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Neural responses to heartbeats in the default network encode the self in spontaneous thoughts
(2016)
The default network (DN) has been consistently associated with self-related cognition, but alsoto bodily state monitoring and autonomic regulation. We hypothesized that these two seemingly disparate functional roles of the DN are functionally coupled, in line with theories proposing that selfhood is grounded in the neural monitoring of internal organs, such as the heart. We measured with magnetoencephalograhy neural responses evoked by heartbeats while human participants freely mind-wandered. When interrupted by a visual stimulus at random intervals, participants scored the self-relatedness of the interrupted thought. They evaluated their involvement as the firstperson perspective subject or agent inthethought (“I”), and on another scaleto what degreethey werethinking aboutthemselves (“Me”). During the interrupted thought, neural responses to heartbeats in two regions of the DN, the ventral precuneus and the ventromedial prefrontal cortex, covaried, respectively, with the “I” and the “Me” dimensions of the self, even at the single-trial level. No covariation between self-relatedness and peripheral autonomic measures (heart rate, heart rate variability, pupil diameter, electrodermal activity, respiration rate, and phase) or alpha power was observed. Our results reveal a direct link between selfhood and neural responses to heartbeats in the DN and thus directly support theories grounding selfhood in the neural monitoring of visceral inputs. More generally, the tight functional coupling between self-related processing and cardiac monitoring observed here implies that, even in the absence of measured changes in peripheral bodily measures, physiological and cognitive functions have to be considered jointly in the DN.
A body of research demonstrates convincingly a role for synchronization of auditory cortex to rhythmic structure in sounds including speech and music. Some studies hypothesize that an oscillator in auditory cortex could underlie important temporal processes such as segmentation and prediction. An important critique of these findings raises the plausible concern that what is measured is perhaps not an oscillator but is instead a sequence of evoked responses. The two distinct mechanisms could look very similar in the case of rhythmic input, but an oscillator might better provide the computational roles mentioned above (i.e., segmentation and prediction). We advance an approach to adjudicate between the two models: analyzing the phase lag between stimulus and neural signal across different stimulation rates. We ran numerical simulations of evoked and oscillatory computational models, showing that in the evoked case,phase lag is heavily rate-dependent, while the oscillatory model displays marked phase concentration across stimulation rates. Next, we compared these model predictions with magnetoencephalography data recorded while participants listened to music of varying note rates. Our results show that the phase concentration of the experimental data is more in line with the oscillatory model than with the evoked model. This finding supports an auditory cortical signal that (i) contains components of both bottom-up evoked responses and internal oscillatory synchronization whose strengths are weighted by their appropriateness for particular stimulus types and (ii) cannot be explained by evoked responses alone.
To a crucial extent, the efficiency of reading results from the fact that visual word recognition is faster in predictive contexts. Predictive coding models suggest that this facilitation results from pre-activation of predictable stimulus features across multiple representational levels before stimulus onset. Still, it is not sufficiently understood which aspects of the rich set of linguistic representations that are activated during reading—visual, orthographic, phonological, and/or lexical-semantic—contribute to context-dependent facilitation. To investigate in detail which linguistic representations are pre-activated in a predictive context and how they affect subsequent stimulus processing, we combined a well-controlled repetition priming paradigm, including words and pseudowords (i.e., pronounceable nonwords), with behavioral and magnetoencephalography measurements. For statistical analysis, we used linear mixed modeling, which we found had a higher statistical power compared to conventional multivariate pattern decoding analysis. Behavioral data from 49 participants indicate that word predictability (i.e., context present vs. absent) facilitated orthographic and lexical-semantic, but not visual or phonological processes. Magnetoencephalography data from 38 participants show sustained activation of orthographic and lexical-semantic representations in the interval before processing the predicted stimulus, suggesting selective pre-activation at multiple levels of linguistic representation as proposed by predictive coding. However, we found more robust lexical-semantic representations when processing predictable in contrast to unpredictable letter strings, and pre-activation effects mainly resembled brain responses elicited when processing the expected letter string. This finding suggests that pre-activation did not result in “explaining away” predictable stimulus features, but rather in a “sharpening” of brain responses involved in word processing.
Speech production involves widely distributed brain regions. This MEG study focuses on the spectro-temporal dynamics that contribute to the setup of this network. In 21 participants performing a cue-target reading paradigm, we analyzed local oscillations during preparation for overt and covert reading in the time-frequency domain and localized sources using beamforming. Network dynamics were studied by comparing different dynamic causal models of beta phase coupling in and between hemispheres. While a broadband low frequency effect was found for any task preparation in bilateral prefrontal cortices, preparation for overt speech production was specifically associated with left-lateralized alpha and beta suppression in temporal cortices and beta suppression in motor-related brain regions. Beta phase coupling in the entire speech production network was modulated by anticipation of overt reading. We propose that the processes underlying the setup of the speech production network connect relevant brain regions by means of beta synchronization and prepare the network for left-lateralized information routing by suppression of inhibitory alpha and beta oscillations.
Current theories of the pathophysiology of schizophrenia have focused on abnormal temporal coordination of neural activity. Oscillations in the gamma-band range (>25 Hz) are of particular interest as they establish synchronization with great precision in local cortical networks. However, the contribution of high gamma (>60 Hz) oscillations toward the pathophysiology is less established. To address this issue, we recorded magnetoencephalographic (MEG) data from 16 medicated patients with chronic schizophrenia and 16 controls during the perception of Mooney faces. MEG data were analysed in the 25–150 Hz frequency range. Patients showed elevated reaction times and reduced detection rates during the perception of upright Mooney faces while responses to inverted stimuli were intact. Impaired processing of Mooney faces in schizophrenia patients was accompanied by a pronounced reduction in spectral power between 60–120 Hz (effect size: d = 1.26) which was correlated with disorganized symptoms (r = −0.72). Our findings demonstrate that deficits in high gamma-band oscillations as measured by MEG are a sensitive marker for aberrant cortical functioning in schizophrenia, suggesting an important aspect of the pathophysiology of the disorder.
Eine wichtige Eigenschaft des menschlichen Gehirns besteht in der Fähigkeit, flexibel auf eintreffende Reize zu reagieren und sich den Anforderungen und Veränderungen der Umwelt anzupassen. Anpassung oder Adaptation lässt sich in vielen Situationen beobachten. Beispielsweise kommt es in der Retina beim Übergang von einer sehr hellen Umgebung in eine dunkle Umgebung zu Anpassungsleistungen. Neuronale Adaptation wird in den Neurowissenschaften genutzt, um Aussagen über die Funktion bestimmter Hirnareale machen zu können. In sogenannten Adaptationsexperimenten werden Stimuli wiederholt dargeboten und die dadurch erzeugten neuronalen Antworten in verschiedenen Hirnarealen miteinander verglichen. Nimmt das Signal in einem Areal ab, dann wird daraus geschlossen, dass die Zellen in diesem Bereich an der Verarbeitung des Stimulus beteiligt waren. Wiederholte Reizdarbietung führte in zahlreichen Untersuchungen zu einer Abnahme der neuronalen Antwort. Daneben wurde jedoch auch der gegenteilige Effekt, eine Verstärkung der neuronalen Antwort, bei Wiederholung eines Reizes nachgewiesen. In der vorliegenden Arbeit wurde die Verarbeitung frequenzmodulierter Töne im auditorischen Kortex des Menschen mit Hilfe eines Wiederholungsparadigmas untersucht. Frequenzmodulationen sind eine beim Menschen noch wenig untersuchte Reizklasse, die in natürlichen Geräuschen und besonders in der menschlichen Sprache eine wichtige Rolle spielen. Ausgangspunkt dieser Arbeit war die Frage, ob sich im auditorischen Kortex des Menschen eine Sensitivität für die Richtung einer Frequenzmodulation nachweisen lässt. Dieser Frage wurde mit drei Magnetenzephalographie-Studien nachgegangen. In Studie 1 wurde ein Zwei-Ton-Paradigma angewendet. Dabei wurde in jedem Durchgang ein frequenzmodulierter Ton jeweils zwei Mal präsentiert. Lediglich die Richtung, in der der Ton abgespielt wurde, also von den niedrigen zu den hohen oder von den hohen zu den niedrigen Frequenzen, wurde variiert. Die beiden frequenzmodulierten Töne in Studie 1 hatten eine Dauer von 500 ms und wurden in einem Abstand von 1 Sekunde präsentiert. Mit dieser Versuchsanordnung sollte untersucht werden, ob es bei Wiederholung der Frequenzrichtung zu einer Abnahme des neuronalen Signals kommt. Diese Abnahme wurde vor allem in der N1m-Komponente aber auch in späteren Komponenten wie der N2m erwartet. Der Vergleich der N1m-Amplitude für den zweiten Ton zeigte jedoch nur geringe Unterschiede zwischen den Bedingungen. Die Wiederholung derselben Frequenzrichtung bewirkte nur eine schwache Abnahme des Signals. Deutliche Adaptationseffekte konnten nicht gefunden werden. Daneben zeigten sich Hemisphärenunterschiede bei der Verarbeitung der frequenzmodulierten Töne. Über den Sensoren der rechten Hemisphäre war die Antwort signifikant stärker ausgeprägt als über der linken Hemisphäre. Als mögliche Erklärung für die schwach ausgeprägten Adaptationseffekte in Studie 1 wurde der zeitliche Aufbau des Paradigmas herangezogen. In der zweiten Studie wurde daher sowohl die Dauer der präsentierten Stimuli als auch der zeitliche Abstand zwischen den beiden Tönen reduziert. Dieses Paradigma führte zu signifikanten Unterschieden in der Reaktion auf den zweiten Reiz. Entgegen der Erwartung einer Adaptation bei Reizwiederholung bewirkte die Wiederholung derselben Frequenzrichtung eine signifikant höhere neuronale Antwort im Vergleich zu der Präsentation einer abweichenden Frequenzrichtung. Diese Unterschiede traten auf der rechten Hemisphäre über einen Zeitraum von 150 bis 350 ms nach Beginn des zweiten Stimulus auf, während sich auf der linken Hemisphäre 200 bis 300 ms nach Beginn des zweiten Tons signifikante Unterschiede zwischen gleichen und unterschiedlichen Frequenzrichtungen zeigten. In der N1m-Amplitude zeigten sich dagegen keine Wiederholungseffekte. Ähnlich wie in Studie 1 traten auch in Studie 2 Hemisphärenunterschiede auf. Für die Sensoren der rechten Hemisphäre waren die Verstärkungseffekte stärker und über einen längeren Zeitraum zu beobachten. Das unerwartete Ergebnis von Studie 2 stellte die Motivation für den Aufbau der dritten Studie dar. Mithilfe dieser Studie sollte überprüft werden, welche Rolle das Inter-Stimulus-Intervall auf die Verarbeitung eines nachfolgenden Stimulus hat. Zu diesem Zweck wurde in Studie 3 die Länge des ISIs zwischen 100 und 600 ms variiert. Damit sollte zum einen überprüft werden, innerhalb welchen zeitlichen Bereichs es zu einer Verstärkung des Signals kommt und wann beziehungsweise ob es ab einem bestimmten zeitlichen Abstand zwischen den Stimuli zu Adaptationsprozessen kommt. Bei dem kürzesten ISI von 100 ms führte die Wiederholung derselben Frequenzrichtung zu einer signifikant stärkeren N1m-Amplitude als bei der Präsentation einer abweichenden Frequenzrichtung. Bei ISIs > 100 ms zeigte sich keine höhere N1m-Amplitude mehr bei Wiederholung derselben Frequenzrichtung. Deutliche späte Effekte wie sie in Studie 2 über einen Zeitbereich von 150 bis 300 ms nachgewiesen wurden, traten in Studie 3 nicht auf. Bei einem ISI von 300 bis 500 ms waren leichte Verstärkungseffekte in einem Zeitbereich von 200 bis 400 ms zu beobachten. Bei einem ISI von 600 ms zeigten sich keine Unterschiede zwischen den Bedingungen. Durch Studie 3 konnte der in Studie 2 gefundene Effekt in einen größeren Zusammenhang gestellt werden. Zu einer Verstärkung der N1m-Komponente kommt es lediglich bei einem ISI von 100 ms. Liegen die Stimuli 200 ms auseinander, findet eine Verstärkung der späteren Komponenten statt, die bei ISIs > 200 ms immer weiter abnimmt. Offen bleibt, welche Abläufe zu der Verstärkung des Signals bei Wiederholung der Frequenzrichtung geführt haben. Um die dem Verstärkungseffekt zugrunde liegenden Prozesse zu verstehen, sind weitere Studien nötig.
Magnetoencephalography (MEG) and Electroencephalography (EEG) provide direct electrophysiological measures at an excellent temporal resolution, but the spatial resolution of source-reconstructed current activity is limited to several millimetres. Here we show, using simulations of MEG signals and Bayesian model comparison, that non-invasive myelin estimates from high-resolution quantitative magnetic resonance imaging (MRI) can enhance MEG/EEG source reconstruction. Our approach assumes that MEG/EEG signals primarily arise from the synchronised activity of pyramidal cells, and since most of the myelin in the cortical sheet originates from these cells, myelin density can predict the strength of cortical sources measured by MEG/EEG. Leveraging recent advances in quantitative MRI, we exploit this structure-function relationship and scale the leadfields of the forward model according to the local myelin density estimates from in vivo quantitative MRI to inform MEG/EEG source reconstruction. Using Bayesian model comparison and dipole localisation errors (DLEs), we demonstrate that adapting local forward fields to reflect increased local myelination at the site of a simulated source explains the simulated data better than models without such leadfield scaling. Our model comparison framework proves sensitive to myelin changes in simulations with exact coregistration and moderate-to-high sensor-level signal-to-noise ratios (≥10 dB) for the multiple sparse priors (MSP) and empirical Bayesian beamformer (EBB) approaches. Furthermore, we sought to infer the microstructure giving rise to specific functional activation patterns by comparing the myelin-informed model which was used to generate the activation with a set of test forward models incorporating different myelination patterns. We found that the direction of myelin changes, however not their magnitude, can be inferred by Bayesian model comparison. Finally, we apply myelin-informed forward models to MEG data from a visuo-motor experiment. We demonstrate improved source reconstruction accuracy using myelin estimates from a quantitative longitudinal relaxation (R1) map and discuss the limitations of our approach.
Highlights
We use quantitative MRI to implement myelin-informed forward models for M/EEG
Local myelin density was modelled by adapting the local leadfields
Myelin-informed forward models can improve source reconstruction accuracy
We can infer the directionality of myelination patterns, but not their strength
We apply our approach to MEG data from a visuo-motor experiment
We present an approach for combining high resolution MRI-based myelin mapping with functional information from electroencephalography (EEG) or magnetoencephalography (MEG). The main contribution to the primary currents detectable with EEG and MEG comes from ionic currents in the apical dendrites of cortical pyramidal cells, aligned perpendicularly to the local cortical surface. We provide evidence from an in-vivo experiment that the variation in MRI-based myeloarchitecture measures across the cortex predicts the variation of the current density over individuals and thus is of functional relevance. Equivalent current dipole locations and moments due to pitch onset evoked response fields (ERFs) were estimated by means of a variational Bayesian algorithm. The myeloarchitecture was estimated indirectly from individual high resolution quantitative multi-parameter maps (MPMs) acquired at 800 μm isotropic resolution. Myelin estimates across cortical areas correlated positively with dipole magnitude. This correlation was spatially specific: regions of interest in the auditory cortex provided significantly better models than those covering whole hemispheres. Based on the MPM data we identified the auditory cortical area TE1.2 as the most likely origin of the pitch ERFs measured by MEG. We can now proceed to exploit the higher spatial resolution of quantitative MPMs to identify the cortical origin of M/EEG signals, inform M/EEG source reconstruction and explore structure–function relationships at a fine structural level in the living human brain.
Current theories of schizophrenia (ScZ) posit that the symptoms and cognitive dysfunctions arise from a dysconnection syndrome. However, studies that have examined this hypothesis with physiological data at realistic time scales are so far scarce. The current study employed a state-of-the-art approach using Magnetoencephalography (MEG) to test alterations in large-scale phase synchronization in a sample of n = 16 chronic ScZ patients, 10 males and n = 19 healthy participants, 10 males, during a perceptual closure task. We identified large-scale networks from source reconstructed MEG data using data-driven analyses of neuronal synchronization. Oscillation amplitudes and interareal phase-synchronization in the 3–120 Hz frequency range were estimated for 400 cortical parcels and correlated with clinical symptoms and neuropsychological scores. ScZ patients were characterized by a reduction in γ-band (30–120 Hz) oscillation amplitudes that was accompanied by a pronounced deficit in large-scale synchronization at γ-band frequencies. Synchronization was reduced within visual regions as well as between visual and frontal cortex and the reduction of synchronization correlated with elevated clinical disorganization. Accordingly, these data highlight that ScZ is associated with a profound disruption of transient synchronization, providing critical support for the notion that core aspect of the pathophysiology arises from an impairment in coordination of distributed neural activity.
Analyzing non-invasive recordings of electroencephalography (EEG) and magnetoencephalography (MEG) directly in sensor space, using the signal from individual sensors, is a convenient and standard way of working with this type of data. However, volume conduction introduces considerable challenges for sensor space analysis. While the general idea of signal mixing due to volume conduction in EEG/MEG is recognized, the implications have not yet been clearly exemplified. Here, we illustrate how different types of activity overlap on the level of individual sensors. We show spatial mixing in the context of alpha rhythms, which are known to have generators in different areas of the brain. Using simulations with a realistic 3D head model and lead field and data analysis of a large resting-state EEG dataset, we show that electrode signals can be differentially affected by spatial mixing by computing a sensor complexity measure. While prominent occipital alpha rhythms result in less heterogeneous spatial mixing on posterior electrodes, central electrodes show a diversity of rhythms present. This makes the individual contributions, such as the sensorimotor mu-rhythm and temporal alpha rhythms, hard to disentangle from the dominant occipital alpha. Additionally, we show how strong occipital rhythms can contribute the majority of activity to frontal channels, potentially compromising analyses that are solely conducted in sensor space. We also outline specific consequences of signal mixing for frequently used assessment of power, power ratios and connectivity profiles in basic research and for neurofeedback application. With this work, we hope to illustrate the effects of volume conduction in a concrete way, such that the provided practical illustrations may be of use to EEG researchers to in order to evaluate whether sensor space is an appropriate choice for their topic of investigation.