Psychological network approaches propose to see symptoms or questionnaire items as interconnected nodes, with links between them reflecting pairwise statistical dependencies evaluated on cross-sectional, time-series, or panel data. These networks constitute an established methodology to visualise and conceptualise the interactions and relative importance of nodes/indicators, providing an important complement to other approaches such as factor analysis. However, limiting the representation to pairwise relationships can neglect potentially critical information shared by groups of three or more variables (higher-order statistical interdependencies). To overcome this important limitation, here we propose an information-theoretic framework to assess these interdependencies and consequently to use hypergraphs as representations in psychometrics. As edges in hypergraphs are capable of encompassing several nodes together, this extension can thus provide a richer account on the interactions that may exist among sets of psychological variables. Our results show how psychometric hypergraphs can highlight meaningful redundant and synergistic interactions on either simulated or state-of-the-art, re-analysed psychometric datasets. Overall, our framework extends current network approaches while leading to new ways of assessing the data that differ at their core from other methods, enriching the psychometrics toolbox, and opening promising avenues for future investigation.

The discovery that human brain connectivity data can be used as a "fingerprint" to identify a given individual from a population, has become a burgeoning research area in the neuroscience field. Recent studies have identified the possibility to extract these brain signatures from the temporal rich dynamics of resting-state magneto encephalography (MEG) recordings. Nevertheless, it is still uncertain to what extent MEG signatures can serve as an indicator of human identifiability during task-related conduct. Here, using MEG data from naturalistic and neurophysiological tasks, we show that identification improves in tasks relative to resting-state, providing compelling evidence for a task dependent axis of MEG signatures. Notably, improvements in identifiability were more prominent in strictly controlled tasks. Lastly, the brain regions contributing most towards individual identification were also modified when engaged in task activities. We hope that this investigation advances our understanding of the driving factors behind brain identification from MEG signals.

During resting-state EEG recordings, alpha activity is more prominent over the posterior cortex in eyes-closed (EC) conditions compared to eyes-open (EO). In this study, we characterized the difference in spectra between EO and EC conditions using dynamic causal modelling. Specifically, we investigated the role of intrinsic and extrinsic connectivity-within the visual cortex-in generating EC-EO alpha power differences over posterior electrodes. The primary visual cortex (V1) and the bilateral middle temporal visual areas (V5) were equipped with bidirectional extrinsic connections using a canonical microcircuit. The states of four intrinsically coupled subpopulations-within each occipital source-were also modelled. Using Bayesian model selection, we tested whether modulations of the intrinsic connections in V1, V5 or extrinsic connections (or a combination thereof) provided the best evidence for the data. In addition, using parametric empirical Bayes (PEB), we estimated group averages under the winning model. Bayesian model selection showed that the winning model contained both extrinsic connectivity modulations, as well as intrinsic connectivity modulations in all sources. The PEB analysis revealed increased extrinsic connectivity during EC. Overall, we found a reduction in the inhibitory intrinsic connections during EC. The results suggest that the intrinsic modulations in V5 played the most important role in producing EC-EO alpha differences, suggesting an intrinsic disinhibition in higher order visual cortex, during EC resting state.

Objective. To spatio-temporally resolve cardiac signals in functional magnetic resonance imaging (fMRI) time-series of the human brain using neither external physiological measurements nor ad hoc modelling assumptions.Approach. Cardiac pulsation is a physiological confound of fMRI time-series that introduces spurious signal fluctuations in proximity to blood vessels. fMRI alone is not sufficiently fast to resolve cardiac pulsation. Depending on the ratio between the instantaneous heart-rate and the acquisition sampling frequency (1/TR, with TR being the repetition time), the cardiac signal may alias into the frequency band of neural activation so that its removal through spectral filtering techniques is generally not possible. In this paper, we show that it is feasible to temporally and spatially resolve cardiac signals throughout the brain even when cardiac aliasing occurs by combining fMRI hyper-sampling with simultaneous multislice (SMS) imaging. The technique, which we name WHOle-brain CArdiac signal REgression from highly accelerated simultaneous multi-Slice fMRI acquisitions (WHOCARES), was developed on 695 healthy subjects selected from the Human Connectome Project and its performance validated against the RETROICOR, HAPPY and the pulse oxymeter signal regression methods.Main results.WHOCARES is capable of retrieving voxel-wise cardiac signal regressors. This is achieved without employing external physiological recordings nor through ad hoc modelling assumptions. The performance of WHOCARES was, on average, superior to RETROICOR, HAPPY and the pulse oxymeter regression methods.Significance.WHOCARES holds basis for the reliable mapping of cardiac activity in fMRI time-series. WHOCARES can be employed for the retrospective removal of cardiac noise in publicly available fMRI datasets where physiological recordings are not available. WHOCARES is freely available athttps://github.com/gferrazzi/WHOCARES.

Objective: Gamma synchrony is a fundamental functional property of the cerebral cortex, impaired in multiple neuropsychiatric conditions (i.e. schizophrenia, Alzheimer's disease, stroke etc.). Auditory stimulation in the gamma range allows to drive gamma synchrony of the entire cortical mantle and to estimate the efficiency of the mechanisms sustaining it. As gamma synchrony depends strongly on the interplay between parvalbumin-positive interneurons and pyramidal neurons, we hypothesize an association between cortical thickness and gamma synchrony. To test this hypothesis, we employed a combined magnetoencephalography (MEG) - Magnetic Resonance Imaging (MRI) study. Methods: Cortical thickness was estimated from anatomical MRI scans. MEG measurements related to exposure of 40 Hz amplitude modulated tones were projected onto the cortical surface. Two measures of cortical synchrony were considered: (a) inter-trial phase consistency at 40 Hz, providing a vertex-wise estimation of gamma synchronization, and (b) phase-locking values between primary auditory cortices and whole cortical mantle, providing a measure of long-range cortical synchrony. A correlation between cortical thickness and synchronization measures was then calculated for 72 MRI-MEG scans. Results: Both inter-trial phase consistency and phase locking values showed a significant positive correlation with cortical thickness. For inter-trial phase consistency, clusters of strong associations were found in the temporal and frontal lobes, especially in the bilateral auditory and pre-motor cortices. Higher phase-locking values corresponded to higher cortical thickness in the frontal, temporal, occipital and parietal lobes. Discussion and conclusions: In healthy subjects, a thicker cortex corresponds to higher gamma synchrony and connectivity in the primary auditory cortex and beyond, likely reflecting underlying cell density involved in gamma circuitries. This result hints towards an involvement of gamma synchrony together with underlying brain structure in brain areas for higher order cognitive functions. This study contributes to the understanding of inherent cortical functional and structural brain properties, which might in turn constitute the basis for the definition of useful biomarkers in patients showing aberrant gamma synchronization.

We present a dataset of magnetic resonance imaging (MRI) data (T1, diffusion, BOLD) acquired in 25 brain tumor patients before the tumor resection surgery, and six months after the surgery, together with the tumor masks, and in 11 controls (recruited among the patients’ caregivers). The dataset also contains behavioral and emotional scores obtained with standardized questionnaires. To simulate personalized computational models of the brain, we also provide structural connectivity matrices, necessary to perform whole-brain modelling with tools such as The Virtual Brain. In addition, we provide blood-oxygen-level-dependent imaging time series averaged across regions of interest for comparison with simulation results. An average resting state hemodynamic response function for each region of interest, as well as shape maps for each voxel, are also contributed.

The hemodynamic response function (HRF) greatly influences the intra- and inter-subject variability of brain activation and connectivity, and might confound the estimation of temporal precedence in connectivity analyses, making its estimation necessary for a correct interpretation of neuroimaging studies. Additionally, the HRF shape itself is a useful local measure. However, most algorithms for HRF estimation are specific for task-related fMRI data, and only a few can be directly applied to resting-state protocols. Here we introduce rsHRF, a Matlab and Python toolbox that implements HRF estimation and deconvolution from the resting-state BOLD signal. We first provide an overview of the main algorithm, practical implementations, and then demonstrate the feasibility and usefulness of rsHRF by validation experiments with a publicly available resting-state fMRI dataset. We also provide tools for statistical analyses and visualization. We believe that this toolbox may significantly contribute to a better analysis and understanding of the components and variability of BOLD signals.

OBJECTIVE:To quantify whole-brain functional organization after complete hemispherotomy, characterizing unexplored plasticity pathways and the conscious level of the dissected hemispheres.METHODS:Evaluation with multimodal magnetic resonance imaging in two pediatric patients undergoing right hemispherotomy including complete callosotomy with a perithalamic section. Regional cerebral blood flow and fMRI network connectivity assessed the functional integrity of both hemispheres after surgery. The level of consciousness was tested by means of a support vector machine classifier which compared the intrinsic organization of the dissected hemispheres with those of patients suffering from disorders of consciousness.RESULTS:After hemispherotomy, both patients showed typical daily functionality. We found no interhemispheric transfer of functional connectivity in either patient as predicted by the operation. The healthy left hemispheres displayed focal blood hyperperfusion in motor and limbic areas, with preserved network-level organization. Unexpectedly, the disconnected right hemispheres showed sustained network organization despite low regional cerebral blood flow. Subcortically, functional connectivity was increased in the left thalamo-cortical loop and between the cerebelli. One patient further showed unusual ipsilateral right cerebello-cortical connectivity, which was explained by the mediation of the vascular system. The healthy left hemisphere had higher probability to be classified as in a minimally conscious state compared to the isolated right hemisphere.SIGNIFICANCE:Complete hemispherotomy leads to a lateralized whole-brain organization, with the remaining hemisphere claiming most of the brain's energetic reserves supported by subcortical structures. Our results further underline the contribution of nonneuronal vascular signals on contralateral connectivity, shedding light on the nature of network organization in the isolated tissue. The disconnected hemisphere is characterized by a level of consciousness which is necessary but insufficient for conscious processing, paving the way for more specific inquiries about its role in awareness in the absence of behavioral output.

Global signal (GS) is an ubiquitous construct in resting state functional magnetic resonance imaging (rs-fMRI), associated to nuisance, but containing by definition most of the neuronal signal. Global signal regression (GSR) effectively removes the impact of physiological noise and other artifacts, but at the same time it alters correlational patterns in unpredicted ways. Performing GSR taking into account the underlying physiology (mainly the blood arrival time) has been proven to be beneficial. From these observations we aimed to: 1) characterize the effect of GSR on network-level functional connectivity in a large dataset; 2) assess the complementary role of global signal and vessels; and 3) use the framework of partial information decomposition to further look into the joint dynamics of the global signal and vessels, and their respective influence on the dynamics of cortical areas. We observe that GSR affects intrinsic connectivity networks in the connectome in a non-uniform way. Furthermore, by estimating the predictive information of blood flow and the global signal using partial information decomposition, we observe that both signals are present in different amounts across intrinsic connectivity networks. Simulations showed that differences in blood arrival time can largely explain this phenomenon, while using hemodynamic and calcium mouse recordings we were able to confirm the presence of vascular effects, as calcium recordings lack hemodynamic information. With these results we confirm network-specific effects of GSR and the importance of taking blood flow into account for improving de-noising methods. Additionally, and beyond the mere issue of data denoising, we quantify the diverse and complementary effect of global and vessel BOLD signals on the dynamics of cortical areas.

Mutations in the synaptic scaffolding protein SHANK3 are a major cause of autism and are associated with prominent intellectual and language deficits. However, the neural mechanisms whereby SHANK3 deficiency affects higher-order socio-communicative functions remain unclear. Using high-resolution functional and structural MRI in adult male mice, here we show that loss of Shank3 (Shank3B−/−) results in disrupted local and long-range prefrontal and frontostriatal functional connectivity. We document that prefrontal hypoconnectivity is associated with reduced short-range cortical projections density, and reduced gray matter volume. Finally, we show that prefrontal disconnectivity is predictive of social communication deficits, as assessed with ultrasound vocalization recordings. Collectively, our results reveal a critical role of SHANK3 in the development of prefrontal anatomy and function, and suggest that SHANK3 deficiency may predispose to intellectual disability and socio-communicative impairments via dysregulation of higher-order cortical connectivity. SIGNIFICANCE STATEMENT Mutations in the synaptic scaffolding protein SHANK3 are commonly associated with autism, intellectual, and language deficits. Previous research has linked SHANK3 deficiency to basal ganglia dysfunction, motor stereotypies, and social deficits. However, the neural mechanism whereby Shank3 gene mutations affects cortical functional connectivity and higher-order socio-communicative functions remain unclear. Here we show that loss of SHANK3 in mice results in largely disrupted functional connectivity and abnormal gray matter anatomy in prefrontal areas. We also show that prefrontal connectivity disruption is tightly linked to socio-communicative deficits. Our findings suggest that SHANK3 is a critical orchestrator of frontocortical function, and that disrupted connectivity of prefrontal areas may underpin socio-communicative impairments observed in SHANK3 mutation carriers.

Previous studies have characterized the brain regions involved in encoding monetary reward and punishment outcomes. The question of how this information is integrated across brain regions has received less attention. Here, we investigated changes in effective connectivity related to the processing of positive and negative monetary outcomes using functional magnetic resonance imaging data from the Human Connectome Project. Specifically, subjects engaged in a card guessing game which could yield win, loss, or neutral outcomes. A general linear model was used to define a network of regions involved in win and loss outcome processing, including anterior insula, anterior cingulate cortex, and ventral striatum. Dynamic causal modelling (DCM) was implemented to study between-region couplings and outcome-related modulations thereof within this network. In addition, we explored the relation between effective connectivity patterns and choice behavior in the gambling task. Parametric empirical Bayesian modelling was conducted for group-level inferences of both DCM and the choice behavior. Behaviorally, both win and loss outcomes increased the probability of choice switches in subsequent gambles. In terms of connectivity, win outcomes were associated with increased extrinsic connectivity across the network, while loss outcomes featured a balance between increased and decreased extrinsic connectivity. Moreover, self-inhibitory connections tended to decrease for both win and loss outcomes. Interestingly, a substantial discrepancy was observed for occipital cortex connectivity, which was characterized by intrinsic disinhibition in loss but not in win trials. The observed differences in effective connectivity during the processing of positive and negative outcomes, despite similarities in average regional activity and choice behavior, highlight the value of exploring network dynamics in the context of incentive manipulations.

Hemispherotomy is a treatment for drug-resistant epilepsy with the whole hemisphere involved in seizure onset. As recovery mechanisms are still debated, we characterize functional reorganization with multimodal MRI in two children operated on the right hemisphere (RH). We found that interhemispheric functional connectivity was abolished in both patients. The healthy left hemispheres (LH) displayed focal hyperperfusion in motor and limbic areas, and preserved network-level organization. The disconnected RHs were hypoperfused despite sustained network-level organization. Functional connectivity was increased in the left thalamo-cortical loop and between the cerebelli. The classification probability of the RH corresponding to a minimally conscious state was smaller than for the LH. We conclude that after hemispherotomy, neurological rehabilitation is sustained by cortical disinhibition and reinforcement of connectivity driven by subcortical structures in the remaining hemisphere. Our results highlight the effect of vascularization on functional connectivity and raise inquiries about the conscious state of the isolated hemisphere.

Event Abstract Back to Event Is the transfer function between neural and hemodynamic activity at rest modulated by body-specific activity? The case of handedness. Sofie Van Den Bossche1*, Nigel Colenbier1, Frederik Van De Steen1, Hannes Almgren1 and Daniele Marinazzo1 1 Ghent University, Department of Data Analysis, Belgium The body-specificity hypothesis (Casasanto, 2011) states that individuals with different physical characteristics, such as left- and right-handers, tend to process the world differently. This has been supported by numerous task-related brain-imaging studies, which show that executing or merely imagining executing an action, activates the left motor cortex in right-handers and the right motor cortex in left-handers (e.g., Willems & Hagoort, 2009). Even so, these studies only provide evidence for body-specific brain activity while performing tasks. Whether body characteristics, such as handedness, also influence spontaneous, intrinsic brain activity remains unclear. Thus, the aim of this study was to examine whether handedness, as expressed by Edinburgh Handedness Inventory (EHI) scores, modulates the shape of the hemodynamic response function (HRF) at rest. To this end, minimally pre-processed resting-state functional MRI data of 343 participants belonging to the Human Connectome Project were selected (Glasser et al., 2013). Further pre-processing included additional spatial smoothing, removal of linear trends, CSF/WM nuisance regression, head motion regression and low-pass temporal filtering. Participants who displayed excessive movement in the scanner (i.e., a mean framewise displacement of 3 SD above the mean) were removed from the analysis. The shape of the resting-state hemodynamic response function was estimated using the blind hemodynamic deconvolution approach developed by Wu and colleagues (2013; see Figure 1 for a schematic overview). The resulting parameters (response height, time to peak and Full Width at Half Maximum) were entered into a voxel-based whole-brain multiple regression analysis. Edinburgh Handedness Inventory scores ranging from -100 (extreme left-handedness) to 100 (extreme right-handedness), were inserted as the covariate of interest. Three covariates of no interest (age, gender and mean framewise displacement) were added as well. Preliminary results revealed a negative correlation between the EHI scores and the height parameter (see Figure 2). For both sessions, a significant cluster was found in the right thalamus, suggesting that the more left-handed participants were according to their EHI scores, the higher their HRF amplitudes in the thalamus of the right hemisphere. For the second session results yielded an additional cluster in the left-postcentral gyrus, which was not found in the first session. Variation between both sessions could be due to a fluctuation in motion artifacts. Further analyses of the rs-fMRI data will include global signal regression. Figure 1 Figure 2 References Casasanto, D. (2011). Different bodies, different minds: The body specificity of language and thought. Current Directions in Psychological Science, 20(6), 378-383. Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson, T. S., Fischl, B., Andersson, J. L., ... Van Essen, D. C. (2013). The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage, 80, 105-124. Willems, R. M., & Hagoort, P. (2009). Hand preference influences neural correlates of action observation. Brain Research, 1269, 90-104. Wu, G. R., Deshpande, G., Laureys, S., & Marinazzo, D. (2015). Retrieving the Hemodynamic Response Function in resting state fMRI: Methodology and application. Presented at the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6050–6053. Wu, G. R., Liao, W., Stramaglia, S., Ding, J. R., Chen, H., & Marinazzo, D. (2013). A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data. Medical Image Analysis, 17(3), 365-374. Keywords: handedness, Resting-state fMRI, BOLD signal, deconvolution, HRF parameters Conference: 12th National Congress of the Belgian Society for Neuroscience, Gent, Belgium, 22 May - 22 May, 2017. Presentation Type: Poster Presentation Topic: Sensory and Motor Systems Citation: Van Den Bossche S, Colenbier N, Van De Steen F, Almgren H and Marinazzo D (2019). Is the transfer function between neural and hemodynamic activity at rest modulated by body-specific activity? The case of handedness.. Front. Neurosci. Conference Abstract: 12th National Congress of the Belgian Society for Neuroscience. doi: 10.3389/conf.fnins.2017.94.00002 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 02 May 2017; Published Online: 25 Jan 2019. * Correspondence: PhD. Sofie Van Den Bossche, Ghent University, Department of Data Analysis, Ghent, 9000, Belgium, sofie.vandenbossche@ugent.be Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Sofie Van Den Bossche Nigel Colenbier Frederik Van De Steen Hannes Almgren Daniele Marinazzo Google Sofie Van Den Bossche Nigel Colenbier Frederik Van De Steen Hannes Almgren Daniele Marinazzo Google Scholar Sofie Van Den Bossche Nigel Colenbier Frederik Van De Steen Hannes Almgren Daniele Marinazzo PubMed Sofie Van Den Bossche Nigel Colenbier Frederik Van De Steen Hannes Almgren Daniele Marinazzo Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Functional magnetic resonance imaging (fMRI) time series can be modeled as the convolution between the explicit timings of presented events and a hemodynamic response function (HRF). The HRF is a model of the underlying hemodynamic response that indirectly reflects neuronal activity in response to an event. Using the HRF, a lot has been learned about the functional segregation or activation in the brain. However, functional segregation is rarely investigated in a new popular neuroimaging technique called resting-state fMRI (rfMRI) which measures spontaneous brain activity. The reason functional segregation is rarely investigated is due to the fact there are no explicit events where the HRF can be modeled too. By using a recently developed blind HRF retrieval method by Wu et al. (2013), it is possible to retrieve the HRF at rest and thus investigate functional segregation in rfMRI. In this study, we used the blind HRF retrieval method to retrieve the HRF that is characterized by three parameters: response height, time to peak, full-width at half-max. By using behavioral measures that have shown to modulate the brain during cognitive tasks, we can explore if they also regulate the brain during spontaneous activity by doing correlational analyses between the retrieved HRF and the measures. There were no indications that the selected behavioral measures correlated with the HRF parameters. As a result we conclude that they do no modulate the hemodynamics during spontaneous activity. Nederlandse samenvatting De tijdsopname van functionele beeldvorming (fMRI) kan geconstrueerd worden door de convolutie tussen aangeboden items en een hemodynamische respons functie (HRF). De HRF is een model van de onderliggende hemodynamische respons wat een indirecte weergave is van de neuronale activiteit als antwoord op aangeboden items. Door het gebruik van de HRF is er al veel geleerd over de functionele segregatie of activiteit van de hersenen. Functionele segregatie wordt daarentegen bijna niet onderzocht in functionele beeldvorming bij personen in rust (rfMRI). De reden daarvoor is het feit dat er geen items zijn waarnaar de HRF kan geconstrueerd worden. Door een recent ontwikkelde methode door Wu et al. (2013) kan functionele segregatie in rfMRI onderzocht worden door de HRF op een blinde manier te reconstrueren. In deze studie is de blinde HRF methode gebruikt om de HRF te reconstrueren die wordt weergegeven door drie parameters. Door het gebruik van gedragsmaten waarvan er al is aangetoond dat ze de activiteit van de hersenen moduleren tijdens cognitieve taken, kunnen we onderzoeken of ze ook de activiteit van de hersenen in spontane activiteit modeleren door correlationele analyses uit te voeren tussen de gereconstrueerde HRF en de gedragsmaten. Er was geen bewijs dat de geselecteerde gedragsmaten gecorreleerd waren met de HRF parameters. Als gevolg concluderen we dat de gedragsmaten de hemodynamiek tijdens spontane activiteit niet moduleren.

Cardiac pulsation is a physiological confound of fMRI analysis pipelines and so accurate mapping of cardiac contributions to the BOLD signal is highly warranted. To overcome cardiac aliasing associated with the limited temporal resolution in fMRI, we developed a data-driven methodology to spatially and temporally resolve cardiac contributions from the BOLD signal itself (i.e without the need of processing external physiological recordings such as PPU/ECG signals). This is achieved by combining simultaneous multi-slice imaging and a dedicated hyper-sampling decomposition scheme. The proposed methodology is fully data driven and it does not make specific assumptions on the shape of cardiac pulsation.