Slowing of EEG correlates with CSF biomarkers and reduced cognitive speed in elderly with normal cognition over 4 years

Harnessing Brainwave Entrainment: A Non-invasive Strategy To Alleviate Neurological Disorder Symptoms

From 1990-2019, the burden of neurological disorders varied considerably across countries and regions. Psychiatric disorders, often emerging in early to mid-adulthood, are linked to late-life neurodegenerative diseases like Alzheimer's disease and Parkinson's disease. Individuals with conditions such as Major Depressive Disorder, Anxiety Disorder, Schizophrenia, and Bipolar Disorder face up to four times higher risk of developing neurodegenerative disorders. Contrarily, 65 % of those with neurodegenerative conditions experience severe psychiatric symptoms during their illness. Further, the limitation of medical resources continues to make this burden a significant global and local challenge. Therefore, brainwave entrainment provides therapeutic avenues for improving the symptoms of diseases. Brainwaves are rhythmic oscillations produced either spontaneously or in response to stimuli. Key brainwave patterns include gamma, beta, alpha, theta, and delta waves, yet the underlying physiological mechanisms and the brain's ability to shift between these dynamic states remain areas for further exploration. In neurological disorders, brainwaves are often disrupted, a phenomenon termed "oscillopathy". However, distinguishing these impaired oscillations from the natural variability in brainwave activity across different regions and functional states poses significant challenges. Brainwave-mediated therapeutics represents a promising research field aimed at correcting dysfunctional oscillations. Herein, we discuss a range of non-invasive techniques such as non-invasive brain stimulation (NIBS), neurologic music therapy (NMT), gamma stimulation, and somatosensory interventions using light, sound, and visual stimuli. These approaches, with their minimal side effects and cost-effectiveness, offer potential therapeutic benefits. When integrated, they may not only help in delaying disease progression but also contribute to the development of innovative medical devices for neurological care.

Beta-to-Theta Entropy Ratio of EEG in Aging, Frontotemporal Dementia, and Alzheimer's Dementia

BACKGROUND

Aging, frontotemporal dementia (FTD), and Alzheimer's dementia (AD) manifest electroencephalography (EEG) alterations, particularly in the beta-to-theta power ratio derived from linear power spectral density (PSD). Given the brain's nonlinear nature, the EEG nonlinear features could provide valuable physiological indicators of aging and cognitive impairment. Multiscale dispersion entropy (MDE) serves as a sensitive nonlinear metric for assessing the information content in EEGs across biologically relevant time scales.

OBJECTIVE

To compare the MDE-derived beta-to-theta entropy ratio with its PSD-based counterpart to detect differences between healthy young and elderly individuals and between different dementia subtypes.

METHODS

Scalp EEG recordings were obtained from two datasets: 1) Aging dataset: 133 healthy young and 65 healthy older adult individuals; and 2) Dementia dataset: 29 age-matched healthy controls (HC), 23 FTD, and 36 AD participants. The beta-to-theta ratios based on MDE vs. PSD were analyzed for both datasets. Finally, the relationships between cognitive performance and the beta-to-theta ratios were explored in HC, FTD, and AD.

RESULTS

In the Aging dataset, older adults had significantly higher beta-to-theta entropy ratios than young individuals. In the Dementia dataset, this ratio outperformed the beta-to-theta PSD approach in distinguishing between HC, FTD, and AD. The AD participants had a significantly lower beta-to-theta entropy ratio than FTD, especially in the temporal region, unlike its corresponding PSD-based ratio. The beta-to-theta entropy ratio correlated significantly with cognitive performance.

CONCLUSION

Our study introduces the beta-to-theta entropy ratio using nonlinear MDE for EEG analysis, highlighting its potential as a sensitive biomarker for aging and cognitive impairment.

Alzheimer's disease and sleep disorders: A bidirectional relationship

Alzheimer's disease (AD) is the most prevalent dementia, pathologically featuring abnormal accumulation of amyloid-β (Aβ) and hyperphosphorylated tau, while sleep, divided into rapid eye movement sleep (REM) and nonrapid eye movement sleep (NREM), plays a key role in consolidating social and spatial memory. Emerging evidence has revealed that sleep disorders such as circadian disturbances and disruption of neuronal rhythm activity are considered as both candidate risks and consequence of AD, suggesting a bidirectional relationship between sleep and AD. This review will firstly grasp basic knowledge of AD pathogenesis, then highlight macrostructural and microstructural alteration of sleep along with AD progression, explain the interaction between accumulation of Aβ and hyperphosphorylated tau, which are two critical neuropathological processes of AD, as well as neuroinflammation and sleep, and finally introduce several methods of sleep enhancement as strategies to reduce AD-associated neuropathology. Although theories about the bidirectional relationship and relevant therapeutic methods in mice have been well developed in recent years, the knowledge in human is still limited. More studies on how to effectively ameliorate AD pathology in patients by sleep enhancement and what specific roles of sleep play in AD are needed.

Sex differences in the progression to Alzheimer's disease: a combination of functional and structural markers

Mild cognitive impairment (MCI) has been frequently interpreted as a transitional phase between healthy cognitive aging and dementia, particularly of the Alzheimer's disease (AD) type. Of note, few studies explored that transition from a multifactorial perspective, taking into consideration the effect of basic factors such as biological sex. In the present study 96 subjects with MCI (37 males and 59 females) were followed-up and divided into two subgroups according to their clinical outcome: "progressive" MCI (pMCI = 41), if they fulfilled the diagnostic criteria for AD at the end of follow-up; and "stable" MCI (sMCI = 55), if they remained with the initial diagnosis. Different markers were combined to characterize sex differences between groups, including magnetoencephalography recordings, cognitive performance, and brain volumes derived from magnetic resonance imaging. Results indicated that the pMCI group exhibited higher low-frequency activity, lower scores in neuropsychological tests and reduced brain volumes than the sMCI group, being these measures significantly correlated. When sex was considered, results revealed that this pattern was mainly due to the influence of the females' sample. Overall, females exhibited lower cognitive scores and reduced brain volumes. More interestingly, females in the pMCI group showed an increased theta activity that correlated with a more abrupt reduction of cognitive and volumetric scores as compared with females in the sMCI group and with males in the pMCI group. These findings suggest that females' brains might be more vulnerable to the effects of AD pathology, since regardless of age, they showed signs of more pronounced deterioration than males.

Oscillatory characteristics of resting-state magnetoencephalography reflect pathological and symptomatic conditions of cognitive impairment

Background

Dementia and mild cognitive impairment are characterised by symptoms of cognitive decline, which are typically assessed using neuropsychological assessments (NPAs), such as the Mini-Mental State Examination (MMSE) and Frontal Assessment Battery (FAB). Magnetoencephalography (MEG) is a novel clinical assessment technique that measures brain activities (summarised as oscillatory parameters), which are associated with symptoms of cognitive impairment. However, the relevance of MEG and regional cerebral blood flow (rCBF) data obtained using single-photon emission computed tomography (SPECT) has not been examined using clinical datasets. Therefore, this study aimed to investigate the relationships among MEG oscillatory parameters, clinically validated biomarkers computed from rCBF, and NPAs using outpatient data retrieved from hospital records.

Methods

Clinical data from 64 individuals with mixed pathological backgrounds were retrieved and analysed. MEG oscillatory parameters, including relative power (RP) from delta to high gamma bands, mean frequency, individual alpha frequency, and Shannon's spectral entropy, were computed for each cortical region. For SPECT data, three pathological parameters-'severity', 'extent', and 'ratio'-were computed using an easy z-score imaging system (eZIS). As for NPAs, the MMSE and FAB scores were retrieved.

Results

MEG oscillatory parameters were correlated with eZIS parameters. The eZIS parameters associated with Alzheimer's disease pathology were reflected in theta power augmentation and slower shift of the alpha peak. Moreover, MEG oscillatory parameters were found to reflect NPAs. Global slowing and loss of diversity in neural oscillatory components correlated with MMSE and FAB scores, whereas the associations between eZIS parameters and NPAs were sparse.

Conclusion

MEG oscillatory parameters correlated with both SPECT (i.e. eZIS) parameters and NPAs, supporting the clinical validity of MEG oscillatory parameters as pathological and symptomatic indicators. The findings indicate that various components of MEG oscillatory characteristics can provide valuable pathological and symptomatic information, making MEG data a rich resource for clinical examinations of patients with cognitive impairments. SPECT (i.e. eZIS) parameters showed no correlations with NPAs. The results contributed to a better understanding of the characteristics of electrophysiological and pathological examinations for patients with cognitive impairments, which will help to facilitate their co-use in clinical application, thereby improving patient care.

REM Sleep Loss-Induced Elevated Noradrenaline Plays a Significant Role in Neurodegeneration: Synthesis of Findings to Propose a Possible Mechanism of Action from Molecule to Patho-Physiological Changes

Wear and tear are natural processes for all living and non-living bodies. All living cells and organisms are metabolically active to generate energy for their routine needs, including for survival. In the process, the cells are exposed to oxidative load, metabolic waste, and bye-products. In an organ, the living non-neuronal cells divide and replenish the lost or damaged cells; however, as neuronal cells normally do not divide, they need special feature(s) for their protection, survival, and sustenance for normal functioning of the brain. The neurons grow and branch as axons and dendrites, which contribute to the formation of synapses with near and far neurons, the basic scaffold for complex brain functions. It is necessary that one or more basic and instinct physiological process(es) (functions) is likely to contribute to the protection of the neurons and maintenance of the synapses. It is known that rapid eye movement sleep (REMS), an autonomic instinct behavior, maintains brain functioning including learning and memory and its loss causes dysfunctions. In this review we correlate the role of REMS and its loss in synaptogenesis, memory consolidation, and neuronal degeneration. Further, as a mechanism of action, we will show that REMS maintains noradrenaline (NA) at a low level, which protects neurons from oxidative damage and maintains neuronal growth and synaptogenesis. However, upon REMS loss, the level of NA increases, which withdraws protection and causes apoptosis and loss of synapses and neurons. We propose that the latter possibly causes REMS loss associated neurodegenerative diseases and associated symptoms.

PET-validated EEG-machine learning algorithm predicts brain amyloid pathology in pre-dementia Alzheimer's disease

Developing reliable biomarkers is important for screening Alzheimer's disease (AD) and monitoring its progression. Although EEG is non-invasive direct measurement of brain neural activity and has potentials for various neurologic disorders, vulnerability to noise, difficulty in clinical interpretation and quantification of signal information have limited its clinical application. There have been many research about machine learning (ML) adoption with EEG, but the accuracy of detecting AD is not so high or not validated with Aβ PET scan. We developed EEG-ML algorithm to detect brain Aβ pathology among subjective cognitive decline (SCD) or mild cognitive impairment (MCI) population, and validated it with Aβ PET. 19-channel resting-state EEG and Aβ PET were collected from 311 subjects: 196 SCD(36 Aβ +, 160 Aβ -), 115 MCI(54 Aβ +, 61Aβ -). 235 EEG data were used for training ML, and 76 for validation. EEG features were standardized for age and sex. Multiple important features sets were selected by 6 statistics analysis. Then, we trained 8 multiple machine learning for each important features set. Meanwhile, we conducted paired t-test to find statistically different features between amyloid positive and negative group. The best model showed 90.9% sensitivity, 76.7% specificity and 82.9% accuracy in MCI + SCD (33 Aβ +, 43 Aβ -). Limited to SCD, 92.3% sensitivity, 75.0% specificity, 81.1% accuracy (13 Aβ +, 24 Aβ -). 90% sensitivity, 78.9% specificity and 84.6% accuracy for MCI (20 Aβ +, 19 Aβ -). Similar trends of EEG power have been observed from the group comparison between Aβ + and Aβ -, and between MCI and SCD: enhancement of frontal/ frontotemporal theta; attenuation of mid-beta in centroparietal areas. The present findings suggest that accurate classification for beta-amyloid accumulation in the brain based on QEEG alone could be possible, which implies that QEEG is a promising biomarker for beta-amyloid. Since QEEG is more accessible, cost-effective, and safer than amyloid PET, QEEG-based biomarkers may play an important role in the diagnosis and treatment of AD. We expect specific patterns in QEEG could play an important role to predict future progression of cognitive impairment in the preclinical stage of AD. Further feature engineering and validation with larger dataset is recommended.

Motor-Related Mu/Beta Rhythm in Older Adults: A Comprehensive Review

Mu rhythm, also known as the mu wave, occurs on sensorimotor cortex activity at rest, and the frequency range is defined as 8-13Hz, the same frequency as the alpha band. Mu rhythm is a cortical oscillation that can be recorded from the scalp over the primary sensorimotor cortex by electroencephalogram (EEG) and magnetoencephalography (MEG). The subjects of previous mu/beta rhythm studies ranged widely from infants to young and older adults. Furthermore, these subjects were not only healthy people but also patients with various neurological and psychiatric diseases. However, very few studies have referred to the effect of mu/beta rhythm with aging, and there was no literature review about this theme. It is important to review the details of the characteristics of mu/beta rhythm activity in older adults compared with young adults, focusing on age-related mu rhythm changes. By comprehensive review, we found that, compared with young adults, older adults showed mu/beta activity change in four characteristics during voluntary movement, increased event-related desynchronization (ERD), earlier beginning and later end, symmetric pattern of ERD and increased recruitment of cortical areas, and substantially reduced beta event-related desynchronization (ERS). It was also found that mu/beta rhythm patterns of action observation were changing with aging. Future work is needed in order to investigate not only the localization but also the network of mu/beta rhythm in older adults.

Proteostasis failure exacerbates neuronal circuit dysfunction and sleep impairments in Alzheimer's disease

Failed proteostasis is a well-documented feature of Alzheimer's disease, particularly, reduced protein degradation and clearance. However, the contribution of failed proteostasis to neuronal circuit dysfunction is an emerging concept in neurodegenerative research and will prove critical in understanding cognitive decline. Our objective is to convey Alzheimer's disease progression with the growing evidence for a bidirectional relationship of sleep disruption and proteostasis failure. Proteostasis dysfunction and tauopathy in Alzheimer's disease disrupts neurons that regulate the sleep-wake cycle, which presents behavior as impaired slow wave and rapid eye movement sleep patterns. Subsequent sleep loss further impairs protein clearance. Sleep loss is a defined feature seen early in many neurodegenerative disorders and contributes to memory impairments in Alzheimer's disease. Canonical pathological hallmarks, β-amyloid, and tau, directly disrupt sleep, and neurodegeneration of locus coeruleus, hippocampal and hypothalamic neurons from tau proteinopathy causes disruption of the neuronal circuitry of sleep. Acting in a positive-feedback-loop, sleep loss and circadian rhythm disruption then increase spread of β-amyloid and tau, through impairments of proteasome, autophagy, unfolded protein response and glymphatic clearance. This phenomenon extends beyond β-amyloid and tau, with interactions of sleep impairment with the homeostasis of TDP-43, α-synuclein, FUS, and huntingtin proteins, implicating sleep loss as an important consideration in an array of neurodegenerative diseases and in cases of mixed neuropathology. Critically, the dynamics of this interaction in the neurodegenerative environment are not fully elucidated and are deserving of further discussion and research. Finally, we propose sleep-enhancing therapeutics as potential interventions for promoting healthy proteostasis, including β-amyloid and tau clearance, mechanistically linking these processes. With further clinical and preclinical research, we propose this dynamic interaction as a diagnostic and therapeutic framework, informing precise single- and combinatorial-treatments for Alzheimer's disease and other brain disorders.

Alterations in rhythmic and non-rhythmic resting-state EEG activity and their link to cognition in older age

While many structural and biochemical changes in the brain have previously been associated with older age, findings concerning functional properties of neuronal networks, as reflected in their electrophysiological signatures, remain rather controversial. These discrepancies might arise due to several reasons, including diverse factors determining general spectral slowing in the alpha frequency range as well as amplitude mixing between the rhythmic and non-rhythmic parameters. We used a large dataset (N = 1703, mean age 70) to comprehensively investigate age-related alterations in multiple EEG biomarkers taking into account rhythmic and non-rhythmic activity and their individual contributions to cognitive performance. While we found strong evidence for an individual alpha peak frequency (IAF) decline in older age, we did not observe a significant relationship between theta power and age while controlling for IAF. Not only did IAF decline with age, but it was also positively associated with interference resolution in a working memory task primarily in the right and left temporal lobes suggesting its functional role in information sampling. Critically, we did not detect a significant relationship between alpha power and age when controlling for the 1/f spectral slope, while the latter one showed age-related alterations. These findings thus suggest that the entanglement of IAF slowing and power in the theta frequency range, as well as 1/f slope and alpha power measures, might explain inconsistencies reported previously in the literature. Finally, despite the absence of age-related alterations, alpha power was negatively associated with the speed of processing in the right frontal lobe while 1/f slope showed no consistent relationship to cognitive performance. Our results thus demonstrate that multiple electrophysiological features, as well as their interplay, should be considered for the comprehensive assessment of association between age, neuronal activity, and cognitive performance.

The Aging Patterns of Brain Structure, Function, and Energy Metabolism

The normal aging process brings changes in brain structure, function, and energy metabolism, which are presumed to contribute to the age-related decline in brain function and cognitive ability. This chapter aims to summarize the aging patterns of brain structure, function, and energy metabolism to distinguish them from the pathological changes associated with neurodegenerative diseases and explore protective factors in aging. We first described the normal atrophy pattern of cortical gray matter with age, which is negatively affected by some neurodegenerative diseases and is protected by a healthy lifestyle, such as physical exercise. Next, we summarized the main types of age-related white matter lesions, including white matter atrophy and hyperintensity. Age-related white matter changes mainly occurred in the frontal lobe, and white matter lesions in posterior regions may be an early sign of Alzheimer's disease. In addition, the relationship between brain activity and various cognitive functions during aging was discussed based on electroencephalography, magnetoencephalogram, and functional magnetic resonance imaging. An age-related reduction in occipital activity is coupled with increased frontal activity, which supports the posterior-anterior shift in aging (PASA) theory. Finally, we discussed the relationship between amyloid-β deposition and tau accumulation in the brain, as pathological manifestations of neurodegenerative disease and aging.

Who's driving? The default mode network in healthy elderly individuals at risk of cognitive decline

Introduction

Age is the main risk factor for the development of neurocognitive disorders, with Alzheimer's disease being the most common. Its physiopathological features may develop decades before the onset of clinical symptoms. Quantitative electroencephalography (qEEG) is a promising and cost-effective tool for the prediction of cognitive decline in healthy older individuals that exhibit an excess of theta activity. The aim of the present study was to evaluate the feasibility of brain connectivity variable resolution electromagnetic tomography (BC-VARETA), a novel source localization algorithm, as a potential tool to assess brain connectivity with 19-channel recordings, which are common in clinical practice.

Methods

We explored differences in terms of functional connectivity among the nodes of the default mode network between two groups of healthy older participants, one of which exhibited an EEG marker of risk for cognitive decline.

Results

The risk group exhibited increased levels of delta, theta, and beta functional connectivity among nodes of the default mode network, as well as reversed directionality patterns of connectivity among nodes in every frequency band when compared to the control group.

Discussion

We propose that an ongoing pathological process may be underway in healthy elderly individuals with excess theta activity in their EEGs, which is further evidenced by changes in their connectivity patterns. BC-VARETA implemented on 19-channels EEG recordings appears to be a promising tool to detect dysfunctions at the connectivity level in clinical settings.

Selection of the optimal channel configuration for implementing wearable EEG devices for the diagnosis of mild cognitive impairment

Background

Early diagnosis of mild cognitive impairment (MCI) is essential for timely treatment planning. With recent advances in the wearable technology, interest has increasingly shifted toward computer-aided self-diagnosis of MCI using wearable electroencephalography (EEG) devices in daily life. However, no study so far has investigated the optimal electrode configurations for the efficient diagnosis of MCI while considering the design factors of wearable EEG devices. In this study, we aimed to determine the optimal channel configurations of wearable EEG devices for the computer-aided diagnosis of MCI.

Method

We employed an EEG dataset collected from 21 patients with MCI and 21 healthy control subjects. After evaluating the classification accuracies for all possible electrode configurations for the two-, four-, six-, and eight-electrode conditions using a support vector machine, the optimal electrode configurations that provide the highest diagnostic accuracy were suggested for each electrode condition.

Results

The highest classification accuracies of 74.04% ± 4.82, 82.43% ± 6.14, 86.28% ± 2.81, and 86.85% ± 4.97 were achieved for the optimal two-, four-, six-, and eight-electrode configurations, respectively, which demonstrated the possibility of precise machine-learning-based diagnosis of MCI with a limited number of EEG electrodes. Additionally, further simulations with the EEG dataset revealed that the optimal electrode configurations had significantly higher classification accuracies than commercial EEG devices with the same number of electrodes, which suggested the importance of electrode configuration optimization for wearable EEG devices based on clinical EEG datasets.

Conclusions

This study highlighted that the optimization of the electrode configuration, assuming the wearable EEG devices can potentially be utilized for daily life monitoring of MCI, is necessary to enhance the performance and portability.

Mental fatigue decreases complexity: Evidence from multiscale entropy analysis of instantaneous frequency variation in alpha rhythm

Mental fatigue (MF) jeopardizes performance and safety through a variety of cognitive impairments and according to the complexity loss theory, should represent "complexity loss" in electroencephalogram (EEG). However, the studies are few and inconsistent concerning the relationship between MF and loss of complexity, probably because of the susceptibility of brain waves to noise. In this study, MF was induced in thirteen male college students by a simulated flight task. Before and at the end of the task, spontaneous EEG and auditory steady-state response (ASSR) were recorded and instantaneous frequency variation (IFV) in alpha rhythm was extracted and analyzed by multiscale entropy (MSE) analysis. The results show that there were significant differences in IFV in alpha rhythm either from spontaneous EEG or from ASSR for all subjects. Therefore, the proposed method can be effective in revealing the complexity loss caused by MF in spontaneous EEG and ASSR, which may serve as a promising analyzing method to mark mild mental impairments.

Association of Plasma and Electroencephalography Markers With Motor Subtypes of Parkinson’s Disease

Objective

The aim of this study was to investigate the correlations of plasma neurodegenerative proteins and electroencephalography (EEG) dynamic functional network (DFN) parameters with disease progression in early Parkinson’s disease (PD) with different motor subtypes, including tremor-dominant (TD) and postural instability and gait disorder (PIGD).

Methods

In our study, 33 patients with PD (21 TD and 12 PIGD) and 33 healthy controls (HCs) were enrolled. Plasma neurofilament light chain (NfL), α-synuclein (α-syn), total-tau (t-tau), β-amyloid 42 (Aβ42), and β-amyloid 40 (Aβ40) levels were measured using an ultrasensitive single-molecule array (Simoa) immunoassay. All the patients with PD underwent EEG quantified by DFN analysis. The motor and non-motor performances were evaluated by a series of clinical assessments. Subsequently, a correlation analysis of plasma biomarkers and EEG measures with clinical scales was conducted.

Results

In the TD group, plasma NfL exhibited a significant association with MDS-UPDRS III and Montreal Cognitive Assessment (MoCA). A higher Aβ42/40 level was significantly related to a decrease in Hamilton Depression Rating Scale (HAMD) and Hamilton Anxiety Rating Scale (HAMA) in the PIGD group. In terms of the correlation between EEG characteristic parameters and clinical outcomes, trapping time (TT) delta was positively correlated with MDS-UPDRS III and MoCA scores in the TD group, especially in the prefrontal and frontal regions. For other non-motor symptoms, there were significant direct associations of k_PLI theta with HAMD and HAMA, especially in the prefrontal region, and k_PLI gamma was particularly correlated with Rapid Eye Movement Sleep Behavior Disorder Screening Questionnaire (RBDSQ) scores in the prefrontal, frontal, and parietal regions in the TD group. Furthermore, there was a significant positive correlation between plasma t-tau and k_PLI, and pairwise correlations were found among plasma NfL, theta TT, and MoCA scores in the TD group.

Conclusion

These results provide evidence that plasma neurodegenerative proteins and EEG measures have great potential in predicting the disease progression of PD subtypes, especially for the TD subtype. A combination of these two kinds of markers may have a superposition effect on monitoring and estimating the prognosis of PD subtypes and deserves further research in larger, follow-up PD cohorts.

The Effects of a Virtual Reality Rehabilitation Task on Elderly Subjects: An Experimental Study Using Multimodal Data

Ageing populations are becoming a global issue. Against this background, the assessment and treatment of geriatric conditions have become increasingly important. This study draws on the multisensory integration of virtual reality (VR) devices in the field of rehabilitation to assess brain function in young and old people. The study is based on multimodal data generated by combining high temporal resolution electroencephalogram (EEG) and subjective scales and behavioural indicators reflecting motor abilities. The phase locking value (PLV) was chosen as an indicator of functional connectivity (FC), and six brain regions, namely LPFC, RPFC, LOL, ROL, LMC and RMC, were analysed. The results showed a significant difference in the alpha band on comparing the resting and task states in the younger group. A significant difference between the two states in the alpha and beta bands was observed when comparing task states in the younger and older groups. Meanwhile, this study affirms that advancing age significantly affects human locomotor performance and also has a correlation with cognitive level. The study proposes a novel accurate and valid assessment method that offers new possibilities for assessing and rehabilitating geriatric diseases. Thus, this method has the potential to contribute to the field of rehabilitation medicine.

Electrophysiological Biomarkers of Epileptogenicity in Alzheimer's Disease

Cortical network hyperexcitability is an inextricable feature of Alzheimer’s disease (AD) that also might accelerate its progression. Seizures are reported in 10–22% of patients with AD, and subclinical epileptiform abnormalities have been identified in 21–42% of patients with AD without seizures. Accurate identification of hyperexcitability and appropriate intervention to slow the compromise of cognitive functions of AD might open up a new approach to treatment. Based on the results of several studies, epileptiform discharges, especially those with specific features (including high frequency, robust morphology, right temporal location, and occurrence during awake or rapid eye movement states), frequent small sharp spikes (SSSs), temporal intermittent rhythmic delta activities (TIRDAs), and paroxysmal slow wave events (PSWEs) recorded in long-term scalp electroencephalogram (EEG) provide sufficient sensitivity and specificity in detecting cortical network hyperexcitability and epileptogenicity of AD. In addition, magnetoencephalogram (MEG), foramen ovale (FO) electrodes, and computational approaches help to find subclinical seizures that are invisible on scalp EEGs. We performed a comprehensive analysis of the aforementioned electrophysiological biomarkers of AD-related seizures.

Magnetoencephalography Brain Signatures Relate to Cognition and Cognitive Reserve in the Oldest-Old: The EMIF-AD 90 + Study

The oldest-old subjects represent the fastest growing segment of society and are at high risk for dementia with a prevalence of up to 40%. Lifestyle factors, such as lifelong participation in cognitive and leisure activities, may contribute to individual cognitive reserve and reduce the risk for cognitive impairments. However, the neural bases underlying cognitive functioning and cognitive reserve in this age range are still poorly understood. Here, we investigate spectral and functional connectivity features obtained from resting-state MEG recordings in a cohort of 35 cognitively normal (92.2 ± 1.8 years old, 19 women) and 11 cognitively impaired (90.9 ± 1.9 years old, 1 woman) oldest-old participants, in relation to cognitive traits and cognitive reserve. The latter was approximated with a self-reported scale on lifelong engagement in cognitively demanding activities. Cognitively impaired oldest-old participants had slower cortical rhythms in frontal, parietal and default mode network regions compared to the cognitively normal subjects. These alterations mainly concerned the theta and beta band and partially explained inter-subject variability of episodic memory scores. Moreover, a distinct spectral pattern characterized by higher relative power in the alpha band was specifically associated with higher cognitive reserve while taking into account the effect of age and education level. Finally, stronger functional connectivity in the alpha and beta band were weakly associated with better cognitive performances in the whole group of subjects, although functional connectivity effects were less prominent than the spectral ones. Our results shed new light on the neural underpinnings of cognitive functioning in the oldest-old population and indicate that cognitive performance and cognitive reserve may have distinct spectral electrophysiological substrates.

Machine Learning to Predict Brain Amyloid Pathology in Pre-dementia Alzheimer’s Disease Using QEEG Features and Genetic Algorithm Heuristic

The use of positron emission tomography (PET) as the initial or sole biomarker of β-amyloid (Aβ) brain pathology may inhibit Alzheimer’s disease (AD) drug development and clinical use due to cost, access, and tolerability. We developed a qEEG-ML algorithm to predict Aβ pathology among subjective cognitive decline (SCD) and mild cognitive impairment (MCI) patients, and validated it using Aβ PET. We compared QEEG data between patients with MCI and those with SCD with and without PET-confirmed beta-amyloid plaque. We compared resting-state eyes-closed electroencephalograms (EEG) patterns between the amyloid positive and negative groups using relative power measures from 19 channels (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, O2), divided into eight frequency bands, delta (1–4 Hz), theta (4–8 Hz), alpha 1 (8–10 Hz), alpha 2 (10–12 Hz), beta 1 (12–15 Hz), beta 2 (15–20 Hz), beta 3 (20–30 Hz), and gamma (30–45 Hz) calculated by FFT and denoised by iSyncBrain^®. The resulting 152 features were analyzed using a genetic algorithm strategy to identify optimal feature combinations and maximize classification accuracy. Guided by gene modeling methods, we treated each channel and frequency band of EEG power as a gene and modeled it with every possible combination within a given dimension. We then collected the models that showed the best performance and identified the genes that appeared most frequently in the superior models. By repeating this process, we converged on a model that approximates the optimum. We found that the average performance increased as this iterative development of the genetic algorithm progressed. We ultimately achieved 85.7% sensitivity, 89.3% specificity, and 88.6% accuracy in SCD amyloid positive/negative classification, and 83.3% sensitivity, 85.7% specificity, and 84.6% accuracy in MCI amyloid positive/negative classification.

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.

Electroencephalography as a Non-Invasive Biomarker of Alzheimer's Disease: A Forgotten Candidate to Substitute CSF Molecules?

Biomarkers for disease diagnosis and prognosis are crucial in clinical practice. They should be objective and quantifiable and respond to specific therapeutic interventions. Optimal biomarkers should reflect the underlying process (pathological or not), be reproducible, widely available, and allow measurements repeatedly over time. Ideally, biomarkers should also be non-invasive and cost-effective. This review aims to focus on the usefulness and limitations of electroencephalography (EEG) in the search for Alzheimer's disease (AD) biomarkers. The main aim of this article is to review the evolution of the most used biomarkers in AD and the need for new peripheral and, ideally, non-invasive biomarkers. The characteristics of the EEG as a possible source for biomarkers will be revised, highlighting its advantages compared to the molecular markers available so far.

Aperiodic and Periodic Components of Ongoing Oscillatory Brain Dynamics Link Distinct Functional Aspects of Cognition across Adult Lifespan

Signal transmission in the brain propagates via distinct oscillatory frequency bands but the aperiodic component, 1/f activity, almost always co-exists which most of the previous studies have not sufficiently taken into consideration. We used a recently proposed parameterization model that delimits the oscillatory and aperiodic components of neural dynamics on lifespan aging data collected from human participants using magnetoencephalography (MEG). Since healthy aging underlines an enormous change in local tissue properties, any systematic relationship of 1/f activity would highlight their impact on the self-organized critical functional states. Furthermore, we have used patterns of correlation between aperiodic background and metrics of behavior to understand the domain general effects of 1/f activity. We suggest that age-associated global change in 1/f baseline alters the functional critical states of the brain affecting the global information processing impacting critically all aspects of cognition, e.g., metacognitive awareness, speed of retrieval of memory, cognitive load, and accuracy of recall through adult lifespan. This alteration in 1/f crucially impacts the oscillatory features peak frequency (PF) and band power ratio, which relates to more local processing and selective functional aspects of cognitive processing during the visual short-term memory (VSTM) task. In summary, this study leveraging on big lifespan data for the first time tracks the cross-sectional lifespan-associated periodic and aperiodic dynamical changes in the resting state to demonstrate how normative patterns of 1/f activity, PF, and band ratio (BR) measures provide distinct functional insights about the cognitive decline through adult lifespan.

CSF in Epileptic Prodromal Alzheimer's Disease: No Diagnostic Contribution but a Pathophysiological One

Objective: To study whether cerebrospinal fluid (CSF) analysis may serve as a diagnostic test for the screening of epilepsy in sporadic prodromal Alzheimer's disease (AD).

Methods: A total of 29 patients with epileptic prodromal sporadic AD patients (epADs) were included and were retrospectively compared with 38 non-epileptic prodromal AD patients (nepADs) for demographics, clinical features, Mini-Mental Status Examination (MMSE) results, CSF biomarkers, and electro-radiological features.

Results: Our study did not show any significant differences in CSF biomarkers regarding neurodegeneration, albumin levels, and inflammation between epADs and nepADs. The epADs were significantly older at diagnosis (p = 0.001), more hypertensive (p = 0.01), and displayed larger white matter hyperintensities on brain magnetic resonance imaging (MRI; p = 0.05). There was a significant correlation between the CSF Aβ-42 and Aβ-40 levels with interictal epileptiform discharges and delta slowing on EEGs recordings, respectively (p = 0.03).

Conclusions: Our study suggests that CSF may not serve as a surrogate marker of epilepsy in prodromal AD and cannot circumvent the operator-dependent and time-consuming interpretation of EEG recordings. In humans, AD-related epileptogenesis appears to involve the Aβ peptides but likely also additional non-amyloid factors such as small-vessel disease (i.e., white matter hyperintensities).

When the time is right: Temporal dynamics of brain activity in healthy aging and dementia

Brain activity and communications are complex phenomena that dynamically unfold over time. However, in contrast with the large number of studies reporting neuroanatomical differences in activation relative to young adults, changes of temporal dynamics of neural activity during normal and pathological aging have been grossly understudied and are still poorly known. Here, we synthesize the current state of knowledge from MEG and EEG studies that aimed at specifying the effects of healthy and pathological aging on local and network dynamics, and discuss the clinical and theoretical implications of these findings. We argue that considering the temporal dynamics of brain activations and networks could provide a better understanding of changes associated with healthy aging, and the progression of neurodegenerative disease. Recent research has also begun to shed light on the association of these dynamics with other imaging modalities and with individual differences in cognitive performance. These insights hold great potential for driving new theoretical frameworks and development of biomarkers to aid in identifying and treating age-related cognitive changes.

Resting state EEG biomarkers of cognitive decline associated with Alzheimer's disease and mild cognitive impairment

In this paper, we explore the utility of resting-state EEG measures as potential biomarkers for the detection and assessment of cognitive decline in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Neurophysiological biomarkers of AD derived from EEG and FDG-PET, once characterized and validated, would expand the set of existing diagnostic molecular biomarkers of AD pathology with associated biomarkers of disease progression and neural dysfunction. Since symptoms of AD often begin to appear later in life, successful identification of EEG-based biomarkers must account for age-related neurophysiological changes that occur even in healthy individuals. To this end, we collected EEG data from individuals with AD (n = 26), MCI (n = 53), and cognitively normal healthy controls stratified by age into three groups: 18-40 (n = 129), 40-60 (n = 62) and 60-90 (= 55) years old. For each participant, we computed power spectral density at each channel and spectral coherence between pairs of channels. Compared to age matched controls, in the AD group, we found increases in both spectral power and coherence at the slower frequencies (Delta, Theta). A smaller but significant increase in power of slow frequencies was observed for the MCI group, localized to temporal areas. These effects on slow frequency spectral power opposed that of normal aging observed by a decrease in the power of slow frequencies in our control groups. The AD group showed a significant decrease in the spectral power and coherence in the Alpha band consistent with the same effect in normal aging. However, the MCI group did not show any significant change in the Alpha band. Overall, Theta to Alpha ratio (TAR) provided the largest and most significant differences between the AD group and controls. However, differences in the MCI group remained small and localized. We proposed a novel method to quantify these small differences between Theta and Alpha bands' power using empirically derived distributions of spectral power across the time domain as opposed to averaging power across time. We defined Power Distribution Distance Measure (PDDM) as a distance measure between probability distribution functions (pdf) of Theta and Alpha power. Compared to average TAR, using PDDF enhanced the statistical significance, the effect size, and the spatial distribution of significant effects in the MCI group. We designed classifiers for differentiating individual MCI and AD participants from age-matched controls. The classification performance measured by the area under ROC curve after cross-validation were AUC = 0.85 and AUC = 0.6, for AD and MCI classifiers, respectively. Posterior probability of AD, TAR, and the proposed PDDM measure were all significantly correlated with MMSE score and neuropsychological tests in the AD group.

Risk Variants in Three Alzheimer's Disease Genes Show Association with EEG Endophenotypes

Background:

Dementia due to Alzheimer’s disease (AD) is a complex neurodegenerative disorder, which much of heritability remains unexplained. At the clinical level, one of the most common physiological alterations is the slowing of oscillatory brain activity, measurable by electroencephalography (EEG). Relative power (RP) at the conventional frequency bands (i.e., delta, theta, alpha, beta-1, and beta-2) can be considered as AD endophenotypes.

Objective:

The aim of this work is to analyze the association between sixteen genes previously related with AD: APOE, PICALM, CLU, BCHE, CETP, CR1, SLC6A3, GRIN2 β, SORL1, TOMM40, GSK3 β, UNC5C, OPRD1, NAV2, HOMER2, and IL1RAP, and the slowing of the brain activity, assessed by means of RP at the aforementioned frequency bands.

Methods:

An Iberian cohort of 45 elderly controls, 45 individuals with mild cognitive impairment, and 109 AD patients in the three stages of the disease was considered. Genomic information and brain activity of each subject were analyzed.

Results:

The slowing of brain activity was observed in carriers of risk alleles in IL1RAP (rs10212109, rs9823517, rs4687150), UNC5C (rs17024131), and NAV2 (rs1425227, rs862785) genes, regardless of the disease status and situation towards the strongest risk factors: age, sex, and APOE ɛ4 presence.

Conclusion:

Endophenotypes reduce the complexity of the general phenotype and genetic variants with a major effect on those specific traits may be then identified. The found associations in this work are novel and may contribute to the comprehension of AD pathogenesis, each with a different biological role, and influencing multiple factors involved in brain physiology.

A Comparison of Traditional and Serious Game-Based Digital Markers of Cognition in Older Adults with Mild Cognitive Impairment and Healthy Controls

Background:

Electroencephalography (EEG) has been used to assess brain activity while users are playing an immersive serious game.

Objective:

To assess differences in brain activation as measured with a non-intrusive wearable EEG device, differences in game performance and correlations between EEG power, game performance and global cognition, between cognitively impaired and non-impaired older adults, during the administration of a novel self-administered serious game-based test, the Virtual Supermarket Test (VST).

Methods:

43 older adults with subjective cognitive decline (SCD) and 33 older adults with mild cognitive impairment (MCI) were recruited from day centers for cognitive disorders. Global cognition was assessed with the Montreal Cognitive Assessment (MoCA). Brain activity was measured with a non-intrusive wearable EEG device in a resting state condition and while they were administered the VST.

Results:

During resting state condition, the MCI group showed increased alpha, beta, delta, and theta band power compared to the SCD group. During the administration of the VST, the MCI group showed increased beta and theta band power compared to the SCD group. Regarding game performance, alpha, beta, delta, and theta rhythms were positively correlated with average duration, while delta rhythm was positively correlated with mean errors. MoCA correlated with alpha, beta, delta, and theta rhythms and with average game duration and mean game errors indicating that elevated EEG rhythms in MCI may be associated with an overall cognitive decline.

Conclusion:

VST performance can be used as a digital biomarker. Cheap commercially available wearable EEG devices can be used for obtaining brain activity biomarkers.

A multivariate model of time to conversion from mild cognitive impairment to Alzheimer's disease

The present study was aimed at determining which combination of demographic, genetic, cognitive, neurophysiological, and neuroanatomical factors may predict differences in time to progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). To this end, a sample of 121 MCIs was followed up during a 5-year period. According to their clinical outcome, MCIs were divided into two subgroups: (i) the "progressive" MCI group (n = 46; mean time to progression 17 ± 9.73 months) and (ii) the "stable" MCI group (n = 75; mean time of follow-up 31.37 ± 14.58 months). Kaplan-Meier survival analyses were applied to explore each variable's relationship with the progression to AD. Once potential predictors were detected, Cox regression analyses were utilized to calculate a parsimonious model to estimate differences in time to progression. The final model included three variables (in order of relevance): left parahippocampal volume (corrected by intracranial volume, LP_ ICV), delayed recall (DR), and left inferior occipital lobe individual alpha peak frequency (LIOL_IAPF). Those MCIs with LP_ICV volume, DR score, and LIOL_IAPF value lower than the defined cutoff had 6 times, 5.5 times, and 3 times higher risk of progression to AD, respectively. Besides, when the categories of the three variables were "unfavorable" (i.e., values below the cutoff), 100% of cases progressed to AD at the end of follow-up. Our results highlighted the relevance of neurophysiological markers as predictors of conversion (LIOL_IAPF) and the importance of multivariate models that combine markers of different nature to predict time to progression from MCI to dementia.

Obstructive Sleep Apnea and Its Treatment in Aging: Effects on Alzheimer's disease Biomarkers, Cognition, Brain Structure and Neurophysiology

Here we review the impact of obstructive sleep apnea (OSA) on biomarkers of Alzheimer's disease (AD) pathogenesis, neuroanatomy, cognition and neurophysiology, and present the research investigating the effects of continuous positive airway pressure (CPAP) therapy. OSA is associated with an increase in AD markers amyloid-β and tau measured in cerebrospinal fluid (CSF), by Positron Emission Tomography (PET) and in blood serum. There is some evidence suggesting CPAP therapy normalizes AD biomarkers in CSF but since mechanisms for amyloid-β and tau production/clearance in humans are not completely understood, these findings remain preliminary. Deficits in the cognitive domains of attention, vigilance, memory and executive functioning are observed in OSA patients with the magnitude of impairment appearing stronger in younger people from clinical settings than in older community samples. Cognition improves with varying degrees after CPAP use, with the greatest effect seen for attention in middle age adults with more severe OSA and sleepiness. Paradigms in which encoding and retrieval of information are separated by periods of sleep with or without OSA have been done only rarely, but perhaps offer a better chance to understand cognitive effects of OSA than isolated daytime testing. In cognitively normal individuals, changes in EEG microstructure during sleep, particularly slow oscillations and spindles, are associated with biomarkers of AD, and measures of cognition and memory. Similar changes in EEG activity are reported in AD and OSA, such as "EEG slowing" during wake and REM sleep, and a degradation of NREM EEG microstructure. There is evidence that CPAP therapy partially reverses these changes but large longitudinal studies demonstrating this are lacking. A diagnostic definition of OSA relying solely on the Apnea Hypopnea Index (AHI) does not assist in understanding the high degree of inter-individual variation in daytime impairments related to OSA or response to CPAP therapy. We conclude by discussing conceptual challenges to a clinical trial of OSA treatment for AD prevention, including inclusion criteria for age, OSA severity, and associated symptoms, the need for a potentially long trial, defining relevant primary outcomes, and which treatments to target to optimize treatment adherence.

Age-related slowing down in the motor initiation in elderly adults

Age-related changes in the human brain functioning crucially affect the motor system, causing increased reaction time, low ability to control and execute movements, difficulties in learning new motor skills. The lifestyle and lowered daily activity of elderly adults, along with the deficit of motor and cognitive brain functions, might lead to the developed ambidexterity, i.e., the loss of dominant limb advances. Despite the broad knowledge about the changes in cortical activity directly related to the motor execution, less is known about age-related differences in the motor initiation phase. We hypothesize that the latter strongly influences the behavioral characteristics, such as reaction time, the accuracy of motor performance, etc. Here, we compare the neuronal processes underlying the motor initiation phase preceding fine motor task execution between elderly and young subjects. Based on the results of the whole-scalp sensor-level electroencephalography (EEG) analysis, we demonstrate that the age-related slowing down in the motor initiation before the dominant hand movements is accompanied by the increased theta activation within sensorimotor area and reconfiguration of the theta-band functional connectivity in elderly adults.

Neural oscillations and brain stimulation in Alzheimer's disease

Aging is associated with alterations in cognitive processing and brain neurophysiology. Whereas the primary symptom of amnestic mild cognitive impairment (aMCI) is memory problems greater than normal for age and education, patients with Alzheimer's disease (AD) show impairments in other cognitive domains in addition to memory dysfunction. Resting-state electroencephalography (rsEEG) studies in physiological aging indicate a global increase in low-frequency oscillations' power and the reduction and slowing of alpha activity. The enhancement of slow and the reduction of fast oscillations, and the disruption of brain functional connectivity, however, are characterized as major rsEEG changes in AD. Recent rodent studies also support human evidence of age- and AD-related changes in resting-state brain oscillations, and the neuroprotective effect of brain stimulation techniques through gamma-band stimulations. Cumulatively, current evidence moves toward optimizing rsEEG features as reliable predictors of people with aMCI at risk for conversion to AD and mapping neural alterations subsequent to brain stimulation therapies. The present paper reviews the latest evidence of changes in rsEEG oscillations in physiological aging, aMCI, and AD, as well as findings of various brain stimulation therapies from both human and non-human studies.

Ablating Tau Reduces Hyperexcitability and Moderates Electroencephalographic Slowing in Transgenic Mice Expressing A53T Human α-Synuclein

Abnormal intraneuronal accumulation of the presynaptic protein α-synuclein (α-syn) is implicated in the etiology of dementia with Lewy bodies (DLB) and Parkinson's disease with dementia (PDD). Recent work revealed that mice expressing human α-syn with the alanine-53-threonine (A53T) mutation have a similar phenotype to the human condition, exhibiting long-term potentiation deficits, learning and memory deficits, and inhibitory hippocampal remodeling, all of which were reversed by genetic ablation of microtubule-associated protein tau. Significantly, memory deficits were associated with histological signs of network hyperactivity/seizures. Electrophysiological abnormalities are often seen in parkinsonian dementias. Baseline electroencephalogram (EEG) slowing is used as a supportive diagnostic feature in DLB and PDD, and patients with these diseases may exhibit indicators of broad network dysfunction such as sleep dysregulation, myoclonus, and seizures. Given the translational significance, we examined whether human A53T α-syn expressing mice exhibit endogenous-tau-dependent EEG abnormalities, as measured with epidural electrodes over the frontal and parietal cortices. Using template-based waveform sorting, we determined that A53T mice have significantly high numbers of epileptiform events as early as 3-4 months of age and throughout life, and this effect is markedly attenuated in the absence of tau. Epileptic myoclonus occurred in half of A53T mice and was markedly reduced by tau ablation. In spectral analysis, tau ablation partially reduced EEG slowing in 6-7 month transgenic mice. We found abnormal sleeping patterns in transgenic mice that were more pronounced in older groups, but did not find evidence that this was influenced by tau genotype. Together, these data support the notion that tau facilitates A53T α-syn-induced hyperexcitability that both precedes and coincides with associated synaptic, cognitive, and behavioral effects. Tau also contributes to some aspects of EEG slowing in A53T mice. Importantly, our work supports tau-based approaches as an effective early intervention in α-synucleinopathies to treat aberrant network activity.

Decomposed Temporal Complexity Analysis of Neural Oscillations and Machine Learning Applied to Alzheimer's Disease Diagnosis

Despite growing evidence of aberrant neuronal complexity in Alzheimer's disease (AD), it remains unclear how this variation arises. Neural oscillations reportedly comprise different functions depending on their own properties. Therefore, in this study, we investigated details of the complexity of neural oscillations by decomposing the oscillations into frequency, amplitude, and phase for AD patients. We applied resting-state magnetoencephalography (MEG) to 17 AD patients and 21 healthy control subjects. We first decomposed the source time series of the MEG signal into five intrinsic mode functions using ensemble empirical mode decomposition. We then analyzed the temporal complexities of these time series using multiscale entropy. Results demonstrated that AD patients had lower complexity on short time scales and higher complexity on long time scales in the alpha band in temporal regions of the brain. We evaluated the alpha band complexity further by decomposing it into amplitude and phase using Hilbert spectral analysis. Consequently, we found lower amplitude complexity and higher phase complexity in AD patients. Correlation analyses between spectral complexity and decomposed complexities revealed scale-dependency. Specifically, amplitude complexity was positively correlated with spectral complexity on short time scales, whereas phase complexity was positively correlated with spectral complexity on long time scales. Regarding the relevance of cognitive function to the complexity measures, the phase complexity on the long time scale was found to be correlated significantly with the Mini-Mental State Examination score. Additionally, we examined the diagnostic utility of the complexity characteristics using machine learning (ML) methods. We prepared a feature pool using multiple sparse autoencoders (SAEs), chose some discriminating features, and applied them to a support vector machine (SVM). Compared to the simple SVM and the SVM after feature selection (FS + SVM), the SVM with multiple SAEs (SAE + FS + SVM) had improved diagnostic accuracy. Through this study, we 1) advanced the understanding of neuronal complexity in AD patients using decomposed temporal complexity analysis and 2) demonstrated the effectiveness of combining ML methods with information about signal complexity for the diagnosis of AD.

EEG spectral power abnormalities and their relationship with cognitive dysfunction in patients with Alzheimer's disease and type 2 diabetes

Rhythmic neural activity has been proposed to play a fundamental role in cognition. Both healthy and pathological aging are characterized by frequency-specific changes in oscillatory activity. However, the cognitive relevance of these changes across the spectrum from normal to pathological aging remains unknown. We examined electroencephalography (EEG) correlates of cognitive function in healthy aging and 2 of the most prominent and debilitating age-related disorders: type 2 diabetes mellitus (T2DM) and Alzheimer's disease (AD). Relative to healthy controls (HC), patients with AD were impaired on nearly every cognitive measure, whereas patients with T2DM performed worse mainly on learning and memory tests. A continuum of alterations in resting-state EEG was associated with pathological aging, generally characterized by reduced alpha (α) and beta (β) power (AD < T2DM < HC) and increased delta (δ) and theta (θ) power (AD > T2DM > HC), with some variations across different brain regions. There were also reductions in the frequency and power density of the posterior dominant rhythm in AD. The ratio of (α + β)/(δ + θ) was specifically associated with cognitive function in a domain- and diagnosis-specific manner. The results thus captured both similarities and differences in the pathophysiology of cerebral oscillations in T2DM and AD. Overall, pathological brain aging is marked by a shift in oscillatory power from higher to lower frequencies, which can be captured by a single cognitively relevant measure of the ratio of (α + β) over (δ + θ) power.

BOLD and EEG signal variability at rest differently relate to aging in the human brain

Variability of neural activity is regarded as a crucial feature of healthy brain function, and several neuroimaging approaches have been employed to assess it noninvasively. Studies on the variability of both evoked brain response and spontaneous brain signals have shown remarkable changes with aging but it is unclear if the different measures of brain signal variability - identified with either hemodynamic or electrophysiological methods - reflect the same underlying physiology. In this study, we aimed to explore age differences of spontaneous brain signal variability with two different imaging modalities (EEG, fMRI) in healthy younger (25 ​± ​3 years, N ​= ​135) and older (67 ​± ​4 years, N ​= ​54) adults. Consistent with the previous studies, we found lower blood oxygenation level dependent (BOLD) variability in the older subjects as well as less signal variability in the amplitude of low-frequency oscillations (1-12 ​Hz), measured in source space. These age-related reductions were mostly observed in the areas that overlap with the default mode network. Moreover, age-related increases of variability in the amplitude of beta-band frequency EEG oscillations (15-25 ​Hz) were seen predominantly in temporal brain regions. There were significant sex differences in EEG signal variability in various brain regions while no significant sex differences were observed in BOLD signal variability. Bivariate and multivariate correlation analyses revealed no significant associations between EEG- and fMRI-based variability measures. In summary, we show that both BOLD and EEG signal variability reflect aging-related processes but are likely to be dominated by different physiological origins, which relate differentially to age and sex.

Hypersynchronization in mild cognitive impairment: the ‘X’ model

Hypersynchronization has been considered as a biomarker of synaptic dysfunction along the Alzheimeŕs disease continuum. In a longitudinal MEG study, Pusil et al. reveal changes in functional connectivity upon progression from MCI to Alzheimer’s disease. They propose the ‘X’ model to explain their findings, and suggest that hypersynchronization predicts conversion.

Decreased Parietal Beta Power as a Sign of Disease Progression in Patients with Mild Cognitive Impairment

BACKGROUND

Electroencephalography (EEG) power has previously been used to compare mild cognitive impairment (MCI) patients who progress to Alzheimer's disease (pMCI) with patients with MCI who remain stable (sMCI) by using beta power. However, the beta band is very broad and smaller frequency bands may improve accuracy.

OBJECTIVE

In the present study, we wanted to investigate whether it was possible to find any differences between pMCI and sMCI using relative power and whether these differences were correlated to cognitive function or neuropathology markers.

METHODS

17 patients with AD, 27 patients with MCI, and 38 older healthy controls were recruited from two memory clinics and followed for three years. EEGs were recorded at baseline for all participants and relative power was calculated. All participants underwent adjusted batteries of standardized cognitive tests and lumbar puncture.

RESULTS

We found that pMCI showed decreased baseline relative power in the parietal electrodes in the beta1 band (13-17.99 Hz). At 2-year follow-up, we found changes in all baseline beta bands but most pronounced in the beta1 band. In addition, we found that qEEG parietal power was correlated with amyloid-β42 and anterograde memory.

CONCLUSION

These findings suggests that relative power in the parietal electrodes in the beta1 band may be a better way to discriminate between pMCI and sMCI at the time of diagnosis than the broad beta band. Similar findings have also been found with resting state fMRI. In addition, we found that anterograde memory was correlated to qEEG parietal beta1 power.

Electromagnetic signatures of the preclinical and prodromal stages of Alzheimer's disease

Biomarkers useful for the predementia stages of Alzheimer’s disease are needed. Electroencephalography and magnetoencephalography (MEG) are expected to provide potential biomarker candidates for evaluating the predementia stages of Alzheimer’s disease. However, the physiological relevance of EEG/MEG signal changes and their role in pathophysiological processes such as amyloid-β deposition and neurodegeneration need to be elucidated. We evaluated 28 individuals with mild cognitive impairment and 38 cognitively normal individuals, all of whom were further classified into amyloid-β-positive mild cognitive impairment (n = 17, mean age 74.7 ± 5.4 years, nine males), amyloid-β-negative mild cognitive impairment (n = 11, mean age 73.8 ± 8.8 years, eight males), amyloid-β-positive cognitively normal (n = 13, mean age 71.8 ± 4.4 years, seven males), and amyloid-β-negative cognitively normal (n = 25, mean age 72.5 ± 3.4 years, 11 males) individuals using Pittsburgh compound B-PET. We measured resting state MEG for 5 min with the eyes closed, and investigated regional spectral patterns of MEG signals using atlas-based region of interest analysis. Then, the relevance of the regional spectral patterns and their associations with pathophysiological backgrounds were analysed by integrating information from Pittsburgh compound B-PET, fluorodeoxyglucose-PET, structural MRI, and cognitive tests. The results demonstrated that regional spectral patterns of resting state activity could be separated into several types of MEG signatures as follows: (i) the effects of amyloid-β deposition were expressed as the alpha band power augmentation in medial frontal areas; (ii) the delta band power increase in the same region was associated with disease progression within the Alzheimer’s disease continuum and was correlated with entorhinal atrophy and an Alzheimer’s disease-like regional decrease in glucose metabolism; and (iii) the global theta power augmentation, which was previously considered to be an Alzheimer’s disease-related EEG/MEG signature, was associated with general cognitive decline and hippocampal atrophy, but was not specific to Alzheimer’s disease because these changes could be observed in the absence of amyloid-β deposition. The results suggest that these MEG signatures may be useful as unique biomarkers for the predementia stages of Alzheimer’s disease.

EEG evidence of compensatory mechanisms in preclinical Alzheimer’s disease

Early biomarkers are needed to identify individuals at high risk of preclinical Alzheimer’s disease and to better understand the pathophysiological processes of disease progression. Preclinical Alzheimer’s disease EEG changes would be non-invasive and cheap screening tools and could also help to predict future progression to clinical Alzheimer’s disease. However, the impact of amyloid-β deposition and neurodegeneration on EEG biomarkers needs to be elucidated. We included participants from the INSIGHT-preAD cohort, which is an ongoing single-centre multimodal observational study that was designed to identify risk factors and markers of progression to clinical Alzheimer’s disease in 318 cognitively normal individuals aged 70–85 years with a subjective memory complaint. We divided the subjects into four groups, according to their amyloid status (based on 18F-florbetapir PET) and neurodegeneration status (evidenced by 18F-fluorodeoxyglucose PET brain metabolism in Alzheimer’s disease signature regions). The first group was amyloid-positive and neurodegeneration-positive, which corresponds to stage 2 of preclinical Alzheimer’s disease. The second group was amyloid-positive and neurodegeneration-negative, which corresponds to stage 1 of preclinical Alzheimer’s disease. The third group was amyloid-negative and neurodegeneration-positive, which corresponds to ‘suspected non-Alzheimer’s pathophysiology’. The last group was the control group, defined by amyloid-negative and neurodegeneration-negative subjects. We analysed 314 baseline 256-channel high-density eyes closed 1-min resting state EEG recordings. EEG biomarkers included spectral measures, algorithmic complexity and functional connectivity assessed with a novel information-theoretic measure, weighted symbolic mutual information. The most prominent effects of neurodegeneration on EEG metrics were localized in frontocentral regions with an increase in high frequency oscillations (higher beta and gamma power) and a decrease in low frequency oscillations (lower delta power), higher spectral entropy, higher complexity and increased functional connectivity measured by weighted symbolic mutual information in theta band. Neurodegeneration was associated with a widespread increase of median spectral frequency. We found a non-linear relationship between amyloid burden and EEG metrics in neurodegeneration-positive subjects, either following a U-shape curve for delta power or an inverted U-shape curve for the other metrics, meaning that EEG patterns are modulated differently depending on the degree of amyloid burden. This finding suggests initial compensatory mechanisms that are overwhelmed for the highest amyloid load. Together, these results indicate that EEG metrics are useful biomarkers for the preclinical stage of Alzheimer’s disease.

Potential Clinical Applications and Future Prospect of Wireless and Mobile Electroencephalography on the Assessment of Cognitive Impairment

Electroencephalography (EEG) systems have been used for assessing cognitive function in dementia for several decades. Studies have demonstrated that EEG in Alzheimer's disease (AD) patients is generally characterized by significant and specific increases in delta and theta power, a decrease in alpha power, and a decrease in the coherence of the fast bands between different brain areas linked by long corticocortical fibers. Posterior EEG characteristics in dementia with Lewy bodies (DLB) allowed discrimination of DLB from AD and controls with high accuracy. Traditional EEG systems require a long application time and discomfort, which limited its use in dementia patients. Alternative tools for assessing cognition may be simple, low-cost, and mobile medical devices such as wireless and mobile EEG (wmEEG) sensor platforms with flexible electronics and stretchable electrode sheets that could be compatible with long-term EEG monitoring even in dementia patients. In this study, we review the utility of EEG in reflecting cognitive function and the prospects for clinical application of wmEEG monitoring for detecting early dementia and discriminating subtypes of dementia effectively and objectively assessing longitudinal cognitive changes. Repeated and longitudinal documentation of EEG using wmEEG will contribute to detection of specific sleep/wake EEG patterns for patients with sleep and wake-related problems related to dementia.

Slowing is slowing: Delayed neural responses to words are linked to abnormally slow resting state activity in primary progressive aphasia

Neurodegenerative disorders are often characterized by neuronal "slowing," which may be assessed in different ways. In the present study, we examined the latency of neural responses to linguistic stimuli in participants diagnosed with primary progressive aphasia (PPA), as well as changes in the power spectra of resting state activity, both measured with MEG. Compared to both age-matched and younger controls, patients with PPA showed a delayed latency of 8-30 Hz event-related desynchronization (ERD) in response to semantic anomalies. In addition, resting-state MEG revealed increased power in the lower frequency delta and theta bands, but decreased activity in the higher alpha and beta bands. The task-induced and spontaneous measures of neural dynamics were related, such that increased peak latencies in response to words were correlated with a shift of spontaneous oscillatory dynamics towards lower frequencies. In contrast, older controls showed similar task related ERD latencies as younger controls, but also "speeding" of spontaneous activity, i.e. a shift towards faster frequencies. In PPA patients both increased peak latencies on task and increased slow oscillations at rest were associated with less accurate performance on the language task and poorer performance on offline cognitive measures, beyond variance accounted for by structural atrophy. A mediation analysis indicated that increased theta power accounted for the relationship between delayed electrophysiological responses and reduced accuracy in PPA patients. These results indicate that the neuropathological changes in PPA result in slowing of both task-related and spontaneous neuronal activity, linked to functional decline, whereas the speeding of spontaneous activity in healthy aging seems to have a protective or compensatory effect.

Alteration in sleep architecture and electroencephalogram as an early sign of Alzheimer's disease preceding the disease pathology and cognitive decline

OBJECTIVE

The present work aims to evaluate the significance of sleep disturbance and electroencephalogram (EEG) alteration in the early stage of Alzheimer's disease (AD).

BACKGROUND AND RATIONALE

Sleep disturbance is common in patients with AD. It is not known if it can occur at the early stage of AD and if EEG recording may help identify the early sign of the disease.

HISTORICAL EVOLUTION

Sleep disturbance in AD has generally been considered as late consequence of the neurodegenerative process. A growing body of evidence has suggested that the sleep disturbance may occur at the early stage of AD.

UPDATED HYPOTHESIS

Based on the previous epidemiologic studies and our recent findings, we propose that sleep disturbance may play an important role in the development of AD. Sleep EEG changes may serve as a valuable early sign for AD in the prepathological stage.

EARLY EXPERIMENTAL DATA

Our data suggested that AβPP^swe/PS1^ΔE9 transgenic AD mice at preplaque stage (3 and 4 months of age) exhibited different profile of sleep architecture and sleep EEG, which preceded the cognitive deficit and AD neuropathology.

FUTURE EXPERIMENTS AND VALIDATION STUDIES

Future experiments should focus on sleep EEG changes in patients with mild cognitive impairment and early stage of AD. Follow-up studies in high-risk population of the elderly are equally important. In addition, the exact molecular mechanism underlying the sleep disturbance should be thoroughly investigated.

MAJOR CHALLENGES FOR THE HYPOTHESIS

Studies on human participants with early stage of AD, especially the follow-up studies on the presymptomatic elderly in a large population, are difficult and time-consuming.

LINKAGE TO OTHER MAJOR THEORIES

Our hypothesis may link previous theories to establish a bidirectional relationship between sleep disorders and AD, which may finally form a new schematic mechanism to understand the disease pathogenesis and disease progression.

The Importance of the Validation of M/EEG With Current Biomarkers in Alzheimer's Disease

Current biomarkers used in research and in clinical practice in Alzheimer's Disease (AD) are the analysis of cerebral spinal fluid (CSF) to detect levels of Aβ42 and phosphorylated-tau, amyloid and FDG-PET, and MRI volumetry. Some of these procedures are still invasive for patients or expensive. Electroencephalography (EEG) and Magnetoencephalography (MEG) are two non-invasive techniques able to detect the early synaptic dysfunction and track the course of the disease. However, in spite of its added value they are not part of the standard of care in clinical practice in dementia. In this paper we review what these neurophysiological techniques can add to the early diagnosis of AD, whether results in both modalities are related to each other or not, as well as the need of its validation against current biomarkers. We discuss their potential implications for the better understanding of the pathophysiological mechanisms of the disease as well as the need of performing simultaneous M/EEG recordings to better understand discrepancies between these two techniques. Finally, more studies are needed studying M/EEG with amyloid and Tau biomarkers.

Spontaneous oscillatory markers of cognitive status in two forms of dementia

Abnormal oscillatory brain activity in dementia may indicate incipient neuronal/synaptic dysfunction, rather than frank structural atrophy. Leveraging a potential link between the degree of abnormal oscillatory activity and cognitive symptom severity, one could localize brain regions in a diseased but pre-atrophic state, which may be more amenable to interventions. In the current study, we evaluated the relationships among cognitive deficits, regional volumetric changes, and resting-state magnetoencephalography abnormalities in patients with mild cognitive impairment (MCI; N = 10; age: 75.9 ± 7.3) or primary progressive aphasia (PPA; N = 12; 69.7 ± 8.0), and compared them to normal aging [young (N = 18; 24.6 ± 3.5), older controls (N = 24; 67.2 ± 9.7]. Whole-brain source-level resting-state estimates of relative oscillatory power in the delta (1-4 Hz), theta (4-7 Hz), alpha (8-12 Hz), and beta (15-30 Hz) bands were combined with gray matter volumes and cognitive scores to examine between-group differences and brain-behavior correlations. Language and executive function (EF) abilities were impaired in patients with PPA, while episodic memory was impaired in MCI. Widespread oscillatory speeding and volumetric shrinkage was associated with normal aging, whereas the trajectory in PPA indicated widespread oscillatory slowing with additional volumetric reductions. Increases in delta and decreases in alpha power uniquely predicted group membership to PPA. Beyond volumetric reductions, more delta predicted poorer memory. In patients with MCI, no consistent group difference among oscillatory measures was found. The contributions of delta/alpha power on memory abilities were larger than volumetric differences. Spontaneous oscillatory abnormalities in association with cognitive symptom severity can serve as a marker of neuronal dysfunction in dementia, providing targets for promising treatments.

A quick test of cognitive speed in older adults with Alzheimer's disease and mild cognitive impairment: A preliminary behavioral and brain imaging study

The purpose of this study was to assess scores and processing speed distributions of the instrument, A Quick Test of Cognitive Speed (AQT), in Korean older adults through behavioral and brain imaging approaches. Participants were instructed to say the color names, stimuli's form, and both the color and form. Test scores and processing speeds were measured in these three subtests of color, form, and color-form. A total of 67 patients (22 healthy controls (HC), 22 with mild cognitive impairment (MCI), and 23 with Alzheimer's disease (AD)) participated. Only color-form score and processing speed of the three subtests could be used to differentiate AD from MCI and HC. Color-form score showed the largest effects size (partial η² = 0.268) for distinguishing AD, MCI from HC and ROC curve analysis confirmed a high level of sensitivity (0.857) and specificity (0.826) for discrimination between AD and HC. None of the subtests could differentiate HC from MCI. Voxel-based morphometry analysis of brain structure in 27 participants (9 in each group) revealed that gray matter volume of the middle occipital gyrus and inferior parietal cortex were associated with color-form score. This study suggests preliminary evidence in the clinical utility of the AQT for screening AD in older Korean adults. The color-form score could be implemented for clinical utilization in a very brief time. Furthermore, strong positive correlations between color-form scores and the brain areas responsible for visuospatial working memory corroborate the validity of AQT.

Healthy and Pathological Brain Aging: From the Perspective of Oscillations, Functional Connectivity, and Signal Complexity

Healthy aging is associated with impairment in cognitive information processing. Several neuroimaging methods such as functional magnetic resonance imaging, positron emission tomography and near-infrared spectroscopy have been used to explore healthy and pathological aging by relying on hemodynamic or metabolic changes that occur in response to brain activity. Since electroencephalography (EEG) and magnetoencephalography (MEG) are able to measure neural activity directly with a high temporal resolution of milliseconds, these neurophysiological techniques are particularly important to investigate the dynamics of brain activity underlying neurocognitive aging. It is well known that age is a major risk factor for Alzheimer's disease (AD), and that synaptic dysfunction represents an early sign of this disease associated with hallmark neuropathological findings. However, the neurophysiological mechanisms underlying AD are not fully elucidated. This review addresses healthy and pathological brain aging from a neurophysiological perspective, focusing on oscillatory activity changes during the resting state, event-related potentials and stimulus-induced oscillatory responses during cognitive or motor tasks, functional connectivity between brain regions, and changes in signal complexity. We also highlight the accumulating evidence on age-related EEG/MEG changes and biological markers of brain neurodegeneration, including genetic factors, structural abnormalities on magnetic resonance images, and the biochemical changes associated with Aβ deposition and tau pathology.

No association of cortical amyloid load and EEG connectivity in older people with subjective memory complaints

Changes in functional connectivity of cortical networks have been observed in resting-state EEG studies in healthy aging as well as preclinical and clinical stages of AD. Little information, however, exists on associations between EEG connectivity and cortical amyloid load in people with subjective memory complaints. Here, we determined the association of global cortical amyloid load, as measured by florbetapir-PET, with functional connectivity based on the phase-lag index of resting state EEG data for alpha and beta frequency bands in 318 cognitively normal individuals aged 70–85 years with subjective memory complaints from the INSIGHT-preAD cohort. Within the entire group we did not find any significant associations between global amyloid load and phase-lag index in any frequency band. Assessing exclusively the subgroup of amyloid-positive participants, we found enhancement of functional connectivity with higher global amyloid load in the alpha and a reduction in the beta frequency bands. In the amyloid-negative participants, higher amyloid load was associated with lower connectivity in the low alpha band. However, these correlations failed to reach significance after controlling for multiple comparisons. The absence of a strong amyloid effect on functional connectivity may represent a selection effect, where individuals remain in the cognitively normal group only if amyloid accumulation does not impair cortical functional connectivity.

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A confirmatory study in subjective memory complainers (SMC) is presented.

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Amyloid is not associated with functional connectivity in SMC.

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Effects of amyloid on cognition are independent of functional connectivity in SMC.

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Functional connectivity changes may follow amyloid load with a temporal delay.