Adding dynamics to the Human Connectome Project with MEG

Larson‐Prior, Linda J.; Oostenveld, Robert; Penna, Stefania Della; Michalareas, Georgios; Prior, Fred; Babajani‐Feremi, Abbas; Schoffelen, Jan‐Mathijs; Marzetti, Laura; Pasquale, Francesco de; Pompeo, F. De; Stout, Jeffery R.; Woolrich, Mark W.; Luo, Qiang; Bucholz, Richard D.; Fries, Pascal; Pizzella, Vittorio; Romani, Gian Luca; Corbetta, Maurizio; Snyder, Abraham Z.

doi:10.1016/j.neuroimage.2013.05.056

Cited by 212 publications

(254 citation statements)

References 126 publications

(197 reference statements)

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“…The Human Connectome Project (HCP) (Essen and Ugurbil, 2012;Essen et al, 2012b;Glasser et al, 2013) has undertaken the challenge to make thousands of datasets publicly available, making comparison of structural and functional connectivity across imaging modalities and across individual subjects easier (Larson-Prior et al, 2013). Other studies are attempting to validate the white-matter information extracted from tractography against tracing studies performed in postmortem tissues (Seehaus et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Mathematical framework for large-scale brain network modeling in The Virtual Brain

et al. 2015

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In this article, we describe the mathematical framework of the computational model at the core of the tool The Virtual Brain (TVB), designed to simulate collective whole brain dynamics by virtualizing brain structure and function, allowing simultaneous outputs of a number of experimental modalities such as electro- and magnetoencephalography (EEG, MEG) and functional Magnetic Resonance Imaging (fMRI). The implementation allows for a systematic exploration and manipulation of every underlying component of a large-scale brain network model (BNM), such as the neural mass model governing the local dynamics or the structural connectivity constraining the space time structure of the network couplings. Here, a consistent notation for the generalized BNM is given, so that in this form the equations represent a direct link between the mathematical description of BNMs and the components of the numerical implementation in TVB. Finally, we made a summary of the forward models implemented for mapping simulated neural activity (EEG, MEG, sterotactic electroencephalogram (sEEG), fMRI), identifying their advantages and limitations.

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Section: Discussionmentioning

confidence: 99%

Mathematical framework for large-scale brain network modeling in The Virtual Brain

et al. 2015

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“…Earlier work in robotics has meshed together living, electronic, and simulated components (66-68) and dynamical models such as HKB have been incorporated into robots for sensorimotor control (69,70) and interrobot interactions (71). To achieve a desirable degree of realism, the architecture of a neurally grounded HDC may take inspiration from human connectomics (72)(73)(74)(75). Using such an approach, Dumas et al (76) already designed a dyadic model of two interacting brains, in good agreement with empirical evidence at both neural and social levels (77).…”

Section: Discussionmentioning

confidence: 99%

The human dynamic clamp as a paradigm for social interaction

Dumas

Guzman

Tognoli

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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Social neuroscience has called for new experimental paradigms aimed toward real-time interactions. A distinctive feature of interactions is mutual information exchange: One member of a pair changes in response to the other while simultaneously producing actions that alter the other. Combining mathematical and neurophysiological methods, we introduce a paradigm called the human dynamic clamp (HDC), to directly manipulate the interaction or coupling between a human and a surrogate constructed to behave like a human. Inspired by the dynamic clamp used so productively in cellular neuroscience, the HDC allows a person to interact in real time with a virtual partner itself driven by well-established models of coordination dynamics. People coordinate hand movements with the visually observed movements of a virtual hand, the parameters of which depend on input from the subject's own movements. We demonstrate that HDC can be extended to cover a broad repertoire of human behavior, including rhythmic and discrete movements, adaptation to changes of pacing, and behavioral skill learning as specified by a virtual "teacher." We propose HDC as a general paradigm, best implemented when empirically verified theoretical or mathematical models have been developed in a particular scientific field. The HDC paradigm is powerful because it provides an opportunity to explore parameter ranges and perturbations that are not easily accessible in ordinary human interactions. The HDC not only enables to test the veracity of theoretical models, it also illuminates features that are not always apparent in real-time human social interactions and the brain correlates thereof.human-machine interface | artificial agent | computational social neuroscience | multiscale | dynamical systems R eciprocally coupled complex systems in biology and psychology are notoriously difficult to study. Over the course of the last three decades, understanding how real neurons work individually and together has grown significantly in large part due to the so-called dynamic clamp paradigm (see ref. 1 for a review). The initial idea was to combine a standard electrophysiological setup with a computer interface, and then control in real time the current injected into a neuron as a function of its membrane potential measured via an intracellular electrode (2). The original dynamic clamp used tried and tested electrophysiological models such as the Hodgkin-Huxley equations (3) to explore parametrically how neurons behave. The latter, considered one of the most significant accomplishments in biophysics in the 20th century, provides a quantitative description of the electric potential across a cell membrane. The interaction between the real neuron and its artificial counterpart allows simulating artificial membrane or synaptic conductance (4, 5), chemical or electronic inputs (6, 7), and even connections with other neurons (8). Because the dynamic clamp falls midway between computational modeling and experimental electrophysiology, it affords the same degree of precision a...

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“…The dynome adds to such a "functional connectome" an understanding of the regions involved in producing and processing brain signals. Although I will focus on brain rhythms, it should be noted that the dynome extends beyond neural oscillations and includes other temporal structures (Larson-Prior et al, 2013).…”

Section: Dynamic Cognomics: Preliminary Remarksmentioning

confidence: 99%