Compensating for thalamocortical synaptic loss in Alzheimer's disease

Abuhassan, Kamal; Coyle, Damien; Maguire, Liam

doi:10.3389/fncom.2014.00065

Cited by 27 publications

(32 citation statements)

References 75 publications

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“…Significant evidence suggests that thalamo-cortical circuitry underlie the generation and modulation of alpha and theta rhythms and that average power is attenuated in these frequency bands for MCI and AD subjects (Jeong, 2004 ; Koenig et al, 2005 ; Jelles et al, 2008 ; Park et al, 2008 ). Several recent modeling studies have proposed a candidate mechanism citing impairment to thalamic reticular fibers in MCI and AD as the source of the dysfunction (Bhattacharya et al, 2011 , 2013 ; Li et al, 2011 ; Abuhassan et al, 2014 ).…”

Section: Discussionmentioning

confidence: 99%

Disrupted Thalamus White Matter Anatomy and Posterior Default Mode Network Effective Connectivity in Amnestic Mild Cognitive Impairment

Alderson

Kehoe

Maguire

et al. 2017

Front. Aging Neurosci.

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Alzheimer’s disease (AD) and its prodromal state amnestic mild cognitive impairment (aMCI) are characterized by widespread abnormalities in inter-areal white matter fiber pathways and parallel disruption of default mode network (DMN) resting state functional and effective connectivity. In healthy subjects, DMN and task positive network interaction are modulated by the thalamus suggesting that abnormal task-based DMN deactivation in aMCI may be a consequence of impaired thalamo-cortical white matter circuitry. Thus, this article uses a multimodal approach to assess white matter integrity between thalamus and DMN components and associated effective connectivity in healthy controls (HCs) relative to aMCI patients. Twenty-six HC and 20 older adults with aMCI underwent structural, functional and diffusion MRI scanning using the high angular resolution diffusion-weighted acquisition protocol. The DMN of each subject was identified using independent component analysis (ICA) and resting state effective connectivity was calculated between thalamus and DMN nodes. White matter integrity changes between thalamus and DMN were investigated with constrained spherical deconvolution (CSD) tractography. Significant structural deficits in thalamic white matter projection fibers to posterior DMN components posterior cingulate cortex (PCC) and lateral inferior parietal lobe (IPL) were identified together with significantly reduced effective connectivity from left thalamus to left IPL. Crucially, impaired thalamo-cortical white matter circuitry correlated with memory performance. Disrupted thalamo-cortical structure was accompanied by significant reductions in IPL and PCC cortico-cortical effective connectivity. No structural deficits were found between DMN nodes. Abnormal posterior DMN activity may be driven by changes in thalamic white matter connectivity; a view supported by the close anatomical and functional association of thalamic nuclei effected by AD pathology and the posterior DMN nodes. We conclude that dysfunctional posterior DMN activity in aMCI is consistent with disrupted cortico-thalamo-cortical processing and thalamic-based dissemination of hippocampal disease agents to cortical hubs.

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Section: Discussionmentioning

confidence: 99%

Disrupted Thalamus White Matter Anatomy and Posterior Default Mode Network Effective Connectivity in Amnestic Mild Cognitive Impairment

Alderson

Kehoe

Maguire

et al. 2017

Front. Aging Neurosci.

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“…Research has shown a strong correlation between abnormal synchronization and brain disorders including epilepsy, Parkinson's disease, Alzheimer's disease, and schizophrenia. Studying the effects of channel noise on synchronization provides a mathematical framework towards understanding how neurons regulate synchronization in the presence of noise [59][60][61].…”

Section: Discussionmentioning

confidence: 99%

Channel noise effects on first spike latency of a stochastic Hodgkin-Huxley neuron

Maisel

Lindenberg

2017

Phys. Rev. E

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While it is widely accepted that information is encoded in neurons via action potentials or spikes, it is far less understood what specific features of spiking contain encoded information. Experimental evidence has suggested that the timing of the first spike may be an energy-efficient coding mechanism that contains more neural information than subsequent spikes. Therefore, the biophysical features of neurons that underlie response latency are of considerable interest. Here we examine the effects of channel noise on the first spike latency of a Hodgkin-Huxley neuron receiving random input from many other neurons. Because the principal feature of a Hodgkin-Huxley neuron is the stochastic opening and closing of channels, the fluctuations in the number of open channels lead to fluctuations in the membrane voltage and modify the timing of the first spike. Our results show that when a neuron has a larger number of channels, (i) the occurrence of the first spike is delayed and (ii) the variation in the first spike timing is greater. We also show that the mean, median, and interquartile range of first spike latency can be accurately predicted from a simple linear regression by knowing only the number of channels in the neuron and the rate at which presynaptic neurons fire, but the standard deviation (i.e., neuronal jitter) cannot be predicted using only this information. We then compare our results to another commonly used stochastic Hodgkin-Huxley model and show that the more commonly used model overstates the first spike latency but can predict the standard deviation of first spike latencies accurately. We end by suggesting a more suitable definition for the neuronal jitter based upon our simulations and comparison of the two models.

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“…Neural systems could exhibit rhythmic oscillations, which are a type of synchronous state. Synchronization in neural systems is thought to be important for processing of sensory information and motor function [ 1 ], but the occurrence of synchronization in some specific areas of the brain may also be associated with some diseases, such as the epilepsy and Parkinson's disease [ 2 ]. Because of the importance of synchronization in neural systems, it has been studied for a long time from many aspects in neuroscience research [ 3 – 7 ].…”

Section: Introductionmentioning

confidence: 99%

Rhythmic Oscillations of Excitatory Bursting Hodkin-Huxley Neuronal Network with Synaptic Learning

Shi

Han

Wang

et al. 2016

Computational Intelligence and Neuroscience

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Rhythmic oscillations of neuronal network are actually kind of synchronous behaviors, which play an important role in neural systems. In this paper, the properties of excitement degree and oscillation frequency of excitatory bursting Hodkin-Huxley neuronal network which incorporates a synaptic learning rule are studied. The effects of coupling strength, synaptic learning rate, and other parameters of chemical synapses, such as synaptic delay and decay time constant, are explored, respectively. It is found that the increase of the coupling strength can weaken the extent of excitement, whereas increasing the synaptic learning rate makes the network more excited in a certain range; along with the increasing of the delay time and the decay time constant, the excitement degree increases at the beginning, then decreases, and keeps stable. It is also found that, along with the increase of the synaptic learning rate, the coupling strength, the delay time, and the decay time constant, the oscillation frequency of the network decreases monotonically.

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Compensating for thalamocortical synaptic loss in Alzheimer's disease

Cited by 27 publications

References 75 publications

Disrupted Thalamus White Matter Anatomy and Posterior Default Mode Network Effective Connectivity in Amnestic Mild Cognitive Impairment

Disrupted Thalamus White Matter Anatomy and Posterior Default Mode Network Effective Connectivity in Amnestic Mild Cognitive Impairment

Channel noise effects on first spike latency of a stochastic Hodgkin-Huxley neuron

Rhythmic Oscillations of Excitatory Bursting Hodkin-Huxley Neuronal Network with Synaptic Learning

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