A modeling study of cortical waves in primary auditory cortex

Beeman, David

doi:10.1186/1471-2202-14-s1-p23

Cited by 5 publications

(10 citation statements)

References 2 publications

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“…The first model is based on a biophysically detailed model of primary auditory cortex by Beeman [3]. It has recently been used to study ASSR deficits by our group [30].…”

Section: Models Capabilities Tests and Morementioning

confidence: 99%

“…We do not discuss the models in more detail here, since they have been described elsewhere [3,30,58,29]. Furthermore, our focus lies on the framework with which to use, validate and compare models not on the models themselves.…”

Section: Models Capabilities Tests and Morementioning

confidence: 99%

“…In this paper, we propose to adopt this framework for the computational psychiatry community and to collaboratively build common repositories of computational models, tests, test suites and tools. As a case in point, we have implemented a Python module (ASSRUnit) for auditory steady-state response (ASSR) deficits in schizophrenic patients, which are based on observations from several experimental studies ( [23,58,24])and we demonstrate how existing computational models ( [30,3,58,29]) can be validated against these observations and compared with each other.…”

Section: Introductionmentioning

confidence: 99%

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Modules for Automated Validation and Comparison of Models of Neurophysiological and Neurocognitive Biomarkers of Psychiatric Disorders: ASSRUnit—A Case Study

Metzner

Mäki‐Marttunen

Zurowski

et al. 2018

Computational Psychiatry

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The characterisation of biomarkers and endophenotypic measures has been a central goal of research in psychiatry over the last years. While most of this research has focused on the identification of biomarkers and endophenotypes, using various experimental approaches, it has been recognised that their instantiations, through computational models, have a great potential to help us understand and interpret these experimental results. However, the enormous increase in available neurophysiological and neurocognitive as well as computational data also poses new challenges. How can a researcher stay on top of the experimental literature? How can computational modelling data be efficiently compared to experimental data? How can computational modelling most effectively inform experimentalists? Recently, a general scientific framework for the generation of executable tests that automatically compare model results to experimental observations, SciUnit, has been proposed. Here we exploit this framework for research in psychiatry to address the challenges mentioned above. We extend the SciUnit framework by adding an experimental database, which contains a comprehensive collection of relevant experimental observations, and a prediction database, which contains a collection of predictions generated by computational models. Together with appropriately designed SciUnit tests and methods to mine and visualise the databases, model data and test results, this extended framework has the potential to greatly facilitate the use of computational models in psychiatry. As an initial example we present ASSRUnit, a module for auditory steady-state response deficits in psychiatric disorders.

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“…The first model is based on a biophysically detailed model of primary auditory cortex by Beeman [3]. It has recently been used to study ASSR deficits by our group [30].…”

Section: Models Capabilities Tests and Morementioning

confidence: 99%

Section: Models Capabilities Tests and Morementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modules for Automated Validation and Comparison of Models of Neurophysiological and Neurocognitive Biomarkers of Psychiatric Disorders: ASSRUnit—A Case Study

Metzner

Mäki‐Marttunen

Zurowski

et al. 2018

Computational Psychiatry

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“…However, the underlying mechanisms remain elusive. Here we investigated the impact of circuit abnormalities on oscillatory activity in the gamma range (> 30 Hz) by simulating auditory entrainment in an established computational model of the primary auditory cortex [ 3 ]. Auditory click entrainment experiments showed that for schizophrenic patients EEG/MEG power decreased at 40 Hz and increased at 20 Hz in response to 40 Hz drive but no differences between were visible in response to 30 Hz drive [ 4 , 5 ].…”

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confidence: 99%

Modelling impairment of evoked gamma range oscillations in schizophrenia

Metzner¹,

Schweikard²,

Zurowski

2015

BMC Neurosci

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?? 2015 Metzner et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Abnormal oscillatory activity in schizophrenia has been found in a wide range of experimental paradigms [1]. For example, schizophrenic patients show reduced evoked gamma activity, which has been associated with negative symptoms, and increased spontaneous gamma activity, which has been associated with positive symptoms [2]. However, the underlying mechanisms remain elusive. Here we investigated the impact of circuit abnormalities on oscillatory activity in the gamma range (> 30 Hz) by simulating auditory entrainment in an established computational model of the primary auditory cortex [3]. Auditory click entrainment experiments showed that for schizophrenic patients EEG/MEG power decreased at 40 Hz and increased at 20 Hz in response to 40 Hz drive but no differences between were visible in response to 30 Hz drive [4, 5]. Here we used the primary auditory cortex model from Beeman [3] and simulated click train stimulation at 40 Hz, to investigate gamma entrainment deficits, and at 30 Hz as a control condition. Without alterations the model entrained at the driving frequency of 30 and 40 Hz, respectively. Similar to previous approaches [6], however, focusing on evoked rather than spontaneous activity, we next explored the effects of (1) connectivity disturbances (reduced (a) recurrent excitation, (b) pyramidal cell input and (c) total connectivity), (2) prolonged GABAergic decay time constant, and (3) reduced inhibitory output. All three interventions in connectivity (1a-c) led to an increase in 40 Hz power for 40 Hz drive, contrary to human EEG/MEG experiments. A prolonged GABAergic decay time constant produced a reduction of power at 40 Hz and an increase in power at 20 Hz, for the 40 Hz drive, which concurs with [4, 5]. Furthermore, for the 30 Hz drive, no differences to the standard model were observed. Reduction of inhibitory output led to decreases in power at 40 Hz for 40 Hz drive but no increases at 20 Hz. In the 30 Hz drive condition, a decrease was visible, in contrast to experimental data [4, 5]. In conclusion, only prolonged GABAergic decay time constants (2), but not interventions (1) and (3) led to changes in entrainment comparable to experimental evidence in agreement with previous modeling approaches [5]. Our simulations suggest that prolonged time constants at GABAergic synapses might play a key role in abnormal evoked gamma rhythms in schizophrenia. However, since we only investigated one intervention at a time, further studies are needed to investigate the complex interactions of these circuit abnormalities. Furthermore, it remai...

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“…In-silico recordings were performed during the presentation of long sequences of triplets and/or doublets. The network's output was derived with two types of measurements in mind: spiking activity of individual neurons and/or local populations of neurons, and local field potentials [5][6]. The network spiking neural activity reproduced reliably data reports from [1][2], including dependence of responses to the B tone in triplets ABA_ on stimulus parameter DF.…”

Section: Resultsmentioning

confidence: 95%

Dynamic features of neural activity in primary auditory cortex captured by an integrate-and-fire network model for auditory streaming

Mahat¹

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Past decades of auditory research have identified several acoustic features that influence perceptual organization of sound, in particular, the frequency of tones and the rate of presentation [1-2]. One class of stimuli that have been intensively studied are sequences of tones that alternate in frequency. They are typically presented in patterns of repeating doublets ABAB… or repeating triplets ABA_ABA_... where the symbol "−" stands for a gap of silence between triplets repeats. The duration of each tone or silence is typically tens to hundreds of milliseconds, and listeners hearing the sequence perceive either one auditory object ("stream integration") or two separate auditory objects ("stream segregation"). Animal studies have characterized single-and multi-unit neural activity and event-related local field potentials while systematically varying frequency separation between tones (DF) or the presentation rate (PR). They found that the B tone responses in doublets were differentially suppressed with increasing PR and that the B tones responses in triplets decreased with larger DF [1-2]. However, the neural mechanisms underlying these animal data have yet to be explained. In this study, we built an integrate-and-fire network model of the primary auditory cortex (AC) that accurately reproduced the experimental results from [1-2]. Then we extended the model to account for basic spectro-temporal features of electrocorticography (ECoG) recordings from the posteriomedial part of the Heschl's gyrus (HGPM; cortical area equivalent to the AC of monkeys), obtained from humans listening to sequences of triplets ABA_. Finally, we constructed a firing rate reduced model of the proposed integrate-and-fire network and analyzed its dynamics as function of parameters.

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A modeling study of cortical waves in primary auditory cortex

Cited by 5 publications

References 2 publications

Modules for Automated Validation and Comparison of Models of Neurophysiological and Neurocognitive Biomarkers of Psychiatric Disorders: ASSRUnit—A Case Study

Modules for Automated Validation and Comparison of Models of Neurophysiological and Neurocognitive Biomarkers of Psychiatric Disorders: ASSRUnit—A Case Study

Modelling impairment of evoked gamma range oscillations in schizophrenia

Dynamic features of neural activity in primary auditory cortex captured by an integrate-and-fire network model for auditory streaming

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