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

Exploring the neuromagnetic signatures of cognitive decline from mild cognitive impairment to Alzheimer's disease dementia

BACKGROUND

Developing non-invasive and affordable biomarkers to detect Alzheimer's disease (AD) at a prodromal stage is essential, particularly in the context of new disease-modifying therapies. Mild cognitive impairment (MCI) is a critical stage preceding dementia, but not all patients with MCI will progress to AD. This study explores the potential of magnetoencephalography (MEG) to predict cognitive decline from MCI to AD dementia.

METHODS

We analysed resting-state MEG data from the BioFIND dataset including 117 patients with MCI among whom 64 developed AD dementia (AD progression), while 53 remained cognitively stable (stable MCI), using spectral analysis. Logistic regression models estimated the additive explanation of selected clinical, MEG, and MRI variables for AD progression risk. We then built a high-dimensional classification model to combine all modalities and variables of interest.

FINDINGS

MEG 16-38Hz spectral power, particularly over parieto-occipital magnetometers, was significantly reduced in the AD progression group. In logistic regression models, decreased MEG 16-38Hz spectral power and reduced hippocampal volume/total grey matter ratio on MRI were independently linked to higher AD progression risk. The data-driven classification model confirmed, among other factors, the complementary information of MEG covariance (AUC = 0.74, SD = 0.13) and MRI cortical volumes (AUC = 0.77, SD = 0.14) to predict AD progression. Combining all inputs led to markedly improved classification scores (AUC = 0.81, SD = 0.12).

INTERPRETATION

These findings highlight the potential of spectral power and covariance as robust non-invasive electrophysiological biomarkers to predict AD progression, complementing other diagnostic measures, including cognitive scores and MRI.

FUNDING

This work was supported by: Fondation pour la Recherche Médicale (grant FDM202106013579).

Studying the Alzheimer's disease continuum using EEG and fMRI in single-modality and multi-modality settings

Alzheimer's disease (AD) is a biological, clinical continuum that covers the preclinical, prodromal, and clinical phases of the disease. Early diagnosis and identification of the stages of Alzheimer's disease (AD) are crucial in clinical practice. Ideally, biomarkers should reflect the underlying process (pathological or otherwise), be reproducible and non-invasive, and allow repeated measurements over time. However, the currently known biomarkers for AD are not suitable for differentiating the stages and predicting the trajectory of disease progression. Some objective parameters extracted using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are widely applied to diagnose the stages of the AD continuum. While electroencephalography (EEG) has a high temporal resolution, fMRI has a high spatial resolution. Combined EEG and fMRI (EEG-fMRI) can overcome single-modality drawbacks and obtain multi-dimensional information simultaneously, and it can help explore the hemodynamic changes associated with the neural oscillations that occur during information processing. This technique has been used in the cognitive field in recent years. This review focuses on the different techniques available for studying the AD continuum, including EEG and fMRI in single-modality and multi-modality settings, and the possible future directions of AD diagnosis using EEG-fMRI.

A Real-Time Neurophysiologic Stress Test for the Aging Brain: Novel Perioperative and ICU Applications of EEG in Older Surgical Patients

As of 2022, individuals age 65 and older represent approximately 10% of the global population [1], and older adults make up more than one third of anesthesia and surgical cases in developed countries [2, 3]. With approximately > 234 million major surgical procedures performed annually worldwide [4], this suggests that > 70 million surgeries are performed on older adults across the globe each year. The most common postoperative complications seen in these older surgical patients are perioperative neurocognitive disorders including postoperative delirium, which are associated with an increased risk for mortality [5], greater economic burden [6, 7], and greater risk for developing long-term cognitive decline [8] such as Alzheimer's disease and/or related dementias (ADRD). Thus, anesthesia, surgery, and postoperative hospitalization have been viewed as a biological "stress test" for the aging brain, in which postoperative delirium indicates a failed stress test and consequent risk for later cognitive decline (see Fig. 3). Further, it has been hypothesized that interventions that prevent postoperative delirium might reduce the risk of long-term cognitive decline. Recent advances suggest that rather than waiting for the development of postoperative delirium to indicate whether a patient "passed" or "failed" this stress test, the status of the brain can be monitored in real-time via electroencephalography (EEG) in the perioperative period. Beyond the traditional intraoperative use of EEG monitoring for anesthetic titration, perioperative EEG may be a viable tool for identifying waveforms indicative of reduced brain integrity and potential risk for postoperative delirium and long-term cognitive decline. In principle, research incorporating routine perioperative EEG monitoring may provide insight into neuronal patterns of dysfunction associated with risk of postoperative delirium, long-term cognitive decline, or even specific types of aging-related neurodegenerative disease pathology. This research would accelerate our understanding of which waveforms or neuronal patterns necessitate diagnostic workup and intervention in the perioperative period, which could potentially reduce postoperative delirium and/or dementia risk. Thus, here we present recommendations for the use of perioperative EEG as a "predictor" of delirium and perioperative cognitive decline in older surgical patients.

Electroencephalography and mild cognitive impairment research: A scoping review and bibliometric analysis (ScoRBA)

Mild cognitive impairment (MCI) is often considered a precursor to Alzheimer's disease (AD) and early diagnosis may help improve treatment effectiveness. To identify accurate MCI biomarkers, researchers have utilized various neuroscience techniques, with electroencephalography (EEG) being a popular choice due to its low cost and better temporal resolution. In this scoping review, we analyzed 2310 peer-reviewed articles on EEG and MCI between 2012 and 2022 to track the research progress in this field. Our data analysis involved co-occurrence analysis using VOSviewer and a Patterns, Advances, Gaps, Evidence of Practice, and Research Recommendations (PAGER) framework. We found that event-related potentials (ERP), EEG, epilepsy, quantitative EEG (QEEG), and EEG-based machine learning were the primary research themes. The study showed that ERP/EEG, QEEG, and EEG-based machine learning frameworks provide high-accuracy detection of seizure and MCI. These findings identify the main research themes in EEG and MCI and suggest promising avenues for future research in this field.

Toward the Identification of Neurophysiological Biomarkers for Alzheimer's Disease in Down Syndrome: A Potential Role for Cross-Frequency Phase-Amplitude Coupling Analysis

Cross-frequency coupling (CFC) mechanisms play a central role in brain activity. Pathophysiological mechanisms leading to many brain disorders, such as Alzheimer's disease (AD), may produce unique patterns of brain activity detectable by electroencephalography (EEG). Identifying biomarkers for AD diagnosis is also an ambition among research teams working in Down syndrome (DS), given the increased susceptibility of people with DS to develop early-onset AD (DS-AD). Here, we review accumulating evidence that altered theta-gamma phase-amplitude coupling (PAC) may be one of the earliest EEG signatures of AD, and therefore may serve as an adjuvant tool for detecting cognitive decline in DS-AD. We suggest that this field of research could potentially provide clues to the biophysical mechanisms underlying cognitive dysfunction in DS-AD and generate opportunities for identifying EEG-based biomarkers with diagnostic and prognostic utility in DS-AD.

Long-Term Changes in Brain Connectivity Reflected in Quantitative Electrophysiology of Symptomatic Former National Football League Players

Exposure to repetitive head impacts (RHI) has been associated with long-term disturbances in cognition, mood, and neurobehavioral dysregulation, and reflected in neuroimaging. Distinct patterns of changes in quantitative features of the brain electrical activity (quantitative electroencephalogram [qEEG]) have been demonstrated to be sensitive to brain changes seen in neurodegenerative disorders and in traumatic brain injuries (TBI). While these qEEG biomarkers are highly sensitive at time of injury, the long-term effects of exposure to RHI on brain electrical activity are relatively unexplored. Ten minutes of eyes closed resting EEG data were collected from a frontal and frontotemporal electrode montage (BrainScope Food and Drug Administration-cleared EEG acquisition device), as well as assessments of neuropsychiatric function and age of first exposure (AFE) to American football. A machine learning methodology was used to derive a qEEG-based algorithm to discriminate former National Football League (NFL) players (n = 87, 55.40 ± 7.98 years old) from same-age men without history of RHI (n = 68, 54.94 ± 7.63 years old), and a second algorithm to discriminate former players with AFE <12 years (n = 33) from AFE ≥12 years (n = 54). The algorithm separating NFL retirees from controls had a specificity = 80%, a sensitivity = 60%, and an area under curve (AUC) = 0.75. Within the NFL population, the algorithm separating AFE <12 from AFE ≥12 resulted in a sensitivity = 76%, a specificity = 52%, and an AUC = 0.72. The presence of a profile of EEG abnormalities in the NFL retirees and in those with younger AFE includes features associated with neurodegeneration and the disruption of neuronal transmission between regions. These results support the long-term consequences of RHI and the potential of EEG as a biomarker of persistent changes in brain function.

The development of an automated machine learning pipeline for the detection of Alzheimer's Disease

Although Alzheimer's disease is the most prevalent form of dementia, there are no treatments capable of slowing disease progression. A lack of reliable disease endpoints and/or biomarkers contributes in part to the absence of effective therapies. Using machine learning to analyze EEG offers a possible solution to overcome many of the limitations of current diagnostic modalities. Here we develop a logistic regression model with an accuracy of 81% that addresses many of the shortcomings of previous works. To our knowledge, no other study has been able to solve the following problems simultaneously: (1) a lack of automation and unbiased removal of artifacts, (2) a dependence on a high level of expertise in data pre-processing and ML for non-automated processes, (3) the need for very large sample sizes and accurate EEG source localization using high density systems, (4) and a reliance on black box ML approaches such as deep neural nets with unexplainable feature selection. This study presents a proof-of-concept for an automated and scalable technology that could potentially be used to diagnose AD in clinical settings as an adjunct to conventional neuropsychological testing, thus enhancing efficiency, reproducibility, and practicality of AD diagnosis.

Nonlinear Phase Synchronization Analysis of EEG Signals in Amnesic Mild Cognitive Impairment with Type 2 Diabetes Mellitus

Studying the nonlinear synchronization of electroencephalogram (EEG) in type 2 diabetic mellitus (T2DM) to find the EEG characteristics related to cognitive impairment is beneficial to the early prevention and diagnosis of mild cognitive impairment. Correlation between probabilities of recurrence (CPR) is a nonlinear phase synchronization method based on recurrence and recurrence probability, which had shown its superiority in detecting epilepsy. In this study, CPR method was used for the first time to analyze the synchronization of eye-closed resting EEG signals with T2DM. The 27 participants were divided into amnesic mild cognitive impairment (aMCI) group (17 case) and control group (10 cases with age and education matched). The CPR values in two groups were statistically analyzed by Mann-Whitney U test, and the correlation between EEG synchronization and cognitive function was studied by Spearman's correlation. The results showed that aMCI group had lower CPR values at each electrode pair than control group, and two groups had decreased CPR values with the increase of the spatial distance of the electrode pair in inter hemispheric. The CPR values were significantly different in frontal, parietal and temporal regions in intra hemispheric between two groups. The CPR values of C3-F7, F4-C4 and FP2-T6 were significantly positively correlated with the MOCA values. This study showed that the synchronization values of EEG signals obtained by the CPR method were significantly different between aMCI and control group, and they were the EEG characteristics associated with cognitive impairment in T2DM.

Measures of resting state EEG rhythms for clinical trials in Alzheimer's disease: Recommendations of an expert panel

The Electrophysiology Professional Interest Area (EPIA) and Global Brain Consortium endorsed recommendations on candidate electroencephalography (EEG) measures for Alzheimer's disease (AD) clinical trials. The Panel reviewed the field literature. As most consistent findings, AD patients with mild cognitive impairment and dementia showed abnormalities in peak frequency, power, and "interrelatedness" at posterior alpha (8-12 Hz) and widespread delta (< 4 Hz) and theta (4-8 Hz) rhythms in relation to disease progression and interventions. The following consensus statements were subscribed: (1) Standardization of instructions to patients, resting state EEG (rsEEG) recording methods, and selection of artifact-free rsEEG periods are needed; (2) power density and "interrelatedness" rsEEG measures (e.g., directed transfer function, phase lag index, linear lagged connectivity, etc.) at delta, theta, and alpha frequency bands may be use for stratification of AD patients and monitoring of disease progression and intervention; and (3) international multisectoral initiatives are mandatory for regulatory purposes.

The Power of EEG to Predict Conversion from Mild Cognitive Impairment and Subjective Cognitive Decline to Dementia

INTRODUCTION

The aim of this study was to examine if quantitative electroencephalography (qEEG) using the statistical pattern recognition (SPR) method could predict conversion to dementia in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI).

METHODS

From 5 Nordic memory clinics, we included 47 SCD patients, 99 MCI patients, and 67 healthy controls. EEGs analyzed with the SPR method together with clinical data recorded at baseline were evaluated. The patients were followed up for a mean of 62.5 (SD 17.6) months and reexamined.

RESULTS

Of 200 participants with valid clinical information, 70 had converted to dementia, and 52 had developed Alzheimer's disease. Receiver-operating characteristic analysis of the EEG results as defined by a dementia index (DI) ranging from 0 to 100 revealed that the area under the curve was 0.78 (95% CI 0.70-0.85), corresponding to a sensitivity of 71%, specificity of 69%, and accuracy of 69%. A logistic regression analysis showed that by adding results of a cognitive test at baseline to the EEG DI, accuracy could improve.

CONCLUSION

We conclude that applying qEEG using the automated SPR method can be helpful in identifying patients with SCD and MCI that have a high risk of converting to dementia over a 5-year period. As the discriminant power of the method is of moderate degree, it should be used in addition to routine diagnostic methods.

Proof of principle: Preoperative cognitive reserve and brain integrity predicts intra-individual variability in processed EEG (Bispectral Index Monitor) during general anesthesia

BACKGROUND

Preoperative cognitive reserve and brain integrity may explain commonly observed intraoperative fluctuations seen on a standard anesthesia depth monitor used ubiquitously in operating rooms throughout the nation. Neurophysiological variability indicates compromised regulation and organization of neural networks. Based on theories of neuronal integrity changes that accompany aging, we assessed the relative contribution of: 1) premorbid cognitive reserve, 2) current brain integrity (gray and white matter markers of neurodegenerative disease), and 3) current cognition (specifically domains of processing speed/working memory, episodic memory, and motor function) on intraoperative neurophysiological variability as measured from a common intraoperative tool, the Bispectral Index Monitor (BIS).

METHODS

This sub-study included participants from a parent study of non-demented older adults electing unilateral Total Knee Arthroplasty (TKA) with the same surgeon and anesthesia protocol, who also completed a preoperative neuropsychological assessment and preoperative 3T brain magnetic resonance imaging scan. Left frontal two-channel derived EEG via the BIS was acquired preoperatively (un-medicated and awake) and continuously intraoperatively with time from tourniquet up to tourniquet down. Data analyses used correlation and regression modeling.

RESULTS

Fifty-four participants met inclusion criteria for the sub-study. The mean (SD) age was 69.5 (7.4) years, 54% were male, 89% were white, and the mean (SD) American Society of Anesthesiologists score was 2.76 (0.47). We confirmed that brain integrity positively and significantly associated with each of the cognitive domains of interest. EEG intra-individual variability (squared deviation from the mean BIS value between tourniquet up and down) was significantly correlated with cognitive reserve (r = -.40, p = .003), brain integrity (r = -.37, p = .007), and a domain of processing speed/working memory (termed cognitive efficiency; r = -.31, p = .021). Hierarchical regression models that sequentially included age, propofol bolus dose, cognitive reserve, brain integrity, and cognitive efficiency found that intraoperative propofol bolus dose (p = .001), premorbid cognitive reserve (p = .008), and current brain integrity (p = .004) explained a significant portion of intraoperative intra-individual variability from the BIS monitor.

CONCLUSIONS

Older adults with higher premorbid reserve and less brain disease were more stable intraoperatively on a depth of anesthesia monitor. Researchers need to replicate findings within larger cohorts and other surgery types.

Permutation Jaccard Distance-Based Hierarchical Clustering to Estimate EEG Network Density Modifications in MCI Subjects

In this paper, a novel electroencephalographic (EEG)-based method is introduced for the quantification of brain-electrical connectivity changes over a longitudinal evaluation of mild cognitive impaired (MCI) subjects. In the proposed method, a dissimilarity matrix is constructed by estimating the coupling strength between every pair of EEG signals, Hierarchical clustering is then applied to group the related electrodes according to the dissimilarity estimated on pairs of EEG recordings. Subsequently, the connectivity density of the electrodes network is calculated. The technique was tested over two different coupling strength descriptors: wavelet coherence (WC) and permutation Jaccard distance (PJD), a novel metric of coupling strength between time series introduced in this paper. Twenty-five MCI patients were enrolled within a follow-up program that consisted of two successive evaluations, at time T0 and at time T1, three months later. At T1, four subjects were diagnosed to have converted to Alzheimer's Disease (AD). When applying the PJD-based method, the converted patients exhibited a significantly increased PJD (p < 0.05), i.e., a reduced overall coupling strength, specifically in delta and θ bands and in the overall range (0.5-32 Hz). In addition, in contrast to stable MCI patients, converted patients exhibited a network density reduction in every subband (delta, θ, alpha, and beta). When WC was used as coupling strength descriptor, the method resulted in a less sensitive and specific outcome. The proposed method, mixing nonlinear analysis to a machine learning approach, appears to provide an objective evaluation of the connectivity density modifications associated to the MCI-AD conversion, just processing noninvasive EEG signals.

Subject-specific multi-poroelastic model for exploring the risk factors associated with the early stages of Alzheimer's disease

There is emerging evidence suggesting that Alzheimer's disease is a vascular disorder, caused by impaired cerebral perfusion, which may be promoted by cardiovascular risk factors that are strongly influenced by lifestyle. In order to develop an understanding of the exact nature of such a hypothesis, a biomechanical understanding of the influence of lifestyle factors is pursued. An extended poroelastic model of perfused parenchymal tissue coupled with separate workflows concerning subject-specific meshes, permeability tensor maps and cerebral blood flow variability is used. The subject-specific datasets used in the modelling of this paper were collected as part of prospective data collection. Two cases were simulated involving male, non-smokers (control and mild cognitive impairment (MCI) case) during two states of activity (high and low). Results showed a marginally reduced clearance of cerebrospinal fluid (CSF)/interstitial fluid (ISF), elevated parenchymal tissue displacement and CSF/ISF accumulation and drainage in the MCI case. The peak perfusion remained at 8 mm s⁻¹ between the two cases.

Early Changes in Alpha Band Power and DMN BOLD Activity in Alzheimer's Disease: A Simultaneous Resting State EEG-fMRI Study

Simultaneous resting state functional magnetic resonance imaging (rsfMRI)-resting state electroencephalography (rsEEG) studies in healthy adults showed robust positive associations of signal power in the alpha band with BOLD signal in the thalamus, and more heterogeneous associations in cortical default mode network (DMN) regions. Negative associations were found in occipital regions. In Alzheimer's disease (AD), rsfMRI studies revealed a disruption of the DMN, while rsEEG studies consistently reported a reduced power within the alpha band. The present study is the first to employ simultaneous rsfMRI-rsEEG in an AD sample, investigating the association of alpha band power and BOLD signal, compared to healthy controls (HC). We hypothesized to find reduced positive associations in DMN regions and reduced negative associations in occipital regions in the AD group. Simultaneous resting state fMRI-EEG was recorded in 14 patients with mild AD and 14 HC, matched for age and gender. Power within the EEG alpha band (8-12 Hz, 8-10 Hz, and 10-12 Hz) was computed from occipital electrodes and served as regressor in voxel-wise linear regression analyses, to assess the association with the BOLD signal. Compared to HC, the AD group showed significantly decreased positive associations between BOLD signal and occipital alpha band power in clusters in the superior, middle and inferior frontal cortex, inferior temporal lobe and thalamus (p < 0.01, uncorr., cluster size ≥ 50 voxels). This group effect was more pronounced in the upper alpha sub-band, compared to the lower alpha sub-band. Notably, we observed a high inter-individual heterogeneity. Negative associations were only reduced in the lower alpha range in the hippocampus, putamen and cerebellum. The present study gives first insights into the relationship of resting-state EEG and fMRI characteristics in an AD sample. The results suggest that positive associations between alpha band power and BOLD signal in numerous regions, including DMN regions, are diminished in AD.

Neuropsychological comparisons of cocaine versus methamphetamine users: A research synthesis and meta-analysis

BACKGROUND

Previous meta-analytical research examining cocaine and methamphetamine separately suggests potentially different neuropsychological profiles associated with each drug. In addition, neuroimaging studies point to distinct structural changes that might underlie differences in neuropsychological functioning.

OBJECTIVES

This meta-analysis compared the effect sizes identified in cocaine versus methamphetamine studies across 15 neuropsychological domains.

METHOD

Investigators searched and coded the literature examining the neuropsychological deficits associated with a history of either cocaine or methamphetamine use. A total of 54 cocaine and 41 methamphetamine studies were selected, yielding sample sizes of 1,718 and 1,297, respectively. Moderator analyses were conducted to compare the two drugs across each cognitive domain.

RESULTS

Data revealed significant differences between the two drugs. Specifically, studies of cocaine showed significantly larger effect-size estimates (i.e., poorer performance) in verbal working memory when compared to methamphetamine. Further, when compared to cocaine, methamphetamine studies demonstrated significantly larger effect sizes in delayed contextual verbal memory and delayed visual memory.

CONCLUSION

Overall, cocaine and methamphetamine users share similar neuropsychological profiles. However, cocaine appears to be more associated with working memory impairments, which are typically frontally mediated, while methamphetamine appears to be more associated with memory impairments that are linked with temporal and parietal lobe dysfunction.

Cerebral PET glucose hypometabolism in subjects with mild cognitive impairment and higher EEG high-alpha/low-alpha frequency power ratio

In Alzheimer's disease (AD) research, both 2-deoxy-2-(¹⁸F)fluoro-D-glucose (FDG) positron emission tomography (PET) and electroencephalography (EEG) are reliable investigational modalities. The aim of this study was to investigate the associations between EEG High-alpha/Low-alpha (H-alpha/L-alpha) power ratio and cortical glucose metabolism. A total of 23 subjects with mild cognitive impairment (MCI) underwent FDG-PET and EEG examinations. H-alpha/L-alpha power ratio was computed for each subject and 2 groups were obtained based on the increase of the power ratio. The subjects with higher H-alpha/L-alpha power ratio showed a decrease in glucose metabolism in the hub brain areas previously identified as typically affected by AD pathology. In subjects with higher H-alpha/L-alpha ratio and lower metabolism, a "double alpha peak" was identified in the EEG spectrum and a U-shaped correlation between glucose metabolism and increase of H-alpha/L-alpha power ratio has been found. Moreover, in this group, a conversion rate of 62.5% at 24 months was detected, significantly different from the chance percentage expected. The neurophysiological meaning of the interplay between alpha oscillations and glucose metabolism and the possible interest of the H-alpha/L-alpha power ratio as a clinical biomarker in AD have been discussed.

Racial Differences in Insular Connectivity and Thickness and Related Cognitive Impairment in Hypertension

Hypertensive African–Americans have a greater risk of cognitive impairment than hypertensive Caucasian–Americans. The neural basis of this increased risk is yet unknown. Neuroimaging investigations suggest that the normal neural activity comprises complex interactions between brain networks. Recent studies consistently demonstrate that the insula, part of the salience network, provides modulation effects (information flow) over the default-mode and central-executive networks in cognitively normal subjects, and argue that the modulation effect is declined in cognitive impairment. The purpose of this study is to examine the information flow at the nodes of three networks using resting state functional magnetic resonance imaging (MRI) data in cognitively impaired hypertensive individuals with the African–Americans and the Caucasian–Americans races, and to compare the thickness of impaired node between two racial groups. Granger causality methodology was used to calculate information flow between networks using resting state functional MRI data, and FreeSurfer was used to measure cortical thickness from T1-weighted structural images. We found that negative information flow of the insula in both African–Americans and Caucasian–Americans, which was in contrast with previously reported positive information flow in this region of normal individuals. Also, significantly greater negative information flow in insula was found in African–Americans than Caucasian–Americans (Wilcoxon rank sum; Z = 2.06; p < 0.05). Significantly, lower insula thickness was found in African–Americans compared with Caucasian–Americans (median = 2.797 mm vs. 2.897 mm) (Wilcoxon rank sum; Z = 2.09; p < 0.05). Finally, the insula thickness correlated with the global cognitive testing measured by Montreal cognitive assessment (Spearman’s correlation; r = 0.30; p < 0.05). These findings suggest that the insula is a potential biomarker for the racial disparity in cognitive impairment of hypertensive individuals.

Increased long distance event-related gamma band connectivity in Alzheimer's disease

BACKGROUND

Brain oscillatory responses can be used for non-invasive analyses of cortico-cortical connectivity, local neuronal synchronization, and coherence of oscillations in many neuropsychiatric conditions including Alzheimer's disease (AD). In the present paper, we examine sensory-evoked and event-related gamma coherences elicited by visual stimuli in three sub-gamma bands in two sub-groups of patients with AD (i.e., acetylcholinesterase-inhibitor treated and untreated) and healthy controls.

METHODS

We studied a total of 39 patients with probable mild AD (according to NINCDS-ADRDA criteria) who had been sub-divided into untreated (n = 21) and treated (n = 18) (patients either on cholinergic monotherapy or combined therapy with memantine) AD groups, and 21 age-, gender-, and education-matched healthy elderly controls. A simple flash visual paradigm was applied for the acquisition of sensory-evoked coherences. Event-related coherences were elicited using a classical visual oddball paradigm. Both sensory-evoked and event-related gamma coherences were calculated for long-distance intrahemispheric pairs for three frequency ranges: 25-30 Hz, 30-35 Hz, and 40-48 Hz in post-stimulus 0-800 ms duration. The long-distance intrahemispheric pairs from both sides were fronto-parietal, fronto-temporal, fronto-temporoparietal, fronto-occipital, centro-occipital and parieto-occipital.

RESULTS

The sensory-evoked or event-related gamma coherences revealed that both treated and untreated AD patients had significantly increased values compared to healthy controls in all three sub-gamma bands. Moreover, the treated AD patients demonstrated significantly higher fronto-parietal gamma coherences during both sensory stimulation and oddball paradigm and lower occipito-parietal coherences during oddball paradigm in comparison to untreated AD patients.

CONCLUSION

The present study demonstrated that an increase of gamma coherences was present in response to both visual sensory and cognitive stimulation in AD patients in all gamma sub-bands. Therefore, gamma oscillatory activity seems to be fundamental in brain functions at both the sensory and cognitive levels. The increase of gamma coherence values was not due to cholinergic treatment to any significant extent, as both treated and untreated AD patients had increased gamma coherence values compared to healthy controls. The use of coherence values reflecting brain connectivity holds potential for neuroimaging of AD and understanding brain dynamics related to the effects of medication.

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.

Involvement of mirror neuron system in prodromal Alzheimer's disease

BACKGROUND

Mirror neurons have been localized in several locations, including the inferior parietal lobule (IPL). Increase of EEG alpha3/alpha2 frequency power ratio has been detected in mild cognitive impairment (MCI) subjects who will convert in Alzheimer's disease (AD). We investigated the association of alpha3/alpha2 frequency power ratio with cortical thickness in IPL in MCI subjects.

METHODS

74 adult subjects with MCI underwent EEG recording and high resolution MRI. Alpha3/alpha2 frequency power ratio as well as cortical thickness were computed for each subject. Three MCI groups were obtained according to increasing tertile values of alpha3/alpha2 ratio. Difference of cortical thickness among the groups was estimated.

RESULTS

Higher alpha3/alpha2 frequency power ratio group had wider cortical thinning than other groups, mapped on the IPL, supramarginal gyrus and precuneus bilaterally.

CONCLUSIONS

High EEG alpha3/alpha2 frequency power ratio was associated with atrophy of IPL areas in MCI subjects.

GENERAL SIGNIFICANCE

The scientific hypothesis is divided into the following main points: 1) the theoretical background considering two recent theories, an evolutionary perspective theory and the theory of mind (ToM), which both track a possible relationship between prodromal AD and mirror system; 2) the relationship has been focused on the prodromal stage of Alzheimer's disease, that is a peculiar and very debated phase of the disease itself; and 3) not a generical relationship, but a focused anatomo-functional association has been proposed.