Decreased EEG synchronization and its correlation with symptom severity in Alzheimer's disease

Probing latent brain dynamics in Alzheimer's disease via recurrent neural network

The impairment of cognitive function in Alzheimer's disease (AD) is clearly correlated to abnormal changes in cortical rhythm. However, the mechanisms underlying this correlation are still poorly understood. Here, we investigate how network structure and dynamical characteristics alter their abnormal changes in cortical rhythm. To that end, biological data of AD and normal participates are collected. By extracting the energy characteristics of different sub-bands in EEG signals, we find that the rhythm of AD patients is special particularly in theta and alpha bands. The cortical rhythm of normal state is mainly at alpha band, while that of AD state shift to the theta band. Furthermore, recurrent neural network (RNN) is trained to explore the rhythm formation and transformation between two neural states from the perspective view of neurocomputation. It is found that the neural coupling strength decreases significantly under AD state when compared with normal state, which weakens the ability of information transmission in AD state. Besides, the low-dimensional properties of RNN are obtained. By analyzing the relationship between the cortical rhythm transition and the low-dimensional trajectory, it is concluded that the low-dimensional trajectory update is slower and the communication cost is higher in AD state, which explains the abnormal synchronization of AD brain network. Our work reveals the causes for the formation of abnormal brain synchronous functional network status, which may expand our understanding of the mechanism of cognitive impairment in AD and provide an EEG biomarker for early AD.

Global Functional Connectivity is Associated with Mind Wandering Domain of Comfort

The resting-state paradigm is frequently applied to study spontaneous activity of the brain in normal and clinical conditions. To assess the relationship between brain activity and subjective experiences, various questionnaires are used. Previous studies using Amsterdam Resting State Questionnaire were focusing on fMRI functional connectivity or EEG microstates and spectral aspect. Here, we utilized Global Field Synchronization as the parameter to estimate global functional connectivity. By re-analyzing the resting-state data from 226 young healthy participants we showed a strong evidence of relationship between ARSQ domain of Comfort and GFS values in the alpha range (r = 0.210, BF10 = 12.338) and substantial evidence for positive relationship between ARSQ domain of Comfort and GFS in the beta frequency range (r = 196, BF10 = 6.307). Our study indicates the relevance of assessments of spontaneous thought occurring during the resting-state for the understanding of the individual intrinsic electrical brain activity.

qEEG as Biomarker for Alzheimer's Disease: Investigating Relative PSD Difference and Coherence Analysis

Purpose

Electroencephalography (EEG) is a non-intrusive technique that provides comprehensive insights into the electrical activities of the brain's cerebral cortex. The brain signals obtained from EEGs can be used as a neuropsychological biomarker to detect different stages of Alzheimer's disease (AD) through quantitative EEG (qEEG) analysis. This paper investigates the difference in the abnormalities of resting state EEG (rEEG) signals between eyes-open (EOR) and eyes-closed (ECR) in AD by analyzing 19-scalp electrode EEG signals and making a comparison with healthy controls (HC).

Participants and Methods

The rEEG data from 534 subjects (ages 40-90) consisting of 269 HC and 265 AD subjects in South Korea were used in this study. The qEEG for EOR and ECR states were performed separately for HC and AD subjects to measure the relative power spectrum density (PSD) and coherence with functional connectivity to evaluate abnormalities. The rEEG data were preprocessed and analyzed using EEGlab and Brainstorm toolboxes in MATLAB R2021a software, and statistical analyses were carried out using ANOVA.

Results

Based on the Welch method, the relative PSD of the EEG EOR and ECR states difference in the AD group showed a significant increase in the delta frequency band of 19 EEG channels, particularly in the frontal, parietal, and temporal, than the HC groups. The delta power band on the source level was increased for the AD group and decreased for the HC group. In contrast, the source activities of alpha, beta, and gamma frequency bands were significantly reduced in the AD group, with a high decrease in the beta frequency band in all brain areas. Furthermore, the coherence of rEEG among different EEG electrodes was analyzed in the beta frequency band. It showed that pair-wise coherence between different brain areas in the AD group is remarkably increased in the ECR state and decreased after subtracting out the EOR state.

Conclusion

The findings suggest that examining PSD and functional connectivity through coherence analysis could serve as a promising and comprehensive approach to differentiate individuals with AD from normal, which may benefit our understanding of the disease.

Decreased Electroencephalography Global Field Synchronization in Slow-Frequency Bands Characterizes Synaptic Dysfunction in Amnestic Subtypes of Mild Cognitive Impairment

Background

Mild cognitive impairment (MCI) is highly prevalent in a memory clinic setting and is heterogeneous regarding its clinical presentation, underlying pathophysiology, and prognosis. The most prevalent subtypes are single-domain amnestic MCI (sd-aMCI), considered to be a prodromal phase of Alzheimer’s disease (AD), and multidomain amnestic MCI (md-aMCI), which is associated with multiple etiologies. Since synaptic loss and dysfunction are the closest pathoanatomical correlates of AD-related cognitive impairment, we aimed to characterize it in patients with sd-aMCI and md-aMCI by means of resting-state electroencephalography (EEG) global field power (GFP), global field synchronization (GFS), and novel cerebrospinal fluid (CSF) synaptic biomarkers.

Methods

We included 52 patients with sd-aMCI (66.9 ± 7.3 years, 52% women) and 30 with md-aMCI (63.1 ± 7.1 years, 53% women). All patients underwent a detailed clinical assessment, resting-state EEG recordings and quantitative analysis (GFP and GFS in delta, theta, alpha, and beta bands), and analysis of CSF biomarkers of synaptic dysfunction, neurodegeneration, and AD-related pathology. Cognitive subtyping was based on a comprehensive neuropsychological examination. The Mini-Mental State Examination (MMSE) was used as an estimation of global cognitive performance. EEG and CSF biomarkers were included in a multivariate model together with MMSE and demographic variables, to investigate differences between sd-aMCI and md-aMCI.

Results

Patients with sd-aMCI had higher CSF phosphorylated tau, total tau and neurogranin levels, and lower values in GFS delta and theta. No differences were observed in GFP. The multivariate model showed that the most important synaptic measures for group separation were GFS theta, followed by GFS delta, GFP theta, CSF neurogranin, and GFP beta.

Conclusion

Patients with sd-aMCI when compared with those with md-aMCI have a neurophysiological and biochemical profile of synaptic damage, neurodegeneration, and amyloid pathology closer to that described in patients with AD. The most prominent signature in sd-aMCI was a decreased global synchronization in slow-frequency bands indicating that functional connectivity in slow frequencies is more specifically related to early effects of AD-specific molecular pathology.

Neuropsychiatric Symptoms in Dementia: Considering a Clinical Role for Electroencephalography

OBJECTIVE

Degenerative dementia is characterized by progressive cognitive decline and neuropsychiatric symptoms. People with Alzheimer's disease (AD), the most common cause of dementia, show synaptic loss and disruption of functional brain networks along with neuritic plaques and neurofibrillary tangles. Electroencephalography (EEG) directly reflects synaptic activity, and among patients with AD it is associated with slowing of background activity. The purpose of this study was to identify associations between neuropsychiatric symptoms and EEG in patients with dementia and to determine whether EEG parameters could be used for clinical assessment of pharmacological treatment of neuropsychiatric symptoms in dementia (NPSD) with galantamine or risperidone.

METHODS

Seventy-two patients with EEG recordings and a score ≥10 on the Neuropsychiatric Inventory (NPI) were included. Clinical assessments included administration of the NPI, the Mini-Mental State Examination (MMSE), and the Cohen-Mansfield Agitation Inventory (CMAI). Patients underwent EEG examinations at baseline and after 12 weeks of treatment with galantamine or risperidone. EEG frequency analysis was performed. Correlations between EEG and assessment scale scores were statistically examined, as were EEG changes from baseline to the week 12 visit and the relationship with NPI, CMAI, and MMSE scores.

RESULTS

Significant correlations were found between NPI agitation and delta EEG frequencies at baseline and week 12. No other consistent and significant relationships were observed between NPSD and EEG at baseline, after NPSD treatment, or in the change in EEG from baseline to follow-up.

CONCLUSIONS

The limited informative findings in this study suggest that there exists a complex relationship between NPSD and EEG; hence, it is difficult to evaluate and use EEG for clinical assessment of pharmacological NPSD treatment.

EEG Resting-State and Event-Related Potentials as Markers of Learning Success in Older Adults Following Second Language Training: A Pilot Study

OBJECTIVES

In this pilot study, we evaluated the use of electrophysiological measures at rest as paradigm-independent predictors of second language (L2) development for the first time in older adult learners. We then assessed EEG correlates of the learning outcome in a language-switching paradigm after the training, which to date has only been done in younger adults and at intermediate to advanced L2 proficiency.

METHODS

Ten (Swiss) German-speaking adults between 65-74 years of age participated in an intensive 3-week English training for beginners. A resting-state EEG was recorded before the training to predict the ensuing L2 development (Experiment 1). A language-switching ERP experiment was conducted after the training to assess the learning outcome (Experiment 2).

RESULTS

All participants improved their L2 skills but differed noticeably in their individual development. Experiment 1 showed that beta1 oscillations at rest (13-14.5 Hz) predicted these individual differences. We interpret resting-state beta1 oscillations as correlates of attentional capacities and semantic working memory that facilitate the extraction and processing of novel forms and meanings from the L2 input.In Experiment 2, we found that language switching from the L2 into the native language (L1) elicited an N400 component, which was reduced in the more advanced learners. Thus, for learners beginning the acquisition of an L2 in third age, language switching appears to become less effortful with increasing proficiency, suggesting that the lexicons of the L1 and L2 become more closely linked.

CONCLUSIONS

In sum, our findings extend the available evidence of neurological processes in L2 learning from younger to older adults, suggesting that electrophysiological mechanisms are similar across the lifespan.

Functional (un-)Coupling: Impairment, Compensation, and Future Progression in Alzheimer's Disease

Background: Functional (un-)coupling (task-related change of functional connectivity) between different sites of the brain is a mechanism of general importance for cognitive processes. In Alzheimer's disease (AD), prior research identified diminished cortical connectivity as a hallmark of the disease. However, little is known about the relation between the amount of functional (un-)coupling and cognitive performance and decline in AD. Method: Cognitive performance (based on CERAD-Plus scores) and electroencephalogram (EEG)-based functional (un-)coupling measures (connectivity changes from rest to a Face-Name-Encoding task) were assessed in 135 AD patients (age: M = 73.8 years; SD = 9.0). Of these, 68 patients (M = 73.9 years; SD = 8.9) participated in a follow-up assessment of their cognitive performance 1.5 years later. Results: The amounts of functional (un-)coupling in left anterior-posterior and homotopic interhemispheric connections in beta1-band were related to cognitive performance at baseline (β = .340; p < .001; β = .274; P = .001, respectively). For both markers, a higher amount of functional coupling was associated with better cognitive performance. Both markers also were significant predictors for cognitive decline. However, while patients with greater functional coupling in left anterior-posterior connections declined less in cognitive performance (β = .329; P = .035) those with greater functional coupling in interhemispheric connections declined more (β = -.402; P = .010). Conclusion: These findings suggest an important role of functional coupling mechanisms in left anterior-posterior and interhemispheric connections in AD. Especially the complex relationship with cognitive decline in AD patients might be an interesting aspect for future studies.

Decreased Global EEG Synchronization in Amyloid Positive Mild Cognitive Impairment and Alzheimer's Disease Patients-Relationship to APOE ε4

The apolipoprotein E (APOE) ε4 allele is a risk factor for Alzheimer's disease (AD) that has been linked to changes in brain structure and function as well as to different biological subtypes of the disease. The present study aimed to investigate the association of APOE ε4 genotypes with brain functional impairment, as assessed by quantitative EEG (qEEG) in patients on the AD continuum. The study population included 101 amyloid positive patients diagnosed with mild cognitive impairment (MCI) (n = 50) and AD (n = 51) that underwent resting-state EEG recording and CSF Aβ42 analysis. In total, 31 patients were APOE ε4 non-carriers, 42 were carriers of one, and 28 were carriers of two APOE ε4 alleles. Quantitative EEG analysis included computation of the global field power (GFP) and global field synchronization (GFS) in conventional frequency bands. Amyloid positive patients who were carriers of APOE ε4 allele(s) had significantly higher GFP beta and significantly lower GFS in theta and beta bands compared to APOE ε4 non-carriers. Increased global EEG power in beta band in APOE ε4 carriers may represent a brain functional compensatory mechanism that offsets global EEG slowing in AD patients. Our findings suggest that decreased EEG measures of global synchronization in theta and beta bands reflect brain functional deficits related to the APOE ε4 genotype in patients that are on a biomarker-verified AD continuum.

Functional Connectivity Alterations Based on the Weighted Phase Lag Index: An Exploratory Electroencephalography Study on Alzheimer's Disease

OBJECTIVE

Numerous electroencephalography (EEG) studies focus on the alteration of electrical activity in patients with Alzheimer's Disease (AD), but there are no consistent results especially regarding functional connectivity. We supposed that the weighted Phase Lag Index (w- PLI), as phase-based measures of functional connectivity, may be used as an auxiliary diagnostic method for AD.

METHODS

We enrolled 30 patients with AD, 30 patients with Mild Cognitive Impairment (MCI), and 30 Healthy Controls (HC). EEGs were recorded in all participants at baseline during relaxed wakefulness. Following EEG preprocessing, Power Spectral Density (PSD) and wPLI parameters were determined to further analyze whether they were correlated to cognitive scores.

RESULTS

In the patients with AD, the increased PSD in theta band was presented compared with MCI and HC groups, which was associated with disturbances of the directional, computational, and delayed memory capacity. Furthermore, the wPLI revealed a distinctly lower connection strength between frontal and distant areas in the delta band and a higher connection strength of the central and temporo-occipital region in the theta band for AD patients. Moreover,we found a significant negative correlation between theta functional connectivity and cognitive scores.

CONCLUSION

Increased theta PSD and decreased delta wPLI may be one of the earliest changes in AD and associated with disease severity. The parameter wPLI is a novel measurement of phase synchronization and has potentials in understanding underlying functional connectivity and aiding in the diagnostics of AD.

Dorsolateral prefrontal cortex excitability abnormalities in Alzheimer's Dementia: Findings from transcranial magnetic stimulation and electroencephalography study

There is some evidence of cortical hyper-excitability in Alzheimer's Dementia (AD) but its relationship with cognition is not clear. In this study, we assessed dorsolateral prefrontal cortex (DLPFC) excitability and its relationship with cognition in AD. Twenty-four participants with AD (mean [SD] age = 74.1 [7.2] years) and eleven elderly healthy controls (HC) (mean [SD] age = 68.8 [7.3] years) were recruited. Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) was used to assess cortical excitability. Cortical evoked activity (CEA) between 25 and 80 ms post-TMS stimulus was calculated as the primary measure of cortical excitability. TMS-evoked potential peak (TEP) amplitudes (P30, N45 and P60) were also calculated. Cognition was assessed using Montreal Cognitive Assessment (MoCA), Executive Interview (EXIT) and Cambridge Neuropsychological Test Automated Battery Stockings of Cambridge (SOC). There was no difference in TMS stimulus intensity between the groups. DLPFC-CEA was higher in the AD (mean [SD] = 134.64 [90.22] μV) than the HC group (mean [SD] = 82.65 [40.28] μV; t₃₃ = 2.357, p = 0.025). There were no differences in TEP peak amplitudes between the groups. Further, DLPFC-CEA was inversely associated with MoCA and SOC, and positively associated with EXIT scores in AD. These results suggest increased DLPFC excitability in AD, and its inverse associations with global cognition and executive function. Future studies should examine these findings in larger samples and longitudinally, and could also assess these markers of cortical excitability in relation to other established markers of AD and in response to interventions.

Synaptic Molecular and Neurophysiological Markers Are Independent Predictors of Progression in Alzheimer's Disease

Background:

Cerebrospinal fluid (CSF) neurogranin and quantitative electroencephalography (qEEG) are potential molecular and functional markers of synaptic pathology in Alzheimer’s disease (AD). Synaptic markers have emerged as candidate prognostic indicators of AD since synaptic degeneration was shown to be an early event and the best correlate of cognitive deficits in patients along the disease continuum.

Objective:

The present study investigated the association between CSF neurogranin and qEEG measures as well as their potential to predict clinical deterioration in mild cognitive impairment (MCI) patients.

Methods:

Patients diagnosed with MCI (n = 99) underwent CSF conventional AD biomarkers and neurogranin analysis and resting-state EEG recordings. The study population was further stratified into stable (n = 41) and progressive MCI (n = 31), based on the progression to AD dementia during two years follow-up. qEEG analysis included computation of global field power and global field synchronization in four conventional frequency bands.

Results:

CSF neurogranin levels were associated with theta power and synchronization in the progressive MCI group. CSF neurogranin and qEEG measures were significant predictors of progression to AD dementia, independent of baseline amyloid status in MCI patients. A combination of CSF neurogranin with global EEG power in theta and global EEG synchronization in beta band exhibited the highest classification accuracy as compared to either of these markers alone.

Conclusion:

qEEG and CSF neurogranin are independent predictors of progression to AD dementia in MCI patients. Molecular and neurophysiological synaptic markers may have additive value in a multimodal diagnostic and prognostic approach to dementia.

Quantitative Assessment of Cortical Excitability in Alzheimer's Dementia and Its Association with Clinical Symptoms: A Systematic Review and Meta-Analyses

BACKGROUND

Alzheimer's disease (AD) is characterized by cognitive and neuropsychiatric symptoms (NPS) due to underlying neurodegenerative pathology. Some studies using electroencephalography (EEG) have shown increased epileptiform and epileptic activity in AD.

OBJECTIVE

This review and meta-analyses aims to synthesize the existing evidence for quantitative abnormalities of cortical excitability in AD and their relationship with clinical symptoms.

METHODS

We systematically searched and reviewed publications that quantitatively assessed cortical excitability, using transcranial magnetic stimulation (TMS) resting motor threshold (rMT), active motor threshold (aMT), motor evoked potential (MEP) or directly from the cortex using TMS-EEG via TMS-evoked potential (TEP). We meta-analyzed studies that assessed rMT and aMT using random effects model.

RESULTS

We identified 895 publications out of which 37 were included in the qualitative review and 30 studies using rMT or aMT were included in the meta-analyses. The AD group had reduced rMT (Hedges' g = -0.99, 95%CI [-1.29, -0.68], p <  0.00001) and aMT (Hedges' g = -0.87, 95%CI [-1.50, -0.24], p <  0.00001) as compared with control groups, indicative of higher cortical excitability. Qualitative review found some evidence of increased MEP amplitude, whereas findings related to TEP were inconsistent. There was some evidence supporting an inverse association between cortical excitability and global cognition. No publications reported on the relationship between cortical excitability and NPS.

CONCLUSION

There is strong evidence of increased motor cortex excitability in AD and some evidence of an inverse association between excitability and cognition. Future studies should assess cortical excitability from non-motor areas using TMS-EEG and examine its relationship with cognition and NPS.

EEG Source Network for the Diagnosis of Schizophrenia and the Identification of Subtypes Based on Symptom Severity-A Machine Learning Approach

A precise diagnosis and a comprehensive assessment of symptom severity are important clinical issues in patients with schizophrenia (SZ). We investigated whether electroencephalography (EEG) features obtained from EEG source network analyses could be effectively applied to classify the SZ subtypes based on symptom severity. Sixty-four electrode EEG signals were recorded from 119 patients with SZ (53 males and 66 females) and 119 normal controls (NC, 51 males and 68 females) during resting-state with closed eyes. Brain network features (global and local clustering coefficient and global path length) were calculated from EEG source activities. According to positive, negative, and cognitive/disorganization symptoms, the SZ patients were divided into two groups (high and low) by positive and negative syndrome scale (PANSS). To select features for classification, we used the sequential forward selection (SFS) method. The classification accuracy was evaluated using 10 by 10-fold cross-validation with the linear discriminant analysis (LDA) classifier. The best classification accuracy was 80.66% for estimating SZ patients from the NC group. The best classification accuracy between low and high groups in positive, negative, and cognitive/disorganization symptoms were 88.10%, 75.25%, and 77.78%, respectively. The selected features well-represented the pathological brain regions of SZ. Our study suggested that resting-state EEG network features could successfully classify between SZ patients and the NC, and between low and high SZ groups in positive, negative, and cognitive/disorganization symptoms.

Neurophysiological Markers of Alzheimer's Disease: Quantitative EEG Approach

Currently established and employed biomarkers of Alzheimer's disease (AD) predominantly mirror AD-associated molecular and structural brain changes. While they are necessary for identifying disease-specific neuropathology, they lack a clear and robust relationship with the clinical presentation of dementia; they can be altered in healthy individuals, while they often inadequately mirror the degree of cognitive and functional deficits in affected subjects. There is growing evidence that synaptic loss and dysfunction are early events during the trajectory of AD pathogenesis that best correlate with the clinical symptoms, suggesting measures of brain functional deficits as candidate early markers of AD. Resting-state electroencephalography (EEG) is a widely available and noninvasive diagnostic method that provides direct insight into brain synaptic activity in real time. Quantitative EEG (qEEG) analysis additionally provides information on physiologically meaningful frequency components, dynamic alterations and topography of EEG signal generators, i.e. neuronal signaling. Numerous studies have shown that qEEG measures can detect disruptions in activity, topographical distribution and synchronization of neuronal (synaptic) activity such as generalized EEG slowing, reduced global synchronization and anteriorization of neuronal generators of fast-frequency resting-state EEG activity in patients along the AD continuum. Moreover, qEEG measures appear to correlate well with surrogate markers of AD neuropathology and discriminate between different types of dementia, making them promising low-cost and noninvasive markers of AD. Future large-scale longitudinal clinical studies are needed to elucidate the diagnostic and prognostic potential of qEEG measures as early functional markers of AD on an individual subject level.

EEG time signature in Alzheimer´s disease: Functional brain networks falling apart

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EEG microstate topographies differ between controls and memory clinic patients.

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Microstate parameters differ in a gradient-like manner in SCD, MCI and AD patients.

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Changes in topography of microstate class C correlate with CSF Aβ42 levels.

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Changes in topography of microstate class B correlate with CSF p-tau levels.

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EEG microstates detect early disruption of neurocognitive networks in AD.

Spontaneous mental activity is characterized by dynamic alterations of discrete and stabile brain states called functional microstates that are thought to represent distinct steps of human information processing. Electroencephalography (EEG) directly reflects functioning of brain synapses with a uniquely high temporal resolution, necessary for investigation of brain network dynamics. Since synaptic dysfunction is an early event and best correlate of cognitive status and decline in patients along Alzheimer's disease (AD) continuum, EEG microstates might serve as valuable early markers of AD. The present study investigated differences in EEG microstate topographies and parameters (duration, occurrence and contribution) between a large cohort of healthy elderly (n = 308) and memory clinic patients: subjective cognitive decline (SCD, n = 210); mild cognitive impairment (MCI, n = 230) and AD (n = 197) and how they correlate to conventional cerebrospinal fluid (CSF) markers of AD. Four most representative microstate maps assigned as classes A, B (asymmetrical), C and D (symmetrical) were computed from the resting state EEGs since it has been shown previously that this is sufficient to explain most of the resting state EEG data. Statistically different topography of microstate maps were found between the controls and the patient groups for microstate classes A, C and D. Changes in the topography of microstate class C were associated with the CSF Aβ42 levels, whereas changes in the topography of class B were linked with the CSF p-tau levels. Gradient-like increase in the contribution of asymmetrical (A and B) and gradient-like decrease in the contribution of symmetrical (C and D) maps were observed with the more severe stage of cognitive impairment. Our study demonstrated extensive relationship of resting state EEG microstates topographies and parameters with the stage of cognitive impairment and AD biomarkers. Resting state EEG microstates might therefore serve as functional markers of early disruption of neurocognitive networks in patients along AD continuum.

Analysis of long range dependence in the EEG signals of Alzheimer patients

Alzheimer's disease (AD), a cognitive disability is analysed using a long range dependence parameter, hurst exponent (HE), calculated based on the time domain analysis of the measured electrical activity of brain. The electroencephalogram (EEG) signals of controls and mild cognitive impairment (MCI)-AD patients are evaluated under normal resting and mental arithmetic conditions. Simultaneous low pass filtering and total variation denoising algorithm is employed for preprocessing. Larger values of HE observed in the right hemisphere of the brain for AD patients indicated a decrease in irregularity of the EEG signal under cognitive task conditions. Correlations between HE and the neuropsychological indices are analysed using bivariate correlation analysis. The observed reduction in the values of Auto mutual information and cross mutual information in the local antero-frontal and distant regions in the brain hemisphere indicates the loss of information transmission in MCI-AD patients.

Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework

This study addresses the problem of Alzheimer’s disease (AD) diagnosis with Electroencephalography (EEG). The use of EEG as a tool for AD diagnosis has been widely studied by comparing EEG signals of AD patients only to those of healthy subjects. By contrast, we perform automated EEG diagnosis in a differential diagnosis context using a new database, acquired in clinical conditions, which contains EEG data of 169 patients: subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients, possible Alzheimer’s disease (AD) patients, and patients with other pathologies. We show that two EEG features, namely epoch-based entropy (a measure of signal complexity) and bump modeling (a measure of synchrony) are sufficient for efficient discrimination between these groups. We studied the performance of our methodology for the automatic discrimination of possible AD patients from SCI patients and from patients with MCI or other pathologies. A classification accuracy of 91.6% (specificity = 100%, sensitivity = 87.8%) was obtained when discriminating SCI patients from possible AD patients and 81.8% to 88.8% accuracy was obtained for the 3-class classification of SCI, possible AD and other patients.

Analysis of long range dependence in the EEG signals of Alzheimer patients

Alzheimer's disease (AD), a cognitive disability is analysed using a long range dependence parameter, hurst exponent (HE), calculated based on the time domain analysis of the measured electrical activity of brain. The electroencephalogram (EEG) signals of controls and mild cognitive impairment (MCI)-AD patients are evaluated under normal resting and mental arithmetic conditions. Simultaneous low pass filtering and total variation denoising algorithm is employed for preprocessing. Larger values of HE observed in the right hemisphere of the brain for AD patients indicated a decrease in irregularity of the EEG signal under cognitive task conditions. Correlations between HE and the neuropsychological indices are analysed using bivariate correlation analysis. The observed reduction in the values of Auto mutual information and cross mutual information in the local antero-frontal and distant regions in the brain hemisphere indicates the loss of information transmission in MCI-AD patients.

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

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.

Decreased global field synchronization of multichannel frontal EEG measurements in obsessive-compulsive disorders

Global field synchronization (GFS) quantifies the synchronization level of brain oscillations. The GFS method has been introduced to measure functional synchronization of EEG data in the frequency domain. GFS also detects phase interactions between EEG signals acquired from all of the electrodes. If a considerable amount of local brain neurons has the same phase, these neurons appear to interact with each other. EEG data were received from 17 obsessive-compulsive disorder (OCD) patients and 17 healthy controls (HC). OCD effects on local and large-scale brain circuits were studied. Analysis of the GFS results showed significantly decreased values in the delta and full frequency bands. This research suggests that OCD causes synchronization disconnection in both the frontal and large-scale regions. This may be related to motivational, emotional and cognitive dysfunctions.

Global field synchronization in gamma range of the sleep EEG tracks sleep depth: Artifact introduced by a rectangular analysis window

BACKGROUND

Investigating functional connectivity between brain networks has become an area of interest in neuroscience. Several methods for investigating connectivity have recently been developed, however, these techniques need to be applied with care. We demonstrate that global field synchronization (GFS), a global measure of phase alignment in the EEG as a function of frequency, must be applied considering signal processing principles in order to yield valid results.

NEW METHOD

Multichannel EEG (27 derivations) was analyzed for GFS based on the complex spectrum derived by the fast Fourier transform (FFT). We examined the effect of window functions on GFS, in particular of non-rectangular windows.

RESULTS

Applying a rectangular window when calculating the FFT revealed high GFS values for high frequencies (>15Hz) that were highly correlated (r=0.9) with spectral power in the lower frequency range (0.75-4.5Hz) and tracked the depth of sleep. This turned out to be spurious synchronization. With a non-rectangular window (Tukey or Hanning window) these high frequency synchronization vanished. Both, GFS and power density spectra significantly differed for rectangular and non-rectangular windows.

COMPARISON WITH EXISTING METHOD(S)

Previous papers using GFS typically did not specify the applied window and may have used a rectangular window function. However, the demonstrated impact of the window function raises the question of the validity of some previous findings at higher frequencies.

CONCLUSIONS

We demonstrated that it is crucial to apply an appropriate window function for determining synchronization measures based on a spectral approach to avoid spurious synchronization in the beta/gamma range.

Interactive effect of 5-HTTLPR genotype and age on sources of cortical rhythms in healthy women

This study was aimed to localize the effects of 5-HTTLPR (serotonin-transporter-linked polymorphic region) on the age differences of spontaneous EEG activity in women using neuroimaging analysis sLORETA (Standardized Low Resolution brain Electromagnetic Tomography). DNA samples extracted from cheek swabs and resting-state EEG recorded at 60 standard leads were collected from young (YW, N=86, 18-35years) and older (OW, N=45; 55-80years) healthy women. We have shown that advanced age was associated with increased posterior EEG desynchronization in S'/S'. S' (LG allele was grouped with S alleles owing to its functional equivalence and this group was labeled as S') genotype carriers denoted by decrease of delta - beta1 current source density, and to a lesser extent in L/L homozygotes denoted by decrease in delta activity. In heterozygotes OW, as compared with heterozygotes YW, higher source density estimates of beta1 in frontal and temporal cortex were observed. Age differences were more pronounced in the right hemisphere in S'/S' and L/L carriers and in the left hemisphere in heterozygotes. We also found that in OW, current source density estimates of theta, alpha1, alpha2, alpha3 and beta1 sources in the right occipital lobe were higher in S'/L than in S'/S' carriers. These results may have implications for understanding 5-HTT-dependent variation in the effect of aging on brain activity.

A new EEG synchronization strength analysis method: S-estimator based normalized weighted-permutation mutual information

Synchronization is an important mechanism for understanding information processing in normal or abnormal brains. In this paper, we propose a new method called normalized weighted-permutation mutual information (NWPMI) for double variable signal synchronization analysis and combine NWPMI with S-estimator measure to generate a new method named S-estimator based normalized weighted-permutation mutual information (SNWPMI) for analyzing multi-channel electroencephalographic (EEG) synchronization strength. The performances including the effects of time delay, embedding dimension, coupling coefficients, signal to noise ratios (SNRs) and data length of the NWPMI are evaluated by using Coupled Henon mapping model. The results show that the NWPMI is superior in describing the synchronization compared with the normalized permutation mutual information (NPMI). Furthermore, the proposed SNWPMI method is applied to analyze scalp EEG data from 26 amnestic mild cognitive impairment (aMCI) subjects and 20 age-matched controls with normal cognitive function, who both suffer from type 2 diabetes mellitus (T2DM). The proposed methods NWPMI and SNWPMI are suggested to be an effective index to estimate the synchronization strength.

Multiple characteristics analysis of Alzheimer's electroencephalogram by power spectral density and Lempel-Ziv complexity

To investigate the electroencephalograph (EEG) background activity in patients with Alzheimer's disease (AD), power spectrum density (PSD) and Lempel-Ziv (LZ) complexity analysis are proposed to extract multiple effective features of EEG signals from AD patients and further applied to distinguish AD patients from the normal controls. Spectral analysis based on autoregressive Burg method is first used to quantify the power distribution of EEG series in the frequency domain. Compared with the control group, the relative PSD of AD group is significantly higher in the theta frequency band while lower in the alpha frequency bands. In order to explore the nonlinear information, Lempel-Ziv complexity (LZC) and multi-scale LZC is further applied to all electrodes for the four frequency bands. Analysis results demonstrate that the group difference is significant in the alpha frequency band by LZC and multi-scale LZC analysis. However, the group difference of multi-scale LZC is much more remarkable, manifesting as more channels undergo notable changes, particularly in electrodes O1 and O2 in the occipital area. Moreover, the multi-scale LZC value provided a better classification between the two groups with an accuracy of 85.7 %. In addition, we combine both features of the relative PSD and multi-scale LZC to discriminate AD patients from the normal controls by applying a support vector machine model in the alpha frequency band. It is indicated that the two groups can be clearly classified by the combined feature. Importantly, the accuracy of the classification is higher than that of any one feature, reaching 91.4 %. The obtained results show that analysis of PSD and multi-scale LZC can be taken as a potential comprehensive measure to distinguish AD patients from the normal controls, which may benefit our understanding of the disease.

Quantifying synchrony patterns in the EEG of Alzheimer's patients with linear and non-linear connectivity markers

We analyzed the relation of several synchrony markers in the electroencephalogram (EEG) and Alzheimer's disease (AD) severity as measured by Mini-Mental State Examination (MMSE) scores. The study sample consisted of 79 subjects diagnosed with probable AD. All subjects were participants in the PRODEM-Austria study. Following a homogeneous protocol, the EEG was recorded both in resting state and during a cognitive task. We employed quadratic least squares regression to describe the relation between MMSE and the EEG markers. Factor analysis was used for estimating a potentially lower number of unobserved synchrony factors. These common factors were then related to MMSE scores as well. Most markers displayed an initial increase of EEG synchrony with MMSE scores from 26 to 21 or 20, and a decrease below. This effect was most prominent during the cognitive task and may be owed to cerebral compensatory mechanisms. Factor analysis provided interesting insights in the synchrony structures and the first common factors were related to MMSE scores with coefficients of determination up to 0.433. We conclude that several of the proposed EEG markers are related to AD severity for the overall sample with a wide dispersion for individual subjects. Part of these fluctuations may be owed to fluctuations and day-to-day variability associated with MMSE measurements. Our study provides a systematic analysis of EEG synchrony based on a large and homogeneous sample. The results indicate that the individual markers capture different aspects of EEG synchrony and may reflect cerebral compensatory mechanisms in the early stages of AD.

Hippocampal Non-Theta-Contingent Eyeblink Classical Conditioning: A Model System for Neurobiological Dysfunction

Typical information processing is thought to depend on the integrity of neurobiological oscillations that may underlie coordination and timing of cells and assemblies within and between structures. The 3-7 Hz bandwidth of hippocampal theta rhythm is associated with cognitive processes essential to learning and depends on the integrity of cholinergic, GABAergic, and glutamatergic forebrain systems. Since several significant psychiatric disorders appear to result from dysfunction of medial temporal lobe (MTL) neurochemical systems, preclinical studies on animal models may be an important step in defining and treating such syndromes. Many studies have shown that the amount of hippocampal theta in the rabbit strongly predicts the acquisition rate of classical eyeblink conditioning and that impairment of this system substantially slows the rate of learning and attainment of asymptotic performance. Our lab has developed a brain-computer interface that makes eyeblink training trials contingent upon the explicit presence or absence of hippocampal theta. The behavioral benefit of theta-contingent training has been demonstrated in both delay and trace forms of the paradigm with a two- to fourfold increase in learning speed over non-theta states. The non-theta behavioral impairment is accompanied by disruption of the amplitude and synchrony of hippocampal local field potentials, multiple-unit excitation, and single-unit response patterns dependent on theta state. Our findings indicate a significant electrophysiological and behavioral impact of the pretrial state of the hippocampus that suggests an important role for this MTL system in associative learning and a significant deleterious impact in the absence of theta. Here, we focus on the impairments in the non-theta state, integrate them into current models of psychiatric disorders, and suggest how improvement in our understanding of neurobiological oscillations is critical for theories and treatment of psychiatric pathology.

Using static and dynamic canonical correlation coefficients as quantitative EEG markers for Alzheimer's disease severity

We analyzed the relation between Alzheimer's disease (AD) severity as measured by Mini-Mental State Examination (MMSE) scores and quantitative electroencephalographic (qEEG) markers that were derived from canonical correlation analysis. This allowed an investigation of EEG synchrony between groups of EEG channels. In this study, we applied the data from 79 participants in the multi-centric cohort study PRODEM-Austria with probable AD. Following a homogeneous protocol, the EEG was recorded both in resting state and during a cognitive task. A quadratic regression model was used to describe the relation between MMSE and the qEEG synchrony markers. This relation was most significant in the δ and θ frequency bands in resting state, and between left-hemispheric central, temporal and parietal channel groups during the cognitive task. Here, the MMSE explained up to 40% of the qEEG marker's variation. QEEG markers showed an ambiguous trend, i.e. an increase of EEG synchrony in the initial stage of AD (MMSE>20) and a decrease in later stages. This effect could be caused by compensatory brain mechanisms. We conclude that the proposed qEEG markers are closely related to AD severity. Despite the ambiguous trend and the resulting diagnostic ambiguity, the qEEG markers could provide aid in the diagnostics of early-stage AD.

Eyes-closed task-free electroencephalography in clinical trials for Alzheimer's disease: an emerging method based upon brain dynamics

Electroencephalography (EEG) is a longstanding technique to measure electrical brain activity and thereby an indirect measure of synaptic activity. Synaptic dysfunction accompanies Alzheimer’s disease (AD) and EEG can be regarded as a potentially useful biomarker in this disease. Lately, emerging analysis techniques of time series have become available for EEG, such as functional connectivity and network analysis, which have increased the possibilities for use in AD clinical trials. In this review, we report the EEG changes in the course of AD, including slowing of the EEG oscillations, decreased functional connectivity in the higher-frequency bands, and decline in optimal functional network organization. We discuss the use of EEG in clinical trials and provide directions for future research.

Decreased coherence and functional connectivity of electroencephalograph in Alzheimer's disease

In this paper, we investigate the abnormalities of electroencephalograph (EEG) signals in the Alzheimer's disease (AD) by analyzing 16-scalp electrodes EEG signals and make a comparison with the normal controls. Coherence is introduced to measure the pair-wise normalized linear synchrony and functional correlations between two EEG signals in different frequency domains, and graph analysis is further used to investigate the influence of AD on the functional connectivity of human brain. Data analysis results show that, compared with the control group, the pair-wise coherence of AD group is significantly decreased, especially for the theta and alpha frequency bands in the frontal and parieto-occipital regions. Furthermore, functional connectivity among different brain regions is reconstructed based on EEG, which exhibit obvious small-world properties. Graph analysis demonstrates that the local functional connections between regions for AD decrease. In addition, it is found that small-world properties of AD networks are largely weakened, by calculating its average path lengths, clustering coefficients, global efficiency, local efficiency, and small-worldness. The obtained results show that both pair-wise coherence and functional network can be taken as effective measures to distinguish AD patients from the normal, which may benefit our understanding of the disease.

Disruptions in small-world cortical functional connectivity network during an auditory oddball paradigm task in patients with schizophrenia

P300 deficits in patients with schizophrenia have previously been investigated using EEGs recorded during auditory oddball tasks. However, small-world cortical functional networks during auditory oddball tasks and their relationships with symptom severity scores in schizophrenia have not yet been investigated. In this study, the small-world characteristics of source-level functional connectivity networks of EEG responses elicited by an auditory oddball paradigm were evaluated using two representative graph-theoretical measures, clustering coefficient and path length. EEG signals from 34 patients with schizophrenia and 34 healthy controls were recorded while each subject was asked to attend to oddball tones. The results showed reduced clustering coefficients and increased path lengths in patients with schizophrenia, suggesting that the small-world functional network is disrupted in patients with schizophrenia. In addition, the negative and cognitive symptom components of positive and negative symptom scales were negatively correlated with the clustering coefficient and positively correlated with path length, demonstrating that both indices are indicators of symptom severity in patients with schizophrenia. Our study results suggest that disrupted small-world characteristics are potential biomarkers for patients with schizophrenia.

Compensating for thalamocortical synaptic loss in Alzheimer's disease

The study presents a thalamocortical network model which oscillates within the alpha frequency band (8-13 Hz) as recorded in the wakeful relaxed state with closed eyes to study the neural causes of abnormal oscillatory activity in Alzheimer's disease (AD). Incorporated within the model are various types of cortical excitatory and inhibitory neurons, recurrently connected to thalamic and reticular thalamic regions with the ratios and distances derived from the mammalian thalamocortical system. The model is utilized to study the impacts of four types of connectivity loss on the model's spectral dynamics. The study focuses on investigating degeneration of corticocortical, thalamocortical, corticothalamic, and corticoreticular couplings, with an emphasis on the influence of each modeled case on the spectral output of the model. Synaptic compensation has been included in each model to examine the interplay between synaptic deletion and compensation mechanisms, and the oscillatory activity of the network. The results of power spectra and event related desynchronization/synchronization (ERD/S) analyses show that the dynamics of the thalamic and cortical oscillations are significantly influenced by corticocortical synaptic loss. Interestingly, the patterns of changes in thalamic spectral activity are correlated with those in the cortical model. Similarly, the thalamic oscillatory activity is diminished after partial corticothalamic denervation. The results suggest that thalamic atrophy is a secondary pathology to cortical shrinkage in Alzheimer's disease. In addition, this study finds that the inhibition from neurons in the thalamic reticular nucleus (RTN) to thalamic relay (TCR) neurons plays a key role in regulating thalamic oscillations; disinhibition disrupts thalamic oscillatory activity even though TCR neurons are more depolarized after being released from RTN inhibition. This study provides information that can be explored experimentally to further our understanding on the neurodegeneration associated with AD pathology.

Electroencephalogram global field synchronization analysis: a new method for assessing the progress of cognitive decline in Alzheimer's disease

Alzheimer's disease (AD) is the most common cause of dementia. Global field synchronization (GFS) can measure functional synchronization in frequency-domain electroencephalogram (EEG) data. The aim of this study is to explore GFS values and its clinical significance for severity of cognitive decline in AD. EEGs were recorded from 37 AD patients and 37 age-matched healthy individuals. GFS values were calculated in delta, theta, alpha, beta 1, beta 2, beta 3, gamma, and full frequency bands. The Montreal Cognitive Assessment (MoCA) and Clinical Dementia Rating scale (CDR) were employed to assess symptom severity in AD patients. Correlation analysis, clustering analysis, and concordance analysis were performed to analyze the relationship between GFS values and MoCA scores in AD patients. GFS values of the beta 1, beta 2, beta 3, and full bands were lower in AD patients than in healthy individuals, and positively correlated with MoCA and CDR scores in the combined group (AD patients and healthy individuals). GFS values were positively correlated with MoCA socres in 3 beta bands and full bands, and with CDR scores in the delta band. There was a good concordance between K-means clustering algorithm calculating of GFS values and MoCA scoring (κ = .913, P < .001). In conclusion, the present results indicated that GFS can serve as an indicator of cognitive decline or impairment in AD patients. Furthermore, the GFS method of EEG holds considerable promise to distinguish mild cognitive impairment from serious cognitive impairment in patients with AD.

A global coupling index of multivariate neural series with application to the evaluation of mild cognitive impairment

Recently, the synchronization between neural signals has been widely used as a key indicator of brain function. To understand comprehensively the effect of synchronization on the brain function, accurate computation of the synchronization strength among multivariate neural series from the whole brain is necessary. In this study, we proposed a method named global coupling index (GCI) to estimate the synchronization strength of multiple neural signals. First of all, performance of the GCI method was evaluated by analyzing simulated EEG signals from a multi-channel neural mass model, including the effects of the frequency band, the coupling coefficient, and the signal noise ratio. Then, the GCI method was applied to analyze the EEG signals from 12 mild cognitive impairment (MCI) subjects and 12 normal controls (NC). The results showed that GCI method had two major advantages over the global synchronization index (GSI) or S-estimator. Firstly, simulation data showed that the GCI method provided both a more robust result on the frequency band and a better performance on the coupling coefficients. Secondly, the actual EEG data demonstrated that GCI method was more sensitive in differentiating the MCI from control subjects, in terms of the global synchronization strength of neural series of specific alpha, beta1 and beta2 frequency bands. Hence, it is suggested that GCI is a better method over GSI and S-estimator to estimate the synchronization strength of multivariate neural series for predicting the MCI from the whole brain EEG recordings.

The effects of automated artifact removal algorithms on electroencephalography-based Alzheimer's disease diagnosis

Over the last decade, electroencephalography (EEG) has emerged as a reliable tool for the diagnosis of cortical disorders such as Alzheimer's disease (AD). EEG signals, however, are susceptible to several artifacts, such as ocular, muscular, movement, and environmental. To overcome this limitation, existing diagnostic systems commonly depend on experienced clinicians to manually select artifact-free epochs from the collected multi-channel EEG data. Manual selection, however, is a tedious and time-consuming process, rendering the diagnostic system “semi-automated.” Notwithstanding, a number of EEG artifact removal algorithms have been proposed in the literature. The (dis)advantages of using such algorithms in automated AD diagnostic systems, however, have not been documented; this paper aims to fill this gap. Here, we investigate the effects of three state-of-the-art automated artifact removal (AAR) algorithms (both alone and in combination with each other) on AD diagnostic systems based on four different classes of EEG features, namely, spectral, amplitude modulation rate of change, coherence, and phase. The three AAR algorithms tested are statistical artifact rejection (SAR), blind source separation based on second order blind identification and canonical correlation analysis (BSS-SOBI-CCA), and wavelet enhanced independent component analysis (wICA). Experimental results based on 20-channel resting-awake EEG data collected from 59 participants (20 patients with mild AD, 15 with moderate-to-severe AD, and 24 age-matched healthy controls) showed the wICA algorithm alone outperforming other enhancement algorithm combinations across three tasks: diagnosis (control vs. mild vs. moderate), early detection (control vs. mild), and disease progression (mild vs. moderate), thus opening the doors for fully-automated systems that can assist clinicians with early detection of AD, as well as disease severity progression assessment.

The effect of souvenaid on functional brain network organisation in patients with mild Alzheimer's disease: a randomised controlled study

Background

Synaptic loss is a major hallmark of Alzheimer’s disease (AD). Disturbed organisation of large-scale functional brain networks in AD might reflect synaptic loss and disrupted neuronal communication. The medical food Souvenaid, containing the specific nutrient combination Fortasyn Connect, is designed to enhance synapse formation and function and has been shown to improve memory performance in patients with mild AD in two randomised controlled trials.

Objective

To explore the effect of Souvenaid compared to control product on brain activity-based networks, as a derivative of underlying synaptic function, in patients with mild AD.

Design

A 24-week randomised, controlled, double-blind, parallel-group, multi-country study.

Participants

179 drug-naïve mild AD patients who participated in the Souvenir II study.

Intervention

Patients were randomised 1∶1 to receive Souvenaid or an iso-caloric control product once daily for 24 weeks.

Outcome

In a secondary analysis of the Souvenir II study, electroencephalography (EEG) brain networks were constructed and graph theory was used to quantify complex brain structure. Local brain network connectivity (normalised clustering coefficient gamma) and global network integration (normalised characteristic path length lambda) were compared between study groups, and related to memory performance.

Results

The network measures in the beta band were significantly different between groups: they decreased in the control group, but remained relatively unchanged in the active group. No consistent relationship was found between these network measures and memory performance.

Conclusions

The current results suggest that Souvenaid preserves the organisation of brain networks in patients with mild AD within 24 weeks, hypothetically counteracting the progressive network disruption over time in AD. The results strengthen the hypothesis that Souvenaid affects synaptic integrity and function. Secondly, we conclude that advanced EEG analysis, using the mathematical framework of graph theory, is useful and feasible for assessing the effects of interventions.

Trial registration

Dutch Trial Register NTR1975.

Cognitive and physical training for the elderly: evaluating outcome efficacy by means of neurophysiological synchronization

Recent neuroscientific research has demonstrated that both healthy and pathological aging induces alterations in the co-operative capacity of neuronal populations in the brain. Both compensatory and neurodegenerative mechanisms contribute to neurophysiological synchronization patterns, which provide a valuable marker for age-related cognitive decline. In this study, we propose that neuroplasticity-based training may facilitate coherent interaction of distant brain regions and consequently enhance cognitive performance in elderly people. If this is true, this would make neurophysiological synchronization a valid outcome measure to assess the efficacy of non-pharmacological interventions to prevent or delay age-related cognitive decline. The present study aims at providing an objective, synchronization-based tool to assess cognitive and/or physical interventions, adopting the notion of Relative Wavelet Entropy. This mathematical model employs a robust and parameter-free synchronization metric. By using data mining techniques, a distance value was computed for all participants so as to quantify the proximity of their individual profile to the mean group synchronization increase. In support of our hypothesis, results showed a significant increase in synchronization, for four electrode pairs, in the intervention group as compared to the active control group. It is concluded that the novel introduction of neurophysiological synchronization features could be used as a valid and reliable outcome measure; while the distance-based analysis could provide a reliable means of evaluating individual benefits.

Resting-state network disruption and APOE genotype in Alzheimer's disease: a lagged functional connectivity study

Background

The apolipoprotein E epsilon 4 (APOE-4) is associated with a genetic vulnerability to Alzheimer's disease (AD) and with AD-related abnormalities in cortical rhythms. However, it is unclear whether APOE-4 is linked to a specific pattern of intrinsic functional disintegration of the brain after the development of the disease or during its different stages. This study aimed at identifying spatial patterns and effects of APOE genotype on resting-state oscillations and functional connectivity in patients with AD, using a physiological connectivity index called “lagged phase synchronization”.

Methodology/Principal Findings

Resting EEG was recorded during awake, eyes-closed state in 125 patients with AD and 60 elderly controls. Source current density and functional connectivity were determined using eLORETA. Patients with AD exhibited reduced parieto-occipital alpha oscillations compared with controls, and those carrying the APOE-4 allele had reduced alpha activity in the left inferior parietal and temporo-occipital cortex relative to noncarriers. There was a decreased alpha2 connectivity pattern in AD, involving the left temporal and bilateral parietal cortex. Several brain regions exhibited increased lagged phase synchronization in low frequencies, specifically in the theta band, across and within hemispheres, where temporal lobe connections were particularly compromised. Areas with abnormal theta connectivity correlated with cognitive scores. In patients with early AD, we found an APOE-4-related decrease in interhemispheric alpha connectivity in frontal and parieto-temporal regions.

Conclusions/Significance

In addition to regional cortical dysfunction, as indicated by abnormal alpha oscillations, there are patterns of functional network disruption affecting theta and alpha bands in AD that associate with the level of cognitive disturbance or with the APOE genotype. These functional patterns of nonlinear connectivity may potentially represent neurophysiological or phenotypic markers of AD, and aid in early detection of the disorder.

Employing neuronal networks to investigate the pathophysiological basis of abnormal cortical oscillations in Alzheimer's disease

This paper describes an investigation into the pathophysiological causes of abnormal cortical oscillations in Alzheimer's disease (AD) using two heterogeneous neuronal network models. The effect of excitatory circuit disruption on the beta band power (13-30 Hz) using a conductance-based network model of 200 neurons is assessed. Then, the neural correlates of abnormal cortical oscillations in different frequency bands based on a larger network model of 1000 neurons consisting of different types of cortical neurons is also analyzed. The results show that, despite the heterogeneity of the network models, the beta band power is significantly affected by excitatory neural and synaptic loss. Secondly, the results of modeling a functional impairment in the excitatory circuit shows that beta band power exhibits the most decrease compared with other bands. Previous biological experiments on different types of cultural excitatory neurons show that cortical neuronal death is mediated by dysfunctional ionic behavior that might specifically contribute to the pathogenesis of p-amyloid peptide (Ap)-induced neuronal death in AD. Our study also shows that beta band power was the first affected component when the modeled excitatory circuit begins to lose neurons and synapses. Alpha (8-12 Hz), gamma (30-50 Hz) and Full frequency (1-70 Hz) band power are affected in a later stage when more severe synaptic loss occurs.

Investigating the neural correlates of pathological cortical networks in Alzheimer's disease using heterogeneous neuronal models

This paper describes an investigation into the pathophysiological causes of abnormal cortical oscillations in Alzheimer's disease (AD) using two heterogeneous neuronal network models. The effect of excitatory circuit disruption on the beta band power (13-30 Hz) using a conductance-based network model of 200 neurons is assessed. Then, the neural correlates of abnormal cortical oscillations in different frequency bands based on a larger network model of 1000 neurons consisting of different types of cortical neurons are also analyzed. EEG studies in AD patients have shown that beta band power (13-30 Hz) decreased in the early stages of the disease with a parallel increase in theta band power (4-7 Hz). This abnormal change progresses with the later stages of the disease but with decreased power spectra in other fast frequency bands plus an increase in delta band power (1-3 Hz). Our results show that, despite the heterogeneity of the network models, the beta band power is significantly affected by excitatory neural and synaptic loss. Second, the results of modeling a functional impairment in the excitatory circuit shows that beta band power exhibits the most decrease compared with other bands. Previous biological experiments on different types of cultural excitatory neurons show that cortical neuronal death is mediated by dysfunctional ionic behavior that might specifically contribute to the pathogenesis of β-amyloid-peptide-induced neuronal death in AD. Our study also shows that beta band power was the first affected component when the modeled excitatory circuit begins to lose neurons and synapses.

EEG theta and alpha reactivity on opening the eyes in the diagnosis of Alzheimer's disease

The objective of this study was to evaluate the contribution of EEG theta and alpha reactivity on opening the eyes, in the diagnosis of slight and moderate Alzheimer's disease (AD). Thirty four patients with AD and a control group of 30 individuals were studied, all being assessed using a neurological evaluation, CERAD neuropsychological battery (consortium to establish a registry for Alzheimer's disease), incorporating the Mini Mental State Examination (MMSE), Clinical Dementia Rating (CDR) and a qEEG analysis of the absolute band power at rest, with the eyes both open and closed. The theta and alpha reactivity indices were calculated on opening the eyes, defined from the relationship between the absolute powers in the respective bands in the periods with the eyes open and with them closed, the quotient of the relationship between the alpha and theta indices, the alpha/theta ratio, was also calculated. Multiple regression models were used to determine the accuracy in discriminating between the AD and control groups. A regression model using only cognitive data provided an accuracy of 92.2%, whereas a regression model combining cognitive data and qEEG measurements provided an accuracy of 95.3% in the classification between AD and the controls. The variable for the qEEG was the left hemisphere alpha/theta index, since the other parameters were shown to be inferior with respect to the clinical data in the regression analysis. The integrated study of the theta and alpha reactivity indices on opening the eyes and the alpha/theta index, was shown to be a useful approach in qEEG in the evaluation of AD and should be evaluated with larger samples and with other data analysis methods, with the aim of increasing the accuracy.