The ongoing activity of the brain at rest, i.e., under no stimulation and in absence of any task, is astonishingly highly structured into/nspatiotemporal patterns. These spatiotemporal patterns, called resting state networks, display low-frequency characteristics (<0.1 Hz)/nobserved typically in the BOLD-fMRI signal of human subjects. We aim here to understand the origins of resting state activity through/nmodeling via a global spiking attractor network of the brain. This approach offers a realistic mechanistic model at the level of each single/nbrain area based on spiking neurons and realistic AMPA, NMDA, and GABA synapses. Integrating the biologically realistic diffusion/ntensor imaging/diffusion spectrum imaging-based neuroanatomical connectivity into the brain model, the resultant emerging resting/nstate functional connectivity of the brain network fits quantitatively best the experimentally observed functional connectivity in humans/nwhen the brain network operates at the edge of instability. Under these conditions, the slow fluctuating (
Coherent oscillations in the theta-to-gamma frequency range have/nbeen proposed as a mechanism that/ncoordinates neural activity/nin large-scale cortical networks i/nn sensory, motor, and cognitive/ntasks. Whether this mechanism also involves coherent oscillations/nat delta frequencies (1/n–/n4 Hz) is not known. Rather, delta oscilla-/ntions have been associated with slow-wave sleep. Here, we show/ncoherent oscillations in the delta frequency band between parietal/nand frontal cortices during the decision-making component of a so-/nmatosensory discrimination task. Importantly, the magnitude of/nthis delta-band coherence is modulated by the different decision/nalternatives. Furthermore, during control conditions not requiring/ndecision making, delta-band coherences are typically much reduced./nOur work indicates an important role for synchronous activity in/nthe delta frequency band when large-scale, distant cortical net-/nworks coordinate their neural activity during decision making. ; R.R./n'/ns research was partially supported by/nInternational Research Scholars Award 55005959 from the Howard Hughes/nMedical Institute, Dirección de Personal Académico de la Universidad Nacio-/nnal Autónoma de México Grant IN203210 and Consejo Nacional de Ciencia y/nTecnología Grant CB-2009-01-130863. V.N. was supported by a Ministerio de/nEducación y Ciencia (MEC)-Fullbright Postdoctoral Fellowship from the Span-/nish Ministry of Science and Technology and Dirección de Personal Académico/nde la Universidad Nacional Autónoma de México. A.L. was supported by the/nRamón y Cajal program from the Spanish government. G.D. was supported by/nthe European Research Council Advanced Grant DYSTRUCTURE 295129 and/nEuropean Union Seventh Framework Programme FP7/2007-2013 under Grant/n269921 (BrainScaleS).
A key unresolved problem in neuroscience is to determine the relevant timescale for understanding spatiotemporal dynamics across the whole brain. While resting state fMRI reveals networks at an ultraslow timescale (below 0.1 Hz), other neuroimaging modalities such as MEG and EEG suggest that much faster timescales may be equally or more relevant for discovering spatiotemporal structure. Here, we introduce a novel way to generate wholebrain neural dynamical activity at the millisecond scale from fMRI signals. This method allows us to study the different timescales through binning the output of the model. These timescales can then be investigated using a method (poetically named brain songs) to extract the spacetime motifs at a given timescale. Using independent measures of entropy and hierarchy to characterize the richness of the dynamical repertoire, we show that both methods find a similar optimum at a timescale of around 200 ms in resting state and in task data. ; G.D. is supported by the Spanish Research Project PSI2016-75688-P (AEI/FEDER, EU), by the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement Nos. 720270 (HBP SGA1) and 785907 (HBP SGA2), and by the Catalan AGAUR Programme 2017 SGR 1545. M.L.K. is supported by the ERC Consolidator Grant: CAREGIVING (No. 615539), and Center for Music in the Brain, funded by the Danish National Research Foundation (DNRF117).
Graph theory constitutes a widely used and established field providing powerful tools for the characterization of complex networks. The intricate topology of networks can also be investigated by means of the collective dynamics observed in the interactions of self-sustained oscillations (synchronization patterns) or propagationlike processes such as random walks. However, networks are often inferred from real-data-forming dynamic systems, which are different from those employed to reveal their topological characteristics. This stresses the necessity for a theoretical framework dedicated to the mutual relationship between the structure and dynamics in complex networks, as the two sides of the same coin. Here we propose a rigorous framework based on the network response over time (i.e., Green function) to study interactions between nodes across time. For this purpose we define the flow that describes the interplay between the network connectivity and external inputs. This multivariate measure relates to the concepts of graph communicability and the map equation. We illustrate our theory using the multivariate Ornstein-Uhlenbeck process, which describes stable and non-conservative dynamics, but the formalism can be adapted to other local dynamics for which the Green function is known. We provide applications to classical network examples, such as small-world ring and hierarchical networks. Our theory defines a comprehensive framework that is canonically related to directed and weighted networks, thus paving a way to revise the standards for network analysis, from the pairwise interactions between nodes to the global properties of networks including community detection. ; M.G. acknowledges funding from the Marie SkłodowskaCurie Action (Grant No. H2020-MSCA-656547) of the European Commission. G.Z.L., N.E.K., and G.D. acknowledge funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 720270 (HBP SGA1). G.D. also acknowledges funding from the European Research Council Advanced Grant DYSTRUCTURE (No. 295129) and the Spanish Research Project (No. PSI2013-42091-P). N.E.K. acknowledges support by the "MOVE-IN Louvain" fellowship cofunded by the Marie Skłodowska-Curie Action of the European Commission.
Abstract The focal lesion alters the excitation–inhibition (E–I) balance and healthy functional connectivity patterns, which may recover over time. One possible mechanism for the brain to counter the insult is global reshaping functional connectivity alterations. However, the operational principles by which this can be achieved remain unknown. We propose a novel equivalence principle based on structural and dynamic similarity analysis to predict whether specific compensatory areas initiate lost E–I regulation after lesion. We hypothesize that similar structural areas (SSAs) and dynamically similar areas (DSAs) corresponding to a lesioned site are the crucial dynamical units to restore lost homeostatic balance within the surviving cortical brain regions. SSAs and DSAs are independent measures, one based on structural similarity properties measured by Jaccard Index and the other based on post-lesion recovery time. We unravel the relationship between SSA and DSA by simulating a whole brain mean field model deployed on top of a virtually lesioned structural connectome from human neuroimaging data to characterize global brain dynamics and functional connectivity at the level of individual subjects. Our results suggest that wiring proximity and similarity are the 2 major guiding principles of compensation-related utilization of hemisphere in the post-lesion functional connectivity re-organization process.
In the human brain, spontaneous activity during resting state consists of rapid transitions between functional network states over time but the underlying mechanisms are not understood. We use connectome based computational brain network modeling to reveal fundamental principles of how the human brain generates large-scale activity observable by noninvasive neuroimaging. We used structural and functional neuroimaging data to construct whole- brain models. With this novel approach, we reveal that the human brain during resting state operates at maximum metastability, i.e. in a state of maximum network switching. In addition, we investigate cortical heterogeneity across areas. Optimization of the spectral characteristics of each local brain region revealed the dynamical cortical core of the human brain, which is driving the activity of the rest of the whole brain. Brain network modelling goes beyond correlational neuroimaging analysis and reveals non-trivial network mechanisms underlying non-invasive observations. Our novel findings significantly pertain to the important role of computational connectomics in understanding principles of brain function. ; GD is supported by the ERC Advanced Grant DYSTRUCTURE (n. 295129), by the Spanish Research Project PSI2016-75688-P. MLK is supported by the ERC Consolidator Grant: CAREGIVING (n. 615539) and Center for Music in the Brain, funded by the Danish National Research Foundation (DNRF117). VJ and GD are supported by the European Union's Horizon 2020 research and innovation programme under grant agreement n. 720270 (HBP SGA1). VJ and PR are supported by the James S. McDonnell Foundation (Brain Network Recovery Group JSMF22002082). VJ is supported by FHU EPINEXT [A*MIDEX project (ANR-11-IDEX-0001-02) funded by the 'Investissements d'Avenir' French Government]. PR is supported the German Ministry of Education and Research (US-German Collaboration in Computational Neuroscience 100258846 and Bernstein Focus State Dependencies of Learning 01GQ0971-5), the Max-Planck Society and funding from the European Union Horizon 2020 (ERC Consolidator grant BrainModes 683049).
In the human brain, spontaneous activity during resting state consists of rapid transitions between functional network states over time but the underlying mechanisms are not understood. We use connectome based computational brain network modeling to reveal fundamental principles of how the human brain generates large-scale activity observable by noninvasive neuroimaging. We used structural and functional neuroimaging data to construct whole- brain models. With this novel approach, we reveal that the human brain during resting state operates at maximum metastability, i.e. in a state of maximum network switching. In addition, we investigate cortical heterogeneity across areas. Optimization of the spectral characteristics of each local brain region revealed the dynamical cortical core of the human brain, which is driving the activity of the rest of the whole brain. Brain network modelling goes beyond correlational neuroimaging analysis and reveals non-trivial network mechanisms underlying non-invasive observations. Our novel findings significantly pertain to the important role of computational connectomics in understanding principles of brain function. ; GD is supported by the ERC Advanced Grant DYSTRUCTURE (n. 295129), by the Spanish Research Project PSI2016-75688-P. MLK is supported by the ERC Consolidator Grant: CAREGIVING (n. 615539) and Center for Music in the Brain, funded by the Danish National Research Foundation (DNRF117). VJ and GD are supported by the European Union's Horizon 2020 research and innovation programme under grant agreement n. 720270 (HBP SGA1). VJ and PR are supported by the James S. McDonnell Foundation (Brain Network Recovery Group JSMF22002082). VJ is supported by FHU EPINEXT [A*MIDEX project (ANR-11-IDEX-0001-02) funded by the 'Investissements d'Avenir' French Government]. PR is supported the German Ministry of Education and Research (US-German Collaboration in Computational Neuroscience 100258846 and Bernstein Focus State Dependencies of Learning 01GQ0971-5), the Max-Planck Society and funding from the European Union Horizon 2020 (ERC Consolidator grant BrainModes 683049).
It is well-established that patterns of functional connectivity (FC) - measures of correlated activity between pairs of voxels or regions observed in the human brain using neuroimaging - are robustly expressed in spontaneous activity during rest. These patterns are not static, but exhibit complex spatio-temporal dynamics. Over the last years, a multitude of methods have been proposed to reveal these dynamics on the level of the whole brain. One finding is that the brain transitions through different FC configurations over time, and substantial effort has been put into characterizing these configurations. However, the dynamics governing these transitions are more elusive, specifically, the contribution of stationary vs. non-stationary dynamics is an active field of inquiry. In this study, we use a whole-brain approach, considering FC dynamics between 66 ROIs covering the entire cortex. We combine an innovative dimensionality reduction technique, tensor decomposition, with a mean field model which possesses stationary dynamics. It has been shown to explain resting state FC averaged over time and multiple subjects, however, this average FC summarizes the spatial distribution of correlations while hiding their temporal dynamics. First, we apply tensor decomposition to resting state scans from 24 healthy controls in order to characterize spatio-temporal dynamics present in the data. We simultaneously utilize temporal and spatial information by creating tensors that are subsequently decomposed into sets of brain regions ("communities") that share similar temporal dynamics, and their associated time courses. The tensors contain pairwise FC computed inside of overlapping sliding windows. Communities are discovered by clustering features pooled from all subjects, thereby ensuring that they generalize. We find that, on the group level, the data give rise to four distinct communities that resemble known resting state networks (RSNs): default mode network, visual network, control networks, and somatomotor network. Second, we simulate data with our stationary mean field model whose nodes are connected according to results from DTI and fiber tracking. In this model, all spatio-temporal structure is due to noisy fluctuations around the average FC. We analyze the simulated data in the same way as the empirical data in order to determine whether stationary dynamics can explain the emergence of distinct FC patterns (RSNs) which have their own time courses. We find that this is the case for all four networks using the spatio-temporal information revealed by tensor decomposition if nodes in the simulation are connected according to model-based effective connectivity. Furthermore, we find that these results require only a small part of the FC values, namely the highest values that occur across time and ROI pair. Our findings show that stationary dynamics can account for the emergence of RSNs. We provide an innovative method that does not make strong assumptions about the underlying data and is generally applicable to resting state or task data from different subject populations. ; This work was supported by the European Union, FP7 Marie Curie ITN "INDIREA" (Grant N. 606901; KG), FP7 FET ICT Flagship Human Brain Project (Grant N. 604102; MG), ERC Advanced Human Brain Project (Grant N. 604102; GD), European Union Horizon2020 (ERC Consolidator grant BrainModes 683049; PR); the Spanish Ministry for Economy, Industry and Competitiveness (MINECO) project "PIRE-PICCS" (Grant N. PCIN-2015-079; KG), SEMAINE ERA-Net NEURON Project (Grant N. PCIN2013-026; APA), and ICoBAM (Grant N. PSI2013-42091-P; GD); the James S. McDonnell Foundation (Brain Network Recovery Group, Grant N. JSMF22002082; PR); the German Ministry of Education and Research (Grant N. 01GQ1504A and 01GQ0971-5; PR); the Max-Planck Society (Minerva Program; PR).
Functionally relevant network patterns form transiently in brain activity during rest, where a given subset of brain areas exhibits temporally synchronized BOLD signals. To adequately assess the biophysical mechanisms governing intrinsic brain activity, a detailed characterization of the dynamical features of functional networks is needed from the experimental side to constrain theoretical models. In this work, we use an open-source fMRI dataset from 100 healthy participants from the Human Connectome Project and analyze whole-brain activity using Leading Eigenvector Dynamics Analysis (LEiDA), which serves to characterize brain activity at each time point by its whole-brain BOLD phase-locking pattern. Clustering these BOLD phase-locking patterns into a set of k states, we demonstrate that the cluster centroids closely overlap with reference functional subsystems. Borrowing tools from dynamical systems theory, we characterize spontaneous brain activity in the form of trajectories within the state space, calculating the Fractional Occupancy and the Dwell Times of each state, as well as the Transition Probabilities between states. Finally, we demonstrate that within-subject reliability is maximized when including the high frequency components of the BOLD signal (>0.1 Hz), indicating the existence of individual fingerprints in dynamical patterns evolving at least as fast as the temporal resolution of acquisition (here TR = 0.72 s). Our results reinforce the mechanistic scenario that resting-state networks are the expression of erratic excursions from a baseline synchronous steady state into weakly-stable partially-synchronized states – which we term ghost attractors. To better understand the rules governing the transitions between ghost attractors, we use methods from dynamical systems theory, giving insights into high-order mechanisms underlying brain function. ; This work has been funded by FEDER through the Competitiveness Factors Operational Program (COMPETE), by National funds through the Foundation for Science and Technology (FCT) under the scope of the project UID/Multi/50026; and by the projects NORTE-01-0145-FEDER-000013 and NORTE-01-0145-FEDER-000023, supported by the Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). JC was supported by Portuguese Foundation for Science and Technology CEECIND/03325/2017, Portugal. GD acknowledges funding from the European Union's Horizon 2020 FET Flagship Human Brain Project under Grant Agreement 785907 HBP SGA2, the Spanish Ministry Project PSI2016-75688-P (AEI/FEDER) and the Catalan Research Group Support 2017 SGR 1545. MK was supported by the European Research Council Consolidator Grant: CAREGIVING (615539), Pettit Foundation, Carlsberg Foundation and Center for Music in the Brain, funded by the Danish National Research Foundation (DNRF117). BC was supported by the French Government through the UCA-Jedi project managed by the National Research Agency (ANR-15-IDEX-01) and, in particular, by the interdisciplinary Institute for Modeling in Neuroscience and Cognition (NeuroMod) of the Université Côte d'Azur.
Cognition is hypothesized to require the globally coordinated, functionally relevant integration of otherwise segregated information processing carried out by specialized brain regions. Studies of the macroscopic connectome as well as recent neuroimaging and neuromodeling research have suggested a densely connected collective of cortical hubs, termed the rich club, to provide a central workspace for such integration. In order for rich club regions to fulfill this role they must dispose of a dynamic mechanism by which they can actively shape networks of brain regions whose information processing needs to be integrated. A potential candidate for such a mechanism comes in the form of oscillations which might be employed to establish communication channels among relevant brain regions. We explore this possibility using an integrative approach combining whole-brain computational modeling with neuroimaging, wherein we investigate the local dynamics model brain regions need to exhibit in order to fit (dynamic) network behavior empirically observed for resting as well as a range of task states. We find that rich club regions largely exhibit oscillations during task performance but not during rest. Furthermore, oscillations exhibited by rich club regions can harmonize a set of asynchronous brain regions thus supporting functional coupling among them. These findings are in line with the hypothesis that the rich club can actively shape integration using oscillations. ; Authors MS and RG were supported by the European Research Council under the European Union's Seventh Framework Programme (ERC-2010-AdG, ERC grant agreement no. 269853). Author GD was supported by the ERC Advanced Grant: DYSTRUCTURE (no. 295129), by the Spanish Research ProjectSAF2010-16085 and by European Community's Seventh Framework Programme under the project "BrainScales" (project number 269921). Author MPvdH was supported by a VENI grant of The Netherlands Organization for Scientific Research (NWO) (451-12-001) and by a Fellowship of the Brain Center Rudolf Magnus.
Previous studies suggest that the brain operates at a critical point in which phases of order and disorder coexist, producing emergent patterned dynamics at all scales and optimizing several brain functions. Here, we combined light-sheet microscopy with GCaMP zebrafish larvae to study whole-brain dynamics in vivo at near single-cell resolution. We show that spontaneous activity propagates in the brain's three-dimensional space, generating scaleinvariant neuronal avalanches with time courses and recurrence times that exhibit statistical self-similarity at different magnitude, temporal, and frequency scales. This suggests that the nervous system operates close to a non-equilibrium phase transition, where a large repertoire of spatial, temporal, and interactive modes can be supported. Finally, we show that gap junctions contribute to the maintenance of criticality and that, during interactions with the environment (sensory inputs and self-generated behaviors), the system is transiently displaced to a more ordered regime, conceivably to limit the potential sensory representations and motor outcomes. ; A.P.-A. was supported by a Juan de la Cierva fellowship (IJCI-2014-21066) from the Spanish Ministry of Economy and Competitiveness. A.J. was supported by the Fondation pour la Recherche Medicale (FRM:FDT20140930915) and the ENS Cachan. M.P. was supported by the ENS Lyon. G.D. was funded by the European Research Council (ERC) Advanced Grant DYSTRUCTURE (No. 295129), by the Spanish Research Project PSI2016-75688-P (AEI/FEDER), and by the European Union's Horizon 2020 research and innovation program under grant agreement No. 720270 (HBP SGA1). G.S. was supported by ERC StG 243106, ERC CoG 726280, ANR-10-LABX-54 MEMO LIFE, and ANR-11-IDEX-0001-02 PSL Research University. We thank J. Boulanger-Weill for technical assistance and discussions, Patricia Gongal for editorial assistance, and David Hildebrand for providing GCaMP6f line.
Perceptual bistability arises when two conflicting interpretations of an ambiguous stimulus or images in binocular rivalry (BR) compete for perceptual dominance. From a computational point of view, competition models based on cross-inhibition and adaptation have shown that noise is a crucial force for rivalry, and operates in balance with adaptation. In particular, noise-driven transitions and adaptation-driven oscillations define two dynamical regimes and the system explains the observed alternations in perception when it operates near their boundary. In order to gain insights into the microcircuit dynamics mediating spontaneous perceptual alternations, we used a reduced recurrent attractor-based biophysically realistic spiking network, well known for working memory, attention, and decision making, where a spike-frequency adaptation mechanism is implemented to account for perceptual bistability. We thus derived a consistently reduced four-variable population rate model using mean-field techniques, and we tested it on BR data collected from human subjects. Our model accounts for experimental data parameters such as mean time dominance, coefficient of variation, and gamma distribution fit. In addition, we show that our model operates near the bifurcation that separates the noise-driven transitions regime from the adaptation-driven oscillations regime, and agrees with Levelt's second revised and fourth propositions. These results demonstrate for the first time that a consistent reduction of a biophysically realistic spiking network of leaky integrate-and-fire neurons with spike-frequency adaptation could account for BR. Moreover, we demonstrate that BR can be explained only through the dynamics of competing neuronal pools, without taking into account the adaptation of inhibitory interneurons. However, the adaptation of interneurons affects the optimal parametric space of the system by decreasing the overall adaptation necessary for the bifurcation to occur, and introduces oscillations in the spontaneous state. ; The authors would like to thank KongFatt Wong, Antoni Guillamon, James Rankin, Robert Ton, for their useful communications, and Tristan Nakagawa for his comments on the manuscript. The research leading to these results has received funding from the European Community's Seventh Framework Programme FP7/2007-2013 under grant agreement number 214728-2, and the Max Planck Society. It was also supported by the European Union grant "Brainsynch" and "BrainScaleS" by the Spanish Research Project SAF2010-16085, and by the CONSOLIDER – INGENIO 2010 Programme CSD2007-00012.
Computational modeling of the spontaneous dynamics over the whole brain provides critical insight into the spatiotemporal organization of brain dynamics at multiple resolutions and their alteration to changes in brain structure (e.g. in diseased states, aging, across individuals). Recent experimental evidence further suggests that the adverse effect of lesions is visible on spontaneous dynamics characterized by changes in resting state functional connectivity and its graph theoretical properties (e.g. modularity). These changes originate from altered neural dynamics in individual brain areas that are otherwise poised towards a homeostatic equilibrium to maintain a stable excitatory and inhibitory activity. In this work, we employ a homeostatic inhibitory mechanism, balancing excitation and inhibition in the local brain areas of the entire cortex under neurological impairments like lesions to understand global functional recovery (across brain networks and individuals). Previous computational and empirical studies have demonstrated that the resting state functional connectivity varies primarily due to the location and specific topological characteristics of the lesion. We show that local homeostatic balance provides a functional recovery by re-establishing excitation-inhibition balance in all areas that are affected by lesion. We systematically compare the extent of recovery in the primary hub areas (e.g. default mode network (DMN), medial temporal lobe, medial prefrontal cortex) as well as other sensory areas like primary motor area, supplementary motor area, fronto-parietal and temporo-parietal networks. Our findings suggest that stability and richness similar to the normal brain dynamics at rest are achievable by re-establishment of balance. ; This study was funded by A.B. Ramalingaswami fellowship (BT/RLF/Re-entry/31/2011) and Innovative Young Bio-technologist Award (IYBA) (BT/07/IYBA/2013) from the Department of Biotechnology, Ministry of Science & Technology, Government of India. GD is supported by the ERC Advanced Grant: DYSTRUCTURE (n. 295129), by the Spanish Research ProjectPSI2013-42091-P, and funding from the European Union Seventh Framework Programme (FP7-ICT Human Brain Project (grant no. 60402)).
Brain function relies on the flexible integration of a diverse set of segregated cortical/nmodules, with the structural connectivity of the brain being a fundamentally important factor/nin shaping the brain"s functional dynamics. Following up on macroscopic studies showing the/nexistence of centrally connected nodes in the mammalian brain, combined with the notion that/nthese putative brain hubs may form a dense interconnected "rich club" collective, we/nhypothesized that brain connectivity might involve a rich club type of architecture to promote/na repertoire of different and flexibly accessible brain functions. With the rich club suggested/nto play an important role in global brain communication, examining the effects of a rich club/norganization on the functional repertoire of physical systems in general, and the brain in/nparticular, is of keen interest. Here we elucidate these effects using a spin glass model of/nneural networks for simulating stable configurations of cortical activity. Using simulations,/nwe show that the presence of a rich club increases the set of attractors and hence the diversity/nof the functional repertoire over and above the effects produced by scale free type topology/nalone. Within the networks" overall functional repertoire rich nodes are shown to be important/nfor enabling a high level of dynamic integrations of low-degree nodes to form functional/nnetworks. This suggests that the rich club serves as an important backbone for numerous co-/nactivation patterns among peripheral nodes of the network. In addition, applying the spin/nglass model to empirical anatomical data of the human brain, we show that the positive effects/non the functional repertoire attributed to the rich club phenomenon can be observed for the/nbrain as well. We conclude that a rich club organization in network architectures may be/ncrucial for the facilitation and integration of a diverse number of segregated functions. ; Authors MS and RG were supported by the European Research Council under the European Union's Seventh Framework Programme (ERC-2010-AdG, ERC grant agreement no. 269853). Author GD was supported by the ERC Advanced Grant: DYSTRUCTURE (n. 295129), by the Spanish Research ProjectSAF2010-16085 and by European Community's Seventh Framework Programme under the project "BrainScales" (project number 269921). Author MPvdH was supported by a VENI grant of The Netherlands Organization for Scientific Research (NWO) (451-12-001) and by a Fellowship of the Brain Center Rudolf Magnus.
In this study, we recorded single unit activity from rat auditory cortex while the animals performed an interval-discrimination task. The animals had to decide whether two auditory stimuli were separated by either 150 or 300 ms, and go to the left or right nose poke accordingly. Spontaneous firing in between auditory responses was compared in the attentive versus non-attentive brain states. We describe the firing rate modulation detected during intervals while there was no auditory stimulation. Nearly 18% of neurons (n = 14) showed a prominent neuronal discharge during the interstimulus interval, in the form of an upward or downward ramp towards the second auditory stimulus. These patterns of spontaneous activity were often modulated in the attentive versus passive trials. Modulation of the spontaneous firing rate during the task was observed not only between auditory stimuli, but also in the interval preceding the stimulus. These slow modulatory components could be locally generated or the result of a top-down influence originated in higher associative association areas. Such a neuronal discharge may be related to the computation of the interval time and contribute to the perception of the auditory stimulus. ; Supported by a grant from the Ministerio de Ciencia e Innovación/n(BFU2008-01371/BFI) to MVS–V. MM–G and GD were supported/nby the European Union grant BRAINSCALES, by the Spanish/nResearch Project SAF2010-16085 and by the CONSOLIDERINGENIO/n2010 Programme CSD2007-00012, and EU FP7/2007-/n2013 under grant agreement 214728-2.
