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      A DCM study of spectral asymmetries in feedforward and feedback connections between visual areas V1 and V4 in the monkey

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          Abstract

          This paper reports a dynamic causal modeling study of electrocorticographic (ECoG) data that addresses functional asymmetries between forward and backward connections in the visual cortical hierarchy. Specifically, we ask whether forward connections employ gamma-band frequencies, while backward connections preferentially use lower (beta-band) frequencies. We addressed this question by modeling empirical cross spectra using a neural mass model equipped with superficial and deep pyramidal cell populations—that model the source of forward and backward connections, respectively. This enabled us to reconstruct the transfer functions and associated spectra of specific subpopulations within cortical sources. We first established that Bayesian model comparison was able to discriminate between forward and backward connections, defined in terms of their cells of origin. We then confirmed that model selection was able to identify extrastriate (V4) sources as being hierarchically higher than early visual (V1) sources. Finally, an examination of the auto spectra and transfer functions associated with superficial and deep pyramidal cells confirmed that forward connections employed predominantly higher (gamma) frequencies, while backward connections were mediated by lower (alpha/beta) frequencies. We discuss these findings in relation to current views about alpha, beta, and gamma oscillations and predictive coding in the brain.

          Highlights

          • We briefly review evidence for canonical microcircuits (CMC) and predictive coding.

          • This evidence is incorporated into a novel Dynamic Causal Model (DCM).

          • We model observed cross-spectral densities from monkey visual cortex (V1 and V4).

          • We establish the face and predictive validity of this new DCM.

          • Gamma rhythms subserve feedforward, and alpha/beta rhythms feedback influences.

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          Most cited references66

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          Identifying true brain interaction from EEG data using the imaginary part of coherency.

          The main obstacle in interpreting EEG/MEG data in terms of brain connectivity is the fact that because of volume conduction, the activity of a single brain source can be observed in many channels. Here, we present an approach which is insensitive to false connectivity arising from volume conduction. We show that the (complex) coherency of non-interacting sources is necessarily real and, hence, the imaginary part of coherency provides an excellent candidate to study brain interactions. Although the usual magnitude and phase of coherency contain the same information as the real and imaginary parts, we argue that the Cartesian representation is far superior for studying brain interactions. The method is demonstrated for EEG measurements of voluntary finger movement. We found: (a) from 5 s before to movement onset a relatively weak interaction around 20 Hz between left and right motor areas where the contralateral side leads the ipsilateral side; and (b) approximately 2-4 s after movement, a stronger interaction also at 20 Hz in the opposite direction. It is possible to reliably detect brain interaction during movement from EEG data. The method allows unambiguous detection of brain interaction from rhythmic EEG/MEG data.
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            Neuronal circuits of the neocortex.

            We explore the extent to which neocortical circuits generalize, i.e., to what extent can neocortical neurons and the circuits they form be considered as canonical? We find that, as has long been suspected by cortical neuroanatomists, the same basic laminar and tangential organization of the excitatory neurons of the neocortex is evident wherever it has been sought. Similarly, the inhibitory neurons show characteristic morphology and patterns of connections throughout the neocortex. We offer a simple model of cortical processing that is consistent with the major features of cortical circuits: The superficial layer neurons within local patches of cortex, and within areas, cooperate to explore all possible interpretations of different cortical input and cooperatively select an interpretation consistent with their various cortical and subcortical inputs.
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              A quantitative map of the circuit of cat primary visual cortex.

              We developed a quantitative description of the circuits formed in cat area 17 by estimating the "weight" of the projections between different neuronal types. To achieve this, we made three-dimensional reconstructions of 39 single neurons and thalamic afferents labeled with horseradish peroxidase during intracellular recordings in vivo. These neurons served as representatives of the different types and provided the morphometrical data about the laminar distribution of the dendritic trees and synaptic boutons and the number of synapses formed by a given type of neuron. Extensive searches of the literature provided the estimates of numbers of the different neuronal types and their distribution across the cortical layers. Applying the simplification that synapses between different cell types are made in proportion to the boutons and dendrites that those cell types contribute to the neuropil in a given layer, we were able to estimate the probable source and number of synapses made between neurons in the six layers. The predicted synaptic maps were quantitatively close to the estimates derived from the experimental electron microscopic studies for the case of the main sources of excitatory and inhibitory input to the spiny stellate cells, which form a major target of layer 4 afferents. The map of the whole cortical circuit shows that there are very few "strong" but many "weak" excitatory projections, each of which may involve only a few percentage of the total complement of excitatory synapses of a single neuron.
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                Author and article information

                Contributors
                Journal
                Neuroimage
                Neuroimage
                Neuroimage
                Academic Press
                1053-8119
                1095-9572
                1 March 2015
                March 2015
                : 108
                : 460-475
                Affiliations
                [a ]Ernst Strüngmann Institute (ESI) in Cooperation with Max Planck Society, Deutschordenstraße 46, Frankfurt 60528, Germany
                [b ]Center for Neuroscience and Center for Mind and Brain, University of California, Davis, Davis, CA 95618, USA
                [c ]The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK
                [d ]Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Kapittelweg 29, Nijmegen 6535 EN, Netherlands
                [e ]Cognitive and Systems Neuroscience Group, Center for Neuroscience, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam 1098 XH, Netherlands
                Author notes
                [* ]Corresponding author at: Picower Institute for Learning and Memory, MIT, Cambridge, MA 02139, USA. Fax: + 1 617 258 7978. andrembastos@ 123456gmail.com
                Article
                S1053-8119(15)00011-7
                10.1016/j.neuroimage.2014.12.081
                4334664
                25585017
                a8b3d46d-8a56-4027-94b1-4f62c2f45c88
                © 2015 The Authors. Published by Elsevier Inc.

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 30 December 2014
                Categories
                Technical Note

                Neurosciences
                neuronal,connectivity,computation,dynamic causal modeling,synchronization coherence,transfer functions,gamma oscillations,beta oscillations

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