36 research outputs found
An information-theoretic approach to build hypergraphs in psychometrics
No full textPsychological network approaches propose to see symptoms or questionnaire items as interconnected nodes, with linksbetween them reflecting pairwise statistical dependencies evaluated on cross-sectional, time-series, or panel data. Thesenetworks constitute an established methodology to visualise and conceptualise the interactions and relative importance ofnodes/indicators, providing an important complement to other approaches such as factor analysis. However, limiting therepresentation 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-theoreticframework to assess these interdependencies and consequently to use hypergraphs as representations in psychometrics. Asedges in hypergraphs are capable of encompassing several nodes together, this extension can thus provide a richer account onthe interactions that may exist among sets of psychological variables. Our results show how psychometric hypergraphs canhighlight meaningful redundant and synergistic interactions on either simulated or state-of-the-art, re-analysed psychometricdatasets. Overall, our framework extends current network approaches while leading to new ways of assessing the data thatdiffer at their core from other methods, enriching the psychometrics toolbox, and opening promising avenues for futureinvestigation
Identification of single subject cognitive networks : from morphology to dynamical computations
No full textDisambiguating the role of blood flow and global signal with partial information decomposition
No full textFigures from the paper ' Disambiguating the role of blood flow and global signal with partial information decomposition'.https://www.biorxiv.org/content/10.1101/596247v2<br
Poster_OHBM_GS_Colenbier.pdf
No full textPoster presented at OHBM and Resting State conferences in 2018, describing the partial information decomposition approach to disambiguate the role of global signal and BOLD in vessels in influencing the grey matter regions
Deletion of autism risk gene Shank3 disrupts prefrontal connectivity
No full textMutations 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 fronto-striatal functional connectivity. We document that prefrontal hypo-connectivity 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
voxelwise resting state HRF shape (WM and GM)
No full textWe used our rsHRF tool https://www.nitrc.org/projects/rshrf to retrieve a proxy of the hemodynamic response function from BOLD signal recorded at each voxel, both in grey and in white matter.These figures report the average relevant parameters (Height, Full Width at Half Maximum, Time to Peak) from Human Connectome Project data.We report results using two sets of basis functions: Canonical Shape with two derivatives (Canon2dd), and Finite Impulse Response (FIR).The 3D nifti images can be found here https://neurovault.org/collections/3584/</div
Effective connectivity modulations related to win and loss outcomes
No full textPrevious 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
WHOCARES: data-driven WHOle-brain CArdiac signal REgression from highly accelerated simultaneous multi-Slice fMRI acquisitions
No full textCardiac pulsation is a physiological confound of functional magnetic resonance imaging (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. The introduction of simultaneous multi-slice (SMS) imaging has significantly reduced the chances of cardiac aliasing. However, the necessity of covering the entire brain at high spatial resolution restrain the shortest TR to just over 0.5 seconds, which is in turn not sufficiently short to resolve cardiac pulsation beyond 60 beats per minute. Recently, hyper-sampling of the fMRI time-series has been introduced to overcome this issue. While each anatomical location is sampled every TR seconds, the time between consecutive excitations is shorter and thus adequate to resolve cardiac pulsation. In this study, we show that it is feasible to temporally and spatially resolve cardiac waveforms at each voxel location by combining a dedicated hyper-sampling decomposition scheme with SMS. We developed the technique on 774 healthy subjects selected from the Human Connectome Project (HCP) and validated the technology against the RETROICOR method. The proposed approach, which we name Data-driven WHOle-brain CArdiac signal REgression from highly accelerated simultaneous multi-Slice fMRI acquisitions (WHOCARES), is fully data-driven, does not make specific assumptions on cardiac pulsatility, and is independent from external physiological recordings so that the retrospective correction of fMRI data becomes possible when such measurements are not available. WHOCARES is freely available at https://github.com/gferrazzi/WHOCARES
