Model-based bifurcation and power spectral analyses of thalamocortical alpha rhythm slowing in Alzheimer's Disease

Bhattacharya, Basabdatta Sen; Cakir, Yuksel; Şengör, Neslihan Serap; Maguire, Liam; Coyle, Damien

doi:10.1016/j.neucom.2012.10.023

Cited by 33 publications

(38 citation statements)

References 66 publications

(123 reference statements)

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“…In a previous research (Bhattacharya et al, 2011b), we have proposed a more elaborate alpha-function based neural mass model that have considered these vital pathways in the thalamocorticothalamic loop. Also, we have performed a non-linear dynamical analysis of a simple thalamocortical model based on alpha functions in Bhattacharya et al (2013) to understand EEG power spectra abnormalities associated with several neurological disorders. Such research directions will be considered as an extended work based on the model presented herein.…”

Section: Discussionmentioning

confidence: 99%

“…However, the emphasis here is on post-synaptic membrane attributes as in von Krosigk et al (1993) and Golomb et al (1996). Thus these parameters (θ s =-35 and σ s = 2) are set by trial and error at values just before the model undergoes bifurcation from a “point-attractor” mode to a “limit-cycle” mode, based on a recent study where we observed rich model dynamics and power spectral behavior around the bifurcation point (Bhattacharya et al, 2013); T max = 1 mM (Destexhe et al, 1994). The input noise mean μ = −45 mV and standard deviation φ = 20 mV 2 are set by trial and error and represents the resting state membrane potential fluctuations in retinal cells.…”

Section: Methodsmentioning

confidence: 99%

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Implementing the cellular mechanisms of synaptic transmission in a neural mass model of the thalamo-cortical circuitry

Bhattacharya¹

2013

Front. Comput. Neurosci.

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A novel direction to existing neural mass modeling technique is proposed where the commonly used “alpha function” for representing synaptic transmission is replaced by a kinetic framework of neurotransmitter and receptor dynamics. The aim is to underpin neuro-transmission dynamics associated with abnormal brain rhythms commonly observed in neurological and psychiatric disorders. An existing thalamocortical neural mass model is modified by using the kinetic framework for modeling synaptic transmission mediated by glutamatergic and GABA (gamma-aminobutyric-acid)-ergic receptors. The model output is compared qualitatively with existing literature on in vitro experimental studies of ferret thalamic slices, as well as on single-neuron-level model based studies of neuro-receptor and transmitter dynamics in the thalamocortical tissue. The results are consistent with these studies: the activation of ligand-gated GABA receptors is essential for generation of spindle waves in the model, while blocking this pathway leads to low-frequency synchronized oscillations such as observed in slow-wave sleep; the frequency of spindle oscillations increase with increased levels of post-synaptic membrane conductance for AMPA (alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic-acid) receptors, and blocking this pathway effects a quiescent model output. In terms of computational efficiency, the simulation time is improved by a factor of 10 compared to a similar neural mass model based on alpha functions. This implies a dramatic improvement in computational resources for large-scale network simulation using this model. Thus, the model provides a platform for correlating high-level brain oscillatory activity with low-level synaptic attributes, and makes a significant contribution toward advancements in current neural mass modeling paradigm as a potential computational tool to better the understanding of brain oscillations in sickness and in health.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Implementing the cellular mechanisms of synaptic transmission in a neural mass model of the thalamo-cortical circuitry

Bhattacharya¹

2013

Front. Comput. Neurosci.

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“…Functional connectivity can be estimated in specific frequency bands using electroencephalography (EEG) recordings. Frequency bands of particular interest are theta and alpha, as they play a crucial role in cognitive processes (Başar and Güntekin 2012;Cavanagh and Frank 2014;Klimesch 1999;Klimesch 2012) and in cognitive ageing and dementia (Bhattacharya et al 2013;Klimesch 1999). Alpha band power decreases, and theta power increases, from early to late adulthood (Dustman, Shearer, and Emmerson 1999); and compared to age-matched participants, people with dementia show higher theta and lower alpha power, and a shift in alpha activity from posterior to anterior recording sites (Başar and Güntekin 2012;Klimesch 1999).…”

Section: Introductionmentioning

confidence: 99%

Associations between EEG functional brain connectivity and a cognitive reserve proxy in healthy older adults

Moezzi

Lavrencic

Goldsworthy

et al. 2019

Preprint

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Cognitive reserve is a concept that explains individual differences in vulnerability to cognitive impairment due to age and dementia-related brain changes. Mechanisms underlying the cognitive reserve effect are poorly understood. We investigated associations between a comprehensive cognitive reserve proxy (Lifetime Experiences Questionnaire/LEQ) and functional connectivity of the prefrontal cortex across the whole scalp, covarying for the level of current cognitive functioning (Addenbrookes Cognitive Examination Revised/ACE-R), using multiblock parallel and orthogonalized partial least squares regression. EEG data were collected from 34 healthy older adults (63 to 83 years) in eyes-open and eyes-closed restingstates, and during 0-back and 1-back tasks. Functional connectivity was estimated using imaginary coherence in the theta and alpha frequency bands, as these bands have been heavily implicated in cognitive ageing, attention and executive function. We found three clusters of electrodes where the absolute values of the regression coefficient were above threshold when covarying for ACE-R: (1) a cluster approximating the right frontocentral region during the eyes-open condition in the theta band with seed electrodes approximating the left prefrontal cortex with positive associations of medium effect size; (2) a cluster approximating the right parietotemporal region during the 0-back task in the theta band with seed electrodes approximating the right prefrontal cortex with negative associations of medium to large effect sizes; and (3) a cluster approximating the occipitoparietal region in the eyes-closed condition in the alpha band with seed electrodes approximating the left prefrontal cortex with negative associations of medium effect size. These relationships between a cognitive reserve proxy and functional connectivity, within key networks and frequency bands associated with attention and executive function, may reflect greater neural capacity and efficiency.Hordacre, Brenton, Bahar Moezzi, and Michael C Ridding. 2018. 'Neuroplasticity and network connectivity of the motor cortex following stroke: A transcranial direct current stimulation study', Human Brain Mapping.

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“…Such 'multi-modal' analytical techniques are being used in several researches to understand Electroencephalograph (EEG) anomalies in brain disorders [1][2], in contrast to 'power spectra-only' analytical studies that were more common during the early days of EEG analysis. In a recent work, a combined analysis of a simple thalamo-cortical neural mass model in context to EEG abnormality in Alzheimer's disease (AD) is presented [3]. The study shows that 'unimodal' analytical techniques such as power spectra-only studies without a simultaneous observation of the time-series model output may lead to anomalous conclusions and hypotheses.…”

mentioning

confidence: 99%

“…Towards this, in this work, a 'multi-modal' analytical technique is applied on a thalamocorticothalamic (tct) model, which was earlier studied using power-spectra analysis only [4]. The tct model is an enhanced version of that used in [3] and is based on biological data available in current literature. Furthermore, it aims to mimic thalalmocortical oscillations such as observed in the EEG of both healthy and diseased brain.…”

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confidence: 99%

Combined study of time-series bifurcation and power spectral behaviour of a thalamo-cortico-thalamic neural mass model

Bhattacharya

2013

BMC Neurosci

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A combined power spectral and time-series bifurcation analysis of a neural mass model is presented. Such 'multi-modal' analytical techniques are being used in several researches to understand Electroencephalograph (EEG) anomalies in brain disorders [1][2], in contrast to 'power spectra-only' analytical studies that were more common during the early days of EEG analysis. In a recent work, a combined analysis of a simple thalamo-cortical neural mass model in context to EEG abnormality in Alzheimer's disease (AD) is presented [3]. The study shows that 'unimodal' analytical techniques such as power spectra-only studies without a simultaneous observation of the time-series model output may lead to anomalous conclusions and hypotheses. Towards this, in this work, a 'multi-modal' analytical technique is applied on a thalamocorticothalamic (tct) model, which was earlier studied using power-spectra analysis only [4]. The tct model is an enhanced version of that used in [3] and is based on biological data available in current literature. Furthermore, it aims to mimic thalalmocortical oscillations such as observed in the EEG of both healthy and diseased brain.\ud \ud Here, the power spectra of the tct model output is observed within the δ (1-3 Hz), θ (4-7 Hz), α (8-13 Hz), β (14-30 Hz) bands, along with a simultaneous analysis of the time series behaviour, the latter showing three behavioural modes: noisy point-attractor, spindle and limit-cycle. With all parameters at their basal values, the output time series is in a noisy point-attractor mode with maximum power within the alpha band (Figure 1). However the model shifts into a limit cycle oscillatory mode with a decrease in inhibitory connectivity parameters in the model (Figure 1); the corresponding power spectra show an increase in peak power within the θ and δ bands along with a simultaneous decrease in power within the α and β bands. The model behaviour is very much in agreement with in-vitro studies [5] which report an increased theta band power and a simultaneous decreased alpha band power during transition from wakefulness to sleep. Furthermore, the in-vitro time-series are qualitatively very similar to those obtained using the model. Thus, the model indicates a decreased inhibitory activity to be the neural correlate of the transitive state between wakefulness and sleep. On the other hand, increased mean firing activity of the extrinsic model inputs pushes the model, first into a spindling mode, and then into a limit cycle mode. In this state, the power within the delta band shows a significant increase compared to those within the other frequency bands. This behaviour is more similar to in-vivo studies of awake-to-sleep transition as reported in [5]

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Model-based bifurcation and power spectral analyses of thalamocortical alpha rhythm slowing in Alzheimer's Disease

Cited by 33 publications

References 66 publications

Implementing the cellular mechanisms of synaptic transmission in a neural mass model of the thalamo-cortical circuitry

Implementing the cellular mechanisms of synaptic transmission in a neural mass model of the thalamo-cortical circuitry

Associations between EEG functional brain connectivity and a cognitive reserve proxy in healthy older adults

Combined study of time-series bifurcation and power spectral behaviour of a thalamo-cortico-thalamic neural mass model

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