Alpha and theta rhythm abnormality in Alzheimer's Disease: a study using a computational model

Transcranial alternating current stimulation (tACS) at gamma frequency: an up-and-coming tool to modify the progression of Alzheimer's Disease

The last decades have witnessed huge efforts devoted to deciphering the pathological mechanisms underlying Alzheimer's Disease (AD) and to testing new drugs, with the recent FDA approval of two anti-amyloid monoclonal antibodies for AD treatment. Beyond these drug-based experimentations, a number of pre-clinical and clinical trials are exploring the benefits of alternative treatments, such as non-invasive stimulation techniques on AD neuropathology and symptoms. Among the different non-invasive brain stimulation approaches, transcranial alternating current stimulation (tACS) is gaining particular attention due to its ability to externally control gamma oscillations. Here, we outline the current knowledge concerning the clinical efficacy, safety, ease-of-use and cost-effectiveness of tACS on early and advanced AD, applied specifically at 40 Hz frequency, and also summarise pre-clinical results on validated models of AD and ongoing patient-centred trials.

Mechanism of an Intrinsic Oscillation in Rat Geniculate Interneurons

Depolarizing current injections produced a rhythmic bursting of action potentials - a bursting oscillation - in a set of local interneurons in the lateral geniculate nucleus (LGN) of rats. The current dynamics underlying this firing pattern have not been determined, though this cell type constitutes an important cellular component of thalamocortical circuitry, and contributes to both pathologic and non-pathologic brain states. We thus investigated the source of the bursting oscillation using pharmacological manipulations in LGN slices in vitro and in silico. 1. Selective blockade of calcium channel subtypes revealed that high-threshold calcium currentsI L andI P contributed strongly to the oscillation. 2. Increased extracellular K+ concentration (decreased K+currents) eliminated the oscillation. 3. Selective blockade of K+ channel subtypes demonstrated that the calcium-sensitive potassium current (I A H P ) was of primary importance. A morphologically simplified, multicompartment model of the thalamic interneuron characterized the oscillation as follows: 1. The low-threshold calcium currentI T provided the strong initial burst characteristic of the oscillation. 2. Alternating fluxes through high-threshold calcium channels andI A H P then provided the continuing oscillation's burst and interburst periods respectively. This interplay betweenI L andI A H P contrasts with the current dynamics underlying oscillations in thalamocortical and reticularis neurons, which primarily involveI T andI H , orI T andI A H P respectively. These findings thus point to a novel electrophysiological mechanism for generating intrinsic oscillations in a major thalamic cell type. Because local interneurons can sculpt the behavior of thalamocortical circuits, these results suggest new targets for the manipulation of ascending thalamocortical network activity.

Substance-dependent EEG during recovery from anesthesia and optimization of monitoring

The electroencephalographic (EEG) activity during anesthesia emergence contains information about the risk for a patient to experience postoperative delirium, but the EEG dynamics during emergence challenge monitoring approaches. Substance-specific emergence characteristics may additionally limit the reliability of commonly used processed EEG indices during emergence. This study aims to analyze the dynamics of different EEG indices during anesthesia emergence that was maintained with different anesthetic regimens. We used the EEG of 45 patients under general anesthesia from the emergence period. Fifteen patients per group received sevoflurane, isoflurane (+ sufentanil) or propofol (+ remifentanil) anesthesia. One channel EEG and the bispectral index (BIS A-1000) were recorded during the study. We replayed the EEG back to the Conox, Entropy Module, and the BIS Vista to evaluate and compare the index behavior. The volatile anesthetics induced significantly higher EEG frequencies, causing higher indices (AUC > 0.7) over most parts of emergence compared to propofol. The median duration of "awake" indices (i.e., > 80) before the return of responsiveness (RoR) was significantly longer for the volatile anesthetics (p < 0.001). The different indices correlated well under volatile anesthesia (rs > 0.6), with SE having the weakest correlation. For propofol, the correlation was lower (rs < 0.6). SE was significantly higher than BIS and, under propofol anesthesia, qCON. Systematic differences of EEG-based indices depend on the drugs and devices used. Thus, to avoid early awareness or anesthesia overdose using an EEG-based index during emergence, the anesthetic regimen, the monitor used, and the raw EEG trace should be considered for interpretation before making clinical decisions.

Altered synaptic plasticity at hippocampal CA1-CA3 synapses in Alzheimer's disease: integration of amyloid precursor protein intracellular domain and amyloid beta effects into computational models

Alzheimer's disease (AD) is a progressive memory loss and cognitive dysfunction brain disorder brought on by the dysfunctional amyloid precursor protein (APP) processing and clearance of APP peptides. Increased APP levels lead to the production of AD-related peptides including the amyloid APP intracellular domain (AICD) and amyloid beta (Aβ), and consequently modify the intrinsic excitability of the hippocampal CA1 pyramidal neurons, synaptic protein activity, and impair synaptic plasticity at hippocampal CA1-CA3 synapses. The goal of the present study is to build computational models that incorporate the effect of AD-related peptides on CA1 pyramidal neuron and hippocampal synaptic plasticity under the AD conditions and investigate the potential pharmacological treatments that could normalize hippocampal synaptic plasticity and learning in AD. We employ a phenomenological N-methyl-D-aspartate (NMDA) receptor-based voltage-dependent synaptic plasticity model that includes the separate receptor contributions on long-term potentiation (LTP) and long-term depression (LTD) and embed it into the a detailed compartmental model of CA1 pyramidal neuron. Modeling results show that partial blockade of Glu2NB-NMDAR-gated channel restores intrinsic excitability of a CA1 pyramidal neuron and rescues LTP in AICD and Aβ conditions. The model provides insight into the complex interactions in AD pathophysiology and suggests the conditions under which the synchronous activation of a cluster of synaptic inputs targeting the dendritic tree of CA1 pyramidal neuron leads to restored synaptic plasticity.

Reduced alpha2 power is associated with slowed information processing speed in multiple sclerosis

OBJECTIVE

Cognitive impairment is common in multiple sclerosis (MS), significantly impacts daily functioning, is time-consuming to assess, and is prone to practice effects. We examined whether the alpha band power measured with magnetoencephalography (MEG) is associated with the different cognitive domains affected by MS.

METHODS

Sixty-eight MS patients and 47 healthy controls underwent MEG, T1- and FLAIR-weighted magnetic resonance imaging (MRI), and neuropsychological testing. Alpha power in the occipital cortex was quantified in the alpha1 (8-10 Hz) and alpha2 (10-12 Hz) bands. Next, we performed best subset regression to assess the added value of neurophysiological measures to commonly available MRI measures.

RESULTS

Alpha2 power significantly correlated with information processing speed (p < 0.001) and was always retained in all multilinear models, whereas thalamic volume was retained in 80% of all models. Alpha1 power was correlated with visual memory (p < 0.001) but only retained in 38% of all models.

CONCLUSIONS

Alpha2 (10-12 Hz) power in rest is associated with IPS, independent of standard MRI parameters. This study stresses that a multimodal assessment, including structural and functional biomarkers, is likely required to characterize cognitive impairment in MS. Resting-state neurophysiology is thus a promising tool to understand and follow up changes in IPS.

Alterations of resting-state Gamma frequency characteristics in aging and Alzheimer's disease

Alzheimer's disease (AD) is an important brain disease associated with aging. It involves various functional and structural changes which alter the EEG characteristics. Although numerous studies have found changes in delta, theta, alpha, and beta power, fewer studies have looked at the changes in the resting state EEG gamma activity characteristics in AD. This study aimed to investigate the alterations in the frequency and power values of AD patients' resting-state EEG gamma oscillations compared with healthy elderly and young subjects. We performed Fast Fourier Transform (FFT) on the resting state EEG data from 179 participants, including 59 early stage AD patients, 60 healthy elderly, and 60 healthy young subjects. We averaged FFT performed epochs to investigate the power values in the gamma frequency range (28-48 Hz). We then sorted the peaks of power values in the gamma frequency range, and the average of the identified highest three values was named as the gamma dominant peak frequency. The gamma dominant peak frequency of AD patients (Meyes-opened = 33.4 Hz, Meyes-closed = 32.7 Hz) was lower than healthy elderly (Meyes-opened = 35.5 Hz, Meyes-closed = 35.0 Hz) and healthy young subjects (Meyes-opened = 37.2 Hz, Meyes-closed = 37.0 Hz). These results could be related to AD progression and therefore critical for the recent findings regarding the 40 Hz gamma entrainment because it seems they entrain the gamma frequency of AD towards that of healthy young.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09873-4.

Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures

INTRODUCTION

The early diagnosis and classification of social anxiety disorder (SAD) are crucial clinical support tasks for medical practitioners in designing patient treatment programs to better supervise the progression and development of SAD. This paper proposes an effective method to classify the severity of SAD into different grading (severe, moderate, mild, and control) by using the patterns of brain information flow with their corresponding graphical networks.

METHODS

We quantified the directed information flow using partial directed coherence (PDC) and the topological networks by graph theory measures at four frequency bands (delta, theta, alpha, and beta). The PDC assesses the causal interactions between neuronal units of the brain network. Besides, the graph theory of the complex network identifies the topological structure of the network. Resting-state electroencephalogram (EEG) data were recorded for 66 patients with different severities of SAD (22 severe, 22 moderate, and 22 mild) and 22 demographically matched healthy controls (HC).

RESULTS

PDC results have found significant differences between SAD groups and HCs in theta and alpha frequency bands (p < 0.05). Severe and moderate SAD groups have shown greater enhanced information flow than mild and HC groups in all frequency bands. Furthermore, the PDC and graph theory features have been used to discriminate three classes of SAD from HCs using several machine learning classifiers. In comparison to the features obtained by PDC, graph theory network features combined with PDC have achieved maximum classification performance with accuracy (92.78%), sensitivity (95.25%), and specificity (94.12%) using Support Vector Machine (SVM).

DISCUSSION

Based on the results, it can be concluded that the combination of graph theory features and PDC values may be considered an effective tool for SAD identification. Our outcomes may provide new insights into developing biomarkers for SAD diagnosis based on topological brain networks and machine learning algorithms.

Electrophysiological and Behavioral Effects of Alpha-Band Sensory Entrainment: Neural Mechanisms and Clinical Applications

Alpha-band (7-13 Hz) activity has been linked to visuo-attentional performance in healthy participants and to impaired functionality of the visual system in a variety of clinical populations including patients with acquired posterior brain lesion and neurodevelopmental and psychiatric disorders. Crucially, several studies suggested that short uni- and multi-sensory rhythmic stimulation (i.e., visual, auditory and audio-visual) administered in the alpha-band effectively induces transient changes in alpha oscillatory activity and improvements in visuo-attentional performance by synchronizing the intrinsic brain oscillations to the external stimulation (neural entrainment). The present review aims to address the current state of the art on the alpha-band sensory entrainment, outlining its potential functional effects and current limitations. Indeed, the results of the alpha-band entrainment studies are currently mixed, possibly due to the different stimulation modalities, task features and behavioral and physiological measures employed in the various paradigms. Furthermore, it is still unknown whether prolonged alpha-band sensory entrainment might lead to long-lasting effects at a neural and behavioral level. Overall, despite the limitations emerging from the current literature, alpha-band sensory entrainment may represent a promising and valuable tool, inducing functionally relevant changes in oscillatory activity, with potential rehabilitative applications in individuals characterized by impaired alpha activity.

Analysis of the neural mechanism of spectra decrease in MCI by a thalamo-cortical coupled neural mass model

Objective.In order to deeply understand the neurophysiological mechanism of the spectra decrease in mild cognitive impairment (MCI), this paper studies a new neural mass model, which can simulate various intracerebral electrophysiological activities.Approach. In this study, a thalamo-cortical coupled neural mass model (TCC-NMM) is proposed. The influences of the coupling coefficients and other key parameters on the model spectra are simulated. Then, the unscented Kalman filter (UKF) algorithm is used to reversely identify the parameters in the TCC-NMM. Furthermore, the TCC-NMM and UKF are combined to analyze the spectra reduction mechanism of electroencephalogram (EEG) signals in MCI patients. The independent sample t-test is carried out to statistical analyze the differences of the identified parameters between MCI and normal controls. The Pearson correlation analysis is used to analyze the intrinsic relationship between parameters and the scores of the comprehensive competence assessment scale.Main results.The simulation results show that the decreased cortical synaptic connectivity constantsC₁can result in spectra decrease of the TCC-NMM outputs. The real EEG analysis results show that the identified values of parameterC₁are significant lower in the MCI group than in control group in frontal and occipital areas and the parametersC₁are positively correlated with the Montreal Cognitive Assessment (MoCA) scores in the two areas. This consistency suggests that the cortical synaptic connectivity loss from pyramidal cells to excitatory interneurons (eIN) may be one of the neural mechanisms of EEG spectra decrease in MCI.Significance. (a) In this study, a new mathematical model TCCNMM based on anatomy and neurophysiology is proposed. (b) All key parameters in TCC-NMM are studied in detail through the forward and reverse analysis and the influence of these parameters on the output spectra of the model is pointed out. (c) The possible neural mechanism of the decreased spectra in MCI patients is pointed out by the joint analysis of simulation in forward with TCC-NMM and analysis of the actual EEG signals in reverse with UKF identification algorithm. (d) We find that the identified parameter C1 of MCI patients is significantly lower than that of the control group, which is consistent with the simulation analysis of TCC-NMM. So, we suggest that the decreased MCI alpha power spectrum is likely related to the cortical synaptic connection loss from pyramidal cells to eIN.

EEG markers and subjective memory complaints in young and older people

Subjective memory complaints (SMCs) have been related to subtle cognitive deficits and neural changes. In this study, we investigated whether EEG rhythms, usually altered in mild cognitive impairment and Alzheimer's disease, are also affected in SMCs compared to people without SMCs. Seventy-one older adults (55-74 years old) and 75 young people (18-34 years old) underwent 3 min of EEG recording in a resting-state condition with their eyes open (EO) and eyes closed (EC) and a comprehensive neuropsychological evaluation. The EEG measures included were power spectral delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (13-30 Hz), and EEG reactivity to EO. Compared to controls, older people with SMCs showed increased theta power and a loss of alpha reactivity to EO. Additionally, in older participants with SMCs, the theta power spectral was related to deficits in verbal memory. In contrast, we failed to find differences in the young people with SMCs, compared to the control group, in the power spectral or the EEG reactivity to EO. Our findings suggest that neurophysiological markers of brain dysfunction may identify cognitive changes even before they are observed on objective neuropsychological tests, at least in older people.

Predicting the Effects of Repetitive Transcranial Magnetic Stimulation on Cognitive Functions in Patients With Alzheimer's Disease by Automated EEG Analysis

Alzheimer's disease (AD) is a progressive, neurodegenerative brain disorder that generally affects the elderly. Today, after the limited benefit of the pharmacological treatment strategies, numerous noninvasive brain stimulation techniques have been developed. Transcranial magnetic stimulation (TMS), based on electromagnetic stimulation, is one of the most widely used methods. The main problem in the use of TMS is the existence of large individual variability in the results. This causes a waste of money, time, and more importantly, a burden for delicate patients. Hence, it is a necessity to form an efficient and personalized TMS application protocol. In this paper, we performed a machine-learning analysis to see whether it is possible to predict the responses of patients with AD to TMS by analyzing their electroencephalography (EEG) signals. For that purpose, we analyzed both the EEG signals collected before and after the TMS application (EEG1 and EEG2, respectively). Through correlating EEG1 and repetitive transcranial magnetic stimulation (rTMS) outcomes, we tried to see whether it is possible to predict patients' responses before the treatment application. On the other hand, by EEG2 analysis, we investigated TMS impacts on EEG, more importantly if this impact is correlated with patients' response to the treatment. We used the support vector machine (SVM) classifier due to its multiple advantages for the current task with feature selection processes by stepwise linear discriminant analysis (SWLDA) and SVM. However, to justify our numerical analysis framework, we examined and compared the performances of different feature selection and classification techniques. Since we have a limited sample number, we used the leave-one-out method for the validation with the Monte Carlo technique to eliminate bias by a small sample size. In the conclusion, we observed that the correlation between rTMS outcomes and EEG2 is stronger than EEG1, since we observed, respectively, 93 and 79% of accuracies during our data analysis. Besides the informative features of EEG2 are focused on theta band, it indicates that TMS is characterizing the theta band signals in patients with AD in direct relation to patients' response to rTMS. This shows that it is more possible to determine patients' benefit from the TMS at the early stages of the treatment, which would increase the efficiency of rTMS applications in patients with Alzheimer's disease.

Theta and gamma oscillatory dynamics in mouse models of Alzheimer's disease: A path to prospective therapeutic intervention

Understanding the neural basis of cognitive deficits, a key feature of Alzheimer's disease (AD), is imperative for achieving the therapy of the disease. Rhythmic oscillatory activities in neural systems are a fundamental mechanism for diverse brain functions, including cognition. In several neurological conditions like AD, aberrant neural oscillations have been shown to play a central role. Furthermore, manipulation of brain oscillations in animals has confirmed their impact on cognition and disease. In this article, we review the evidence from mouse models that shows how synchronized oscillatory activity is intricately linked to AD machinery. We primarily focus on recent reports showing abnormal oscillatory activities at theta and gamma frequencies in AD condition and their influence on cellular disturbances and cognitive impairments. A thorough comprehension of the role that neuronal oscillations play in AD pathology should pave the way to therapeutic interventions that can curb the disease.

Controlling Alzheimer's Disease Through the Deep Brain Stimulation to Thalamic Relay Cells

Experimental and clinical studies have shown that the technique of deep brain stimulation (DBS) plays a potential role in the regulation of Alzheimer’s disease (AD), yet it still desires for ongoing studies including clinical trials, theoretical approach and action mechanism. In this work, we develop a modified thalamo-cortico-thalamic (TCT) model associated with AD to explore the therapeutic effects of DBS on AD from the perspective of neurocomputation. First, the neuropathological state of AD resulting from synapse loss is mimicked by decreasing the synaptic connectivity strength from the Inter-Neurons (IN) neuron population to the Thalamic Relay Cells (TRC) neuron population. Under such AD condition, a specific deep brain stimulation voltage is then implanted into the neural nucleus of TRC in this TCT model. The symptom of AD is found significantly relieved by means of power spectrum analysis and nonlinear dynamical analysis. Furthermore, the therapeutic effects of DBS on AD are systematically examined in different parameter space of DBS. The results demonstrate that the controlling effect of DBS on AD can be efficient by appropriately tuning the key parameters of DBS including amplitude A, period P and duration D. This work highlights the critical role of thalamus stimulation for brain disease, and provides a theoretical basis for future experimental and clinical studies in treating AD.

Reviewing Evidence for the Relationship of EEG Abnormalities and RTT Phenotype Paralleled by Insights from Animal Studies

Rett syndrome (RTT) is a rare neurodevelopmental disorder that is usually caused by mutations of the MECP2 gene. Patients with RTT suffer from severe deficits in motor, perceptual and cognitive domains. Electroencephalogram (EEG) has provided useful information to clinicians and scientists, from the very first descriptions of RTT, and yet no reliable neurophysiological biomarkers related to the pathophysiology of the disorder or symptom severity have been identified to date. To identify consistently observed and potentially informative EEG characteristics of RTT pathophysiology, and ascertain areas most worthy of further systematic investigation, here we review the literature for EEG abnormalities reported in patients with RTT and in its disease models. While pointing to some promising potential EEG biomarkers of RTT, our review identify areas of need to realize the potential of EEG including (1) quantitative investigation of promising clinical-EEG observations in RTT, e.g., shift of mu rhythm frequency and EEG during sleep; (2) closer alignment of approaches between patients with RTT and its animal models to strengthen the translational significance of the work (e.g., EEG measurements and behavioral states); (3) establishment of large-scale consortium research, to provide adequate Ns to investigate age and genotype effects.

Effects of Several Classes of Voltage-Gated Ion Channel Conductances on Gamma and Theta Oscillations in a Hippocampal Microcircuit Model

Gamma and theta oscillations have been functionally associated with cognitive processes, such as learning and memory. Synaptic conductances play an important role in the generation of intrinsic network rhythmicity, but few studies have examined the effects of voltage-gated ion channels (VGICs) on these rhythms. In this report, we have used a pyramidal-interneuron-gamma (PING) network consisting of excitatory pyramidal cells and two types of inhibitory interneurons. We have constructed a conductance-based neural network incorporating a persistent sodium current (I_NaP), a delayed rectifier potassium current (I_KDR), a inactivating potassium current (I_A) and a hyperpolarization-activated current (I_H). We have investigated the effects of several conductances on network theta and gamma frequency oscillations. Variation of all conductances of interest changed network rhythmicity. Theta power was altered by all conductances tested. Gamma rhythmogenesis was dependent on I_A and I_H. The I_KDR currents in excitatory pyramidal cells as well as both types of inhibitory interneurons were essential for theta rhythmogenesis and altered gamma rhythm properties. Increasing I_NaP suppressed both gamma and theta rhythms. Addition of noise did not alter these patterns. Our findings suggest that VGICs strongly affect brain network rhythms. Further investigations in vivo will be of great interest, including potential effects on neural function and cognition.

The effect of fornix deep brain stimulation in brain diseases

Deep brain stimulation is used to alleviate symptoms of neurological and psychiatric disorders including Parkinson's disease, epilepsy, and obsessive-compulsive-disorder. Electrically stimulating limbic structures has been of great interest, and in particular, the region of the fornix. We conducted a systematic search for studies that reported clinical and preclinical outcomes of deep brain stimulation within the fornix up to July 2019. We identified 13 studies (7 clinical, 6 preclinical) that examined the effects of fornix stimulation in Alzheimer's disease (n = 9), traumatic brain injury (n = 2), Rett syndrome (n = 1), and temporal lobe epilepsy (n = 1). Overall, fornix stimulation can lead to decreased rates of cognitive decline (in humans), enhanced memory (in humans and animals), visuo-spatial memorization (in humans and animals), and improving verbal recollection (in humans). While the exact mechanisms of action are not completely understood, studies suggest fornix DBS to be involved with increased functional connectivity and neurotransmitter levels, as well as enhanced neuroplasticity.

Using structural connectivity to augment community structure in EEG functional connectivity

Recently, EEG recording techniques and source analysis have improved, making it feasible to tap into fast network dynamics. Yet, analyzing whole-cortex EEG signals in source space is not standard, partly because EEG suffers from volume conduction: Functional connectivity (FC) reflecting genuine functional relationships is impossible to disentangle from spurious FC introduced by volume conduction. Here, we investigate the relationship between white matter structural connectivity (SC) and large-scale network structure encoded in EEG-FC. We start by confirming that FC (power envelope correlations) is predicted by SC beyond the impact of Euclidean distance, in line with the assumption that SC mediates genuine FC. We then use information from white matter structural connectivity in order to smooth the EEG signal in the space spanned by graphs derived from SC. Thereby, FC between nearby, structurally connected brain regions increases while FC between nonconnected regions remains unchanged, resulting in an increase in genuine, SC-mediated FC. We analyze the induced changes in FC, assessing the resemblance between EEG-FC and volume-conduction- free fMRI-FC, and find that smoothing increases resemblance in terms of overall correlation and community structure. This result suggests that our method boosts genuine FC, an outcome that is of interest for many EEG network neuroscience questions.

Atypical neuromagnetic resting activity associated with thalamic volume and cognitive outcome in very preterm children

Children born very preterm, even in the absence of overt brain injury or major impairment, are at increased risk of cognitive difficulties. This risk is associated with developmental disruptions of the thalamocortical system during critical periods while in the neonatal intensive care unit. The thalamus is an important structure that not only relays sensory information but acts as a hub for integration of cortical activity which regulates cortical power across a range of frequencies. In this study, we investigate the association between atypical power at rest in children born very preterm at school age using magnetoencephalography (MEG), neurocognitive function and structural alterations related to the thalamus using MRI. Our results indicate that children born extremely preterm have higher power at slow frequencies (delta and theta) and lower power at faster frequencies (alpha and beta), compared to controls born full-term. A similar pattern of spectral power was found to be associated with poorer neurocognitive outcomes, as well as with normalized T1 intensity and the volume of the thalamus. Overall, this study provides evidence regarding relations between structural alterations related to very preterm birth, atypical oscillatory power at rest and neurocognitive difficulties at school-age children born very preterm.

Alpha rhythm slowing in a modified thalamo-cortico-thalamic model related with Alzheimer's disease

A decrease in alpha band power is defined as a hallmark of electroencephalogram (EEG) in Alzheimer’s disease (AD). This study devotes to understanding the neuronal correlates of alpha rhythm slowing associated with AD from the view of neurocomputation. Firstly, a modified computational model of thalamo-cortico-thalamic (TCT) circuitry is constructed by incorporating two important biologically plausible ingredients. One is the disinhibition property between different inhibitory interneurons in the cortical module. The other is the full relay function of thalamic relay nucleus (TCR) to the cortical module. Then, by decreasing synaptic connectivity parameters to mimic the neuropathological condition of synapse loss in AD, the correlation between neuronal synaptic behavior and abnormal alpha rhythm is simulated by means of power spectral analysis. The results indicate that these decreases of synaptic activity, i.e., not only the excitatory synaptic connections from TCR to fast inhibitory interneurons C_fte and from excitatory interneurons to pyramidal neurons C_pxe but also the inhibitory synaptic connections from fast inhibitory interneurons to slow inhibitory interneurons C_lfi and from inhibitory interneurons to TCR C_tii, can significantly diminish the peak power density over the alpha band of the thalamic output, which implies that there is a slowing of alpha band. Furthermore, the underlying mechanism behind the alpha rhythmic changes is analyzed using nonlinear dynamical technique. The results reveal that decreases of C_fte, C_pxe, C_lfi and C_tii can make the thalamic module transfer from a limit cycle mode to a point attractor mode, which may lead to the alpha rhythm slowing in the modified TCT model. We expect this work can be helpful in identifying early biomarkers of AD’s EEG and understanding potential pathogenesis of AD.

Nicotinic Acetylcholine Receptor Agonists for the Treatment of Alzheimer's Dementia: An Update

A significant portion of the clinical phenotype observed in Alzheimer's disease (AD) occurs through nicotinic acetylcholine receptors (nAChRs). Degeneration of cholinergic neurons, combined with aberrant nAChR expression and activation partially through amyloid-beta peptide (Aβ)-nAChR leads to upregulation of pro-inflammatory pathways and subsequently the progressive cognitive decline of AD. Interestingly, the cholinergic anti-inflammatory pathway is also mediated through nAChR particularly α7 nAChR. Thus, agonists of these receptors will likely exert pro-cognitive benefits through multiple mechanisms including stimulating the cholinergic pathway, modulating inflammation, and buffering the effects of amyloid. Despite this promising theoretical use, trials thus far have been complicated by adverse effects or minimal improvement. This review will provide an update on several pharmacological nAChR agonists tested in clinical trials and reasons that further investigation of nAChR agonists is merited.

IMPLICATIONS

nAChRs have consistently presented a promising theoretical use in the treatment of AD; however, trials thus far have been complicated by adverse effects or minimal improvement. This review will provide an update on several pharmacological nAChR agonists trialed and reasons that further investigation of nAChR agonists is merited.

Knowledge-driven computational modeling in Alzheimer's disease research: Current state and future trends

Neurodegenerative diseases such as Alzheimer's disease (AD) follow a slowly progressing dysfunctional trajectory, with a large presymptomatic component and many comorbidities. Using preclinical models and large-scale omics studies ranging from genetics to imaging, a large number of processes that might be involved in AD pathology at different stages and levels have been identified. The sheer number of putative hypotheses makes it almost impossible to estimate their contribution to the clinical outcome and to develop a comprehensive view on the pathological processes driving the clinical phenotype. Traditionally, bioinformatics approaches have provided correlations and associations between processes and phenotypes. Focusing on causality, a new breed of advanced and more quantitative modeling approaches that use formalized domain expertise offer new opportunities to integrate these different modalities and outline possible paths toward new therapeutic interventions. This article reviews three different computational approaches and their possible complementarities. Process algebras, implemented using declarative programming languages such as Maude, facilitate simulation and analysis of complicated biological processes on a comprehensive but coarse-grained level. A model-driven Integration of Data and Knowledge, based on the OpenBEL platform and using reverse causative reasoning and network jump analysis, can generate mechanistic knowledge and a new, mechanism-based taxonomy of disease. Finally, Quantitative Systems Pharmacology is based on formalized implementation of domain expertise in a more fine-grained, mechanism-driven, quantitative, and predictive humanized computer model. We propose a strategy to combine the strengths of these individual approaches for developing powerful modeling methodologies that can provide actionable knowledge for rational development of preventive and therapeutic interventions. Development of these computational approaches is likely to be required for further progress in understanding and treating AD.

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

BACKGROUND

Presenilin-1 (PSEN1) mutations are the most common cause of familial early onset Alzheimer's disease (AD). The PSEN1 E280A (E280A) mutation has an autosomal dominant inheritance and is involved in the production of amyloid-β. The largest family group of carriers with E280A mutation is found in Antioquia, Colombia. The study of mutation carriers provides a unique opportunity to identify brain changes in stages previous to AD. Electroencephalography (EEG) is a low cost and minimally invasiveness technique that enables the following of brain changes in AD.

OBJECTIVE

To examine how previous reported differences in EEG for Theta and Alpha-2 rhythms in E280A subjects are related to specific regions in cortex and could be tracked across different ages.

METHODS

EEG signals were acquired during resting state from non-carriers and carriers, asymptomatic and symptomatic subjects from E280A kindred from Antioquia, Colombia. Independent component analysis (ICA) and inverse solution methods were used to locate brain regions related to differences in Theta and Alpha-2 bands.

RESULTS

ICA identified two components, mainly related to the Precuneus, where the differences in Theta and Alpha-2 exist simultaneously at asymptomatic and symptomatic stages. When the ratio between Theta and Alpha-2 is used, significant correlations exist with age and a composite cognitive scale.

CONCLUSION

Theta and Alpha-2 rhythms are altered in E280A subjects. The alterations are possible to track at Precuneus regions using EEG, ICA, and inverse solution methods.

Causal Role of Thalamic Interneurons in Brain State Transitions: A Study Using a Neural Mass Model Implementing Synaptic Kinetics

Experimental studies on the Lateral Geniculate Nucleus (LGN) of mammals and rodents show that the inhibitory interneurons (IN) receive around 47.1% of their afferents from the retinal spiking neurons, and constitute around 20–25% of the LGN cell population. However, there is a definite gap in knowledge about the role and impact of IN on thalamocortical dynamics in both experimental and model-based research. We use a neural mass computational model of the LGN with three neural populations viz. IN, thalamocortical relay (TCR), thalamic reticular nucleus (TRN), to study the causality of IN on LGN oscillations and state-transitions. The synaptic information transmission in the model is implemented with kinetic modeling, facilitating the linking of low-level cellular attributes with high-level population dynamics. The model is parameterized and tuned to simulate alpha (8–13 Hz) rhythm that is dominant in both Local Field Potential (LFP) of LGN and electroencephalogram (EEG) of visual cortex in an awake resting state with eyes closed. The results show that: First, the response of the TRN is suppressed in the presence of IN in the circuit; disconnecting the IN from the circuit effects a dramatic change in the model output, displaying high amplitude synchronous oscillations within the alpha band in both TCR and TRN. These observations conform to experimental reports implicating the IN as the primary inhibitory modulator of LGN dynamics in a cognitive state, and that reduced cognition is achieved by suppressing the TRN response. Second, the model validates steady state visually evoked potential response in humans corresponding to periodic input stimuli; however, when the IN is disconnected from the circuit, the output power spectra do not reflect the input frequency. This agrees with experimental reports underpinning the role of IN in efficient retino-geniculate information transmission. Third, a smooth transition from alpha to theta band is observed by progressive decrease of neurotransmitter concentrations in the synaptic clefts; however, the transition is abrupt with removal of the IN circuitry in the model. The results imply a role of IN toward maintaining homeostasis in the LGN by suppressing any instability that may arise due to anomalous synaptic attributes.

Electroencephalography-driven approach to prodromal Alzheimer's disease diagnosis: from biomarker integration to network-level comprehension

Decay of the temporoparietal cortex is associated with prodromal Alzheimer's disease (AD). Additionally, shrinkage of the temporoparietal cerebral area has been connected with an increase in α3/α2 electroencephalogram (EEG) power ratio in prodromal AD. Furthermore, a lower regional blood perfusion has been exhibited in patients with a higher α3/α2 proportion when contrasted with low α3/α2 proportion. Furthermore, a lower regional blood perfusion and reduced hippocampal volume has been exhibited in patients with higher α3/α2 when contrasted with lower α3/α2 EEG power ratio. Neuropsychological evaluation, EEG recording, and magnetic resonance imaging were conducted in 74 patients with mild cognitive impairment (MCI). Estimation of cortical thickness and α3/α2 frequency power ratio was conducted for each patient. A subgroup of 27 patients also underwent single-photon emission computed tomography evaluation. In view of α3/α2 power ratio, the patients were divided into three groups. The connections among cortical decay, cerebral perfusion, and memory loss were evaluated by Pearson's r coefficient. Results demonstrated that higher α3/α2 frequency power ratio group was identified with brain shrinkage and cutdown perfusion inside the temporoparietal projections. In addition, decay and cutdown perfusion rate were connected with memory shortfalls in patients with MCI. MCI subgroup with higher α3/α2 EEG power ratio are at a greater risk to develop AD dementia.

Functions and Mechanisms of Sleep

Sleep is a complex physiological process that is regulated globally, regionally, and locally by both cellular and molecular mechanisms. It occurs to some extent in all animals, although sleep expression in lower animals may be co-extensive with rest. Sleep regulation plays an intrinsic part in many behavioral and physiological functions. Currently, all researchers agree there is no single physiological role sleep serves. Nevertheless, it is quite evident that sleep is essential for many vital functions including development, energy conservation, brain waste clearance, modulation of immune responses, cognition, performance, vigilance, disease, and psychological state. This review details the physiological processes involved in sleep regulation and the possible functions that sleep may serve. This description of the brain circuitry, cell types, and molecules involved in sleep regulation is intended to further the reader's understanding of the functions of sleep.

Conversion of mild cognitive impairment patients in Alzheimer's disease: prognostic value of Alpha3/Alpha2 electroencephalographic rhythms power ratio

INTRODUCTION

The increase in electroencephalogram (EEG) alpha3/alpha2 frequency power ratio has been demonstrated as a biomarker characteristic of subjects with mild cognitive impairment (MCI) who will develop Alzheimer's disease (AD).

METHODS

Seventy-four adult subjects with MCI underwent clinical and neuropsychological evaluation, EEG recording, and high-resolution 3D magnetic resonance imaging (MRI). This group has been evaluated after a three years follow-up. Twenty-seven of these subjects underwent perfusion single-photon emission computed tomography (SPECT) evaluation also. Increasing alpha3/alpha2 power ratio, was computed for each subject. Differences in EEG markers, cortical thickness, brain perfusion among the groups were estimated.

RESULTS

In the higher alpha3/alpha2 frequency power ratio group, greater memory impairment was correlated with greater cortical atrophy and lower perfusional rate in the temporo-parietal cortex. After a follow-up of three years, these patients converted in AD.

CONCLUSION

High EEG upper/low alpha power ratio was associated with cortical thinning and lower perfusion in the temporo-parietal lobe. Moreover, atrophy and lower perfusion rate were both significantly correlated with memory impairment in MCI subjects. The increase of EEG upper/low alpha frequency power ratio could be useful for identifying individuals at risk for progression to AD dementia and may be of value in the clinical context.

Association of EEG, MRI, and regional blood flow biomarkers is predictive of prodromal Alzheimer's disease

BACKGROUND

Thinning in the temporoparietal cortex, hippocampal atrophy, and a lower regional blood perfusion is connected with prodromal stage of Alzheimer's disease (AD). Of note, an increase of electroencephalography (EEG) upper/low alpha frequency power ratio has also been associated with these major landmarks of prodromal AD.

METHODS

Clinical and neuropsychological assessment, EEG recording, and high-resolution three-dimensional magnetic resonance imaging were done in 74 grown up subjects with mild cognitive impairment. This information was gathered and has been assessed 3 years postliminary. EEG recording and perfusion single-photon emission computed tomography assessment was done in 27 subjects. Alpha3/alpha2 frequency power ratio, including cortical thickness, was figured for every subject. Contrasts in cortical thickness among the groups were assessed. Pearson's r relationship coefficient was utilized to evaluate the quality of the relationship between cortical thinning, brain perfusion, and EEG markers.

RESULTS

The higher alpha3/alpha2 frequency power ratio group corresponded with more prominent cortical decay and a lower perfusional rate in the temporoparietal cortex. In a subsequent meetup after 3 years, these patients had AD.

CONCLUSION

High EEG upper/low alpha power ratio was connected with cortical diminishing and lower perfusion in the temporoparietal brain area. The increase in EEG upper/low alpha frequency power ratio could be helpful in recognizing people in danger of conversion to AD dementia and this may be quality information in connection with clinical assessment.

Characterization of complexity in the electroencephalograph activity of Alzheimer's disease based on fuzzy entropy

In this paper, experimental neurophysiologic recording and statistical analysis are combined to investigate the nonlinear characteristic and the cognitive function of the brain. Fuzzy approximate entropy and fuzzy sample entropy are applied to characterize the model-based simulated series and electroencephalograph (EEG) series of Alzheimer's disease (AD). The effectiveness and advantages of these two kinds of fuzzy entropy are first verified through the simulated EEG series generated by the alpha rhythm model, including stronger relative consistency and robustness. Furthermore, in order to detect the abnormality of irregularity and chaotic behavior in the AD brain, the complexity features based on these two fuzzy entropies are extracted in the delta, theta, alpha, and beta bands. It is demonstrated that, due to the introduction of fuzzy set theory, the fuzzy entropies could better distinguish EEG signals of AD from that of the normal than the approximate entropy and sample entropy. Moreover, the entropy values of AD are significantly decreased in the alpha band, particularly in the temporal brain region, such as electrode T3 and T4. In addition, fuzzy sample entropy could achieve higher group differences in different brain regions and higher average classification accuracy of 88.1% by support vector machine classifier. The obtained results prove that fuzzy sample entropy may be a powerful tool to characterize the complexity abnormalities of AD, which could be helpful in further understanding of the disease.

Mild Cognitive Impairment: Structural, Metabolical, and Neurophysiological Evidence of a Novel EEG Biomarker

BACKGROUND

Recent studies demonstrate that the alpha3/alpha2 power ratio correlates with cortical atrophy, regional hypoperfusion, and memory impairment in subjects with mild cognitive impairment (MCI).

METHODS

Evidences were reviewed in subjects with MCI, who underwent EEG recording, magnetic resonance imaging (MRI) scans, and memory evaluation. Alpha3/alpha2 power ratio (alpha2 8.9-10.9 Hz range; alpha3 10.9-12.9 Hz range), cortical thickness, linear EEG coherence, and memory impairment have been evaluated in a large group of 74 patients. A subset of 27 subjects within the same group also underwent single photon emission computed tomography (SPECT) evaluation.

RESULTS

In MCI subjects with higher EEG upper/low alpha power ratio, a greater temporo-parietal and hippocampal atrophy was found as well as a decrease in regional blood perfusion and memory impairment. In this group, an increase of theta oscillations is associated with a greater interhemispheric coupling between temporal areas.

CONCLUSION

The increase of alpha3/alpha2 power ratio is a promising novel biomarker in identifying MCI subjects at risk for Alzheimer's disease.

Theta and alpha EEG frequency interplay in subjects with mild cognitive impairment: evidence from EEG, MRI, and SPECT brain modifications

BACKGROUND

Temporo-parietal and medial temporal cortex atrophy are associated with mild cognitive impairment (MCI) due to Alzheimer disease (AD) as well as the reduction of regional cerebral blood perfusion in hippocampus. Moreover, the increase of EEG alpha3/alpha2 power ratio has been associated with MCI due to AD and with an increase in theta frequency power in a group of subjects with impaired cerebral perfusion in hippocampus.

METHODS

Seventy four adult subjects with MCI underwent clinical and neuropsychological evaluation, electroencephalogram (EEG) recording and high resolution 3D magnetic resonance imaging (MRI). Among the patients, a subset of 27 subjects underwent also perfusion single-photon emission computed tomography and hippocampal atrophy evaluation. Alpha3/alpha2 power ratio as well as cortical thickness was computed for each subject. Three MCI groups were detected according to increasing tertile values of alpha3/alpha2 power ratio and difference of cortical thickness among the groups estimated.

RESULTS

Higher alpha3/alpha2 power ratio group had wider cortical thinning than other groups, mapped to the Supramarginal and Precuneus bilaterally. Subjects with higher alpha3/alpha2 frequency power ratio showed a constant trend to a lower perfusion than lower alpha3/alpha2 group. Moreover, this group correlates with both a bigger hippocampal atrophy and an increase of theta frequency power.

CONCLUSION

Higher EEG alpha3/alpha2 power ratio was associated with temporo-parietal cortical thinning, hippocampal atrophy and reduction of regional cerebral perfusion in medial temporal cortex. In this group an increase of theta frequency power was detected inMCI subjects. The combination of higher EEG alpha3/alpha2 power ratio, cortical thickness measure and regional cerebral perfusion reveals a complex interplay between EEG cerebral rhythms, structural and functional brain modifications.

Electroencephalography reveals lower regional blood perfusion and atrophy of the temporoparietal network associated with memory deficits and hippocampal volume reduction in mild cognitive impairment due to Alzheimer's disease

BACKGROUND

An increased electroencephalographic (EEG) upper/lower alpha power ratio has been associated with less regional blood perfusion, atrophy of the temporoparietal region of the brain, and reduction of hippocampal volume in subjects affected by mild cognitive impairment due to Alzheimer's disease as compared with subjects who do not develop the disease. Moreover, EEG theta frequency activity is quite different in these groups. This study investigated the correlation between biomarkers and memory performance.

METHODS

EEG α3/α2 power ratio and cortical thickness were computed in 74 adult subjects with prodromal Alzheimer's disease. Twenty of these subjects also underwent assessment of blood perfusion by single-photon emission computed tomography (SPECT). Pearson's r was used to assess the correlation between cortical thinning, brain perfusion, and memory impairment.

RESULTS

In the higher α3/α2 frequency power ratio group, greater cortical atrophy and lower regional perfusion in the temporoparietal cortex was correlated with an increase in EEG theta frequency. Memory impairment was more pronounced in the magnetic resonance imaging group and SPECT groups.

CONCLUSION

A high EEG upper/low alpha power ratio was associated with cortical thinning and less perfusion in the temporoparietal area. Moreover, atrophy and less regional perfusion were significantly correlated with memory impairment in subjects with prodromal Alzheimer's disease. The EEG upper/lower alpha frequency power ratio could be useful for identifying individuals at risk for progression to Alzheimer's dementia and may be of value in the clinical context.

Understanding early dementia: EEG, MRI, SPECT and memory evaluation

BACKGROUND

An increase in the EEG upper/low α power ratio has been associated with mild cognitive impairment (MCI) due to Alzheimer's disease (AD) and to the atrophy of temporoparietal brain areas. Subjects with a higher α3/α2 frequency power ratio showed lower brain perfusion than in the low α3/α2 group. The two groups show significantly different hippocampal volumes and correlation with θ frequency activity.

METHODS

Seventy-four adult subjects with MCI underwent clinical and neuropsychological evaluation, electroencephalogram (EEG) recording, and high resolution 3D magnetic resonance imaging (MRI). Twenty-seven of them underwent EEG recording and perfusion single-photon emission computed tomography (SPECT) evaluation. The α3/α2 power ratio and cortical thickness were computed for each subject. The difference in cortical thickness between the groups was estimated.

RESULTS

In the higher upper/low α group, memory impairment was more pronounced in both the MRI group and the SPECT MCI groups. An increase in the production of θ oscillations was associated with greater interhemisperic coupling between temporal areas. It also correlated with greater cortical atrophy and lower perfusional rate in the temporoparietal cortex.

CONCLUSION

High EEG upper/low α power ratio was associated with cortical thinning and lower perfusion in temporoparietal areas. Moreover, both atrophy and lower perfusion rate significantly correlated with memory impairment in MCI subjects. Therefore, the increase in the EEG upper/low α frequency power ratio could be useful in identifying individuals at risk for progression to AD dementia in a clinical context.

Electroencephalographic upper/low alpha frequency power ratio relates to cortex thinning in mild cognitive impairment

OBJECTIVE

Temporoparietal cortex thinning is associated with mild cognitive impairment (MCI) due to Alzheimer disease (AD). The increase in EEG upper/low α frequency power ratio has been associated with AD converter MCI subjects. We investigated the association of the EEG upper/low α frequency power ratio with patterns of cortical thickness in MCI.

METHODS

74 adult subjects with MCI underwent clinical and neuropsychological evaluation, electroencephalography (EEG) recording and high-resolution 3-dimensional magnetic resonance imaging (MRI). The EEG upper/low α frequency power ratio as well as cortical thickness were computed for each subject. Three MCI groups were detected according to increasing tertile values of EEG upper/low α frequency power ratios, and the difference of cortical thickness among the groups was estimated.

RESULTS

The EEG high upper/low α frequency power ratio group had a total cortical grey matter volume reduction of 471 mm(2), greater than that of the EEG low upper/low α frequency power ratio group (p < 0.001). The EEG high upper/low α frequency power ratio group showed a similar but less marked pattern (160 mm(2)) of cortical thinning when compared to the EEG middle upper/low α frequency power ratio group (p < 0.001). Moreover, the EEG high upper/low α frequency power ratio group had wider cortical thinning than other groups, mapped to the supramarginal gyrus and precuneus bilaterally. No significant regional cortical thickness differences were found between middle and low EEG upper/low α frequency power ratio groups.

CONCLUSION

A high EEG upper/low α frequency power ratio was associated with temporoparietal cortical thinning in MCI subjects. The combination of upper/low α frequency power ratio and cortical thickness measurement could be useful for identifying individuals at risk for progression to AD dementia and may be of value in the clinical context.

EEG upper/low alpha frequency power ratio relates to temporo-parietal brain atrophy and memory performances in mild cognitive impairment

OBJECTIVE

Temporo-parietal cortex thinning is associated to mild cognitive impairment (MCI) due to Alzheimer disease (AD). The increase of EEG upper/low alpha power ratio has been associated with AD-converter MCI subjects. We investigated the association of alpha3/alpha2 ratio with patterns of cortical thickness in MCI.

MATERIALS AND METHODS

Seventy-four adult subjects with MCI underwent clinical and neuropsychological evaluation, electroencephalogram (EEG) recording and high resolution 3D magnetic resonance imaging. Alpha3/alpha2 power ratio as well as cortical thickness was computed for each subject. Three MCI groups were detected according to increasing tertile values of upper/low alpha power ratio. Difference of cortical thickness among the groups was estimated. Pearson's r was used to assess the topography of the correlation between cortical thinning and memory impairment.

RESULTS

High upper/low alpha power ratio group had total cortical gray matter volume reduction of 471 mm(2) than low upper/low alpha power ratio group (p < 0.001). Upper/low alpha group showed a similar but less marked pattern (160 mm(2)) of cortical thinning when compared to middle upper/low alpha power ratio group (p < 0.001). Moreover, high upper/low alpha group had wider cortical thinning than other groups, mapped to the Supramarginal and Precuneus bilaterally. Finally, in high upper/low alpha group temporo-parietal cortical thickness was correlated to memory performance. No significant cortical thickness differences was found between middle and low alpha3/alpha2 power ratio groups.

CONCLUSION

High EEG upper/low alpha power ratio was associated with temporo-parietal cortical thinning and memory impairment in MCI subjects. The combination of EEG upper/low alpha ratio and cortical thickness measure could be useful for identifying individuals at risk for progression to AD dementia and may be of value in clinical context.

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

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.

Activity dependent degeneration explains hub vulnerability in Alzheimer's disease

Brain connectivity studies have revealed that highly connected 'hub' regions are particularly vulnerable to Alzheimer pathology: they show marked amyloid-β deposition at an early stage. Recently, excessive local neuronal activity has been shown to increase amyloid deposition. In this study we use a computational model to test the hypothesis that hub regions possess the highest level of activity and that hub vulnerability in Alzheimer's disease is due to this feature. Cortical brain regions were modeled as neural masses, each describing the average activity (spike density and spectral power) of a large number of interconnected excitatory and inhibitory neurons. The large-scale network consisted of 78 neural masses, connected according to a human DTI-based cortical topology. Spike density and spectral power were positively correlated with structural and functional node degrees, confirming the high activity of hub regions, also offering a possible explanation for high resting state Default Mode Network activity. 'Activity dependent degeneration' (ADD) was simulated by lowering synaptic strength as a function of the spike density of the main excitatory neurons, and compared to random degeneration. Resulting structural and functional network changes were assessed with graph theoretical analysis. Effects of ADD included oscillatory slowing, loss of spectral power and long-range synchronization, hub vulnerability, and disrupted functional network topology. Observed transient increases in spike density and functional connectivity match reports in Mild Cognitive Impairment (MCI) patients, and may not be compensatory but pathological. In conclusion, the assumption of excessive neuronal activity leading to degeneration provides a possible explanation for hub vulnerability in Alzheimer's disease, supported by the observed relation between connectivity and activity and the reproduction of several neurophysiologic hallmarks. The insight that neuronal activity might play a causal role in Alzheimer's disease can have implications for early detection and interventional strategies.