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The amplitude envelope of speech is crucial for accurate comprehension. Considered a key stage in speech processing, the phase of neural activity in the theta-delta bands (1-10 Hz) tracks the phase of the speech amplitude envelope during listening. However, the mechanisms underlying this envelope representation have been heavily debated. A dominant model posits that envelope tracking reflects entrainment of endogenous low-frequency oscillations to the speech envelope. Alternatively, envelope tracking reflects a series of evoked responses to acoustic landmarks within the envelope. It has proven challenging to distinguish these two mechanisms. To address this, we recorded MEG while participants (n= 12, 6 female) listened to natural speech, and compared the neural phase patterns to the predictions of two computational models: an oscillatory entrainment model and a model of evoked responses to peaks in the rate of envelope change. Critically, we also presented speech at slowed rates, where the spectro-temporal predictions of the two models diverge. Our analyses revealed transient theta phase-locking in regular speech, as predicted by both models. However, for slow speech, we found transient theta and delta phase-locking, a pattern that was fully compatible with the evoked response model but could not be explained by the oscillatory entrainment model. Furthermore, encoding of acoustic edge magnitudes was invariant to contextual speech rate, demonstrating speech rate normalization of acoustic edge representations. Together, our results suggest that neural phase-locking to the speech envelope is more likely to reflect discrete representation of transient information rather than oscillatory entrainment.SIGNIFICANCE STATEMENTThis study probes a highly debated topic in speech perception: the neural mechanisms underlying the cortical representation of the temporal envelope of speech. It is well established that the slow intensity profile of the speech signal, its envelope, elicits a robust brain response that “tracks” these envelope fluctuations. The oscillatory entrainment model posits that envelope tracking reflects phase alignment of endogenous neural oscillations. Here the authors provide evidence for a distinct mechanism. They show that neural speech envelope tracking arises from transient evoked neural responses to rapid increases in the speech envelope. Explicit computational modeling provides direct and compelling evidence that evoked responses are the primary mechanism underlying cortical speech envelope representations, with no evidence for oscillatory entrainment.

This article describes a case of Foreign accent syndrome (FAS) in an Italian woman who developed a Canadian-like foreign accent without brain damage (functional FAS). The patient underwent an in-depth neuroimaging and (neuro)psychological evaluation. Language networks in the frontotemporal-parietal areas were typically activated bilaterally through fMRI and MEG assessments based on task-based data. Resting-state fMRI showed preserved connectivity between language areas. An obsessive-compulsive personality profile and mild anxiety were found, suggesting psychological and psychiatric factors may be relevant. Accordingly with our findings, multimodal imaging is beneficial to understand FAS neurological and functional etiologies.

Publisher Copyright: © The Author(s) 2022. Published by Oxford University Press. Studying white matter connections with tractography is a promising approach to understand the development of different brain processes, such as proprioception. An emerging method is to use functional brain imaging to select the cortical seed points for tractography, which is considered to improve the functional relevance and validity of the studied connections. However, it is unknown whether different functional seeding methods affect the spatial and microstructural properties of the given white matter connection. Here, we compared functional magnetic resonance imaging, magnetoencephalography, and manual seeding of thalamocortical proprioceptive tracts for finger and ankle joints separately. We showed that all three seeding approaches resulted in robust thalamocortical tracts, even though there were significant differences in localization of the respective proprioceptive seed areas in the sensorimotor cortex, and in the microstructural properties of the obtained tracts. Our study shows that the selected functional or manual seeding approach might cause systematic biases to the studied thalamocortical tracts. This result may indicate that the obtained tracts represent different portions and features of the somatosensory system. Our findings highlight the challenges of studying proprioception in the developing brain and illustrate the need for using multimodal imaging to obtain a comprehensive view of the studied brain process.

Purpose Resective epilepsy surgery is a well-established, evidence-based treatment option in patients with drug-resistant focal epilepsy. A major predictive factor of good surgical outcome is visualization and delineation of a potential epileptogenic lesion by MRI. However, frequently, these lesions are subtle and may escape detection by conventional MRI (≤ 3 T). Methods We present the EpiUltraStudy protocol to address the hypothesis that application of ultra-high field (UHF) MRI increases the rate of detection of structural lesions and functional brain aberrances in patients with drug-resistant focal epilepsy who are candidates for resective epilepsy surgery. Additionally, therapeutic gain will be addressed, testing whether increased lesion detection and tailored resections result in higher rates of seizure freedom 1 year after epilepsy surgery. Sixty patients enroll the study according to the following inclusion criteria: aged ≥ 12 years, diagnosed with drug-resistant focal epilepsy with a suspected epileptogenic focus, negative conventional 3 T MRI during pre-surgical work-up. Results All patients will be evaluated by 7 T MRI; ten patients will undergo an additional 9.4 T MRI exam. Images will be evaluated independently by two neuroradiologists and a neurologist or neurosurgeon. Clinical and UHF MRI will be discussed in the multidisciplinary epilepsy surgery conference. Demographic and epilepsy characteristics, along with postoperative seizure outcome and histopathological evaluation, will be recorded. Conclusion This protocol was reviewed and approved by the local Institutional Review Board and complies with the Declaration of Helsinki and principles of Good Clinical Practice. Results will be submitted to international peer-reviewed journals and presented at international conferences. Trial registration number www.trialregister.nl: NTR7536.

Brainstem degeneration is a prominent feature of spinocerebellar ataxia type 3 (SCA3), involving structures that execute binaural synchronization with microsecond precision. As a consequence, auditory processing may deteriorate during the course of disease. We tested whether the binaural “Huggins pitch” effect is suitable to study the temporal precision of brainstem functioning in SCA3 mutation carriers. We expected that they would have difficulties perceiving Huggins pitch at high frequencies, and that they would show attenuated neuromagnetic responses to Huggins pitch. The upper limit of Huggins pitch perception was psychoacoustically determined in 18 pre-ataxic and ataxic SCA3 mutation carriers and in 18 age-matched healthy controls. Moreover, the cortical N100 response following Huggins pitch onset was acquired by means of magnetoencephalography (MEG). MEG recordings were analyzed using dipole source modeling and comprised a monaural pitch condition and a no-pitch condition with simple binaural correlation changes. Compared with age-matched controls, ataxic but not pre-ataxic SCA3 mutation carriers had significantly lower frequency limits up to which Huggins pitch could be heard. Listeners with lower frequency limits also showed diminished MEG responses to Huggins pitch, but not in the two control conditions. Huggins pitch is a promising tool to assess brainstem functioning in ataxic SCA3 patients. Future studies should refine the psychophysiological setup to capture possible performance decrements also in pre-ataxic mutation carriers. Longitudinal observations will be needed to prove the potential of the assessment of Huggins pitch as a biomarker to track brainstem functioning during the disease course in SCA3.

Traveling waves are reconstructed from the data presented on the https://neuroimage.usc.edu/brainstorm/ resource according to MEG (STF) data during median nerve stimulation., {"references":["Tadel F, Baillet S, Mosher JC, Pantazis D, Leahy RM (2011) Brainstorm: A User-Friendly Application for MEG/EEG Analysis Computational Intelligence and Neuroscience, vol. 2011, ID 879716"]}

Ancient philosophy and contemporary neuroscience support the notion that the self is a malleable mental construct influencing perception of ourselves and the environment. Accumulating evidence shows this self-construct can be ‘loosened’ in altered states of consciousness (ASCs) induced by meditation and psychedelics. However, whether and how these state alterations coalesce as long-term changes conducive to human-thriving is still unknown. Deeper understanding of these issues can inform design of safe and scalable interventions for fostering human-thriving in clinical and general settings. Our overarching aim is to uncover predictive processing (PP)-based neural correlates of self-dissolution induced by meditation and psychedelics, as both state and long-term effects, examine their relation to human-thriving, and develop a neurofeedback tool for manipulating the self-construct. We employ a multidisciplinary approach grounded in neurophenomenology: combining neuroimaging, neurofeedback, in-depth phenomenology, computational modeling, cognitive tasks and self-reports. The project comprises 5 work-packages (WP, see figures 1 and 2): WP1. Self-dissolution state effects: Analyze neuroimaging data of meditation/psychedelic-induced self-dissolution states, using information complexity and transfer measures, link these with phenomenology/self-reports, and test EEG-MEG translation. WP2. Self-dissolution long-term effects and their impact on human thriving: Assess resting-state WP1 neural indices of meditators/psychedelic-users with substantial self-dissolution life experiences vs. matched controls, and test whether they predict human-thriving, including (i) existential resilience (neural paradigm), (ii) pro-sociality (behavioral-tasks and self-report), (iii) mental health and well-being (scales). WP3. Computational modeling: Build a computational model of self-dissolution based on PP principles that reproduces the qualitative behavior of information complexity metrics in M/EEG data and yields mechanistic insights. WP4. Neurofeedback proof-of-concept: Implement an EEG neurofeedback-assisted training based on indices found in WP1 to test and compare their causal efficacy via neurofeedback in expert meditators. WP5. Test neurofeedback tool in meditation-naives: An ambitious longitudinal study synthesizing knowledge from WP1-4, that will scale the neurofeedback tool to meditation-naives and assess its efficacy and safety. The project capitalizes on recent advances in computational neuroscience and neuroimaging, measuring beta spectral power, Lempel-Ziv complexity and transfer-entropy (effective global markers of ASCs) in default-mode-network regions (well-established self-processing regions). Leveraging existing published neural/phenomenological meditation/psychedelics datasets of various sizes and modalities (Figure 1), we hypothesize that (WP1) self-dissolution states are marked by higher DMN Lempel-Ziv complexity and altered DMN transfer-entropy, as well as lower beta-power in the posterior-cingulate; that (WP2) self-dissolution leaves a long-term neural imprint differentiating meditators/psychedelic-users from neurotypical populations, and predicts human-thriving; and that (WP3) computational modeling is capable of uncovering PP mechanisms that can explain self-dissolution effects in terms of relaxed high-level priors. Furthermore, by collecting new EEG data using measures informed by WP1-3 as well as a pilot EEG-neurofeedback study, we hypothesize that (WP4) the self-dissolution effects can be reliably induced through a neurofeedback-assisted training for expert meditators, and (WP5) in the general population, laying out a path to increased long-term human thriving at scale. Outputs include causally-informed neural indices of self-dissolution and their relation to human thriving, an underlying and unifying PP computational model, and an EEG-neurofeedback protocol that will serve as a prototype for use in the field.

This systematic review examines power spectrum and connectivity measures in different frequency bands using EEG/MEG recordings in developmental dyslexia (reading difficulty). This review expands on earlier review research on dyslexia using MEG/EEG with a wider age range, including adults and infants at risk for dyslexia. Focusing specifically on resting state measurements will allow for a deeper exploration and synthesis of the results of resting state research on developmental dyslexia.

Data, scripts and code to reproduce the analyses reported in 'Moving from ERP to MVPA, and comparing EEG to MEG using the Amsterdam Decoding and Modeling toolbox (ADAM)'

This review seeks to consolidate, summarize and critically appraise existing functional neuroimaging-based research regarding semantic representations in healthy multilingual speakers.

MEG is a neuroimaging technique often used to study changes in the brain’s functional organization. In order to assess changes in functional organization with respect to learning processes or pathology, the localization of the cortical representations of interest must be as accurate as possible. The present study aims to investigate the accuracy of the localization of representations in the primary somatosensory cortex using a classical paradigm to study changes in the brain’s functional organization. We will apply tactile, non-painful stimuli to both corners of the lips and thumbs to estimate the localization of the respective representation in S1. The retest reliability between two measurements (measurements 1 and 2) on the same day and at an interval of one week (measurements 3 and 4) will be considered to assess the reproducibility and stability of the dipole localization. We consider reliability on the same day and on two different measurement days. The comparison of two measurements on the same day (measurement 1 vs. measurement 2 and measurement 3 vs. measurement 4) indicates localization errors due to the methodology itself, e.g., due to slight variations in the stimulation position or the digitization of the head shape. The additional comparison of the measurements at one-week intervals (measurement 1 vs. measurement 3 and measurement 2 vs. measurement 4) allows us to observe a change over time and the stability of the representation over time.

Measuring implicit prejudice against vaccine-hesitant individuals and attempting to moderate it using paradoxical thinking intervention and the inconsistency intervention. The prejudice will be measured using Implicit Association Test (IAT) inside MEG. The interventions will be carried out via auditory statements. Control group will hear unrelated statements while paradoxical group will listen to paradoxical statements about vaccine hesitancy and inconsistent group will listen to statements inconsistent with their own worldview about vaccine hesitancy. MEG signals will be recorded during the IAT-s and intervention.

Gliomas are a rare form of primary brain tumor, and the rate of morbidity and mortality is very high (Ho et al., 2014). Glioma patients often suffer from cognitive deficits, particularly in Executive Functioning (EF), which can decrease their quality of life (Caramanna et al., 2021; van Kessel et al., 2017; Weyer-Jamora et al., 2021). EF refers to a set of cognitive processes that are important in the cognitive control of behaviors such as attention, decision-making and working memory. EF deficits exist before surgery, and although studies have shown that tumor resection may be helpful in alleviating such problems, research has demonstrated varying trajectories in EF changes in patients that have undergone tumor resection, with some noting improving as well as deteriorating EF after surgery (Lemaitre et al., 2021; Ng et al., 2019; Noll et al., 2021; Sinha et al., 2020; Talacchi et al., 2011). Research has shown that gliomas are often localized in the frontal regions of the brain, residing in the white matter under the frontal cortices (Larjavaara et al., 2007, Wang et al., 2020). These brain regions have been shown to play an important role in the regulation of EF. Importantly, gliomas seem to directly integrate into their neural environment. The area in the vicinity of the tumor, the peritumoral area, has been shown to be hyperactive (Venkatesh et al., 2019, Numan et al., 2021). Investigations into the relationship between gliomas and neuronal activity in rodents, have revealed a direct link between the two, whereby synaptic and electric integration of gliomas into the neuronal networks occurs, which in turn promotes glioma progression (Buckingham et al., 2011; Campbell et al., 2012; Gibson et al., 2014; Venkatesh et al., 2015; Venkatesh et al., 2017; Venkatesh et al., 2019). Studies in rodents have further revealed that increased neuronal activity promotes glioma proliferation and growth (Gibson et al., 2014; Venkatesh et al., 2015). Various molecular mechanisms underlying this phenomenon have been researched, with some studies focusing on neuroligin-3 (NLGN3), whereby stimulation of neurons causes the cleavage and secretion of NLGN3, stimulating glioma growth (Venkatesh et al., 2015; Venkatesh et al., 2017). Other studies have shown similar mechanisms to occur through the secretion of brain derived neurotrophic factor (BDNF), glutamate and dopamine, all of which seem to promote tumor growth (Xiong et al., 2013; Dolma et al., 2016; Ishiuchi et al., 2002; Ishiuchi et al., 2007). Conversely, gliomas are also able to stimulate neuronal activity by forming synapses with healthy neurons through glutamate release (Buckingham et al., 2011; Campbell et al., 2012). Overall, such studies have shown an intricate bidirectional relationship between neurons and gliomas which impacts tumor growth. Further studies, in glioma patients, have also linked tumor progression and brain activity, as measured with magnetoencephalography (MEG), which may contribute to the progression of the disease. In patients, it has been shown that activity, as measured with magnetoencephalography (MEG), is pathologically heightened in comparison to healthy controls (HCs), not only around the tumor but throughout the entire brain (Numan et al., 2022; Zimmermann et al., 2023) Lower levels of oscillatory brain activity were correlated with lower levels of NLGN3 and with longer progression free survival (PFS) in glioma patients (Derks et al., 2018). Similarly, higher levels of broadband power, a proxy for neural activity, postoperatively, predicted shorter PFS and overall survival (OS) in this patient group and were shown to serve as an additional prognostic marker (Belgers et al., 2020). Further supporting the concept of glioma induced neuronal circuits remodeling, are studies that have observed altered electrophysiological properties throughout the brain of patients with glioma, when comparing to HCs. Distinct electrophysiological patterns have been detected, characterized by an increase in slow wavelengths such as higher delta band activity in comparison to HCs (Aabedi et al., 2022; Baayen et al., 2003; Bosma et al., 2008; De Jongh et al., 2003; Oshino et al., 2007). Such alterations have also been correlated with differences in cognition, such as language (Wolthuis et al., 2022). Moreover, recent studies using magnetoencephalography (MEG) demonstrated differences in network clustering (Derks et al., 2021) and connectivity in the brain as compared to HCs (Bartolomei et al., 2006a; Bartolomei et al., 2006b). These findings in turn have been associated with cognitive functioning (Bosma et al., 2008; Bosma et al., 2009; van Dellen et al., 2012; Derks et al., 2019). A recent study by van Lingen et al., (2023) investigated the link between alterations in multilayer network topology in the frontoparietal network (FPN), a network that plays an important role in facilitating EF, and changes in EF in glioma patients from the pre-operative state to a one-year follow up (FU). Results showed that lower set shifting correlated with a decrease in integration. Although studies, such as the previously mentioned one, have investigated how gliomas may affect cognitive functioning in patients, most of the current research has focused on linking alterations in neuronal networks and EF. However, neuronal activity has yet to be investigated with regard to its relationship to cognitive functioning in glioma patients. This is of particular interest due to the previously illustrated findings that demonstrated the integration of the tumor into its neural surroundings and the finding that activity is heightened throughout the brain in these patients in comparison to controls (Numan et al., 2021; Zimmermann et al., 2023). Additionally, it is unknown how activity changes from pre- to post-tumor resection in such patients. This study will therefore aim to bridge these gaps of knowledge by building upon previous work conducted by van Lingen et al., (2023). In addition to analyzing the relationship between activity and EF in patients, this study aims to explore the same in healthy individuals. Patient studies have found that greater brain activity in the default mode network (DMN) is associated with better cognitive flexibility (Sambataro et al., 2010). Furthermore, Zou et al. (2012) discovered that intrinsic resting-state brain activity was significantly correlated with working memory task performance. In addition to such research, brain stimulation, through techniques such as transcranial alternating current stimulation (tACS) and transcranial random noise stimulation (TRNS), have been shown to enhance long-term memory and other cognitive functions (Grover et al., 2022; Qi et al., 2021; Snowball et al., 2023). Although these findings suggest that activity may be related to cognitive performance, the association between EF and activity in the FPN in healthy individuals is unknown. This study therefore intends to investigate the relationship between activity in the FPN and EF in glioma patients and HCs. As brain activity has been shown to impact various aspects of glioma pathology, the question remains how this may relate to cognition.

Synaptic loss occurs early in many neurodegenerative diseases and contributes to cognitive impairment even in the absence of gross atrophy. Currently, for human disease there are few formal models to explain how cortical networks underlying cognition are affected by synaptic loss. We advocate that biophysical models of neurophysiology offer both a bridge from clinical to preclinical models of pathology, and quantitative assays for experimental medicine. Such biophysical models can also disclose hidden neuronal dynamics generating neurophysiological observations like electro- and magneto-encephalography (MEG). Here, we augment a biophysically informed mesoscale model of human cortical function by inclusion of synaptic density estimates as captured by [11C]UCB-J positron emission tomography, and provide insights into how regional synapse loss affects neurophysiology. We use the primary tauopathy of progressive supranuclear palsy (Richardson’s syndrome) as an exemplar condition, with high clinicopathological correlations. Progressive supranuclear palsy causes a marked change in cortical neurophysiology in the presence of mild atrophy and is associated with a decline in cognitive functions associated with the frontal lobe. Using (parametric empirical) Bayesian inversion of a conductance-based canonical microcircuit model of MEG data, we show that the inclusion of regional synaptic density—as a subject-specific prior on laminar specific neuronal populations—markedly increases model evidence. Specifically, model comparison suggests that a reduction in synaptic density in inferior frontal cortex affects superficial and granular layer glutamatergic excitation. This predicted individual differences in behaviour, demonstrating the link between synaptic loss, neurophysiology, and cognitive deficits. The method we demonstrate is not restricted to progressive supranuclear palsy or the effects of synaptic loss: such pathology-enriched dynamic causal models can be used to assess the mechanisms of other neurological disorders, with diverse non-invasive measures of pathology, and is suitable to test the effects of experimental pharmacology.

3.1. A topic of significant and rapidly growing interest in the public is the phenomenon of “microdosing”. Microdosing refers to consumption of a ‘subthreshold’ dose of a psychedelic substance, most commonly LSD or psilocybin. These doses involve only minimal noticeable changes in conscious experience. Early research indicates that microdosing is low risk and may have benefits for mental health and cognitive performance. To date, there have been only a small number of studies investigating the neurobiological effects of microdosing and the fundamental mechanisms involved. The proposed study will use a self-blinding design to investigate neurological, biological, and behavioral differences between microdosing and placebo conditions. These data will allow for substantial progress in our understanding of what microdosing does in the brain, and potentially elucidate neurobiological correlates of psychological effects.

This magnetoencephalography (MEG) study addresses (i) how Friedreich ataxia (FRDA) affects the sub‐second dynamics of resting‐state brain networks, (ii) the main determinants of their dynamic alterations, and (iii) how these alterations are linked with FRDA‐related changes in resting‐state functional brain connectivity (rsFC) over long timescales. For that purpose, 5 min of resting‐state MEG activity were recorded in 16 FRDA patients (mean age: 27 years, range: 12–51 years; 10 females) and matched healthy subjects. Transient brain network dynamics was assessed using hidden Markov modeling (HMM). Post hoc median‐split, nonparametric permutations and Spearman rank correlations were used for statistics. In FRDA patients, a positive correlation was found between the age of symptoms onset (ASO) and the temporal dynamics of two HMM states involving the posterior default mode network (DMN) and the temporo‐parietal junctions (TPJ). FRDA patients with an ASO 11 years or healthy subjects. The temporal dynamics of the DMN state also correlated with minute‐long DMN rsFC. This study demonstrates that ASO is the main determinant of alterations in the sub‐second dynamics of posterior associative neocortices in FRDA patients and substantiates a direct link between sub‐second network activity and functional brain integration over long timescales., This magnetoencephalography (MEG) study addresses (i) how Friedreich ataxia (FRDA) affects the sub‐second dynamics of resting‐state brain networks, (ii) the main determinants of their dynamic alterations, and (iii) how these alterations are linked with FRDA‐related changes in resting‐state functional brain connectivity (rsFC) over long timescales. Transient brain network dynamics was assessed using hidden Markov modeling (HMM). In FRDA patients, a positive correlation was found between the age of symptoms onset (ASO) and the temporal dynamics of two HMM states involving the posterior default mode network (DMN) and the temporo‐parietal junctions (TPJ). The temporal dynamics of the DMN state also correlated with minute‐long DMN rsFC. FRDA patients with an ASO

The latency of neural responses in the visual cortex changes systematically across the lifespan. Here, we test the hypothesis that development of visual white matter pathways mediates maturational changes in the latency of visual signals. Thirty‐eight children participated in a cross‐sectional study including diffusion magnetic resonance imaging (MRI) and magnetoencephalography (MEG) sessions. During the MEG acquisition, participants performed a lexical decision and a fixation task on words presented at varying levels of contrast and noise. For all stimuli and tasks, early evoked fields were observed around 100 ms after stimulus onset (M100), with slower and lower amplitude responses for low as compared to high contrast stimuli. The optic radiations and optic tracts were identified in each individual's brain based on diffusion MRI tractography. The diffusion properties of the optic radiations predicted M100 responses, especially for high contrast stimuli. Higher optic radiation fractional anisotropy (FA) values were associated with faster and larger M100 responses. Over this developmental window, the M100 responses to high contrast stimuli became faster with age and the optic radiation FA mediated this effect. These findings suggest that the maturation of the optic radiations over childhood accounts for individual variations observed in the developmental trajectory of visual cortex responses., This study elucidates the relationship between structural and functional properties of the visual pathways and how they change during childhood. Developmental changes of visual white matter pathways account for latency variations in electrophysiological responses of visual cortex. The present findings are an example of how relating white matter properties to functional aspects of the brain can help us reach a more complete understanding of the link between development of brain connectivity and changes in electrophysiology.

People change their preferences when exposed to others’ opinions. We examine the neural basis of how peer feedback influences an individual’s recommendation behavior. In addition, we investigate if the personality trait of ‘agreeableness’ modulates behavioral change and neural responses. In our experiment, participants with low and high agreeableness indicated their degree of recommendation of commercial brands, while subjected to peer group feedback. The associated neural responses were recorded with concurrent magnetoencephalography. After a delay, the participants were asked to reevaluate the brands. Recommendations changed consistently with conflicting feedback only when peer recommendation was lower than the initial recommendation. On the neural level, feedback evoked neural responses in the medial frontal and lateral parietal cortices, which were stronger for conflicting peer opinions. Conflict also increased neural oscillations in 4–10 Hz and decreased oscillations in 13–30 Hz in medial frontal and parietal cortices§. The change in recommendation behavior was not different between the low and high agreeableness groups. However, the groups differed in neural oscillations in the alpha and beta bands, when recommendation matched with feedback. In addition to corroborating earlier findings on the role of conflict monitoring in feedback processing, our results suggest that agreeableness modulates neural processing of peer feedback. peerReviewed

Funding Information: We wish to thank the reviewers and editors for the useful comments to improve the paper a lot. We thank Dr. Hiroshi Morioka for the useful discussion at the beginning of the project. L.P. was funded in part by the European Research Council (No. 678578 ). A.H. was supported by a Fellowship from CIFAR, and the Academy of Finland. The authors acknowledge the computational resources provided by the Aalto Science-IT project, and also wish to thank the Finnish Grid and Cloud Infrastructure (FGCI) for supporting this project with computational and data storage resources. | openaire: EC/H2020/678578/EU//HRMEG Resting-state magnetoencephalography (MEG) data show complex but structured spatiotemporal patterns. However, the neurophysiological basis of these signal patterns is not fully known and the underlying signal sources are mixed in MEG measurements. Here, we developed a method based on the nonlinear independent component analysis (ICA), a generative model trainable with unsupervised learning, to learn representations from resting-state MEG data. After being trained with a large dataset from the Cam-CAN repository, the model has learned to represent and generate patterns of spontaneous cortical activity using latent nonlinear components, which reflects principal cortical patterns with specific spectral modes. When applied to the downstream classification task of audio-visual MEG, the nonlinear ICA model achieves competitive performance with deep neural networks despite limited access to labels. We further validate the generalizability of the model across different datasets by applying it to an independent neurofeedback dataset for decoding the subject's attentional states, providing a real-time feature extraction and decoding mindfulness and thought-inducing tasks with an accuracy of around 70% at the individual level, which is much higher than obtained by linear ICA or other baseline methods. Our results demonstrate that nonlinear ICA is a valuable addition to existing tools, particularly suited for unsupervised representation learning of spontaneous MEG activity which can then be applied to specific goals or tasks when labelled data are scarce.

People with HIV (PWH) use cannabis at a higher rate than the general population, but the influence on neural activity is not well characterized. Cannabis use among PWH may have a beneficial effect, as neuroinflammation is known to be a critical problem in PWH and cannabis use has been associated with a reduction in proinflammatory markers. Thus, it is important to understand the net impact of cannabis use on brain and cognitive function in PWH. In this study, we collected magnetoencephalographic (MEG) brain imaging data on 81 participants split across four demographically matched groups (i.e., PWH using cannabis, controls using cannabis, non‐using PWH, and non‐using controls). Participants completed a visuospatial processing task during MEG. Time–frequency resolved voxel time series were extracted to identify the dynamics of oscillatory and pre‐stimulus baseline neural activity. Our results indicated strong theta (4–8 Hz), alpha (10–16 Hz), and gamma (62–72 Hz) visual oscillations in parietal–occipital brain regions across all participants. PWH exhibited significant behavioral deficits in visuospatial processing, as well as reduced theta oscillations and elevated pre‐stimulus gamma activity in visual cortices, all of which replicate prior work. Strikingly, chronic cannabis use was associated with a significant reduction in pre‐stimulus gamma activity in the visual cortices, such that PWH no longer statistically differed from controls. These results provide initial evidence that cannabis use may normalize some neural aberrations in PWH. This study fills an important gap in understanding the impact of cannabis use on brain and cognitive function in PWH., This work used a visuospatial processing paradigm in combination with magnetoencephalographic (MEG) imaging. Voxel‐level timeseries analyses were applied to study the impact of cannabis use on behavior and neural activity in people with HIV (PWH).

In order to describe how humans represent meaning in the brain, one must be able to account for not just concrete words but, critically, also abstract words, which lack a physical referent. Hebbian formalism and optimization are basic principles of brain function, and they provide an appealing approach for modeling word meanings based on word co-occurrences. We provide proof of concept that a statistical model of the semantic space can account for neural representations of both concrete and abstract words, using MEG. Here, we built a statistical model using word embeddings extracted from a text corpus. This statistical model was used to train a machine learning algorithm to successfully decode the MEG signals evoked by written words. In the model, word abstractness emerged from the statistical regularities of the language environment. Representational similarity analysis further showed that this salient property of the model co-varies, at 280–420 ms after visual word presentation, with activity in regions that have been previously linked with processing of abstract words, namely the left-hemisphere frontal, anterior temporal and superior parietal cortex. In light of these results, we propose that the neural encoding of word meanings can arise through statistical regularities, that is, through grounding in language itself.

Matlab code used in the following experimental design: Zilber, N., Ciuciu, P., Gramfort, A., Azizi, L., & Van Wassenhove, V. (2014). Supramodal processing optimizes visual perceptual learning and plasticity. Neuroimage, 93, 32-46.

Stage 1 IPA at Cortex

Stage 1 IPA at Cortex

Stage 1 IPA at Cortex

Code and data for project on power contours

Pre-registration of MEG study

Work published as: Goddard E, Carlson T, Woolgar A (2021) Spatial and feature-selective attention have distinct, interacting, effects on population-level tuning. Journal of Cognitive Neuroscience (in press Nov 2021). DOI: https://doi.org/10.1162/jocn_a_01796. Please cite this paper if you use the data on this project. This project contains a magnetoencephalography (MEG) data set. Participants (n = 20) made judgements about visual stimuli in different attention conditions. For details of the folder structure and how to use this data see the project wiki. For details of the stimuli and methodology, see the paper above.

This dataset contains behavioural and MEG data from 22 healthy volunteers who performed a random-dot motion task with two features, color and motion, one of which was relevant on any given trial.

Pre-registration for Microphenomenologically informed neuroimaging

MEG recordings of a melon implanted with deep brain stimulation (DBS)

This project contains the stimuli, the raw MEG data and the analysis code used in 'A humanness dimension to visual object coding in the brain' by Contini, Goddard, Grootswagers, Williams and Carlson (2020) Neuroimage (https://doi.org/10.1016/j.neuroimage.2020.117139). Please refer to this work for full details of the methodology, and cite this paper if using any of this data in future work. - To use the high resolution, segmented object images from this study, download the 'STIMULI' folder. - To recreate the MEG data analyses from scratch, download the 'MEG_DATA', 'BEHAVIOURAL' and 'ANALYSIS_CODE' folders and start with the Matlab function 'CategoryAndPosition_MEG_Analysis.m' located in the 'ANALYSIS_CODE' directory. To skip any of the analysis steps, download the 'ANALYSED_DATA' folder. - To recreate the manuscript figures, download only the 'FIGURES' and 'STIMULI' folders and use the Matlab scripts in the 'FIGURES' folder. - In the 'ONLINE_EXP' folder, humanness-online.zip contains the raw data and experiment script for the online shape-similarity-rt part of the paper (also publicly available on https://gitlab.pavlovia.org/tijl/humanness).

Recognition memory refers to the ability to detect whether a stimulus has been encountered previously. Impairments of recognition memory are common in healthy ageing and neurodegenerative disorders such as Alzheimer’s Disease. However, it is possible that behavioural impairments in explicit tests of recognition memory are caused by differences in motivation and strategy (e.g., rehearsal), rather than underlying memory capability. Direct brain measures of recognition memory can potentially bypass such confounding effects, particularly in implicit tests in which stimulus repetition is irrelevant to task performance. EEG and MEG are techniques for measuring brain activity directly, which are more patient-friendly than fMRI and not confounded by vascular changes, for example in aging. We have recently advocated a paradigm based on Fast Periodic Visual Stimulation (FPVS) in which brain activity associated with (rapid, periodic) stimulus repetition can be measured within a few minutes, by examining the power in frequencies associated with that repetition. However, attributing repetition-related changes in brain activity during implicit tests to recognition memory is tricky because that activity could also reflect repetition priming, a form of unconscious memory that is not typically impaired in aging or dementia. While some have argued that repetition priming and recognition memory are supported by the same underlying memory signal (Berry et al., 2012), others have argued that they are distinct at both a functional and neural level (Squire, 1992, 2009; Squire & Dede, 2015). Yet others have argued that recognition memory is itself supported by two processes – familiarity and recollection – that are also supported by different brain systems (Staresina et al., 2013; Yonelinas, 2002; Yonelinas et al., 2005). Recollection in particular – the retrieval of additional spatiotemporal context – is believed to be supported by the hippocampus, a brain region strongly affected by ageing and Alzheimer’s Disease. This is the third experiment in a series of three. The first experiment (Stothart, Smith, & Milton, 2020) used EEG to demonstrate that pre-FPVS encoding strengthened the FPVS memory response. However, repetition during FPVS (without pre-encoding) elicited a (smaller) signal at the same frequency, suggesting that repetition priming also plays a role. The second EEG experiment (Nurdal et al., 2021) varied repetition frequency during FPVS, and provided further evidence that repetition priming plays a role in the FPVS response. Importantly however, a condition was included with pre-encoding but no repetition during FPVS, and this still produced an FPVS response. This suggests that immediate repetition priming (over a few seconds during FPVS) is not necessary to produce an FPVS response, though it remains possible that the FPVS “memory” response reflects longer-lasting repetition priming from the prior encoding phase. Indeed, it is difficult to separate contributions from priming, familiarity and recollection using a single scalar response, since pre-task encoding and repetition frequency can potentially affect all three. This third experiment uses MEG, as well as EEG. MEG has greater spatial resolution, and thereby allows us to potentially dissociate the three types of memory processes in terms of their underlying brain sources (using topographic analyses or source reconstruction). To manipulate the degree of priming, familiarity and recollection, we varied: 1) the “depth” of the pre-FPVS encoding, and 2) the frequency of repetition during the FPVS phase. This resulted in 5 conditions: C1: Deep encoding: Participants name each object, make a semantic decision and perform a 2AFC task to indicate the object just learned (that will later be the oddball in the FPVS phase). This should maximise encoding and subsequent recollection, relative to C2 below. C2: Shallow encoding: Participants decide whether the object is more red or blue during the encoding phase. This should result in weaker recollection than C1, but stronger familiarity relative to C3 below. C3: No encoding, high repetition: 8 oddball objects appear 12 times during FPVS phase (as in C1-C2 above), but with no encoding phase. This should increase immediate repetition priming relative to C4 below. C4: No encoding, low repetition: 16 oddball objects appear 6 times during FPVS phase. C5: Control: No oddballs (i.e., no object repeated in FPVS phase).

This repository provides raw data and scripts used to evaluate non-linear properties of tACS stimulation and recording harware. These non-linearities can lead to spurious low-frequency artifacts during amplitude modulated transcranial alternating current stimulation. For details see: Kasten, F.H., Negahbani, E., Fröhlich, F., Herrmann, C.S., Non-linear transfer characteristics of stimulation and recording hardware account for spurious low-frequency artifacts during amplitude modulated transcranial alternating current stimulation (AM-tACS), NeuroImage (2018), doi: 10.1016/j.neuroimage.2018.05.068. https://www.sciencedirect.com/science/article/pii/S1053811918304944 If you find this code and/or data useful, please consider citing the above reference.

Cognitive impairment is a common symptom in patients with glioma (Caramanna et al., 2021; Klein, Duffau, & De Witt Hamer, 2012; van Kessel, Baumfalk, van Zandvoort, Robe, & Snijders, 2017), seen across multiple cognitive domains including executive functioning. The latter is a set of higher-order mental skills that include working memory, flexible thinking, and self-control. Integration of information across the brain is crucial to maintain executive functioning performance (Bullmore & Sporns, 2012). Moreover, further deterioration of executive functioning has been observed after surgical removal of a brain tumor (Ng et al., 2019; Noll et al., 2015; Satoer et al., 2012; Talacchi, Santini, Savazzi, & Gerosa, 2011; Wu et al., 2011). Neural correlates of this cognitive impairment and subsequent deficit are sparse. Magnetoencephalography (MEG) studies have revealed that post-operative cognitive changes are associated with both preoperative characteristics and temporal changes of the functional brain network using graph theory measures including functional connectivity and hub load (Carbo et al., 2017; Derks, Reijneveld, & Douw, 2014; van Dellen et al., 2012). That is, Van Dellen et al. (2012) showed that higher lower alpha band connectivity within the default mode resting state network after surgery was related to better verbal memory. Within the upper alpha band, higher connectivity of the right frontoparietal resting state network correlated with better attention, working memory and executive functioning. As also suggested from cross-sectional studies, increasing integration of the network related to improving cognitive functioning (Carbo et al., 2017; Derks, Reijneveld, & Douw, 2014): deteriorating verbal memory after tumor resection correlated with decreasing dynamic integrative hub functioning. More importantly, cognitive decline could be predicted by lower baseline hub integration (Carbo et al., 2017). Hub nodes are crucial for information flow throughout the system, serving as a connector to other nodes within or between brain networks. The extent to which hubs play an integrative or central role within the entire brain network seems generally related to cognitive functioning in glioma. However, all the mentioned studies calculated graph theory network measures in each MEG frequency band separately (alpha, beta, delta etc). Recent work shows that applying a multilayer network approach that calculates network measures across different layers (frequency bands in the case of MEG data) yields additional information compared to single layer results (Brookes et al., 2016; De Domenico, Sasai, & Arenas, 2016; Guillon et al., 2017; Tewarie et al., 2016; Yu et al., 2017). For example, (Breedt et al., 2021) showed that higher multilayer centrality in the frontoparietal network related to better executive functioning in healthy controls, whereas this association was not observed in single layer analyses. In this study, we therefore aim to explore multilayer MEG connectivity before and after resective surgery in relation to executive functioning in glioma patients.

This is a proposal funded by the BRAIN Initiative at NIH to advance scientific discovery and understanding the brain by developing methods (a standard) that will facilitate data sharing. Data sharing can effectively impact the understanding of health and disease by promoting data use from goals beyond those initially supporting the data collection process. The project will deliver a community-developed standard describing brain connectivity experiments and accompanied by software to validate the datasets.

Past work in the cognitive neuroscience of intuitive physics has found regions of the dorsal visual stream that respond when people view and think about physical events (Fisher et al. 2016). Additionally, fMRI evidence shows that these regions, dubbed the brain’s ‘physics engine,’ hold information about object mass when participants view dynamic scenes (Schwettmann et al. 2019; Buckingham et al 2018; Loh et al 2010). Previous studies investigated neural representations of object mass using real objects or naturalistic videos. However, we do not know whether viewing static images of familiar objects will also invoke reliable representations of their masses. In this study, we will investigate this topic using magnetoencephalography (MEG) to address the following questions: 1. can the mass of familiar objects be decoded from static images using MEG? (This question will be addressed using: a. Binary classification of Heavy vs Light objects (Qualitative prediction), b. Regression-based analysis to predict mass of individual objects from MEG data (Quantitative prediction)), and 2. at what latency after stimulus onset can mass be decoded?

Facial expression recognition is central to social communication and dependent on environmental conditions. For example, in a dangerous situation, we tend to identify threat better than safety information. Such recognition biases in favour of threat detection can prevent harm and injury but, if exaggerated, also contribute to the development and maintenance of anxious psychopathology. We will examine interindividual differences in threat perception and its malleability through threat-safety reversal learning using whole-head magnetoencephalography. This will involve an emotion recognition test with subtle expressions of fearful or happy faces in contextual settings of threat-of-shock or safety. Previous findings show early neural priming of context-congruent face processing (i.e., fearful faces during threat). Based on this, we predict this threat-selective bias is pronounced in individuals prone to anxious psychopathology who benefit less from threat reversal to safety. The results will improve our understanding of perceptual biases from healthy functional to the anxiety disorder spectrum.

Speaking and listening are complex tasks that we perform on a daily basis, almost without conscious effort. Interestingly, speaking almost never occurs without listening: whenever we speak, we at least hear our own speech. The research in this thesis is concerned with how the perception of our own speech influences our speaking behavior. We show that unconsciously, we actively monitor this auditory feedback of our own speech. This way, we can efficiently take action and adapt articulation when an error occurs and auditory feedback does not correspond to our expectation. Processing the auditory feedback of our speech does not, however, automatically affect speech production. It is subject to a number of constraints. For example, we do not just track auditory feedback, but also its consistency. If auditory feedback is more consistent over time, it has a stronger influence on speech production. In addition, we investigated how auditory feedback during speech is processed in the brain, using magnetoencephalography (MEG). The results suggest the involvement of a broad cortical network including both auditory and motor-related regions. This is consistent with the view that the auditory center of the brain is involved in comparing auditory feedback to our expectation of auditory feedback. If this comparison yields a mismatch, motor-related regions of the brain can be recruited to alter the ongoing articulations. Franken, M. K. (2018). Listening for speaking: Investigations of the relationship between speech perception and production. PhD Thesis, Radboud University, Nijmegen. http://repository.ubn.ru.nl/handle/2066/180052

The aim of this project is to examine the nature of object representations during dynamic occlusion. We recorded MEG data to test whether object features such as shape, luminance, and object position are represented during occlusion.

This project contains the MEG data, behavioural data and the analysis code used in 'Reaction times predict dynamic brain representations measured with MEG for only some object categorisation tasks' by Contini, Goddard and Wardle (2020) (https://doi.org/10.1016/j.neuropsychologia.2020.107687). Please refer to this work for full details of the methodology, and cite this paper if using any of this data in future work. - To recreate the analyses from scratch, start with the Matlab script 'Analysis_corrRTwithMEG.m' located in the 'CODE' folder. The options selected in this script reflect those used in the manuscript, but other options are available (e.g. running the analysis using MEG sensor data without PCA). - To recreate the manuscript figures, use the Matlab script 'create_figures_for_manuscript.m' located in the 'CODE' folder.

The relation between brain functional activity and the underlying structure is complex and varies depending on the specific brain region. Recently, we used graph signal processing to introduce the structural-decoupling index (SDI), a novel metric quantifying structure-function coupling in brain regions, based on graph spectral filtering of functional activity. At slow temporal scales accessible with resting-state functional magnetic resonance imaging, the SDI showed a meaningful spatial gradient from unimodal (more coupled) to transmodal regions (more liberal). It also showed to perform very well for brain fingerprinting; i.e., individuals could be classified with near perfect accuracy based on their SDI. Here, we investigate structure-function coupling at faster temporal scales and its specificity to individuals, by means of resting-state magnetoencephalography (MEG) of 84 healthy subjects. We found that the MEG SDI forms a cortical gradient from task-positive regions, more coupled, to task-negative regions, highly decoupled. Great specificity of the SDI to individuals was confirmed, with largest subject classification accuracies in the beta and alpha bands. We conclude that structure-function coupling changes across temporal scales of investigation and provides rich signatures of individual brain organization at rest.

International audience; The development of the Brain Imaging Data Structure (BIDS; Gorgolewski et al., 2016) gave the neuroscientific community a standard to organize and share data. BIDS prescribes file naming conventions and a folder structure to store data in a set of already existing file formats. Next to rules about organization of the data itself, BIDS provides standardized templates to store associated metadata in the form of Javascript Object Notation (JSON) and tab separated value (TSV) files. It thus facilitates data sharing, eases metadata querying, and enables automatic data analysis pipelines. BIDS is a rich system to curate, aggregate, and annotate neuroimaging databases. While BIDS was originally intended for magnetic resonance imaging (MRI) data, it has extensions for other data modalities including: magnetoencephalography (MEG; Niso et al., 2018), electroencephalography (EEG; Pernet et al., 2019), and intracranial encephalography (iEEG; Holdgraf et al., 2019). Software packages analyzing MEG, EEG, and iEEG are now starting to support data organized using the BIDS standard, thereby becoming “BIDS compatible”. Within the Python ecosystem, MNE-Python (Gramfort et al., 2013) is a major software package for electrophysiology data analysis, and extending its functionality to support BIDS would be a great benefit for its growing user base. For this reason, we developed a dedicated Python software package MNE-BIDS with the goal of providing a programmable interface for BIDS datasets in electrophysiology with MNE-Python. MNE-BIDS allows users to re-organize data into BIDS formats, store associated metadata after anonymization, extract information necessary for preprocessing, and read the data into MNE-Python objects, ready for source localization. Starting with a single directory full of data files with arbitrary names, MNE-BIDS can be used to extract existing metadata, reorganize the files into the BIDS format, and write additional

Photovoltaic devices have an efficiency limit of 33% set by fundamental loss processes in the semiconductor materials. The largest loss comes about as a result of charges excited far above the bandgap losing their excess energy as they cool to the bandedge, a process known as thermalisation. Carrier multiplication offers a means of reducing thermalisation losses using a system in which excess energy from one electron-hole pair is transferred to produce a second electron-hole pair. Fully harnessing carrier multiplication would raise the efficiency limit of photovoltaic devices to 44%. Typically, various challenges mean the benefits of carrier multiplication have not yet been realised. In quantum dot materials, the fundamental understanding of the carrier multiplication process is still lacking. A more thorough understanding of the cooling and recombination mechanisms is needed to fully exploit this phenomenon. In organic materials, an excited singlet exciton splits to form two triplet excitons on neighbouring molecules. The process in organics is referred to as singlet fission. Producing a device that can make use of the singlet fission process however, has proved difficult, limited in part by the instability of singlet fission materials and also by challenges brought about by the need to couple to another material. In this thesis, we will examine both quantum dot and organic systems. The initial work in this project involved the fabrication of quantum dot devices. Steps were taken to improve the efficiency and reproducibility of the devices with the aim of investigating carrier multiplication in device stacks. However, the devices work showed limited success and the decision was taken to move to ultrafast spectroscopy. In examining quantum dot films using picosecond transient absorption spectroscopy, we found clear signatures of carrier multiplication occurring in films of dots synthesis here. We also found that some of our results were at odds with explanations in the literature. To explain our results, we took data under a range of excitation conditions and compared them to the predicted results of various explanations in the literature. We also used ultrafast spectroscopy to examine singlet fission in diketopyrrolopyrrole-based molecules. Such molecules are much more stable than the polyacenes typically used in singlet fission research. We looked for the slow recombination signature of triplet excitons in films of a diketopyrrolopyrrole-based molecule using transient absorption spectroscopy. We assigned the formation mechanism of these triplet molecules using the timescale of the singlet to triplet transition. In this work, we have considered means of increasing the harvestable energy from photovoltaic devices and improved our understanding of how such efficiency increases may be possible., EPSRC Centre for Doctoral Training in New & Sustainable Photovoltaics

Auditory communication is an essential form of human social interaction. However, the intra-brain cortical-oscillatory drivers of auditory communication exchange remain relatively unexplored. We used improvisational music performance to simulate and capture the creativity and turn-taking dynamics of natural auditory communication. Using magnetoencephalography (MEG) hyperscanning in musicians, we targeted brain activity during periods of music communication imagery, and separately analyzed theta (5–7 Hz), alpha (8–13 Hz), and beta (15–29 Hz) source-level activity using a within-subjects, two-factor approach which considered the assigned social role of the subject (leader or follower) and whether communication responses were improvisational (yes or no). Theta activity related to improvisational communication and social role significantly interacted in the left isthmus cingulate cortex. Social role was furthermore differentiated by pronounced occipital alpha and beta amplitude increases suggestive of working memory retention engagement in Followers but not Leaders. The results offer compelling evidence for both musical and social neuroscience that the cognitive strategies, and correspondingly the memory and attention-associated oscillatory brain activities of interlocutors during communication differs according to their social role/hierarchy, thereby indicating that social role/hierarchy needs to be controlled for in social neuroscience research.

Delayed recall (DR) impairment is one of the most significant predictive factors in defining the progression to Alzheimer’s disease (AD). Changes in brain functional connectivity (FC) could accompany this decline in the DR performance even in a resting state condition from the preclinical stages to the diagnosis of AD itself, so the characterization of the relationship between the two phenomena has attracted increasing interest. Another aspect to contemplate is the potential moderator role of the APOE genotype in this association, considering the evidence about their implication for the disease. 379 subjects (118 mild cognitive impairment (MCI) and 261 cognitively intact (CI) individuals) underwent an extensive evaluation, including MEG recording. Applying cluster-based permutation test, we identified a cluster of differences in FC and studied which connections drove such an effect in DR. The moderation effect of APOE genotype between FC results and delayed recall was evaluated too. Higher FC in beta band in the right occipital region is associated with lower DR scores in both groups. A significant anteroposterior link emerged in the seed-based analysis with higher values in MCI. Moreover, APOE genotype appeared as a moderator between beta FC and DR performance only in the CI group. An increased beta FC in the anteroposterior brain region appears to be associated with lower memory performance in MCI. This finding could help discriminate the pattern of the progression of healthy aging to MCI and the relation between resting state and memory performance.

Highlights • Semantic task demands affect activity and connectivity at different processing stages. • Earliest task modulations occurred in posterior visual brain regions. • ATL, PTC and IFG effects reflect task-relevant retrieval of multimodal information. • ATL effects are left-lateralised for activation but bilateral for functional connectivity. • Dynamic evoked and connectivity data are essential to study semantic networks., How does brain activity in distributed semantic brain networks evolve over time, and how do these regions interact to retrieve the meaning of words? We compared spatiotemporal brain dynamics between visual lexical and semantic decision tasks (LD and SD), analysing whole-cortex evoked responses and spectral functional connectivity (coherence) in source-estimated electroencephalography and magnetoencephalography (EEG and MEG) recordings. Our evoked analysis revealed generally larger activation for SD compared to LD, starting in primary visual area (PVA) and angular gyrus (AG), followed by left posterior temporal cortex (PTC) and left anterior temporal lobe (ATL). The earliest activation effects in ATL were significantly left-lateralised. Our functional connectivity results showed significant connectivity between left and right ATL, PTC and right ATL in an early time window, as well as between left ATL and IFG in a later time window. The connectivity of AG was comparatively sparse. We quantified the limited spatial resolution of our source estimates via a leakage index for careful interpretation of our results. Our findings suggest that the different demands on semantic information retrieval in lexical and semantic decision tasks first modulate visual and attentional processes, then multimodal semantic information retrieval in the ATLs and finally control regions (PTC and IFG) in order to extract task-relevant semantic features for response selection. Whilst our evoked analysis suggests a dominance of left ATL for semantic processing, our functional connectivity analysis also revealed significant involvement of right ATL in the more demanding semantic task. Our findings demonstrate the complementarity of evoked and functional connectivity analysis, as well as the importance of dynamic information for both types of analyses.

We review recent advances in using mathematical models of the relationship between the brain structure and function that capture features of brain dynamics. We argue the need for models that can jointly capture temporal, spatial, and spectral features of brain functional activity. We present recent work on spectral graph theory based models that can accurately capture spectral as well as spatial patterns across multiple frequencies in MEG reconstructions.

Humans do not have an accurate representation of probability information in the environment but distort it in a surprisingly stereotyped way (“probability distortion”), as shown in a wide range of judgment and decision-making tasks. Many theories hypothesize that humans automatically compensate for the uncertainty inherent in probability information (“representational uncertainty”) and probability distortion is a consequence of uncertainty compensation. Here we examined whether and how the representational uncertainty of probability is quantified in the human brain and its relevance to probability distortion behavior. Human subjects (13 female and 9 male) kept tracking the relative frequency of one color of dot in a sequence of dot arrays while their brain activity was recorded by MEG. We found converging evidence from both neural entrainment and time-resolved decoding analysis that a mathematically derived measure of representational uncertainty is automatically computed in the brain, despite it is not explicitly required by the task. In particular, the encodings of relative frequency and its representational uncertainty, respectively, occur at latencies of ∼300 and 400 ms. The relative strength of the brain responses to these two quantities correlates with the probability distortion behavior. The automatic and fast encoding of the representational uncertainty provides neural basis for the uncertainty compensation hypothesis of probability distortion. More generally, since representational uncertainty is closely related to confidence estimation, our findings exemplify how confidence might emerge before perceptual judgment.SIGNIFICANCE STATEMENTHuman perception of probabilities and relative frequencies can be markedly distorted, which is a potential source of disastrous decisions. But the brain is not just ignorant of probability; probability distortions are highly patterned and similar across different tasks. Recent theoretical work suggests that probability distortions arise from the brain's compensation of its own uncertainty in representing probability. Is such representational uncertainty really computed in the brain? To answer this question, we asked human subjects to track an ongoing stimulus sequence of relative frequencies and recorded their brain responses using MEG. Indeed, we found that the neural encoding of representational uncertainty accompanies that of relative frequency, although the former is not explicitly required by the task.