Precuneus Failures in Subjects of the PSEN1 E280A Family at Risk of Developing Alzheimer’s Disease Detected Using Quantitative Electroencephalography

Villa-Ochoa, Jhony Alexánder; Alonso, Joan Francesc; Duque, Jon Edinson; Tobón, Carlos; Baena, Ana; Lopera, Francisco; Mañanas, Miguel Ángel

doi:10.3233/jad-161291

Cited by 8 publications

(11 citation statements)

References 88 publications

(125 reference statements)

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“…Based on our findings, this is translated on a network level into higher relative power in the faster frequencies, alpha 2 in particular, as well as a reduction in total power. Although investigated in a small sample, EEG analyses of presymptomatic PS1 mutation carriers have shown higher power in the relative alpha 2 band and lower power in the theta band [ 23 , 56 , 57 ] compared to healthy subjects, potentially indicative of preclinical hyperexcitable inhibitory neurons. Alternatively, the lack of different interneuron subtypes in the model and a distinction between feedback or feedforward inhibition loops may have influenced the results.…”

Section: Discussionmentioning

confidence: 99%

“…1 ) [ 8 , 16 , 63 , 68 , 75 ]. Several functional connectivity studies have examined the temporal correlation in neurophysiological signals between distinct brain regions and provided support for this biphasic theory by showing hypersynchronization in the early stages of AD and a global disconnected network in more late stages [ 16 , 48 , 56 , 57 , 63 , 75 , 77 ]. Results from optogenetic stimulation studies in animal models show a link between specific neuronal firing rates and modulation of oscillatory power and indicate a positive relation between inhibitory interneuron firing rate and high frequency (gamma) oscillatory power, as well as between pyramidal cell activity and power of low-frequency oscillations [ 13 , 74 ].…”

Section: Introductionmentioning

confidence: 99%

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A multiscale brain network model links Alzheimer’s disease-mediated neuronal hyperactivity to large-scale oscillatory slowing

et al. 2022

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Background Neuronal hyperexcitability and inhibitory interneuron dysfunction are frequently observed in preclinical animal models of Alzheimer’s disease (AD). This study investigates whether these microscale abnormalities explain characteristic large-scale magnetoencephalography (MEG) activity in human early-stage AD patients. Methods To simulate spontaneous electrophysiological activity, we used a whole-brain computational network model comprised of 78 neural masses coupled according to human structural brain topology. We modified relevant model parameters to simulate six literature-based cellular scenarios of AD and compare them to one healthy and six contrast (non-AD-like) scenarios. The parameters include excitability, postsynaptic potentials, and coupling strength of excitatory and inhibitory neuronal populations. Whole-brain spike density and spectral power analyses of the simulated data reveal mechanisms of neuronal hyperactivity that lead to oscillatory changes similar to those observed in MEG data of 18 human prodromal AD patients compared to 18 age-matched subjects with subjective cognitive decline. Results All but one of the AD-like scenarios showed higher spike density levels, and all but one of these scenarios had a lower peak frequency, higher spectral power in slower (theta, 4–8Hz) frequencies, and greater total power. Non-AD-like scenarios showed opposite patterns mainly, including reduced spike density and faster oscillatory activity. Human AD patients showed oscillatory slowing (i.e., higher relative power in the theta band mainly), a trend for lower peak frequency and higher total power compared to controls. Combining model and human data, the findings indicate that neuronal hyperactivity can lead to oscillatory slowing, likely due to hyperexcitation (by hyperexcitability of pyramidal neurons or greater long-range excitatory coupling) and/or disinhibition (by reduced excitability of inhibitory interneurons or weaker local inhibitory coupling strength) in early AD. Conclusions Using a computational brain network model, we link findings from different scales and models and support the hypothesis of early-stage neuronal hyperactivity underlying E/I imbalance and whole-brain network dysfunction in prodromal AD.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A multiscale brain network model links Alzheimer’s disease-mediated neuronal hyperactivity to large-scale oscillatory slowing

et al. 2022

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“…The implication that network alterations occur early in the disease course is supported by the oscillatory slowing (an increased power in the theta band) observed in early AD patients accompanied by neuronal hypersynchronisation. Neuronal loss and a global discoordination of network activity then follows in later stages, alongside power alterations in other oscillatory bands such as alpha and delta [22, 41, 42, 52, 53, 76, 95, 96, 101, 106, 115, 116].…”

Section: Discussionmentioning

confidence: 99%

Tau in cerebrospinal fluid induces neuronal hyperexcitability and alters hippocampal theta oscillations

Brown

Camporesi

Lantero‐Rodriguez

et al. 2023

Preprint

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Alzheimer's disease (AD) is the leading cause of dementia worldwide. In AD animal models, soluble tau aggregates have been shown to disrupt neuronal function, alter synaptic plasticity and impair cognitive function. In humans, small fractions of these toxic tau species are secreted into cerebrospinal fluid (CSF), some of which can be measured as diagnostic and prognostic biomarkers, starting from early stages of disease. However, the mechanisms of how these tau forms alter neuronal and network function are not fully understood. Here, we have developed and applied a novel approach to examine the electrophysiological effects of CSF from patients with a tau-positive biomarker profile. The method involves incubation of acutely-isolated wild-type mouse hippocampal brain slices with small volumes of diluted human CSF, followed by a suite of electrophysiological recording methods to evaluate their effects on neuronal function from single cells through to the network level. Comparison of the toxicity profiles of the same CSF samples, with and without immuno-depletion for tau, has enabled a pioneering demonstration that CSF-tau potently modulates neuronal function. We demonstrate that CSF-tau mediates an increase in neuronal excitability in single cells. We then observed, at the network level, increased input-output responses and enhanced paired-pulse facilitation as well as an increase in long-term potentiation. Finally, we show that CSF-tau modifies the generation and maintenance of hippocampal theta oscillations, which have important roles in learning and memory and are known to be altered in AD patients. Together, we describe a novel method for screening human CSF-tau to understand functional effects on neuron and network activity, which could have far-reaching benefits in understanding tau pathology, thus allowing for the development of better targeted treatments for tauopathies in the future.

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“…In addition to the ROIs, one of the independent components found by Ochoa et al, (2017a) (Figure 2B) was evaluated. This component is associated by the author to the precuneus region.…”

Section: Comparisons Between Groupsmentioning

confidence: 99%

Evaluation of Strategies Based on Wavelet-ICA and ICLabel for Artifact Correction in EEG Recordings

et al. 2023

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In quantitative electroencephalography, it is of vital importance to eliminate non-neural components, as these can lead to an erroneous analysis of the acquired signals, limiting their use in diagnosis and other clinical applications. In light of this drawback, preprocessing pipelines based on the joint use of the Wavelet Transform and the Independent Component Analysis technique (wICA) were proposed in the 2000s. Recently, with the advent of data-driven methods, deep learning models were developed for the automatic labeling of independent components, which constitutes an opportunity for the optimization of ICA-based techniques. In this paper, ICLabel, one of these deep learning models, was added to the wICA methodology in order to explore its improvement. To assess the usefulness of this approach, it was compared to different pipelines which feature the use of wICA and ICLabel independently and a lack thereof. The impact of each pipeline was measured by its capacity to highlight known statistical differences between asymptomatic carriers of the PSEN-1 E280A mutation and a healthy control group. Specifically, the between-group effect size and the P-values were calculated to compare the pipelines. The results show that using ICLabel for artifact removal can improve the effect size (ES) and that, by leveraging it with wICA, an artifact smoothing approach that is less prone to the loss of neural information can be built.

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Precuneus Failures in Subjects of the PSEN1 E280A Family at Risk of Developing Alzheimer’s Disease Detected Using Quantitative Electroencephalography

Cited by 8 publications

References 88 publications

A multiscale brain network model links Alzheimer’s disease-mediated neuronal hyperactivity to large-scale oscillatory slowing

A multiscale brain network model links Alzheimer’s disease-mediated neuronal hyperactivity to large-scale oscillatory slowing

Tau in cerebrospinal fluid induces neuronal hyperexcitability and alters hippocampal theta oscillations

Evaluation of Strategies Based on Wavelet-ICA and ICLabel for Artifact Correction in EEG Recordings

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