Biology subjects

Hauk, O.

Publications and source records attributed to Hauk, O..

17 recordsLinked to original sources

bioRxiv2026

Anterior temporal lobes show route-dependent sensitivity to conceptual specificity: Evidence from MEG and EEG

Understanding how the brain represents meaning at different levels of specificity, from general categories (bird) through combined phrases (nocturnal bird) to specific exemplars (owl), is a central question in semantic cognition and its disorders. The anterior temporal lobes (ATL) have been implicated in both activating stored conceptual representations and combining them, yet whether these two routes to conceptual content engage the ATL in the same way remains an open question. We recorded concurrent MEG-EEG from 36 participants reading 100 conceptual triads varying parametrically in specificity: general words (bird), adjective-noun phrases (nocturnal bird), and specific words (owl). Source analyses revealed a non-linear pattern in bilateral ATL (400-520ms): high-specificity concepts elicited greater activity than both low-specificity words and mid-specificity phrases. Bilateral inferior frontal cortex showed this effect with similar timing (430-630ms). N400 amplitude and ATL source activity showed a functional dissociation in their sensitivity to word frequency, ruling out a purely frequency account of the ATL and IFC effects. Results imply that accessing high-specificity concepts requires individuating stored representations among competitors, whereas phrases construct meaning through a different route. These findings extend the unified semantic hub account by revealing that computational operations within the ATL vary depending on task and stimulus demands.

neuroscience↗

bioRxiv2025

Dynamic feedforward and feedback influences on left ventral occipitotemporal cortex: evidence from word and pseudoword reading

Left ventral occipitotemporal cortex (vOT) is crucial in reading, yet its functional role is viewed either as a prelexical feedforward hub or a bidirectional interface between sensory and higher-order linguistic systems. The two competing views have usually been considered mutually exclusive. To comprehensively explain the functional complexity of left vOT, we investigated the temporal and spectral dynamics of information flows involving left vOT during visual word and pseudoword reading using magnetoencephalography (MEG) and two directed connectivity metrics, i.e., phase slope index (PSI) and Granger causality (GC). Specifically, feedforward connectivity from low-level visual areas to vOT was observed for all conditions, with the strength of orthographic information flow to left superior temporal cortex (ST) modulated by stimulus word-likeness. Conversely, feedback flow from left ST to vOT appeared for pseudowords that allow top-down linguistic constraints to facilitate reading, and occurred later for word-like than complete pseudowords. Our findings suggest that left vOT during word reading functions in a hybrid manner: operating in efficient feedforward mode for familiar word recognition while flexibly recruiting bidirectional processing for unfamiliar pseudowords. By disentangling feedforward and feedback dynamics with high temporal and spectral resolution, we empirically reconcile competing theories of vOT function.

neuroscience↗

bioRxiv2025

A common framework for semantic memory and semantic composition

How the brain constructs meaning from individual words and phrases is a fundamental question for research in semantic cognition, language and their disorders. These two aspects of meaning are traditionally studied separately, resulting in two large, multi-method literatures, which we sought to bring together in this study. Not only would this address basic cognitive questions of how semantic cognition operates but also because, despite their distinct focuses, both literatures ascribe a critical role to the anterior temporal lobe (ATL) in each aspect of semantics. Given these considerations, we explored the notion that these systems rely on common underlying computational principles when activating conceptual semantic representations via single words, vs. building a coherent semantic representation across sequences of words. The present pre-registered study used magnetoencephalography and electroencephalography to track brain activity in participants reading nouns and adjective-noun phrases, whilst integrating conceptual variables from both literatures: the concreteness of nouns (e.g., "lettuce" vs. "fiction") and the denotational semantics of adjectives (subsective vs. privative, e.g., "bad" vs. "fake"). Region-of-interest analyses show that bilateral ATLs responded more strongly to phrases at different timepoints, irrespective of concreteness. Decoding analyses on ATL signals further revealed a time-varying representational format for adjective semantics, whereas representations of noun concreteness were more stable and maintained for around 300 ms. Further, the neural representation of noun concreteness was modulated by the preceding adjectives: decoders learning concreteness signals in single words generalised better to subsective relative to privative phrases. These findings point to a unified ATL function for semantic memory and composition.

neuroscience↗

bioRxiv2024

Word-selective EEG/MEG responses in the English language obtained with Fast Periodic Visual Stimulation (FPVS)

Fast periodic visual stimulation (FPVS) allows the recording of objective brain responses of human word discrimination (i.e., reproducible word-category-selective responses) with a high signal-to-noise ratio. This approach has been successfully employed over the last decade in a number of scalp electroencephalography (EEG) studies. Three important advances were achieved in this study: (1) robust measures of written word-selective responses with this approach have not been reported in English; (2) responses have only been reported in EEG but not with MEG, and (3) without source localization. Thus, we presented English words periodically (2 Hz) among different types of letter strings (10Hz; consonant strings, non-words, pseudowords) whilst recording simultaneous EEG and MEG in 25 participants who performed a simple non-linguistic color detection task. Data were analyzed in sensor and in source space (L2-minimum-norm estimation, MNE). With only 4 minutes of stimulation we observed a robust word discrimination response in each condition including, importantly, even when words were embedded in word-like pseudowords. This response was larger in nonwords and largest in consonant strings. We observed left-lateralized responses in all conditions in the majority of our participants. Cluster-based permutation tests revealed that these responses were left-lateralized in sensor as well as in source space, with peaks in left posterior regions. Our results demonstrate that the FPVS paradigm can elicit robust English word-discrimination responses in EEG and MEG within only a few minutes of recording time. Together with source estimation, this can provide novel insights into the neural basis of visual word recognition in healthy and clinical populations.

neuroscience↗

bioRxiv2024

Multivariate Time-Lagged Multidimensional Pattern Connectivity (mvTL-MDPC) for EEG/MEG Functional Connectivity Analysis

Multidimensional connectivity methods are critical to reveal the full pattern of complex interactions between brain regions over time. However, to date only bivariate multidimensional methods are available for time-resolved EEG/MEG data, which may overestimate connectivity due to the confounding effects of spurious and indirect dependencies. Here, we introduce a novel functional connectivity method which is both multivariate and multidimensional, Multivariate Time-lagged Multidimensional Pattern Connectivity (mvTL-MDPC), to address this issue in time-resolved EEG/MEG applications. This novel method extends its bivariate counterpart TL-MDPC to estimate how well patterns in an ROI 1 at time point t1 can be linearly predicted from patterns of an ROI 2 at time point t2 while partialling out the multivariate contributions from other brain regions. We compared the performance of mvTL-MDPC and TL-MDPC on simulated data designed to test their ability to identify true direct connections, using the Euclidean distance to the ground truth to measure goodness-of-fit. These simulations demonstrate that mvTL-MDPC produces more reliable and accurate results than the bivariate method. We therefore applied this method to an existing EEG/MEG dataset contrasting words presented in more or less demanding semantic tasks, to identify the dynamic brain network underlying controlled semantic cognition. As expected, mvTL-MDPC was more selective than TL-MDPC, identifying fewer connections, likely due to a reduction in the detection of spurious or indirect connections. Dynamic connections were identified between bilateral anterior temporal lobes, posterior temporal cortex and inferior frontal gyrus, in line with recent neuroscientific models of semantic cognition.

neuroscience↗

bioRxiv2023

Decoding semantics from dynamic brain activation patterns: From trials to task in EEG/MEG source space

The temporal dynamics within the semantic brain network and its dependence on stimulus and task parameters are still not well understood. Here, we addressed this by decoding task as well as stimulus information from source-estimated EEG/MEG data. We presented the same visual word stimuli in a lexical decision (LD) and three semantic decision (SD) tasks. The meanings of the presented words varied across five semantic categories. Source space decoding was applied over time in five ROIs in the left hemisphere (Anterior and Posterior Temporal Lobe, Inferior Frontal Gyrus, Primary Visual Areas, and Angular Gyrus) and one in the right hemisphere (Anterior Temporal Lobe). Task decoding produced sustained significant effects in all ROIs from 50-100 ms, both when categorising tasks with different semantic demands (LD-SD) as well as for similar semantic tasks (SD-SD). In contrast, semantic word category could only be decoded in lATL, rATL, PTC and IFG, between 250-500 ms. Furthermore, we compared two approaches to source space decoding: Conventional ROI-by-ROI decoding and combined-ROI decoding with back-projected activation patterns. The former produced more reliable results for word-category decoding while the latter was more informative for task-decoding. This indicates that task effects are distributed across the whole semantic network while stimulus effects are more focal. Our results demonstrate that the semantic network is widely distributed but that bilateral anterior temporal lobes together with control regions are particularly relevant for the processing of semantic information. Significance StatementMost previous decoding analyses of EEG/MEG data have focussed on decoding performance over time in sensor space. Here for the first time we compared two approaches to source space decoding in order to reveal the spatio-temporal dynamics of both task and stimulus features in the semantic brain network. This revealed that even semantic tasks with similar task demands can be decoded across the network from early latencies, despite reliable differences in their evoked responses. Furthermore, stimulus features can be decoded in both tasks but only for a subset of ROIs and following the earliest task effects. These results inform current neuroscientific models of controlled semantic cognition.

neuroscience↗

bioRxiv2023

Linguistic and attentional factors -not statistical regularities- contribute to word-selective neural responses with FPVS-oddball paradigms

In recent years, a fast periodic oddball-like paradigm has proved to be highly sensitive to measure category-selective visual word representation and characterize its development and neural basis. In this approach, deviant words are inserted in rapid streams of base stimuli every nth occurrence (e.g., Lochy et al., 2015). To understand the nature of word-selective representation and improve its measurement, we tested 22 adults with EEG, assessing the impact of discrimination coarseness (deviant words among nonwords or pseudowords), the relative frequency of item repetition (set size or item repetition controlled for deviant vs. base stimuli), and the nature of the orthogonal attentional task (focused or deployed spatial attention). In all stimulation sequences, base stimuli were presented at 10 Hz, with words inserted every 5 stimuli generating word-selective responses in the EEG spectra at 2 Hz and harmonics. Word-selective occipito-temporal responses were robust at the individual level, left-lateralized and sensitive to wordlikeness of base stimuli, being stronger in the coarser categorical contrast (among nonwords). Amplitudes were not affected by item repetition, showing that implicit statistical learning about a relative token frequency difference for deviant stimuli does not contribute to the word-selective neural activity, at least with relatively large stimulus set sizes (n=30). Finally, the broad attentional deployment task produced stronger responses than a focused task, an important finding for future studies in the field. Taken together, these results confirm the linguistic nature of word-selective responses, strengthen the validity and increase the sensitivity of the FPVS-EEG oddball paradigm to measure visual word recognition. HighlightsO_LIWord-selective responses measured in fast periodic visual stimulation with EEG are linguistic in nature C_LIO_LIWord-selective responses reflect prelexical or lexical processes depending on the contrast (words in nonwords or pseudowords respectively) C_LIO_LIUsing sufficiently large sets (30 items) prevents the extraction of statistical regularities and hence, statistical learning C_LIO_LIUsing an orthogonal task involving broad, rather than focused, spatial attention increases amplitude of the neural responses C_LIO_LISensitivity of the paradigm to detect significant responses at the individual level is very good (95% for prelexical and about 80% for lexical word responses) C_LI

neuroscience↗

bioRxiv2023

Identifying nonlinear Functional Connectivity with EEG/MEG using Nonlinear Time-Lagged Multidimensional Pattern Connectivity (nTL-MDPC)

Investigating task- and stimulus-dependent connectivity is key to understanding how brain regions interact to perform complex cognitive processes. Most existing connectivity analysis methods reduce activity within brain regions to unidimensional measures, resulting in a loss of information. While recent studies have introduced new functional connectivity methods that exploit multidimensional information, i.e., pattern-to-pattern relationships across regions, they have so far mostly been applied to fMRI data and therefore lack temporal information. We recently developed Time-Lagged Multidimensional Pattern Connectivity for EEG/MEG data, which detects linear dependencies between patterns for pairs of brain regions and latencies in event-related experimental designs (Rahimi et al., 2022b). Due to the linearity of this method, it may miss important nonlinear relationships between activity patterns. Thus, we here introduce nonlinear Time-Lagged Multidimensional Pattern Connectivity (nTL-MDPC) as a novel bivariate functional connectivity metric for event-related EEG/MEG applications. nTL-MDPC describes how well patterns in ROI X at time point tx can predict patterns of ROI Y at time point ty using artificial neural networks (ANNs). We evaluated this method on simulated data as well as on an existing EEG/MEG dataset of semantic word processing, and compared it to its linear counterpart (TL-MDPC). We found that nTL-MDPC indeed detected nonlinear relationships more reliably than TL-MDPC in simulations with moderate to high numbers of trials. However, in real brain data the differences were subtle, with identification of some connections over greater time lags but no change in the connections identified. The simulations and EEG/MEG results demonstrate that differences between the two methods are not dramatic, i.e. the linear method can approximate linear and nonlinear dependencies well. HighlightsO_LInTL-MDPC is a bivariate functional connectivity method for event-related EEG/MEG C_LIO_LInTL-MDPC detects linear and nonlinear connectivity at zero and non-zero lags C_LIO_LInTL-MDPC revealed connectivity between ATL hub and semantic control regions C_LIO_LIDifferences between linear and nonlinear TL-MDPC were small C_LI

neuroscience↗

bioRxiv2022

Time Lagged Multidimensional Pattern Connectivity (TL MDPC): An EEG/MEG Pattern Transformation Based Functional Connectivity Metric

Functional and effective connectivity methods are essential to study the complex information flow in brain networks underlying human cognition. Only recently have connectivity methods begun to emerge that make use of the full multidimensional information contained in patterns of brain activation, rather than univariate summary measures of these patterns. To date, these methods have mostly been applied to fMRI data, and no method allows vertex-vertex transformation with the temporal specificity of EEG/MEG data. Here, we introduce time-lagged multidimensional pattern connectivity (TL-MDPC) as a novel bivariate functional connectivity metric for EEG/MEG research. TL-MDPC estimates the vertex-to-vertex transformations among multiple brain regions and across different latency ranges. It determines how well patterns in ROI X at time point tx can linearly predict patterns of ROI Y at time point ty. In the present study, we use simulations to demonstrate TL-MDPCs increased sensitivity to multidimensional effects compared to a univariate approach across realistic choices of number of trials and signal-to-noise ratio. We applied TL-MDPC, as well as its univariate counterpart, to an existing dataset varying the depth of semantic processing of visually presented words by contrasting a semantic decision and a lexical decision task. TL-MDPC detected significant effects beginning very early on, and showed stronger task modulations than the univariate approach, suggesting that it is capable of capturing more information. With TL-MDPC only, we observed rich connectivity between core semantic representation (left and right anterior temporal lobes) and semantic control (inferior frontal gyrus and posterior temporal cortex) areas with greater semantic demands. TL-MDPC is a promising approach to identify multidimensional connectivity patterns, typically missed by univariate approaches. HighlightsO_LITL-MDPC is a multidimensional functional connectivity method for event-related EMEG C_LIO_LITL-MDPC captures both univariate and multidimensional connectivity C_LIO_LITL-MDPC yields both zero-lag and time-lagged dependencies C_LIO_LITL-MDPC produced richer connectivity than univariate approaches in a semantic task C_LIO_LITL-MDPC identified connectivity between the ATL hubs and semantic control regions C_LI

neuroscience↗

bioRxiv2021

Investigating brain mechanisms underlying natural reading by co-registering eye tracking with combined EEG and MEG

Linking brain and behavior is one of the great challenges in cognitive neuroscience. Ultimately, we want to understand how the brain processes information to guide every-day behavior. However, most neuroscientific studies employ very simplistic experimental paradigms whose ecological validity is doubtful. Reading is a case in point, since most neuroscientific studies to date have used unnatural word-by-word stimulus presentation and have often focused on single word processing. Previous research has therefore actively avoided factors that are important for natural reading, such as rapid self-paced voluntary saccadic eye movements. Recent methodological developments have made it possible to deal with associated problems such as eye movement artefacts and the overlap of brain responses to successive stimuli, using a combination of eye-tracking and neuroimaging. A growing number of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are successfully using this methodology. Here, we provide a proof-of-concept that this methodology can be applied to combined EEG and magnetoencephalography (MEG) data. Our participants naturally read 4-word sentences that could end in a plausible or implausible word while eye-tracking, EEG and MEG were being simultaneously recorded. Eye-movement artefacts were removed using independent-component analysis. Fixation-related potentials and fields for sentence-final words were subjected to minimum-norm source estimation. We detected an N400-type brain response in our EEG data starting around 200 ms after fixation of the sentence-final word. The brain sources of this effect, estimated from combined EEG and MEG data, were mostly located in left temporal lobe areas. We discuss the possible use of this method for future neuroscientific research on language and cognition.

neuroscience↗

bioRxiv2021

Task modulation of spatiotemporal dynamics in semantic brain networks: an EEG/MEG study

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 ATLs and 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 semantic task demands modulate visual and attentional processes early-on, followed by modulation of multimodal semantic information retrieval in ATLs and then 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. HighlightsO_LISemantic task demands affect activity and connectivity at different processing stages C_LIO_LIEarliest task modulations occurred in posterior visual brain regions C_LIO_LIATL, PTC and IFG effects reflect task-relevant retrieval of multimodal information C_LIO_LIATL effects left-lateralised for activation but bilateral for functional connectivity C_LIO_LIDynamic evoked and connectivity data are essential to study semantic networks C_LI

neuroscience↗

bioRxiv2019

Towards an Objective Evaluation of EEG/MEG Source Estimation Methods: The Linear Tool Kit

The question \"What is the spatial resolution of EEG/MEG?\" can only be answered with many ifs and buts, as the answer depends on a large number of parameters. Here, we describe a framework for resolution analysis of EEG/MEG source estimation, focusing on linear methods. The spatial resolution of linear methods can be evaluated using the resolution matrix, which contains the point-spread and cross-talk functions (PSFs and CTFs), respectively. Both of them have to be taken into account for a full characterization of spatial resolution. They can be used to compute a range of quantitative resolution metrics, which should cover at the last three aspects of those functions: localization accuracy, spatial extent, and relative amplitude. Here, we first provide a tutorial-style introduction to resolution analysis of linear source estimation methods. We then apply these concepts to evaluate the benefit of combining EEG and MEG measurements, and to compare weighted and normalized L2-minimum-norm estimation and spatial filters. We confirm and extend previous results, showing that adding EEG to MEG improves spatial resolution, and that different methods offer different compromises among different resolution criteria. We hope that our approach will help EEG/MEG researchers in the interpretation of source estimation results, the design of new experiments, and the development of new MEG systems.

neuroscience↗

bioRxiv2019

Distinct roles for the Anterior Temporal Lobe and Angular Gyrus in the spatio-temporal cortical semantic network

It is now well recognised that human semantic knowledge is supported by a large neural network distributed over multiple brain regions, but the dynamic organisation of this network remains unknown. Some studies have proposed that a central semantic hub coordinates this network. We explored the possibility of different types of semantic hubs; namely "representational hubs", whose neural activity is modulated by semantic variables, and "connectivity hubs", whose connectivity to distributed areas is modulated by semantic variables. We utilised the spatio-temporal resolution of source-estimated Electro-/Magnetoencephalography data in a word-concreteness task (17 participants, 12 female) in order to: (i) find representational hubs at different timepoints based on semantic modulation of evoked brain activity in source space; (ii) identify connectivity hubs among left Anterior Temporal Lobe (ATL), Angular Gyrus (AG), Middle Temporal Gyrus and Inferior Frontal Gyrus based on their functional connectivity to the whole cortex, in particular sensory-motor-limbic systems; and (iii) explicitly compare network models with and without an intermediate hub linking sensory input to other candidate hub regions using Dynamic Causal Modelling (DCM) of evoked responses. ATLs activity was modulated as early as 150ms post-stimulus, while both ATL and AG showed modulations of functional connectivity with sensory-motor-limbic areas from 150-450ms. DCM favoured models with one intermediate hub, namely ATL in an early time window and AG in a later time-window. Our results support ATL as a single representational hub with an early onset, but suggest that both ATL and AG function as connectivity hubs depending on the stage of semantic processing.

neuroscience↗

bioRxiv2018

Analysing linear multivariate pattern transformations in neuroimaging data

Most connectivity metrics in neuroimaging research reduce multivariate activity patterns in regions-of-interests (ROIs) to one dimension, which leads to a loss of information. Importantly, it prevents us from investigating the transformations between patterns in different ROIs. Here, we applied linear estimation theory in order to robustly estimate the linear transformations between multivariate fMRI patterns with a cross-validated Tikhonov regularisation approach. We derived three novel metrics that describe different features of these voxel-by-voxel mappings: goodness-of-fit, sparsity and pattern deformation. The goodness-of-fit describes the degree to which the patterns in an input region can be described as a linear transformation of patterns in an output region. The sparsity metric, which relies on a Monte Carlo procedure, was introduced in order to test whether the transformation mostly consists of one-to-one mappings between voxels in different regions. Furthermore, we defined a metric for pattern deformation, i.e. the degree to which the transformation rotates or rescales the input patterns. As a proof of concept, we applied these metrics to an event-related fMRI data set consisting of four subjects that has been used in previous studies. We focused on the transformations from early visual cortex (EVC) to inferior temporal cortex (ITC), fusiform face area (FFA) and parahippocampal place area (PPA). Our results suggest that the estimated linear mappings are able to explain a significant amount of variance of the three output ROIs. The transformation from EVC to ITC shows the highest goodness-of-fit, and those from EVC to FFA and PPA show the expected preference for faces and places as well as animate and inanimate objects, respectively. The pattern transformations are sparse, but sparsity is lower than would have been expected for one-to-one mappings, thus suggesting the presence of one-to-few voxel mappings. ITC, FFA and PPA patterns are not simple rotations of an EVC pattern, indicating that the corresponding transformations amplify or dampen certain dimensions of the input patterns. While our results are only based on a small number of subjects, they show that our pattern transformation metrics can describe novel aspects of multivariate functional connectivity in neuroimaging data.

neuroscience↗

bioRxiv2018

Brain correlates of action word memory

When understanding language semantically related to actions, the motor cortex is active and may be sensitive to semantic information, for example about the body-part-relationship of displayed action-related words. Conversely, movements of the hands or feet can impair memory performance for arm- and leg-related action words respectively, suggesting that the role of motor systems extends to verbal working memory. Here, we studied brain correlates of verbal memory load for action-related words using event-related fMRI during the encoding and memory maintenance of word lists. Seventeen participants saw either four identical or four different words from the same category, semantically related to actions typically performed either with the arms or with the legs. After a variable delay of 4-14 seconds, they performed a nonmatching-to-sample task. Hemodynamic activity related to the information load of words at presentation was most prominent in left temporo-occipital and bilateral posterior-parietal areas. In contrast, larger demand on verbal memory maintenance produced greater activation in left premotor and supplementary motor cortex, along with posterior-parietal areas, indicating that verbal memory circuits for action-related words include the cortical action system. Somatotopic memory load effects of arm- and leg-related words were not present at the typical precentral loci where earlier studies had found such word-category differences in reading tasks, although traces of somatotopic semantic mappings were observed at more anterior cortical regions. These results support a neurocomputational model of distributed action-perception circuits (APCs), according to which language understanding is manifest as full ignition of APCs, whereas working memory is realized as reverberant activity gradually receding to multimodal prefrontal and lateral temporal areas.

neuroscience↗

bioRxiv2018

Is there a problem with methods skills in cognitive neuroscience? Evidence from an online survey.

Cognitive neuroscience increasingly relies on complex data analysis methods. Researchers in this field come from highly diverse scientific backgrounds, such as psychology, engineering and medicine. This poses challenges with respect to acquisition of appropriate scientific computing and data analysis skills, as well as communication among researchers with different knowledge and skills sets. Are researchers in cognitive neuroscience adequately equipped to address these challenges? Here, we present evidence from an online survey of methods skills. Respondents (n=305) mainly comprised students and post-doctoral researchers working in the cognitive neurosciences. Multiple choice questions addressed a variety of basic and fundamental aspects of neuroimaging data analysis, such as signal analysis, linear algebra, and statistics. We analysed performance with respect to the following factors: undergraduate degree (grouped into Psychology, Methods, Biology), current researcher status (undergraduate student, PhD student, post-doctoral researcher), gender, and self-rated expertise levels. Overall accuracy was 72%. Not surprisingly, the Methods group performed best (87%), followed by Biology (73%) and Psychology (66%). Accuracy increased from undergraduate (59%) to PhD (74%) level, but not from PhD to post-doctoral (74%) level. The difference in performance for the Methods versus non-methods (Psychology/Biology) groups was particularly striking for questions related to signal analysis and linear algebra, two areas especially relevant to neuroimaging research. Self-rated methods expertise was not strongly predictive of performance. The majority of respondents (93%) indicated they would like to receive at least some additional training on the topics covered in this survey. In conclusion, methods skills among junior researchers in cognitive neuroscience can be improved, researchers are aware of this, and there is strong demand for more skills-oriented training opportunities. We hope that this survey will provide an empirical basis for the development of bespoke skills-oriented training programmes in cognitive neuroscience institutions.

scientific communication and education↗

bioRxiv2017

Adaptive Cortical Parcellations for Source Reconstructed EEG/MEG Connectomes

There is growing interest in the rich temporal and spectral properties of the brain's functional connectome that are provided by Electro- and Magnetoencephalography (EEG/MEG). However, the problem of leakage between brain sources that arises when reconstructing brain activity from EEG/MEG recordings outside the head makes it difficult to distinguish true connections from spurious connections, even when connections are based on measures that ignore zero-lag dependencies. In particular, standard anatomical parcellations for potential cortical sources tend to over- or under-sample the real spatial resolution of EEG/MEG. By using information from cross-talk functions (CTFs) that objectively describe leakage for a given sensor configuration and distributed source reconstruction method, we introduce methods for optimising the number of parcels while simultaneously minimising the leakage between them. More specifically, we compare two image segmentation algorithms: 1) a split-and-merge (SaM) algorithm based on standard anatomical parcellations and 2) a region growing (RG) algorithm based on all the brain vertices with no prior parcellation. Interestingly, when applied to minimum-norm reconstructions for EEG/MEG configurations from real data, both algorithms yielded approximately 70 parcels despite their different starting points, suggesting that this reflects the resolution limit of this particular sensor configuration and reconstruction method. Importantly, when compared against standard anatomical parcellations, resolution matrices of adaptive parcellations showed notably higher sensitivity and distinguishability of parcels. Furthermore, extensive simulations of realistic networks under various circumstances revealed significant improvements in network reconstruction accuracies, particularly in reducing false leakage-induced connections. Adaptive parcellations therefore allow a more accurate reconstruction of functional EEG/MEG connectomes.\n\nHighlightsO_LIWe introduce adaptive cortical parcellation algorithms for E/MEG source estimation.\nC_LIO_LIAlgorithms are based on cross-talk functions and image segmentation methods.\nC_LIO_LIThe resulting parcellations yielded ~70 parcels regardless of starting point.\nC_LIO_LISensitivity and distinguishability improved compared to anatomical parcellations.\nC_LIO_LIAccuracy of realistic whole-brain network reconstruction improved significantly.\nC_LI

neuroscience↗