Non-invasive Electrophysiological Recordings Research Articles

Solvent-Free and Cost-Efficient Fabrication of a High-Performance Nanocomposite Sensor for Recording of Electrophysiological Signals.

Carbon nanotube (CNT)-based nanocomposites have found applications in making sensors for various types of physiological sensing. However, the sensors' fabrication process is usually complex, multistep, and requires longtime mixing and hazardous solvents that can be harmful to the environment. Here, we report a flexible dry silver (Ag)/CNT/polydimethylsiloxane (PDMS) nanocomposite-based sensor made by a solvent-free, low-temperature, time-effective, and simple approach for electrophysiological recording. By mechanical compression and thermal treatment of Ag/CNT, a connected conductive network of the fillers was formed, after which the PDMS was added as a polymer matrix. The CNTs make a continuous network for electrons transport, endowing the nanocomposite with high electrical conductivity, mechanical strength, and durability. This process is solvent-free and does not require a high temperature or complex mixing procedure. The sensor shows high flexibility and good conductivity. High-quality electroencephalography (EEG) and electrooculography (EOG) were performed using fabricated dry sensors. Our results show that the Ag/CNT/PDMS sensor has comparable skin-sensor interface impedance with commercial Ag/AgCl-coated dry electrodes, better performance for noninvasive electrophysiological signal recording, and a higher signal-to-noise ratio (SNR) even after 8 months of storage. The SNR of electrophysiological signal recording was measured to be 26.83 dB for our developed sensors versus 25.23 dB for commercial Ag/AgCl-coated dry electrodes. Our process of compress-heating the functional fillers provides a universal approach to fabricate various types of nanocomposites with different nanofillers and desired electrical and mechanical properties.

Non-Invasive Electroretinogram Recording with Simultaneous Optogenetics to Dissect Retinal Ganglion Cells Electrophysiological Dynamics

Electroretinography (ERG) is a non-invasive electrophysiological recording technique that detects the electrical signaling of neuronal cells in the visual system. In conventional ERG recordings, the signals are considered a collective electrical response from various neuronal cell populations, including rods, cones, bipolar cells, and retinal ganglion cells (RGCs). However, due to the limited ability to control electrophysiological responses from different types of cells, the detailed information underlying ERG signals has not been analyzed and interpreted. Linking the features of ERG signals to the specific neuronal response will advance the understanding of neuronal electrophysiological dynamics and provide more evidence to elucidate pathological mechanisms, such as RGC loss during the progression of glaucoma. Herein, we developed an advanced ERG recording system integrated with a programmable, non-invasive optogenetic stimulation method in mice. In this system, we applied an automatic and unbiased ERG data analysis approach to differentiate a, b wave, negative response, and oscillatory potentials. To differentiate the electrophysiological response of RGCs in ERG recordings, we sensitized mouse RGCs with red-light opsin, ChRmine, through adeno-associated virus (AAV) intravitreal injection. Features of RGC dynamics under red-light stimulation were identified in the ERG readout. This non-invasive ERG recording system, associated with the programmable optogenetics stimulation method, provides a new methodology to dissect neural dynamics under variable physiological and pathological conditions in vivo. With the merits of non-invasiveness, improved sensitivity, and specificity, we envision this system can be further applied for early-stage detection of RGC degeneration and functional progression in neural degenerative diseases, such as glaucoma.

A Waveform-Independent Measure of Recurrent Neural Activity.

Rhythmic neural activity, so-called oscillations, plays a key role in neural information transmission, processing, and storage. Neural oscillations in distinct frequency bands are central to physiological brain function, and alterations thereof have been associated with several neurological and psychiatric disorders. The most common methods to analyze neural oscillations, e.g., short-time Fourier transform or wavelet analysis, assume that measured neural activity is composed of a series of symmetric prototypical waveforms, e.g., sinusoids. However, usually, the models generating the signal, including waveform shapes of experimentally measured neural activity are unknown. Decomposing asymmetric waveforms of nonlinear origin using these classic methods may result in spurious harmonics visible in the estimated frequency spectra. Here, we introduce a new method for capturing rhythmic brain activity based on recurrences of similar states in phase-space. This method allows for a time-resolved estimation of amplitude fluctuations of recurrent activity irrespective of or specific to waveform shapes. The algorithm is derived from the well-established field of recurrence analysis, which, in comparison to Fourier-based analysis, is still very uncommon in neuroscience. In this paper, we show its advantages and limitations in comparison to short-time Fourier transform and wavelet convolution using periodic signals of different waveform shapes. Furthermore, we demonstrate its application using experimental data, i.e., intracranial and noninvasive electrophysiological recordings from the human motor cortex of one epilepsy patient and one healthy adult, respectively.

Energy-Based Hierarchical Clustering of Cortical Slow Waves in Multi-Electrode Recordings.

The recent development of novel multi-electrode recording technologies has revealed the existence of traveling patterns of cortical activity in many species and under different states of awareness. Among these, slow activation waves occurring under sleep and anesthesia have been widely investigated as they provide unique insights into network features such as excitability, connectivity, structure, and dynamics of the cerebral cortex. Such characterization is usually based on clustering methods which are constrained by a priori assumptions as to the number of clusters to be used or rely on wave-by-wave pattern reconstruction. Here, we introduce a new computational tool based on modal analysis of fluid flows which is robustly applied to multivariate electrophysiological data from cortical networks, namely the Energy-based Hierarchical Waves Clustering method (EHWC). EHWC is composed of three main steps: (1) detecting the occurrence of global waves; (2) reducing the data dimensionality via singular value decomposition; (3) clustering hierarchically the singled-out waves. The analysis does not require the single-channel contribution to the waves, which is a typical bottleneck in this kind of analysis due to the unavoidable intrinsic variability of locally recorded activity. For testing and validation, here we used in vivo extracellular recordings from mice cortex under three different levels of anesthesia. As a result, we found slow waves with an increasing number of propagation modes as the anesthesia level decreases, giving an estimate of the increasing complexity of network dynamics. This and other wave's features replicate and extend the findings from previous literature, paving the way to extend the same approach to non-invasive electrophysiological recordings like EEG and fMRI used clinically for the characterization of brain dynamics and clinical stratification in brain lesions.

Cellular Electrophysiological Recording: Mirroring Action Potentials: Label‐Free, Accurate, and Noninvasive Electrophysiological Recordings of Human‐Derived Cardiomyocytes (Adv. Mater. 7/2021)

Cellular Electrophysiological Recording: Mirroring Action Potentials: Label‐Free, Accurate, and Noninvasive Electrophysiological Recordings of Human‐Derived Cardiomyocytes (Adv. Mater. 7/2021)

A non-invasive multimodal foetal ECG\u2013Doppler dataset for antenatal cardiology research

Non-invasive foetal electrocardiography (fECG) continues to be an open topic for research. The development of standard algorithms for the extraction of the fECG from the maternal electrophysiological interference is limited by the lack of publicly available reference datasets that could be used to benchmark different algorithms while providing a ground truth for foetal heart activity when an invasive scalp lead is unavailable. In this work, we present the Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research (NInFEA), the first open-access multimodal early-pregnancy dataset in the field that features simultaneous non-invasive electrophysiological recordings and foetal pulsed-wave Doppler (PWD). The dataset is mainly conceived for researchers working on fECG signal processing algorithms. The dataset includes 60 entries from 39 pregnant women, between the 21st and 27th week of gestation. Each dataset entry comprises 27 electrophysiological channels (2048 Hz, 22 bits), a maternal respiration signal, synchronised foetal trans-abdominal PWD and clinical annotations provided by expert clinicians during signal acquisition. MATLAB snippets for data processing are also provided.

Mirroring Action Potentials: Label-Free, Accurate, and Noninvasive Electrophysiological Recordings of Human-Derived Cardiomyocytes.

The electrophysiological recording of action potentials in human cells is a long-sought objective due to its pivotal importance in many disciplines. Among the developed techniques, invasiveness remains a common issue, causing cytotoxicity or altering unpredictably cell physiological response. In this work, a new approach for recording intracellular signals of outstanding quality and with noninvasiveness is introduced. By taking profit of the concept of mirror charge in classical electrodynamics, the new proposed device transduces cell ionic currents into mirror charges in a microfluidic chamber, thus realizing a virtual mirror cell. By monitoring mirror charge dynamics, it is possible to effectively record the action potentials fired by the cells. Since there is no need for accessing or interacting with the cells, the method is intrinsically noninvasive. In addition, being based on optical recording, it shows high spatial resolution and high parallelization. As shown through a set of experiments, the presented methodology is an ideal candidate for the next generation devices for the reliable assessment of cardiotoxicity on human-derived cardiomyocytes. More generally, it paves the way toward a new family of in vitro biodevices that will lay a new milestone in the field of electrophysiology.

Reduced effective connectivity between right parietal and inferior frontal cortex during audiospatial perception in neglect patients with a right-hemisphere lesion

A lesion to the right hemisphere of the brain in humans commonly leads to perceptual neglect of the left side of the sensorium. The clinical observation that lesions to disparate cortical and subcortical areas converge upon similar behavioural symptoms points to neglect as a dysconnection syndrome that may result from the disruption of a distributed network, rather than aberrant computations in any particular brain region. To test this hypothesis, we used Bayesian analysis of effective connectivity based on electroencephalographic recordings in ten patients (6 male, 4 female; age range 41–68) with left-sided neglect following a right-hemisphere lesion. In line with previous research, age-matched healthy controls showed a contralateral increase in connection strength between parietal and frontal cortex with respect to the laterality of audiospatial oddball stimuli. Neglect patients, however, showed a dysconnection between parietal and frontal cortex in the right hemisphere when oddballs appeared on their left side, but preserved connectivity in the left hemisphere when stimuli appeared on their right. This preserved fronto-parietal connectivity was associated with lower neglect severity. Moreover, we saw ipsilateral fronto-temporal connectivity increases for oddballs appearing on the neglected side, which might be a compensatory mechanism for residual left side awareness. No group differences were found in intrinsic (within-region) connectivity. While further validation is required in a bigger sample, our findings are in keeping with the idea that neglect results from the disruption of a distributed network, rather than a lesion to any single brain region. Significance statementLesions to the right hemisphere of the brain commonly lead to neglect syndrome, characterized by perceptual deficits where patients are unaware of the left side of their body and environment. Using analysis of non-invasive electrophysiological recordings, we provide evidence that patients with left-sided neglect have reduced connectivity between the right parietal and frontal cortex during audiospatial stimuli, but preserved connectivity between regions in the non-lesioned left hemisphere. Moreover, for these intact connections we observed an ipsilateral fronto-temporal increase in connectivity during oddballs appearing on the neglected side, which might be a compensatory mechanism for residual perception. Crucially, we found that patients with more severe neglect symptoms had reduced connectivity between parietal and frontal cortex in the left hemisphere. This suggests that neglect may be caused by the disruption of a distributed network in the brain, rather than a lesion to any particular brain region.

A Glial-Silicon Nanowire Electrode Junction Enabling Differentiation and Noninvasive Recording of Slow Oscillations from Primary Astrocytes.

The correct human brain function is dependent on the activity of non-neuronal cells called astrocytes. The bioelectrical properties of astrocytes in vitro do not closely resemble those displayed in vivo and the former are incapable of generating action potential; thus, reliable approaches in vitro for noninvasive electrophysiological recording of astrocytes remain challenging for biomedical engineering. Here it is found that primary astrocytes grown on a device formed by a forest of randomly oriented gold coated-silicon nanowires, resembling the complex structural and functional phenotype expressed by astrocytes in vivo. The device enables noninvasive extracellular recording of the slow-frequency oscillations generated by differentiated astrocytes, while flat electrodes failed on recording signals from undifferentiated cells. Pathophysiological concentrations of extracellular potassium, occurring during epilepsy and spreading depression, modulate the power of slow oscillations generated by astrocytes. A reliable approach to study the role of astrocytes function in brain physiology and pathologies is presented.

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms.

Non-invasive electrophysiological recordings are useful for the evaluation of nervous system function. These techniques are inexpensive, fast, replicable, and less resource-intensive than imaging. Further, the functional data produced have excellent temporal resolution, which is not achievable with structural imaging. Current applications of electroencephalograms (EEG) are limited by data processing methods. Standard analysis techniques using raw time series data at individual channels are very limited methods of interrogating nervous system activity. More detailed information about cortical function can be achieved by examining relationships between channels and deriving statistical models of how areas are interacting, allowing visualization of connectivity between networks. This manuscript describes a method for deriving statistical models of cortical network activity by recording EEG in a standard manner, then examining the interelectrode coherence measures to assess relationships between the recorded areas. Higher order interactions can be further examined by assessing the covariance between the coherence pairs, producing high-dimensional "maps" of network interactions. These data constructs can be examined to assess cortical network function and its relationship to pathology in ways not achievable with traditional techniques. This approach offers greater sensitivity to network level interactions than is achievable with raw time series analysis. It is, however, limited by the complexity of drawing specific mechanistic conclusions about the underlying neural populations and the high volumes of data generated, requiring more advanced statistical techniques for evaluation, including dimensionality reduction and classifier-based approaches.

Two-Chambered Chitosan Nerve Guides With Increased Bendability Support Recovery of Skilled Forelimb Reaching Similar to Autologous Nerve Grafts in the Rat 10 mm Median Nerve Injury and Repair Model.

Tension-free surgical reconstruction of transected digital nerves in humans is regularly performed using autologous nerve grafts (ANGs) or bioartificial nerve grafts. Nerve grafts with increased bendability are needed to protect regenerating nerves in highly mobile extremity parts. We have recently demonstrated increased bendability and regeneration supporting properties of chitosan nerve guides with a corrugated outer wall (corrCNGs) in the common rat sciatic nerve model (model of low mobility). Here, we further modified the hollow corrCNGs into two-chambered nerve guides by inserting a perforated longitudinal chitosan-film (corrCNG[F]s) and comprehensively monitored functional recovery in the advanced rat median nerve model. In 16 adult female Lewis rats, we bilaterally reconstructed 10 mm median nerve gaps with either ANGs, standard chitosan nerve guides (CNGs), CNGs (CNG[F]s), or corrCNG[F]s (n = 8, per group). Over 16 weeks, functional recovery of each forelimb was separately surveyed using the grasping test (reflex-based motor task), the staircase test (skilled forelimb reaching task), and non-invasive electrophysiological recordings from the thenar muscles. Finally, regenerated tissue harvested from the distal part of the nerve grafts was paraffin-embedded and cross-sections were analyzed regarding the number of Neurofilament 200-immunopositive axons and the area of newly formed blood vessels. Nerve tissue harvested distal to the grafts was epon-embedded and semi-thin cross-sections underwent morphometrical analyses (e.g., number of myelinated axons, axon and fiber diameters, and myelin thicknesses). Functional recovery was fastest and most complete in the ANG group (100% recovery rate regarding all parameters), but corrCNG[F]s accelerated the recovery of all functions evaluated in comparison to the other nerve guides investigated. Furthermore, corrCNG[F]s supported recovery of reflex-based grasping (87.5%) and skilled forelimb reaching (100%) to eventually significantly higher rates than the other nerve guides (grasping test: CNGs: 75%, CNG[F]s: 62.5%; staircase test: CNGs: 66.7%, CNG[F]s: 83.3%). Histological and nerve morphometrical evaluations, in accordance to the functional results, demonstrated best outcome in the ANG group and highest myelin thicknesses in the corrCNG[F] group compared to the CNG and CNG[F] groups. We thus clearly demonstrate that corrCNG[F]s represent promising innovative nerve grafts for nerve repair in mobile body parts such as digits.

Phase shift invariant imaging of coherent sources (PSIICOS) from MEG data

Increasing evidence suggests that neuronal communication is a defining property of functionally specialized brain networks and that it is implemented through synchronization between population activities of distinct brain areas. The detection of long-range coupling in electroencephalography (EEG) and magnetoencephalography (MEG) data using conventional metrics (such as coherence or phase-locking value) is by definition contaminated by spatial leakage. Methods such as imaginary coherence, phase-lag index or orthogonalized amplitude correlations tackle spatial leakage by ignoring zero-phase interactions. Although useful, these metrics will by construction lead to false negatives in cases where true zero-phase coupling exists in the data and will underestimate interactions with phase lags in the vicinity of zero. Yet, empirically observed neuronal synchrony in invasive recordings indicates that it is not uncommon to find zero or close-to-zero phase lag between the activity profiles of coupled neuronal assemblies.Here, we introduce a novel method that allows us to mitigate the undesired spatial leakage effects and detect zero and near zero phase interactions. To this end, we propose a projection operation that operates on sensor-space cross-spectrum and suppresses the spatial leakage contribution but retains the true zero-phase interaction component. We then solve the network estimation task as a source estimation problem defined in the product space of interacting source topographies. We show how this framework provides reliable interaction detection for all phase-lag values and we thus refer to the method as Phase Shift Invariant Imaging of Coherent Sources (PSIICOS).Realistic simulations demonstrate that PSIICOS has better detector characteristics than existing interaction metrics. Finally, we illustrate the performance of PSIICOS by applying it to real MEG dataset recorded during a standard mental rotation task. Taken together, using analytical derivations, data simulations and real brain data, this study presents a novel source-space MEG/EEG connectivity method that overcomes previous limitations and for the first time allows for the estimation of true zero-phase coupling via non-invasive electrophysiological recordings.

First-night effect on sleep time in dairy cows.

In human sleep studies, the probability of discomfort from the electrodes and the change in environment usually results in first-night recordings being discarded. Sleep recordings from the first night in human subjects often differ in amount of REM (rapid eye movement) sleep and the overall sleep architecture. This study investigated whether recordings of sleep states in dairy cows also show a first-night effect. Non-invasive electrophysiological recordings were carried out on nine cows of the Swedish Red breed during three consecutive 24-hour periods (recording days 1–3). Overall, cows spent 12.9 ± 1.4 hours awake, 8.2 ± 1 hours ruminating, 57.2 ± 20.3 min drowsing, 44.1 ± 20.2 min in REM sleep and 64.3 ± 38.1 min in NREM (non-rapid eye movement) sleep (mean ± SD) and there were no significant differences between recording days in total duration for any of the sleep and awake states. However, the bouts of REM sleep and rumination were longer, and the awake bouts were shorter, at night time compared to daytime, regardless of recording day. The awake bouts also showed an interaction effect with longer bouts at daytime during day 1 compared to daytime on day 3. Data on sleep and awake states recorded in adult dairy cows during three consecutive 24-h periods showed great variation in sleep time between cows, but total time for each state was not significantly affected by recording day. Further and more detailed studies of how sleep architecture is affected by recording day is necessary to fully comprehend the first-night effect in dairy cows.

Reflex-based grasping, skilled forelimb reaching, and electrodiagnostic evaluation for comprehensive analysis of functional recovery-The 7-mm rat median nerve gap repair model revisited.

IntroductionThe rat median nerve injury and repair model gets increasingly important for research on novel bioartificial nerve grafts. It allows follow‐up evaluation of the recovery of the forepaw functional ability with several sensitive techniques. The reflex‐based grasping test, the skilled forelimb reaching staircase test, as well as electrodiagnostic recordings have been described useful in this context. Currently, no standard values exist, however, for comparison or comprehensive correlation of results obtained in each of the three methods after nerve gap repair in adult rats.MethodsHere, we bilaterally reconstructed 7‐mm median nerve gaps with autologous nerve grafts (ANG) or autologous muscle‐in‐vein grafts (MVG), respectively. During 8 and 12 weeks of observation, functional recovery of each paw was separately monitored using the grasping test (weekly), the staircase test, and noninvasive electrophysiological recordings from the thenar muscles (both every 4 weeks). Evaluation was completed by histomorphometrical analyses at 8 and 12 weeks postsurgery.ResultsThe comprehensive evaluation detected a significant difference in the recovery of forepaw functional motor ability between the ANG and MVG groups. The correlation between the different functional tests evaluated precisely displayed the recovery of distinct levels of forepaw functional ability over time.ConclusionThus, this multimodal evaluation model represents a valuable preclinical model for peripheral nerve reconstruction approaches.

Classification of Mental States Based on Spatiospectral Patterns of Brain Electrical Activity

Abstract: Classification of human thought is an emerging research field that may allow us to understand human brainfunctions and further develop advanced brain-computer interface (BCI) systems. In the present study, we introducea new approach to classify various mental states from noninvasive electrophysiological recordings of human brainactivity. We utilized the full spatial and spectral information contained in the electroencephalography (EEG) signalsrecorded while a subject is performing a specific mental task. For this, the EEG data were converted into a 2D spa-tiospectral pattern map, of which each element was filled with 1, 0, and -1 reflecting the degrees of event-relatedsynchronization (ERS) and event-related desynchronization (ERD). We evaluated the similarity between a current(input) 2D pattern map and the template pattern maps (database), by taking the inner-product of pattern matrices.Then, the current 2D pattern map was assigned to a class that demonstrated the highest similarity value. For theverification of our approach, eight participants took part in the present study; their EEG data were recorded whilethey performed four different cognitive imagery tasks. Consistent ERS/ERD patterns were observed more frequentlybetween trials in the same class than th ose in different classes, indicating that th ese spatiospectral pattern maps couldbe used to classify different mental states. The classification accuracy was evaluated for each participant from boththe proposed approach and a conventional mental state classification method based on the inter-hemispheric spectralpower asymmetry, using the leave-one-out cross-validation (LOOCV). An average accuracy of 68.13% (± 9.64%) wasattained for the proposed method; whereas an average accuracy of 57% (± 5.68%) was attained for the conventionalmethod (significance was assessed by the one-tail paired t-test, p < 0.01), showing that the proposed simple clas-sification approach might be one of the promising methods in discriminating various mental states.Key words:brain-computer interface (BCI), spatiospectral pattern, mental state classification, mind reading, elec-troencephalography (EEG)

Neural mechanisms underlying neurooptometric rehabilitation following traumatic brain injury.

Mild to severe traumatic brain injuries have lasting effects on everyday functioning. Issues relating to sensory problems are often overlooked or not addressed until well after the onset of the injury. In particular, vision problems related to ambient vision and the magnocellular pathway often result in posttrauma vision syndrome or visual midline shift syndrome. Symptoms from these syndromes are not restricted to the visual domain. Patients commonly experience proprioceptive, kinesthetic, vestibular, cognitive, and language problems. Neurooptometric rehabilitation often entails the use of corrective lenses, prisms, and binasal occlusion to accommodate the unstable magnocellular system. However, little is known regarding the neural mechanisms engaged during neurooptometric rehabilitation, nor how these mechanisms impact other domains. Event-related potentials from noninvasive electrophysiological recordings can be used to assess rehabilitation progress in patients. In this case report, high-density visual event-related potentials were recorded from one patient with posttrauma vision syndrome and secondary visual midline shift syndrome during a pattern reversal task, both with and without prisms. Results indicate that two factors occurring during the end portion of the P148 component (168–256 milliseconds poststimulus onset) map onto two separate neural systems that were engaged with and without neurooptometric rehabilitation. Without prisms, neural sources within somatosensory, language, and executive brain regions engage inefficient magnocellular system processing. However, when corrective prisms were worn, primary visual areas were appropriately engaged. The impact of using early neurooptometric rehabilitation for posttrauma vision syndrome, visual midline shift syndrome, and other similar subtle vision disorders to support neural reorganization is discussed.

Quantifying additive evoked contributions to the event-related potential

Event-related potentials (ERPs) are widely used in basic neuroscience and in clinical diagnostic procedures. In contrast, neurophysiological insights from ERPs have been limited, as several different mechanisms lead to ERPs. Apart from stereotypically repeated responses (additive evoked responses), these mechanisms are asymmetric amplitude modulations and phase-resetting of ongoing oscillatory activity. Therefore, a method is needed that differentiates between these mechanisms and moreover quantifies the stability of a response. We propose a constrained subspace independent component analysis that exploits the multivariate information present in the all-to-all relationship of recordings over trials. Our method identifies additive evoked activity and quantifies its stability over trials. We evaluate identification performance for biologically plausible simulation data and two neurophysiological test cases: Local field potential (LFP) recordings from a visuo-motor-integration task in the awake behaving macaque and magnetoencephalography (MEG) recordings of steady-state visual evoked fields (SSVEFs). In the LFPs we find additive evoked response contributions in visual areas V2/4 but not in primary motor cortex A4, although visually triggered ERPs were also observed in area A4. MEG-SSVEFs were mainly created by additive evoked response contributions. Our results demonstrate that the identification of additive evoked response contributions is possible both in invasive and in non-invasive electrophysiological recordings.

A mutual information analysis of neural coding of speech by low-frequency MEG phase information

Recent work has implicated low-frequency (<20 Hz) neuronal phase information as important for both auditory (<10 Hz) and speech [theta (∼4-8 Hz)] perception. Activity on the timescale of theta corresponds linguistically to the average length of a syllable, suggesting that information within this range has consequences for segmentation of meaningful units of speech. Longer timescales that correspond to lower frequencies [delta (1-3 Hz)] also reflect important linguistic features-prosodic/suprasegmental-but it is unknown whether the patterns of activity in this range are similar to theta. We investigate low-frequency activity with magnetoencephalography (MEG) and mutual information (MI), an analysis that has not yet been applied to noninvasive electrophysiological recordings. We find that during speech perception each frequency subband examined [delta (1-3 Hz), theta(low) (3-5 Hz), theta(high) (5-7 Hz)] processes independent information from the speech stream. This contrasts with hypotheses that either delta and theta reflect their corresponding linguistic levels of analysis or each band is part of a single holistic onset response that tracks global acoustic transitions in the speech stream. Single-trial template-based classifier results further validate this finding: information from each subband can be used to classify individual sentences, and classifier results that utilize the combination of frequency bands provide better results than single bands alone. Our results suggest that during speech perception low-frequency phase of the MEG signal corresponds to neither abstract linguistic units nor holistic evoked potentials but rather tracks different aspects of the input signal. This study also validates a new method of analysis for noninvasive electrophysiological recordings that can be used to formally characterize information content of neural responses and interactions between these responses. Furthermore, it bridges results from different levels of neurophysiological study: small-scale multiunit recordings and local field potentials and macroscopic magneto/electrophysiological noninvasive recordings.

Dynamic Causal Modeling of Subcortical Connectivity of Language

Subcortical-cortical interactions in the language network were investigated using dynamic causal modeling of magnetoencephalographic data recorded during auditory comprehension. Participants heard sentences that either were correct or contained violations. Sentences containing violations had syntactic or prosodic violations or both. We show that a hidden source, modeling magnetically silent deep nuclei, is required to explain the data best. This is in line with recent brain imaging studies and intracranial recordings suggesting an involvement of subcortical structures in language processing. Here, the processing of syntactic and prosodic violations elicited a global increase in the amplitude of evoked responses, both at the cortical and subcortical levels. As estimated by Bayesian model averaging, this was accompanied by various changes in cortical-cortical and subcortical-cortical connectivity. The most consistent findings in relation to violations were a decrease of reentrant inputs to Heschl's gyrus (HG) and of transcallosal lateral connections. These results suggest that in conditions where one hemisphere detects a violation, possibly via fast thalamocortical (HG) loops, the intercallosal connectivity is reduced to allow independent processing of syntax (left hemisphere) and of prosody (right hemisphere). This study is the first demonstration in cognitive neuroscience that subcortical-cortical loops can be empirically investigated using noninvasive electrophysiological recordings.

Dynamic causal models of steady-state responses

In this paper, we describe a dynamic causal model (DCM) of steady-state responses in electrophysiological data that are summarised in terms of their cross-spectral density. These spectral data-features are generated by a biologically plausible, neural-mass model of coupled electromagnetic sources; where each source comprises three sub-populations. Under linearity and stationarity assumptions, the model's biophysical parameters (e.g., post-synaptic receptor density and time constants) prescribe the cross-spectral density of responses measured directly (e.g., local field potentials) or indirectly through some lead-field (e.g., electroencephalographic and magnetoencephalographic data). Inversion of the ensuing DCM provides conditional probabilities on the synaptic parameters of intrinsic and extrinsic connections in the underlying neuronal network. This means we can make inferences about synaptic physiology, as well as changes induced by pharmacological or behavioural manipulations, using the cross-spectral density of invasive or non-invasive electrophysiological recordings. In this paper, we focus on the form of the model, its inversion and validation using synthetic and real data. We conclude with an illustrative application to multi-channel local field potential data acquired during a learning experiment in mice.