Associating spontaneous with evoked activity in a neural mass model of visual cortex

Trong, Manh Nguyen; Bojak, Ingo; Knösche, Thomas R.

doi:10.1016/j.neuroimage.2012.10.024

Cited by 7 publications

(7 citation statements)

References 47 publications

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“…In order to describe the dynamics of cortical macrocolumns we use a neural mass model (NMM) originally developed by Jansen and co-workers to reproduce the evoked EEG activity elicited in neuronal populations by visual stimuli [ 34 , 35 ], in terms of their average postsynaptic potential [ 36 ]. Since their introduction [ 37 ], neural mass models have been widely used, in both the temporal [ 38 ] and spectral domains [ 39 ], to explore a broad range of phenomena that includes spontaneous activity in the visual cortex [ 40 ], local generation of multiple rhythms [ 41 ], rhythm propagation across cortical areas [ 42 ], inter-laminar dynamics and plasticity [ 43 ], generation [ 44 ] and termination [ 45 , 46 ] of self-organized transients in epileptogenic tissue, and critical dynamics near instability regions [ 47 ], among many other phenomena. Due to their versatility and relevance for brain modeling, neural mass models have become one of the main levels of description in computational modeling environments such as the Virtual Brain simulator [ 48 ].…”

Section: Introductionmentioning

confidence: 99%

Mesoscopic Segregation of Excitation and Inhibition in a Brain Network Model

et al. 2015

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Neurons in the brain are known to operate under a careful balance of excitation and inhibition, which maintains neural microcircuits within the proper operational range. How this balance is played out at the mesoscopic level of neuronal populations is, however, less clear. In order to address this issue, here we use a coupled neural mass model to study computationally the dynamics of a network of cortical macrocolumns operating in a partially synchronized, irregular regime. The topology of the network is heterogeneous, with a few of the nodes acting as connector hubs while the rest are relatively poorly connected. Our results show that in this type of mesoscopic network excitation and inhibition spontaneously segregate, with some columns acting mainly in an excitatory manner while some others have predominantly an inhibitory effect on their neighbors. We characterize the conditions under which this segregation arises, and relate the character of the different columns with their topological role within the network. In particular, we show that the connector hubs are preferentially inhibitory, the more so the larger the node's connectivity. These results suggest a potential mesoscale organization of the excitation-inhibition balance in brain networks.

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Section: Introductionmentioning

confidence: 99%

Mesoscopic Segregation of Excitation and Inhibition in a Brain Network Model

et al. 2015

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“…A typical example is provided by zizanoic acid (87): initially it was identified as 'khusenic acid' in Khus oil [56,57] and subsequently in Japanese VEO by Kido, who named it 'zizanoic acid'. [58] Such situations produced several different synonyms that could be attributed to a single compound, as in the case of khusimol (77), which was also described as 'tricyclovetivenol' [59][60][61] and 'khusenol' [62] until Nigam demonstrated that all these names referred to the same unique structure. [63] A similar example is found with ziza-6(13)-ene (62), [64] tricyclovetivene, [65,66] khusene [56] and khusimene.…”

Section: Uses Of Vetivermentioning

confidence: 99%

“…[140] Eudesmanes Many eudesmane derivatives are present in VEO (Table 4), and among them, junenol 130 was the first constituent for which absolute stereochemistry could be determined. [60,141] As a major constituent of Khus oil, it could be easily extracted and fully characterized by chemical transformations and IR spetroscopy, and its absolute configuration has been confirmed by synthesis from santanolide. [141,142] Later, Weyerstahl reported its presence in Haitian VEO.…”

Section: Zizaanes and Prezizaanesmentioning

confidence: 99%

Volatile constituents of vetiver: a review

Belhassen

Filippi

Brevard³

et al. 2014

Flavour & Fragrance J

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A comprehensive and critical compilation of the volatile constituents of vetiver root (Chrysopogon zizanioides L.) is presented, including those present in its essential oil. This extremely complex material has stimulated analytical research for many decades in the hope of unravelling the secrets of its composition. Up to now, more than 300 molecules have been reported, mostly sesquiterpenoids possessing a large variety of skeletons, together with their norterpenic counterparts. This review attempts to clarify some of the discrepancies related to the characterization of these constituents, which were often due to the misuses of analytical methods without enough critical consideration of the literature. The identification of the main odour impact components of vetiver is also discussed, because much contradictory information appears in the literature on this subject. Copyright © 2014 John Wiley & Sons, Ltd.

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“…Cowan (1972, 1973) and Amari (1977) type of neural fields have been used to model the VSD response of the visual cortex as well (Markounikau et al 2010;Meyer 2007). Finally, the Jansen and Rit (1995) neural mass has been used in a description of VSD data concerning the connection between spontaneous and evoked activity in the visual cortex (Nguyen Trong et al 2012). …”

Section: Electrophysiological Signals: Eeg Meg and Vsdimentioning

confidence: 99%

Neuroimaging, Neural Population Models for

Bojak¹,

Breakspear²

2014

Encyclopedia of Computational Neuroscience

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SynonymsEEG, neural population models of; fMRI BOLD, neural population models of; Multimodal neural population models DefinitionNeuroimaging denotes measurements of the brain with sufficient spatial resolution to construct maps of anatomy (structural neuroimaging) and activity (functional neuroimaging), respectively. Where a functional neuroimaging modality can capture spatial snapshots at sufficient speed, its data can be used to infer spatiotemporal brain dynamics. The spatial resolution of noninvasive functional neuroimaging in humans is limited to about one millimeter and requires the coherent activity of a large number of neurons to generate a signal that exceeds the noise floor. This is an ideal match for neural population models (NPMs), which are designed to capture the mass activity of neural groupings at mesoscopic scales and above. However, in order to compare NPM predictions to neuroimaging data, one also has to include signal expression -a measurement function -that maps the NPM-generated activity onto the sensor recordings. For most popular neuroimaging modalities, including multimodal approaches, such forward modelling "pipelines" are now available and have been used to predict and analyze data.

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Associating spontaneous with evoked activity in a neural mass model of visual cortex

Cited by 7 publications

References 47 publications

Mesoscopic Segregation of Excitation and Inhibition in a Brain Network Model

Mesoscopic Segregation of Excitation and Inhibition in a Brain Network Model

Volatile constituents of vetiver: a review

Neuroimaging, Neural Population Models for

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