaches to describe the data effectively. Here, we propose a new generative model for functional connectivity as a time-varying linear mixture of spatially distributed statistical "modes". The temporal evolution of this mixture is governed by a recurrent neural network, which enables the model to generate data with a rich temporal structure. We use a Bayesian framework known as amortised variational inference to learn model parameters from observed data. We call the approach DyNeMo (for Dynamic Network Modes), and show using simulations it outperforms the HMM when the assumption of mutual exclusivity is violated. In resting-state MEG, DyNeMo reveals a mixture of modes that activate on fast time scales of 100-150 ms, which is similar to state lifetimes found using an HMM. In task MEG data, DyNeMo finds modes with plausible, task-dependent evoked responses without any knowledge of the task timings. Overall, DyNeMo provides decompositions that are an approximate remapping of the HMM's while showing improvements in overall explanatory power. However, the magnitude of the improvements suggests that the HMM's assumption of mutual exclusivity can be reasonable in practice. Nonetheless, DyNeMo provides a flexible framework for implementing and assessing future modelling developments.

Bacterial diketopiperazines stimulate diatom growth and lipid accumulation.

Diatoms are photosynthetic microalgae that fix a significant fraction of the world's carbon. Because of their photosynthetic efficiency and high-lipid content, diatoms are priority candidates for biofuel production. Here, we report that sporulating Bacillus thuringiensis and other members of the Bacillus cereus group, when in co-culture with the marine diatom Phaeodactylum tricornutum, significantly increase diatom cell count. Bioassay-guided purification of the mother cell lysate of B. thuringiensis led to the identification of two diketopiperazines (DKPs) that stimulate both P. tricornutum growth and increase its lipid content. These findings may be exploited to enhance P. tricornutum growth and microalgae-based biofuel production. As increasing numbers of DKPs are isolated from marine microbes, the work gives potential clues to bacterial-produced growth factors for marine microalgae.

Potentially Preventable Primary Cesarean Sections in Future Placenta Accreta Spectrum.

OBJECTIVE: Prior cesarean delivery is a well-known risk factor for placenta accreta spectrum disorders. While primary cesarean section is unavoidable in some patients, in others it may not be clearly indicated. The aim of the study is to determine the proportion of patients with placenta accreta spectrum who had a potentially preventable primary cesarean section and to identify factors associated with preventable placenta accreta spectrum. STUDY DESIGN: This was a single-center retrospective cohort study of women with pathology-confirmed placenta accreta spectrum from 2007 to 2019. Primary cesarean sections were categorized as potentially preventable or unpreventable based on practice consistent with the American College of Obstetricians and Gynecologists and the Society for Maternal-Fetal Medicine "Safe Prevention of the Primary Cesarean Delivery" recommendations. Fisher's exact test and Mann-Whitney U-test were used for comparison with p <0.05 considered statistically significant. RESULTS: Seventy-two patients had pathology-confirmed placenta accreta spectrum over the course of the study period, 15 (20.8%) of whom required a cesarean hysterectomy at the time of primary cesarean section. Fifty-seven patients had placenta accreta spectrum in a pregnancy following their primary cesarean section. Of these, 29 (50.9%) were considered potentially preventable. Most were performed without clear medical indication (37.9%) or for fetal malpresentation without attempted external cephalic version (37.9%). The remainder were due to arrest of labor not meeting criteria (17.2%) and abnormal or indeterminate fetal heart patterns with documented recovery (6.9%). Of the 11 patients without clear medical indication for primary cesarean section, eight (72.7%) were patient-choice cesarean sections and three (27.3%) were for suspected fetal macrosomia with estimated fetal weights not meeting criteria for cesarean delivery. There was no difference in the incidence of potentially preventable primary cesarean sections before and after the ACOG-SMFM "Safe Prevention of the Primary Cesarean Delivery" publication (48.8 vs. 57.1%, p = 0.59). Privately insured patients were more likely to have a potentially preventable primary cesarean section than those with Medicaid (62.5 vs. 23.5%, p = 0.008) and were more likely to have a primary cesarean section without clear medical indication (81.8 vs. 18.2%, p = 0.004). CONCLUSION: Many patients with placenta accreta spectrum had a potentially preventable primary cesarean section. Most were performed without clear medical indication or for malpresentation without attempted external cephalic version, suggesting that at least a subset of placenta accreta spectrum cases may be preventable. This was particularly true for privately insured patients. These findings call for continued investigation of potentially preventable primary cesarean sections with initiatives to address concerns at the patient, provider, and hospital level. KEY POINTS: · Many patients with placenta accreta spectrum have potentially preventable primary cesarean sections.. · Privately insured patients are more likely to have potentially preventable primary cesarean sections.. · Our findings suggest that at least a subset of placenta accreta spectrum cases may be preventable..

Delineating between-subject heterogeneity in alpha networks with Spatio-Spectral Eigenmodes.

Between subject variability in the spatial and spectral structure of oscillatory networks can be highly informative but poses a considerable analytic challenge. Here, we describe a data-driven modal decomposition of a multivariate autoregressive model that simultaneously identifies oscillations by their peak frequency, damping time and network structure. We use this decomposition to define a set of Spatio-Spectral Eigenmodes (SSEs) providing a parsimonious description of oscillatory networks. We show that the multivariate system transfer function can be rewritten in these modal coordinates, and that the full transfer function is a linear superposition of all modes in the decomposition. The modal transfer function is a linear summation and therefore allows for single oscillatory signals to be isolated and analysed in terms of their spectral content, spatial distribution and network structure. We validate the method on simulated data and explore the structure of whole brain oscillatory networks in eyes-open resting state MEG data from the Human Connectome Project. We are able to show a wide between participant variability in peak frequency and network structure of alpha oscillations and show a distinction between occipital 'high-frequency alpha' and parietal 'low-frequency alpha'. The frequency difference between occipital and parietal alpha components is present within individual participants but is partially masked by larger between subject variability; a 10Hz oscillation may represent the high-frequency occipital component in one participant and the low-frequency parietal component in another. This rich characterisation of individual neural phenotypes has the potential to enhance analyses into the relationship between neural dynamics and a person's behavioural, cognitive or clinical state.

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.

Automatic decomposition of electrophysiological data into distinct nonsinusoidal oscillatory modes.

Neurophysiological signals are often noisy, nonsinusoidal, and consist of transient bursts. Extraction and analysis of oscillatory features (such as waveform shape and cross-frequency coupling) in such data sets remains difficult. This limits our understanding of brain dynamics and its functional importance. Here, we develop iterated masking empirical mode decomposition (itEMD), a method designed to decompose noisy and transient single-channel data into relevant oscillatory modes in a flexible, fully data-driven way without the need for manual tuning. Based on empirical mode decomposition (EMD), this technique can extract single-cycle waveform dynamics through phase-aligned instantaneous frequency. We test our method by extensive simulations across different noise, sparsity, and nonsinusoidality conditions. We find itEMD significantly improves the separation of data into distinct nonsinusoidal oscillatory components and robustly reproduces waveform shape across a wide range of relevant parameters. We further validate the technique on multimodal, multispecies electrophysiological data. Our itEMD extracts known rat hippocampal θ waveform asymmetry and identifies subject-specific human occipital α without any prior assumptions about the frequencies contained in the signal. Notably, it does so with significantly less mode mixing compared with existing EMD-based methods. By reducing mode mixing and simplifying interpretation of EMD results, itEMD will enable new analyses into functional roles of neural signals in behavior and disease.NEW & NOTEWORTHY We introduce a novel, data-driven method to identify oscillations in neural recordings. This approach is based on empirical mode decomposition and reduces mixing of components, one of its main problems. The technique is validated and compared with existing methods using simulations and real data. We show our method better extracts oscillations and their properties in highly noisy and nonsinusoidal datasets.

Within-cycle instantaneous frequency profiles report oscillatory waveform dynamics.

The nonsinusoidal waveform is emerging as an important feature of neuronal oscillations. However, the role of single-cycle shape dynamics in rapidly unfolding brain activity remains unclear. Here, we develop an analytical framework that isolates oscillatory signals from time series using masked empirical mode decomposition to quantify dynamical changes in the shape of individual cycles (along with amplitude, frequency, and phase) with instantaneous frequency. We show how phase-alignment, a process of projecting cycles into a regularly sampled phase grid space, makes it possible to compare cycles of different durations and shapes. "Normalized shapes" can then be constructed with high temporal detail while accounting for differences in both duration and amplitude. We find that the instantaneous frequency tracks nonsinusoidal shapes in both simulated and real data. Notably, in local field potential recordings of mouse hippocampal CA1, we find that theta oscillations have a stereotyped slow-descending slope in the cycle-wise average yet exhibit high variability on a cycle-by-cycle basis. We show how principal component analysis allows identification of motifs of theta cycle waveform that have distinct associations to cycle amplitude, cycle duration, and animal movement speed. By allowing investigation into oscillation shape at high temporal resolution, this analytical framework will open new lines of inquiry into how neuronal oscillations support moment-by-moment information processing and integration in brain networks.NEW & NOTEWORTHY We propose a novel analysis approach quantifying nonsinusoidal waveform shape. The approach isolates oscillations with empirical mode decomposition before waveform shape is quantified using phase-aligned instantaneous frequency. This characterizes the full shape profile of individual cycles while accounting for between-cycle differences in duration, amplitude, and timing. We validated in simulations before applying to identify a range of data-driven nonsinusoidal shape motifs in hippocampal theta oscillations.

Effect of TPE vs medical management on patient outcomes in the setting of hypertriglyceridemia-induced acute pancreatitis with severely elevated triglycerides.

OBJECTIVES: Hypertriglyceridemia-induced acute pancreatitis (HTG-AP) accounts for 1 to 10% of pancreatitis cases, and is associated with a more severe clinical course. Therapeutic plasma exchange (TPE) is a potential treatment option for quickly lowering plasma triglycerides (TG). Current ASFA guidelines define HTG-AP as a Category III disorder, indicating the role of apheresis is not firmly established. Here, we examine clinical data regarding its effectiveness on morbidity and mortality in patients with HTG-AP presenting with severely elevated plasma triglycerides (>4000 mg/dl). METHODS: We retrospectively examined clinical data and outcomes from 67 consecutive episodes of HTG-AP over a 5-year period in which either medical management alone or medical management plus adjunct TPE was employed to reduce plasma triglycerides. RESULTS: 16/67 admissions involved TPE, initiated at a mean of 0.7 days from the time of presentation, while 51 received medical management alone. After only one TPE procedure, the mean TG values decreased from 4103 to 1045 mg/dl (a reduction of 74.7%), and those receiving TPE reached plasma TG < 1000 mg/dl 0.99 days faster than the medical group. One patient in the TPE group died. However, when excluding patients with hospital courses complicated by multiple organ dysfunction, there was no significant difference in mortality or hospital length of stay (LOS) between the groups. CONCLUSIONS: In uncomplicated cases of HTG-AP with an absence of multiorgan dysfunction, there is no significant benefit to either mortality or LOS when adding adjunct TPE to medical management, even when patients present with severely elevated levels of TG.

Post-stimulus beta responses are modulated by task duration.

Modulation of beta-band neural oscillations during and following movement is a robust marker of brain function. In particular, the post-movement beta rebound (PMBR), which occurs on movement cessation, has been related to inhibition and connectivity in the healthy brain, and is perturbed in disease. However, to realise the potential of the PMBR as a biomarker, its modulation by task parameters must be characterised and its functional role determined. Here, we used MEG to image brain electrophysiology during and after a grip-force task, with the aim to characterise how task duration, in the form of an isometric contraction, modulates beta responses. Fourteen participants exerted a 30% maximum voluntary grip-force for 2, 5 and 10 s. Our results showed that the amplitude of the PMBR is modulated by task duration, with increasing duration significantly reducing PMBR amplitude and increasing its time-to-peak. No variation in the amplitude of the movement related beta decrease (MRBD) with task duration was observed. To gain insight into what may underlie these trial-averaged results, we used a Hidden Markov Model to identify the individual trial dynamics of a brain network encompassing bilateral sensorimotor areas. The rapidly evolving dynamics of this network demonstrated similar variation with task parameters to the 'classical' rebound, and we show that the modulation of the PMBR can be well-described in terms of increased frequency of beta events on a millisecond timescale rather than modulation of beta amplitude during this time period. Our results add to the emerging picture that, in the case of a carefully controlled paradigm, beta modulation can be systematically controlled by task parameters and such control can reveal new information as to the processes that generate the average beta timecourse. These findings will support design of clinically relevant paradigms and analysis pipelines in future use of the PMBR as a marker of neuropathology.

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

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

Tracking dynamic brain networks using high temporal resolution MEG measures of functional connectivity.

Fluctuations in functional interactions between brain regions typically occur at the millisecond time scale. Conventional connectivity metrics are not adequately time-resolved to detect such fast fluctuations in functional connectivity. At the same time, attempts to use conventional metrics in a time-resolved manner usually come with the selection of sliding windows of fixed arbitrary length. In the current work, we evaluated the use of high temporal resolution metrics of functional connectivity in conjunction with non-negative tensor factorisation to detect fast fluctuations in connectivity and temporally evolving subnetworks. To this end, we used the phase difference derivative, wavelet coherence, and we also introduced a new metric, the instantaneous amplitude correlation. In order to deal with the inherently noisy nature of magnetoencephalography data and large datasets, we make use of recurrence plots and we used pair-wise orthogonalisation to avoid spurious estimates of functional connectivity due to signal leakage. Firstly, metrics were evaluated in the context of dynamically coupled neural mass models in the presence and absence of delays and also compared to conventional static metrics with fixed sliding windows. Simulations showed that these high temporal resolution metrics outperformed conventional static connectivity metrics. Secondly, the sensitivity of the metrics to fluctuations in connectivity was analysed in post-movement beta rebound magnetoencephalography data, which showed time locked sensorimotor subnetworks that modulated with the post-movement beta rebound. Finally, sensitivity of the metrics was evaluated in resting-state magnetoencephalography, showing similar spatial patterns across metrics, thereby indicating the robustness of the current analysis. The current methods can be applied in cognitive experiments that involve fast modulations in connectivity in relation to cognition. In addition, these methods could also be used as input to temporal graph analysis to further characterise the rapid fluctuation in brain network topology.

Do the posterior midline cortices belong to the electrophysiological default-mode network?

The default-mode network (DMN) and its principal core hubs in the posterior midline cortices (PMC), i.e., the precuneus and the posterior cingulate cortex, play a critical role in the human brain structural and functional architecture. Because of their centrality, they are affected by a wide spectrum of brain disorders, e.g., Alzheimer's disease. Non-invasive electrophysiological techniques such as magnetoencephalography (MEG) are crucial to the investigation of the neurophysiology of the DMN and its alteration by brain disorders. However, MEG studies relying on band-limited power envelope correlation diverge in their ability to identify the PMC as a part of the DMN in healthy subjects at rest. Since these works were based on different MEG recording systems and different source reconstruction pipelines, we compared DMN functional connectivity estimated with two distinct MEG systems (Elekta, now MEGIN, and CTF) and two widely used reconstruction algorithms (Minimum Norm Estimation and linearly constrained minimum variance Beamformer). Our results identified the reconstruction method as the critical factor influencing PMC functional connectivity, which was significantly dampened by the Beamformer. On this basis, we recommend that future electrophysiological studies on the DMN should rely on Minimum Norm Estimation (or close variants) rather than on the classical Beamformer. Crucially, based on analytic knowledge about these two reconstruction algorithms, we demonstrated with simulations that this empirical observation could be explained by the existence of a spontaneous linear, approximately zero-lag synchronization structure between areas of the DMN or among multiple sources within the PMC. This finding highlights a novel property of the neural dynamics and functional architecture of a core human brain network at rest.

Impact of Hormone Therapy on Laboratory Values in Transgender Patients.

BACKGROUND: For transgender individuals taking hormone therapy (HT), data on laboratory values are limited, and the effects on laboratory values cannot be easily predicted. We evaluated the impact on common laboratory analytes in transgender individuals before and after initiation of HT. METHODS: We conducted a retrospective chart review of transgender patients identified at transgender-specific clinics at an urban county hospital and community clinic. Laboratory data were collected on hormone concentrations, hematologic parameters, electrolytes, lipids, and liver and renal markers before and after initiation of HT. RESULTS: We identified 183 transgender women (TW) and 119 transgender men (TM) for whom laboratory data were available. In all, 87 TW and 62 TM had baseline laboratory data, and data were also available for 133 TW and 89 TM on HT for >6 months. The most significant changes were seen in red blood cell count, hemoglobin concentration, hematocrit, and creatinine levels after >6 months of HT, which increased in TM and decreased in TW after HT (P < 0.005; d index > 0.6). Alkaline phosphatase, aspartate aminotransferase, and alanine aminotransferase levels increased in TM; however, the effect size was small (d index < 0.5). Calcium, albumin, and alkaline phosphatase levels significantly decreased in TW (P < 0.001; d > 0.6). Additionally, TM were found to have increased triglycerides and decreased HDL levels (P < 0.005; d > 0.6). CONCLUSIONS: Changes occur in several common laboratory parameters for patients on HT. Some laboratory values changed to match the gender identity, whereas others remained unchanged or were intermediate from the baseline values. These findings will help guide interpretation of laboratory test results in transgender patients taking HT.

Unpacking Transient Event Dynamics in Electrophysiological Power Spectra.

Electrophysiological recordings of neuronal activity show spontaneous and task-dependent changes in their frequency-domain power spectra. These changes are conventionally interpreted as modulations in the amplitude of underlying oscillations. However, this overlooks the possibility of underlying transient spectral 'bursts' or events whose dynamics can map to changes in trial-average spectral power in numerous ways. Under this emerging perspective, a key challenge is to perform burst detection, i.e. to characterise single-trial transient spectral events, in a principled manner. Here, we describe how transient spectral events can be operationalised and estimated using Hidden Markov Models (HMMs). The HMM overcomes a number of the limitations of the standard amplitude-thresholding approach to burst detection; in that it is able to concurrently detect different types of bursts, each with distinct spectral content, without the need to predefine frequency bands of interest, and does so with less dependence on a priori threshold specification. We describe how the HMM can be used for burst detection and illustrate its benefits on simulated data. Finally, we apply this method to empirical data to detect multiple burst types in a task-MEG dataset, and illustrate how we can compute burst metrics, such as the task-evoked timecourse of burst duration.