Prediction of longitudinal cognitive decline in normal elderly with subjective complaints using electrophysiological imaging

Identification of Concussion Subtypes Based on Intrinsic Brain Activity

Importance

The identification of brain activity-based concussion subtypes at time of injury has the potential to advance the understanding of concussion pathophysiology and to optimize treatment planning and outcomes.

Objective

To investigate the presence of intrinsic brain activity-based concussion subtypes, defined as distinct resting state quantitative electroencephalography (qEEG) profiles, at the time of injury.

Design, Setting, and Participants

In this retrospective, multicenter (9 US universities and high schools and 4 US clinical sites) cohort study, participants aged 13 to 70 years with mild head injuries were included in longitudinal cohort studies from 2017 to 2022. Patients had a clinical diagnosis of concussion and were restrained from activity by site guidelines for more than 5 days, with an initial Glasgow Coma Scale score of 14 to 15. Participants were excluded for known neurological disease or history of traumatic brain injury within the last year. Patients were assessed with 2 minutes of artifact-free EEG acquired from frontal and frontotemporal regions within 120 hours of head injury. Data analysis was performed from July 2021 to June 2023.

Main Outcomes and Measures

Quantitative features characterizing the EEG signal were extracted from a 1- to 2-minute artifact-free EEG data for each participant, within 120 hours of injury. Symptom inventories and days to return to activity were also acquired.

Results

From the 771 participants (mean [SD] age, 20.16 [5.75] years; 432 male [56.03%]), 600 were randomly selected for cluster analysis according to 471 qEEG features. Participants and features were simultaneously grouped into 5 disjoint subtypes by a bootstrapped coclustering algorithm with an overall agreement of 98.87% over 100 restarts. Subtypes were characterized by distinctive profiles of qEEG measure sets, including power, connectivity, and complexity, and were validated in the independent test set. Subtype membership showed a statistically significant association with time to return to activity.

Conclusions and Relevance

In this cohort study, distinct subtypes based on resting state qEEG activity were identified within the concussed population at the time of injury. The existence of such physiological subtypes supports different underlying pathophysiology and could aid in personalized prognosis and optimization of care path.

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.

Follow-up of participants with subjective cognitive decline from Tremembé epidemiologic study, Brazil

Subjective cognitive decline is defined as a self-perceived cognitive decline but with normal performance in neuropsychological assessments.

Objective

To verify the evolution of patients diagnosed with subjective cognitive decline compared to the cognitively normal group without any concern.

Methods

This is a follow-up study based on data analysis from the Tremembé epidemiologic study, in Brazil. The 211 individuals classified as cognitively normal and 174 diagnosed as having subjective cognitive decline at baseline were invited to participate.

Results

After a median follow-up time of five years, 108 subjective cognitive decline participants (62.0%) were reassessed. Of these, 58 (53.7%) kept this diagnosis, whereas 14 individuals (12.9%) progressed to mild cognitive impairment and 5 (4.6%) to dementia. In the cognitively normal group, 107 (50.7%) were reassessed, of which 51 (47.7%) were still classified likewise, 6 (5.6%) evolved to mild cognitive impairment and 9 (8.4%) to dementia. The presence of cognitive decline had a significant association with increasing age and depression symptoms. Considering the total number of baseline participants in each group: the subjective cognitive decline group showed higher percentage of mild cognitive impairment (p=0.022) and no difference was found in progression to dementia (p=0.468) between the groups after follow-up assessment.

Conclusion

Most subjective cognitive decline participants at baseline kept their cognitive complaint at follow-up and this group progressed more to mild cognitive impairment than the other group. No difference in the progression to dementia was found, despite the higher incidence of dementia in the cognitively normal group.

Associations of Baseline Sleep Microarchitecture with Cognitive Function After 8 Years in Middle-Aged and Older Men from a Community-Based Cohort Study

Purpose

Prospective studies examining associations between baseline sleep microarchitecture and future cognitive function recruited from small samples with predominantly short follow-up. This study examined sleep microarchitecture predictors of cognitive function (visual attention, processing speed, and executive function) after 8 years in community-dwelling men.

Patients and Methods

Florey Adelaide Male Ageing Study participants (n=477) underwent home-based polysomnography (2010-2011), with 157 completing baseline (2007-2010) and follow-up (2018-2019) cognitive assessments (trail-making tests A [TMT-A] and B [TMT-B] and the standardized mini-mental state examination [SMMSE]). Whole-night F4-M1 sleep EEG recordings were processed following artifact exclusion, and quantitative EEG characteristics were obtained using validated algorithms. Associations between baseline sleep microarchitecture and future cognitive function (visual attention, processing speed, and executive function) were examined using linear regression models adjusted for baseline obstructive sleep apnoea, other risk factors, and cognition.

Results

The final sample included men aged (mean [SD]) 58.9 (8.9) years at baseline, overweight (BMI 28.5 [4.2] kg/m²), and well educated (75.2% ≥Bachelor, Certificate, or Trade), with majorly normal baseline cognition. Median (IQR) follow-up was 8.3 (7.9, 8.6) years. In adjusted analyses, NREM and REM sleep EEG spectral power was not associated with TMT-A, TMT-B, or SMMSE performance (all p>0.05). A significant association of higher N3 sleep fast spindle density with worse TMT-B performance (B=1.06, 95% CI [0.13, 2.00], p=0.026) did not persist following adjustment for baseline TMT-B performance.

Conclusion

In this sample of community-dwelling men, sleep microarchitecture was not independently associated with visual attention, processing speed, or executive function after 8 years.

Measurement of Frontal Midline Theta Oscillations using OPM-MEG

Optically pumped magnetometers (OPMs) are an emerging lightweight and compact sensor that can measure magnetic fields generated by the human brain. OPMs enable construction of wearable magnetoencephalography (MEG) systems, which offer advantages over conventional instrumentation. However, when trying to measure signals at low frequency, higher levels of inherent sensor noise, magnetic interference and movement artefact introduce a significant challenge. Accurate characterisation of low frequency brain signals is important for neuroscientific, clinical, and paediatric MEG applications and consequently, demonstrating the viability of OPMs in this area is critical. Here, we undertake measurement of theta band (4-8 Hz) neural oscillations and contrast a newly developed 174 channel triaxial wearable OPM-MEG system with conventional (cryogenic-MEG) instrumentation. Our results show that visual steady state responses at 4 Hz, 6 Hz and 8 Hz can be recorded using OPM-MEG with a signal-to-noise ratio (SNR) that is not significantly different to conventional MEG. Moreover, we measure frontal midline theta oscillations during a 2-back working memory task, again demonstrating comparable SNR for both systems. We show that individual differences in both the amplitude and spatial signature of induced frontal-midline theta responses are maintained across systems. Finally, we show that our OPM-MEG results could not have been achieved without a triaxial sensor array, or the use of postprocessing techniques. Our results demonstrate the viability of OPMs for characterising theta oscillations and add weight to the argument that OPMs can replace cryogenic sensors as the fundamental building block of MEG systems.

Quantitative Electroencephalography Analysis for Improved Assessment of Consciousness Levels in Deep Coma Patients Using a Proposed Stimulus Stage

"Coma" is defined as an inability to obey commands, to speak, or to open the eyes. So, a coma is a state of unarousable unconsciousness. In a clinical setting, the ability to respond to a command is often used to infer consciousness. Evaluation of the patient's level of consciousness (LeOC) is important for neurological evaluation. The Glasgow Coma Scale (GCS) is the most widely used and popular scoring system for neurological evaluation and is used to assess a patient's level of consciousness. The aim of this study is the evaluation of GCSs with an objective approach based on numerical results. So, EEG signals were recorded from 39 patients in a coma state with a new procedure proposed by us in a deep coma state (GCS: between 3 and 8). The EEG signals were divided into four sub-bands as alpha, beta, delta, and theta, and their power spectral density was calculated. As a result of power spectral analysis, 10 different features were extracted from EEG signals in the time and frequency domains. The features were statistically analyzed to differentiate the different LeOC and to relate with the GCS. Additionally, some machine learning algorithms have been used to measure the performance of the features for distinguishing patients with different GCSs in a deep coma. This study demonstrated that GCS 3 and GCS 8 patients were classified from other levels of consciousness in terms of decreased theta activity. To the best of our knowledge, this is the first study to classify patients in a deep coma (GCS between 3 and 8) with 96.44% classification performance.

Neuronal Hyperactivation in EEG Data during Cognitive Tasks Is Related to the Apolipoprotein J/Clusterin Genotype in Nondemented Adults

The clusterin (CLU) rs11136000 CC genotype is a probable risk factor for Alzheimer's disease (AD). CLU, also known as the apolipoprotein J gene, shares certain properties with the apolipoprotein E (APOE) gene with a well-established relationship with AD. This study aimed to determine whether the electrophysiological patterns of brain activation during the letter fluency task (LFT) depend on CLU genotypes in adults without dementia. Previous studies have shown that LFT performance involves activation of the frontal cortex. We examined EEG alpha1 and alpha2 band desynchronization in the frontal regions during the LFT in 94 nondemented individuals stratified by CLU (rs11136000) genotype. Starting at 30 years of age, CLU CC carriers exhibited more pronounced task-related alpha2 desynchronization than CLU CT&TT carriers in the absence of any differences in LFT performance. In CLU CC carriers, alpha2 desynchronization was significantly correlated with age. Increased task-related activation in individuals at genetic risk for AD may reflect greater "effort" to perform the task and/or neuronal hyperexcitability. The results show that the CLU genotype is associated with neuronal hyperactivation in the frontal cortex during cognitive tasks performances in nondemented individuals, suggesting systematic vulnerability of LFT related cognitive networks in people carrying unfavorable CLU alleles.

Bibliometric analysis of electroencephalogram research in mild cognitive impairment from 2005 to 2022

Background

Electroencephalogram (EEG), one of the most commonly used non-invasive neurophysiological examination techniques, advanced rapidly between 2005 and 2022, particularly when it was used for the diagnosis and prognosis of mild cognitive impairment (MCI). This study used a bibliometric approach to synthesize the knowledge structure and cutting-edge hotspots of EEG application in the MCI.

Methods

Related publications in the Web of Science Core Collection (WosCC) were retrieved from inception to 30 September 2022. CiteSpace, VOSviewer, and HistCite software were employed to perform bibliographic and visualization analyses.

Results

Between 2005 and 2022, 2,905 studies related to the application of EEG in MCI were investigated. The United States had the highest number of publications and was at the top of the list of international collaborations. In terms of total number of articles, IRCCS San Raffaele Pisana ranked first among institutions. The Clinical Neurophysiology published the greatest number of articles. The author with the highest citations was Babiloni C. In descending order of frequency, keywords with the highest frequency were “EEG,” “mild cognitive impairment,” and “Alzheimer’s disease”.

Conclusion

The application of EEG in MCI was investigated using bibliographic analysis. The research emphasis has shifted from examining local brain lesions with EEG to neural network mechanisms. The paradigm of big data and intelligent analysis is becoming more relevant in EEG analytical methods. The use of EEG to link MCI to other related neurological disorders, and to evaluate new targets for diagnosis and treatment, has become a new research trend. The above-mentioned findings have implications in the future research on the application of EEG in MCI.

Longitudinal association between subjective and objective memory in older adults: a study with the Virginia Cognitive Aging Project sample

Using the bivariate dual change score approach, the present study investigated the directionality of the SMC-OMP association in a sample of healthy older adults (N = 2,057) from the Virginia Cognitive Aging Project. The sample was assessed throughout 10 years, five time points, and the impact of education, depressive symptoms, and low-memory functioning was tested. The Memory Functioning Questionnaire was used to assess SMC. There was a lack of longitudinal association with no significant coupling effects found between subjective and objective memory. After including depressive symptoms as a covariate, Frequency of Forgetting significantly predicted subsequent negative changes in OMP . A similar result was found for the low-memory functioning group after the inclusion of depression, with the frequency of memory complaints predicting subsequent memory decline . Our results do not support a predictive value of SMC without accounting for the influence of depressive symptoms and low-memory functioning.

Patterns of structure-function association in normal aging and in Alzheimer's disease: Screening for mild cognitive impairment and dementia with ML regression and classification models

Background

The combined analysis of imaging and functional modalities is supposed to improve diagnostics of neurodegenerative diseases with advanced data science techniques.

Objective

To get an insight into normal and accelerated brain aging by developing the machine learning models that predict individual performance in neuropsychological and cognitive tests from brain MRI. With these models we endeavor to look for patterns of brain structure-function association (SFA) indicative of mild cognitive impairment (MCI) and Alzheimer's dementia.

Materials and methods

We explored the age-related variability of cognitive and neuropsychological test scores in normal and accelerated aging and constructed regression models predicting functional performance in cognitive tests from brain radiomics data. The models were trained on the three study cohorts from ADNI dataset-cognitively normal individuals, patients with MCI or dementia-separately. We also looked for significant correlations between cortical parcellation volumes and test scores in the cohorts to investigate neuroanatomical differences in relation to cognitive status. Finally, we worked out an approach for the classification of the examinees according to the pattern of structure-function associations into the cohorts of the cognitively normal elderly and patients with MCI or dementia.

Results

In the healthy population, the global cognitive functioning slightly changes with age. It also remains stable across the disease course in the majority of cases. In healthy adults and patients with MCI or dementia, the trendlines of performance in digit symbol substitution test and trail making test converge at the approximated point of 100 years of age. According to the SFA pattern, we distinguish three cohorts: the cognitively normal elderly, patients with MCI, and dementia. The highest accuracy is achieved with the model trained to predict the mini-mental state examination score from voxel-based morphometry data. The application of the majority voting technique to models predicting results in cognitive tests improved the classification performance up to 91.95% true positive rate for healthy participants, 86.21%-for MCI and 80.18%-for dementia cases.

Conclusion

The machine learning model, when trained on the cases of this of that group, describes a disease-specific SFA pattern. The pattern serves as a "stamp" of the disease reflected by the model.

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.

Hub structure in functional network of EEG signals supporting high cognitive functions in older individuals

Introduction

Maintaining high cognitive functions is desirable for "wellbeing" in old age and is particularly relevant to a super-aging society. According to their individual cognitive functions, optimal intervention for older individuals facilitates the maintenance of cognitive functions. Cognitive function is a result of whole-brain interactions. These interactions are reflected in several measures in graph theory analysis for the topological characteristics of functional connectivity. Betweenness centrality (BC), which can identify the "hub" node, i.e., the most important node affecting whole-brain network activity, may be appropriate for capturing whole-brain interactions. During the past decade, BC has been applied to capture changes in brain networks related to cognitive deficits arising from pathological conditions. In this study, we hypothesized that the hub structure of functional networks would reflect cognitive function, even in healthy elderly individuals.

Method

To test this hypothesis, based on the BC value of the functional connectivity obtained using the phase lag index from the electroencephalogram under the eyes closed resting state, we examined the relationship between the BC value and cognitive function measured using the Five Cognitive Functions test total score.

Results

We found a significant positive correlation of BC with cognitive functioning and a significant enhancement in the BC value of individuals with high cognitive functioning, particularly in the frontal theta network.

Discussion

The hub structure may reflect the sophisticated integration and transmission of information in whole-brain networks to support high-level cognitive function. Our findings may contribute to the development of biomarkers for assessing cognitive function, enabling optimal interventions for maintaining cognitive function in older individuals.

Differences in Subjective and Objective Cognitive Decline Outcomes Are Associated with Modifiable Protective Factors: A 4-Year Longitudinal Study

Subjective cognitive decline (SCD) in older adults has been identified as a risk factor for dementia. However, the literature is inconsistent, and the underlying mechanisms are not well understood. We aimed to determine whether older adults with SCD had more modifiable protective factors against the risk of dementia and a lower risk of developing objective cognitive decline (OCD). We included 4363 older adults (71.7 ± 5.3 [mean ± standard deviation] years of age; 2239 women) from the National Center for Geriatrics and Gerontology Study of Geriatric Syndromes. SCD, OCD, and protective factors against dementia, such as lifestyle and activity, were assessed using interviews and objective cognitive-assessment tools. Based on initial cognitive status, participants were categorized into normal cognition, SCD-only, OCD-only, and both SCD and OCD groups. After 4 years, participants were classified as having either no impairment or mild or global cognitive impairment (i.e., OCD). Binomial logistic regression analyses were performed with the cognitive statuses of the groups at follow-up and baseline as the dependent and independent variables, respectively. After adjusting for potential confounding factors, we found that the SCD-only group had more modifiable protective factors against the risk of dementia than the OCD-only group. Community-dwelling older adults with normal cognition or those part of the SCD-only group had a lower risk of developing OCD during the 4-year follow-up, which may have been due to having more modifiable protective factors against the risk of dementia. Additionally, these factors may contribute to the inconsistencies in the literature on SCD outcomes.

Early dementia diagnosis, MCI-to-dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for EEG signal analysis

INTRODUCTION

Dementia in its various forms represents one of the most frightening emergencies for the aging population. Cognitive decline-including Alzheimer's disease (AD) dementia-does not develop in few days; disease mechanisms act progressively for several years before clinical evidence.

METHODS

A preclinical stage, characterized by measurable cognitive impairment, but not overt dementia, is represented by mild cognitive impairment (MCI), which progresses to-or, more accurately, is already in a prodromal form of-AD in about half cases; people with MCI are therefore considered the population at risk for AD deserving special attention for validating screening methods.

RESULTS

Graph analysis tools, combined with machine learning methods, represent an interesting probe to identify the distinctive features of physiological/pathological brain aging focusing on functional connectivity networks evaluated on electroencephalographic data and neuropsychological/imaging/genetic/metabolic/cerebrospinal fluid/blood biomarkers.

DISCUSSION

On clinical data, this innovative approach for early diagnosis might provide more insight into pathophysiological processes underlying degenerative changes, as well as toward a personalized risk evaluation for pharmacological, nonpharmacological, and rehabilitation treatments.

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.

Virtual Reality and Exercise Training Enhance Brain, Cognitive, and Physical Health in Older Adults with Mild Cognitive Impairment

We investigated the effectiveness of virtual-reality-based cognitive training (VRCT) and exercise on the brain, cognitive, physical and activity of older adults with mild cognitive impairment (MCI).

METHODS

This study included 99 participants (70.8 ± 5.4) with MCI in the VRCT, exercise, and control groups. The VRCT consisted of a series of games targeting different brain functions such as executive function, memory, and attention. Twenty-four sessions of VRCT (three days/week) were performed, and each session was 100 min long. Exercise intervention consisted of aerobic and resistance trainings performed in 24 sessions for 60 min (2 times/week for 12 weeks). Global cognitive function was measured using the Mini-Mental State Examination (MMSE) test. Resting-state electroencephalography (EEG) of the neural oscillatory activity in different frequency bands was performed. Physical function was measured using handgrip strength (HGS) and gait speed.

RESULTS

After the intervention period, VRCT significantly improved the MMSE scores (p < 0.05), and the exercise group had significantly improved HGS and MMSE scores (p < 0.05) compared to baseline. One-way analysis of variance (ANOVA) of resting-state EEG showed a decreased theta/beta power ratio (TBR) (p < 0.05) in the central region of the brain in the exercise group compared to the control group. Although not statistically significant, the VRCT group also showed a decreased TBR compared to the control group. The analysis of covariance (ANCOVA) test showed a significant decrease in theta band power in the VRCT group compared to the exercise group and a decrease in delta/alpha ratio in the exercise group compared to the VRCT group.

CONCLUSION

Our findings suggest that VRCT and exercise training enhances brain, cognitive, and physical health in older adults with MCI. Further studies with a larger population sample to identify the effect of VRCT in combination with exercise training are required to yield peak benefits for patients with MCI.

Theta Band-Power Shapes Amyloid-Driven Longitudinal EEG Changes in Elderly Subjective Memory Complainers At-Risk for Alzheimer's Disease

BACKGROUND

Alzheimer's disease (AD) includes progressive symptoms spread along a continuum of preclinical and clinical stages. Although numerous studies uncovered the neuro-cognitive changes of AD, very little is known on the natural history of brain lesions and modifications of brain networks in elderly cognitively-healthy memory complainers at risk of AD for carrying pathophysiological biomarkers (amyloidopathy and tauopathy).

OBJECTIVE

We analyzed resting-state electroencephalography (EEG) of 318 cognitively-healthy subjective memory complainers from the INSIGHT-preAD cohort at the time of their first visit (M0) and two-years later (M24).

METHODS

Using 18F-florbetapir PET-scanner, subjects were stratified between amyloid negative (A-; n = 230) and positive (A+; n = 88) groups. Differences between A+ and A-were estimated at source-level in each band-power of the EEG spectrum.

RESULTS

At M0, we found an increase of theta power in the mid-frontal cortex in A+ compared to A-. No significant association was found between mid-frontal theta and the individuals' cognitive performance. At M24, theta power increased in A+ relative to A-individuals in the posterior cingulate cortex and the pre-cuneus. Alpha band revealed a peculiar decremental trend in posterior brain regions in the A+ relative to the A-group only at M24. Theta power increase over the mid-frontal and mid-posterior cortices suggests an hypoactivation of the default-mode network in the A+ individuals and a non-linear longitudinal progression at M24.

CONCLUSION

We provide the first source-level longitudinal evidence on the impact of brain amyloidosis on the EEG dynamics of a large-scale, monocentric cohort of elderly individuals at-risk for AD.

Quantitative electroencephalography changes in patients with mild cognitive impairment after choline alphoscerate administration

There is limited evidence on the effectiveness of choline alphoscerate for mild cognitive impairment (MCI) in studies using neuropsychological markers. The aim of this study was to evaluate the spectral change at a source level using quantitative electroencephalography (qEEG) as a biomarker for cognitive function after choline alphoscerate administration to patients with MCI. This study used the qEEG data of patients with MCI who visited the Department of Neurology of the Chung-Ang University Hospital between April 2017 and December 2018. Resting-state EEG studies were performed on 33 patients with MCI at baseline and compared with those of the 18 normal controls selected from the community. After baseline qEEG, choline alphoscerate 400 mg was administered twice daily for 2 months to the patients with MCI. Follow-up qEEG was performed in 20 subjects. Baseline qEEG of patients with MCI was compared to qEEG after choline alphoscerate administration. We found that the MCI group exhibited a decreased alpha power compared to that of the control group. Patients with MCI treated with choline alphoscerate exhibited a decrease in the theta and delta power of the parietal and temporal lobe and an increase in the alpha power spectrum of the occipital lobes. We also identified the trend of default mode network enhancement after choline alphoscerate administration. Our results suggest that choline alphoscerate may have a positive effect in patients with MCI and support the usefulness of qEEG for monitoring the therapeutic effect of nootropics.

Electroencephalography Theta/Beta Ratio Decreases in Patients with Severe Obstructive Sleep Apnea

Purpose

Accumulating evidence suggests that theta/beta ratio (TBR), an electroencephalographic (EEG) frequency band parameter, might serve as an objective marker of executive cognitive control in healthy adults. Obstructive sleep apnea (OSA) has a detrimental impact on patients' behavior and cognitive performance while whether TBR is different in OSA population has not been reported. This study aimed to explore the difference in relative EEG spectral power and TBR during sleep between patients with severe OSA and non-OSA groups.

Patients and Methods

142 participants with in-laboratory nocturnal PSG recording were included, among which 100 participants suffered severe OSA (apnea hypopnea index, AHI > 30 events/hour; OSA group) and 42 participants had no OSA (AHI ≤ 5 events/h; control group). The fast Fourier transformation was used to compute the EEG power spectrum for total sleep duration within contiguous 30-second epochs of sleep. The demographic and polysomnographic characteristics, relative EEG spectral power and TBR of the two groups were compared.

Results

It was found that the beta band power during NREM sleep and total sleep was significantly higher in the OSA group than controls (p < 0.001, p = 0.012, respectively), and the theta band power during NREM sleep and total sleep was significantly lower in the OSA group than controls (p = 0.019, p = 0.014, respectively). TBR during NREM sleep, REM sleep and total sleep was significantly lower in the OSA group compared to the control group (p < 0.001 for NREM sleep and total sleep, p = 0.015 for REM sleep). TBR was negatively correlated with AHI during NREM sleep (r=-0.324, p < 0.001) and total sleep (r=-0. 312, p < 0.001).

Conclusion

TBR was significantly decreased in severe OSA patients compared to the controls, which was attributed to both increased beta power and decreased theta power. TBR may be a stable EEG-biomarker of OSA patients, which may accurately and reliably identify phenotype of patients.

Distinct Effects of the Apolipoprotein E ε4 Genotype on Associations Between Delayed Recall Performance and Resting-State Electroencephalography Theta Power in Elderly People Without Dementia

Background

Abnormal electroencephalography (EEG) activity has been demonstrated in mild cognitive impairment (MCI), and theta rhythm might be inversely related to memory. The apolipoprotein E (ApoE) epsilon 4 (ε4) allele, as a genetic vulnerability factor for pathologic and normal age-related cognitive decline, may influence different patterns of cognitive dysfunction. Therefore, the present study primarily aimed to verify the role of resting theta rhythm in delayed recall deficits, and further explore the effects of the ApoE genotype on the associations between the resting theta power and delayed recall performance in the elderly individuals without dementia.

Methods

A total of 47 individuals without dementia, including 23 MCI and 24 healthy subjects (HCs), participated in the study. All subjects were administered the Hopkins Verbal Learning Test–Revised (HVLT-R) to measure delayed recall performance. Power spectra based on resting-state EEG data were used to examine brain oscillations. Linear regression was used to examine the relationships between EEG power and delayed recall performance in each subgroup.

Results

The increased theta power in the bilateral central and temporal areas (P_s = 0.02–0.044, uncorrected) was found in the patients with MCI, and were negatively correlated with delayed recall performance (r_s = −0.358 to −0.306, P_s = 0.014–0.036, FDR corrected) in the elderly individuals without dementia. The worse delayed recall performance was associated with higher theta power in the left central and temporal areas, especially in ApoE ε4 non-carriers and not in carriers (r_s = −0.404 to −0.369, P_s = 0.02–0.035, uncorrected).

Conclusion

Our study suggests that theta disturbances might contribute to delayed recall memory decline. The ApoE genotype may have distinct effects on EEG-based neural correlates of episodic memory performance.

Abnormal EEG Signal Energy in the Elderly: A Wavelet Analysis of Event-related Potentials During a Stroop Task

BACKGROUND

Previous work showed that elderly with excess in theta activity in their resting state electroencephalogram (EEG) are at higher risk of cognitive decline than those with a normal EEG. By using event-related potentials (ERP) during a counting Stroop task, our prior work showed that elderly with theta excess have a large P300 component compared with normal EEG group. This increased activity could be related to a higher EEG signal energy used during this task.

NEW METHOD

By wavelet analysis applied to ERP obtained during a counting Stroop task we quantified the energy in the different frequency bands of a group of elderly with altered EEG.

RESULTS

In theta and alpha bands, the total energy was higher in elderly subjects with theta excess, specifically in the stimulus categorization window (258-516 ms). Both groups solved the task with similar efficiency.

COMPARISON WITH EXISTING METHODS

The traditional ERP analysis in elderly compares voltage among conditions and groups for a given time windows, while the frequency composition is not usually examined. We complemented our previous ERP analysis using a wavelet methodology. Furthermore, we showed the advantages of wavelet analysis over Short Time Fourier Transform when exploring EEG signal during this task.

CONCLUSIONS

The higher EEG signal energy in ERP might reflect undergoing neurobiological mechanisms that allow the elderly with theta excess to cope with the cognitive task with similar behavioral results as the normal EEG group. This increased energy could promote a metabolic and cellular dysregulation causing a greater decline in cognitive function.

Individual predictors and electrophysiological signatures of working memory enhancement in aging

A primary goal of translational neuroscience is to identify the neural mechanisms of age-related cognitive decline and develop protocols to maximally improve cognition. Here, we demonstrate how interventions that apply noninvasive neurostimulation to older adults improve working memory (WM). We found that one session of sham-controlled transcranial direct current stimulation (tDCS) selectively improved WM in older adults with more education, extending earlier work and underscoring the importance of identifying individual predictors of tDCS responsivity. Improvements in WM were associated with two distinct electrophysiological signatures. First, a broad enhancement of theta network synchrony tracked improvements in behavioral accuracy, with tDCS effects moderated by education level. Further analysis revealed that accuracy dynamics reflected an anterior-posterior network distribution regardless of cathode placement. Second, specific enhancements of theta-gamma phase-amplitude coupling (PAC) reflecting tDCS current flow tracked improvements in reaction time (RT). RT dynamics further explained inter-individual variability in WM improvement independent of education. These findings illuminate theta network synchrony and theta-gamma PAC as distinct but complementary mechanisms supporting WM in aging. Both mechanisms are amenable to intervention, the effectiveness of which can be predicted by individual demographic factors.

Accelerated Aging Characterizes the Early Stage of Alzheimer's Disease

For Alzheimer's disease (AD), aging is the main risk factor, but whether cognitive impairments due to aging resemble early AD deficits is not yet defined. When working with mouse models of AD, the situation is just as complicated, because only a few studies track the progression of the disease at different ages, and most ignore how the aging process affects control mice. In this work, we addressed this problem by comparing the aging process of PS2APP (AD) and wild-type (WT) mice at the level of spontaneous brain electrical activity under anesthesia. Using local field potential recordings, obtained with a linear probe that traverses the posterior parietal cortex and the entire hippocampus, we analyzed how multiple electrical parameters are modified by aging in AD and WT mice. With this approach, we highlighted AD specific features that appear in young AD mice prior to plaque deposition or that are delayed at 12 and 16 months of age. Furthermore, we identified aging characteristics present in WT mice but also occurring prematurely in young AD mice. In short, we found that reduction in the relative power of slow oscillations (SO) and Low/High power imbalance are linked to an AD phenotype at its onset. The loss of SO connectivity and cortico-hippocampal coupling between SO and higher frequencies as well as the increase in UP-state and burst durations are found in young AD and old WT mice. We show evidence that the aging process is accelerated by the mutant PS2 itself and discuss such changes in relation to amyloidosis and gliosis.

One-Year Follow-Up of Healthy Older Adults with Electroencephalographic Risk for Neurocognitive Disorder After Neurofeedback Training

Background:

In healthy older adults, excess theta activity is an electroencephalographic (EEG) predictor of cognitive impairment. In a previous study, neurofeedback (NFB) treatment reinforcing reductions theta activity resulted in EEG reorganization and cognitive improvement.

Objective:

To explore the clinical applicability of this NFB treatment, the present study performed a 1-year follow-up to determine its lasting effects.

Methods:

Twenty seniors with excessive theta activity in their EEG were randomly assigned to the experimental or control group. The experimental group received an auditory reward when the theta absolute power (AP) was reduced. The control group received the reward randomly.

Results:

Both groups showed a significant decrease in theta activity at the training electrode. However, the EEG results showed that only the experimental group underwent global changes after treatment. These changes consisted of delta and theta decreases and beta increases. Although no changes were found in any group during the period between the posttreatment evaluation and follow-up, more pronounced theta decreases and beta increases were observed in the experimental group when the follow-up and pretreatment measures were compared. Executive functions showed a tendency to improve two months after treatment which became significant one year later.

Conclusion:

These results suggest that the EEG and behavioral benefits of this NFB treatment persist for at least one year, which adds up to the available evidence contributing to identifying factors that increase its efficacy level. The relevance of this study lies in its prophylactic features of addressing a clinically healthy population with EEG risk of cognitive decline.

The Role of Monoaminergic Tones and Brain Metabolism in Cognition in De Novo Parkinson's Disease

BACKGROUND

Cognitive impairment is frequent in Parkinson's disease (PD) and several neurotransmitter changes have been reported since the time of diagnosis, although seldom investigated altogether in the same patient cohort.

OBJECTIVE

Our aim was to evaluate the association between neurotransmitter impairment, brain metabolism, and cognition in a cohort of de novo, drug-naïve PD patients.

METHODS

We retrospectively selected 95 consecutive drug-naïve PD patients (mean age 71.89±7.53) undergoing at the time of diagnosis a brain [18F]FDG-PET as a marker of brain glucose metabolism and proxy measure of neurodegeneration, [123I]FP-CIT-SPECT as a marker and dopaminergic deafferentation in the striatum and frontal cortex, as well as a marker of serotonergic deafferentation in the thalamus, and quantitative electroencephalography (qEEG) as an indirect measure of cholinergic deafferentation. Patients also underwent a complete neuropsychological battery.

RESULTS

Positive correlations were observed between (i) executive functions and left cerebellar cortex metabolism, (ii) prefrontal dopaminergic tone and working memory (r = 0.304, p = 0.003), (iii) qEEG slowing in the posterior leads and both memory (r = 0.299, p = 0.004) and visuo-spatial functions (r = 0.357, p < 0.001).

CONCLUSIONS

In subjects with PD, the impact of regional metabolism and diffuse projection systems degeneration differs across cognitive domains. These findings suggest possible tailored approaches to the treatment of cognitive deficits in PD.

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.

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.

Auto-weighted centralised multi-task learning via integrating functional and structural connectivity for subjective cognitive decline diagnosis

Early diagnosis and intervention of mild cognitive impairment (MCI) and its early stage (i.e., subjective cognitive decline (SCD)) is able to delay or reverse the disease progression. However, discrimination between SCD, MCI and healthy subjects accurately remains challenging. This paper proposes an auto-weighted centralised multi-task (AWCMT) learning framework for differential diagnosis of SCD and MCI. AWCMT is based on structural and functional connectivity information inferred from magnetic resonance imaging (MRI). To be specific, we devise a novel multi-task learning algorithm to combine neuroimaging functional and structural connective information. We construct a functional brain network through a sparse and low-rank machine learning method, and also a structural brain network via fibre bundle tracking. Those two networks are constructed separately and independently. Multi-task learning is then used to identify features integration of functional and structural connectivity. Hence, we can learn each task's significance automatically in a balanced way. By combining the functional and structural information, the most informative features of SCD and MCI are obtained for diagnosis. The extensive experiments on the public and self-collected datasets demonstrate that the proposed algorithm obtains better performance in classifying SCD, MCI and healthy people than traditional algorithms. The newly proposed method has good interpretability as it is able to discover the most disease-related brain regions and their connectivity. The results agree well with current clinical findings and provide new insights into early AD detection based on the multi-modal neuroimaging technique.

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

Background:

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

Objective:

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

Methods:

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

Results:

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

Conclusion:

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

MCI Detection Using Kernel Eigen-Relative-Power Features of EEG Signals

Classification between individuals with mild cognitive impairment (MCI) and healthy controls (HC) based on electroencephalography (EEG) has been considered a challenging task to be addressed for the purpose of its early detection. In this study, we proposed a novel EEG feature, the kernel eigen-relative-power (KERP) feature, for achieving high classification accuracy of MCI versus HC. First, we introduced the relative powers (RPs) between pairs of electrodes across 21 different subbands of 2-Hz width as the features, which have not yet been used in previous MCI-HC classification studies. Next, the Fisher’s class separability criterion was applied to determine the best electrode pairs (five electrodes) as well as the frequency subbands for extracting the most sensitive RP features. The kernel principal component analysis (kernel PCA) algorithm was further performed to extract a few more discriminating nonlinear principal components from the optimal RPs, and these components form a KERP feature vector. Results carried out on 51 participants (24 MCI and 27 HC) show that the newly introduced subband RP feature showed superior classification performance to commonly used spectral power features, including the band power, single-electrode relative power, and also the RP based on the conventional frequency bands. A high leave-one-participant-out cross-validation (LOPO-CV) classification accuracy 86.27% was achieved by the RP feature, using a simple linear discriminant analysis (LDA) classifier. Moreover, with the same classifier, the proposed KERP further improved the accuracy to 88.24%. Finally, cascading the KERP feature to a nonlinear classifier, the support vector machine (SVM), yields a high MCI-HC classification accuracy of 90.20% (sensitivity = 87.50% and specificity = 92.59%). The proposed method demonstrated a high accuracy and a high usability (only five electrodes are required), and therefore, has great potential to further develop an EEG-based computer-aided diagnosis system that can be applied for the early detection of MCI.

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.

Associations of subjective cognitive and memory decline with depression, anxiety, and two-year change in objectively-assessed global cognition and memory

Research studies exploring the association of cognitive complaints with objectively assessed cognitive decline report inconsistent results. However, many of these have methodological limitations. We investigated whether 1) more severe subjective cognitive decline (SCD) and subjective memory decline (SMD) predict change in objectively assessed global cognition, remote memory, recent memory, learning; 2) the predictive value of more severe SMD over change in objectively assessed remote memory, recent memory, and learning is stronger for individuals that report an SMD that started within the past five years than for those that report an SMD that started five or more years previously and/or stronger for those that experienced SMD within the past two years than for those who had not; and 3) greater depression and anxiety are associated with more severe SCD and SMD. We used two-year longitudinal data from the CFAS-Wales study (N = 1,531; mean (SD) age = 73.0 (6.0) years). We fitted linear regression models. More severe SCD and SMD did not predict change in objectively assessed global cognition, remote memory, and recent memory but predicted lower scores in learning. The prediction of SMD over change in learning was not stronger when individuals reported an SMD that started within the past five years compared to when they reported an SMD that started five or more years previously nor when individuals reported an SMD that started within the past two years than those who did not. Greater depression and anxiety were associated with more severe SCD and SMD. More severe SMD may be useful for predicting lower learning ability and for identifying individuals experiencing depression and anxiety.

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.

Effect of Brain and Pulse Waves on Safety Consciousness and Safety Commitment of Workers at Construction Sites

Even though individual mental and health status largely affects the safety in industrial sites, most studies for preventing industrial accidents are mainly focused on external factors such as regulations, education, etc. In this study, the effect of individual factors on safety (i.e., safety consciousness and safety commitment) was analyzed by collecting brainwave and pulse data at construction sites where industrial accidents have occurred with the highest percentage. The effects of brain stress, concentration, brain activity, and left and right brain imbalance on safety accidents were evaluated through brain wave measurements. In addition, the effects of cumulative fatigue, physical vitality, autonomic nerve health, and autonomic balance were identified through pulse wave measurements. Data were acquired for 180 construction workers at various construction sites, and the workers were classified into three grades according to factors that affected safety accidents at construction sites. Then, the safety consciousness and safety commitment levels of workers corresponding to each grade of the influence factors were evaluated by conducting a questionnaire on safety consciousness and safety commitment. As a result, the characteristics of brain and pulse waves required to improve safety consciousness and safety commitment ability of workers at construction sites were explored.

Impaired posterior cingulate cortex-parahippocampus connectivity is associated with episodic memory retrieval problems in amnestic mild cognitive impairment

Episodic memory retention and retrieval decline are the most common impairments observed in amnestic mild cognitive impairment (aMCI) patients who progress to Alzheimer's disease (AD). Clinical electroencephalography research shows that patients with dementia due to AD exhibit a slowing of neural electrical activity in the parietal cortex. Memory research has further suggested that successful memory performance is associated with changes in a posterior cingulate-parahippocampal cortical network together with increased θ-γ oscillatory coupling, where θ oscillations act as carrier waves for γ oscillations, which contain the actual information. However, the neurophysiological link between the memory research and clinical studies investigating aMCI and AD is lacking. In this study, we look at brain activity in aMCI and how it relates to memory performance. We demonstrate decreased γ power in the posterior cingulate cortex and the left and right parahippocampus in aMCI patients in comparison to control participants. This goes together with reduced θ coherence between the posterior cingulate cortex and parahippocampus associated with altered memory performance aMCI patients in comparison to control participants. In addition, comparing patients with aMCI to control participants reveals an effect for θ-γ coupling for the posterior cingulate cortex, and the left and right parahippocampus. Taken together, our results show that parahippocampus and posterior cingulate cortex interact via θ-γ coupling, which is associated with memory recollection and is altered in aMCI patients, offering a potential candidate mechanism for memory decline in aMCI.

Spontaneous Alpha and Theta Oscillations Are Related to Complementary Aspects of Cognitive Control in Younger and Older Adults

The resting-state human electroencephalogram (EEG) power spectrum is dominated by alpha (8-12 Hz) and theta (4-8 Hz) oscillations, and also includes non-oscillatory broadband activity inversely related to frequency (1/f activity). Gratton proposed that alpha and theta oscillations are both related to cognitive control function, though in a complementary manner. Alpha activity is hypothesized to facilitate the maintenance of representations, such as task sets in preparation for expected task conditions. In contrast, theta activity would facilitate changes in representations, such as the updating of task sets in response to unpredicted task demands. Therefore, theta should be related to reactive control (which may prompt changes in task representations), while alpha may be more relevant to proactive control (which implies the maintenance of current task representations). Less is known about the possible relationship between 1/f activity and cognitive control, which was analyzed here in an exploratory fashion. To investigate these hypothesized relationships, we recorded eyes-open and eyes-closed resting-state EEG from younger and older adults and subsequently tested their performance on a cued flanker task, expected to elicit both proactive and reactive control processes. Results showed that alpha power and 1/f offset were smaller in older than younger adults, whereas theta power did not show age-related reductions. Resting alpha power and 1/f offset were associated with proactive control processes, whereas theta power was related to reactive control as measured by the cued flanker task. All associations were present over and above the effect of age, suggesting that these resting-state EEG correlates could be indicative of trait-like individual differences in cognitive control performance, which may be already evident in younger adults, and are still similarly present in healthy older adults.

Abnormal Regional and Global Connectivity Measures in Subjective Cognitive Decline Depending on Cerebral Amyloid Status

BACKGROUND

Amyloid-β accumulation was found to alter precuneus-based functional connectivity (FC) in mild cognitive impairment (MCI) and Alzheimer's disease (AD) dementia, but its impact is less clear in subjective cognitive decline (SCD), which in combination with AD pathologic change is theorized to correspond to stage 2 of the Alzheimer's continuum in the 2018 NIA-AA research framework.

OBJECTIVE

This study addresses how amyloid pathology relates to resting-state fMRI FC in SCD, especially focusing on the precuneus.

METHODS

From the DELCODE cohort, two groups of 24 age- and gender-matched amyloid-positive (SCDAβ+) and amyloidnegative SCD (SCDβ-) patients were selected according to visual [18F]-Florbetaben (FBB) PET readings, and studied with resting-state fMRI. Local (regional homogeneity [ReHo], fractional amplitude of low-frequency fluctuations [fALFF]) and global (degree centrality [DC], precuneus seed-based FC) measures were compared between groups. Follow-up correlation analyses probed relationships of group differences with global and precuneal amyloid load, as measured by FBB standard uptake value ratios (SUVR=⫖FBB).

RESULTS

ReHo was significantly higher (voxel-wise p < 0.01, cluster-level p < 0.05) in the bilateral precuneus for SCDAβ+patients, whereas fALFF was not altered between groups. Relatively higher precuneus-based FC with occipital areas (but no altered DC) was observed in SCDAβ+ patients. In this latter cluster, precuneus-occipital FC correlated positively with global (SCDAβ+) and precuneus SUVRFBB (both groups).

CONCLUSION

While partial confounding influences due to a higher APOE ε4 carrier ratio among SCDAβ+ patients cannot be excluded, exploratory results indicate functional alterations in the precuneus hub region that were related to amyloid-β load, highlighting incipient pathology in stage 2 of the AD continuum.

Differences between memory encoding and retrieval failure in mild cognitive impairment: results from quantitative electroencephalography and magnetic resonance volumetry

BACKGROUND

The memory impairments in mild cognitive impairment (MCI) can be classified into encoding (EF) and retrieval (RF) failure, which can be affected by underlying pathomechanism. We explored the differences structurally and functionally.

METHODS

We compared quantitative electroencephalography (qEEG) power spectra and connectivity between 87 MCI patients with EF and 78 MCI with RF using iSyncBrain® (iMediSync Inc., Republic of Korea) ( https://isyncbrain.com/ ). Voxel-based morphometric analysis of the gray matter (GM) in the MCI groups and 71 cognitive normal controls was also done using the Computational Anatomy Toolbox 12 ( http://www.neuro.uni-jena.de/cat/ ).

RESULTS

qEEG showed higher frontal theta and lower beta2 band power, and higher theta connectivity in the EF. There was no statistically significant difference in GM volume between the EF and RF. However, when compared to normal control, GM volume reductions due to EF in the left thalamus and bilateral hippocampi and reductions due to RF in the left thalamus, right superior frontal lobe, right superior temporal lobe, and right middle cingulum were observed (p < 0.05, family-wise error correction).

CONCLUSIONS

MCI differs functionally and structurally according to their specific memory impairments. The EF findings are structurally and functionally more consistent with the prodromal Alzheimer's disease stage than the RF findings. Since this study is a cross-sectional study, prospective follow-up studies are needed to investigate whether different types of memory impairments can predict the underlying pathology of amnestic MCI. Additionally, insufficient sample size may lead to ambiguous statistical findings in direct comparisons, and a larger patient cohort could more robustly identify differences in GM volume reductions between the EF and the RF group.

Investigating Auditory Electrophysiological Measures of Participants with Mild Cognitive Impairment and Alzheimer's Disease: A Systematic Review and Meta-Analysis of Event-Related Potential Studies

BACKGROUND

Objectively measuring auditory functions has been proposed as an avenue in differentiating normal age-related cognitive dysfunction from Alzheimer's disease (AD) and its prodromal states. Previous research has suggested auditory event-related potentials (AERPs) to be non-invasive, cost-effective, and efficient biomarkers for the diagnosis of AD.

OBJECTIVE

The objective of this paper is to review the published literature on AERPs measures in older adults diagnosed with AD and those at higher risk of developing AD, i.e., mild cognitive impairment (MCI) and subjective cognitive decline.

METHODS

The search was performed on six major electronic databases (Ovid MEDLINE, OVID EMBASE, PsycINFO, PubMed, Scopus, and CINAHL Plus). Articles identified prior to 7 May 2019 were considered for this review. A random effects meta-analysis and analysis of between study heterogeneity was conducted using the Comprehensive Meta-Analysis software.

RESULTS

The search identified 1,076 articles; 74 articles met the full inclusion criteria and were included in the systematic review, and 47 articles were included into the analyses. Pooled analysis suggests that AD participants can be differentiated from controls due to significant delays in ABR, N100, P200, N200, and P300 latencies. P300 amplitude was significantly smaller in AD participants compared to controls. P300 latencies differed significantly between MCI participants and controls based on the pooled analysis.

CONCLUSION

The findings of this review indicate that some AERPs may be valuable biomarkers of AD. In conjunction with currently available clinical and neuropsychological assessments, AERPs can aid in screening and diagnosis of prodromal AD.

Power Spectral Changes of Quantitative EEG in the Subjective Cognitive Decline: Comparison of Community Normal Control Groups

PURPOSE

The purpose of this study is to compare and analyze the power spectral changes between subjective cognitive decline (SCD) subjects and normal controls (NC) while checking the preclinical stage of AD in the SCD subjects and to use the derived data for biomarker research that can diagnose early-stage AD in the future.

METHODS

We recruited 23 SCD patients and 23 normal control subjects and QEEG analysis including power spectral density (PSD) and source-level analysis were performed. An automated preprocessing procedure and statistical analysis were performed by iSync Brain^® (iMediSync Inc., Republic of Korea) (https://isyncbrain.com/) using the international standard 10-20 system (19 electrodes).

RESULTS

Absolute PSD, there was no statistically significant difference in all of the EEG power measurements of the 19 channels. In the relative PSD analysis, the average delta band power of the SCD group was significantly higher in Fp2, F4, and F8 than NC. Alpha1 band power of the O1 channel was 22.56±16.05 for the SCD group and 33.19±19.05 for the NC (p-value <0.05). Source-level analysis did not show a statistically significant difference.

CONCLUSION

SCD subjects showed a partial increase of delta waves in the frontal lobe region and a partial decrease in alpha1, a fast wave in the occipital region, compared to the NC. SCD is considered one of the earliest clinical symptoms of AD and it is predicted to be related to minor nerve damage. We were able to observe the power spectral changes in SCD subjects in this cross-sectional study, a large number of subjects and longitudinal studies are needed to evaluate their predictability for future deterioration such as conversion to MCI.

Structural imaging outcomes in subjective cognitive decline: Community vs. clinical-based samples

Subjective cognitive decline (SCD) has been proposed as a preclinical stage of Alzheimer's disease (AD). Neuroimaging studies have suggested early AD-like structural brain alterations in SCD subjects compared to healthy controls. However, there is substantial heterogeneity in the results, which might depend on whether SCD samples were drawn from the community or from memory clinics. Here we reviewed brain atrophy, assessed through structural magnetic resonance imaging, separately for SCD-community and clinic-based samples. SCD-community samples show a more consistent pattern of atrophy, involving the hippocampus and temporal and parietal cortices. Similarly, in SCD-clinic samples the temporo-parietal cortex showed early vulnerability, however these studies reported a more heterogeneous atrophy pattern. Overall, these studies suggest both commonalities and differences in brain atrophy patterns between SCD clinical and community samples. In SCD-community, the temporal cortex is involved, while SCD-clinical exhibited a more complex pattern of atrophy, which may be related to a more heterogeneous sample reporting neuropsychiatric symptoms along with preclinical AD.

Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer's disease

Subjective cognitive decline (SCD) is regarded as the first clinical manifestation in the Alzheimer’s disease (AD) continuum. Investigating populations with SCD is important for understanding the early pathological mechanisms of AD and identifying SCD-related biomarkers, which are critical for the early detection of AD. With the advent of advanced neuroimaging techniques, such as positron emission tomography (PET) and magnetic resonance imaging (MRI), accumulating evidence has revealed structural and functional brain alterations related to the symptoms of SCD. In this review, we summarize the main imaging features and key findings regarding SCD related to AD, from local and regional data to connectivity-based imaging measures, with the aim of delineating a multimodal imaging signature of SCD due to AD. Additionally, the interaction of SCD with other risk factors for dementia due to AD, such as age and the Apolipoprotein E (ApoE) ɛ4 status, has also been described. Finally, the possible explanations for the inconsistent and heterogeneous neuroimaging findings observed in individuals with SCD are discussed, along with future directions. Overall, the literature reveals a preferential vulnerability of AD signature regions in SCD in the context of AD, supporting the notion that individuals with SCD share a similar pattern of brain alterations with patients with mild cognitive impairment (MCI) and dementia due to AD. We conclude that these neuroimaging techniques, particularly multimodal neuroimaging techniques, have great potential for identifying the underlying pathological alterations associated with SCD. More longitudinal studies with larger sample sizes combined with more advanced imaging modeling approaches such as artificial intelligence are still warranted to establish their clinical utility.

Resting EEG, Hair Cortisol and Cognitive Performance in Healthy Older People with Different Perceived Socioeconomic Status

Successful aging depends upon several internal and external factors that influence the overall aging process. Objective and subjective socioeconomic status emerge as potential psychosocial factors in the ethiopathophysiology of aging-related disorders. Presumably, low socioeconomic status can act as a psychosocial stressor that can affect humans’ physiology via psychoneuroendocrine mechanisms, that may, in turn, affect the brain physiology. In resting-state electroencephalography (EEG), excess theta and delta activity has been related to cognitive decline and dementia. The main aim of this study was to analyze the effect of objective and subjective socioeconomic status (SES) on cognition and brain electrical activity through EEG measures. The present research constitutes a cross-sectional study with thirty healthy older adults (61–82 years old) separated into two clusters: high socioeconomic (HS) and low socioeconomic (LS) status; they were evaluated and compared in cognitive terms using the Wechsler Adult Intelligence Scale (WAIS-IV). An EEG at rest was recorded to measure brain activity and, as an indicator of long-term stress exposure, hair cortisol concentrations (HCC) were measured. Our results show that lower SES is related to a worse performance in working memory tasks (p = 0.009), higher delta (p = 0.002) and theta power (p = 0.039), and lower alpha activity (p = 0.028). However, it seems that SES does not significantly affect HCC in this population of healthy older adults. The effects of SES on long-term cortisol exposure, brain electrical activity, and cognitive functions in healthy older people emphasize the role of psychosocial factors in aging from an integrative perspective that will allow us to implement better prevention programs to target cognitive decline in adults.

Association of serum Apolipoprotein B with cerebrospinal fluid biomarkers of Alzheimer's pathology

Objective

To examine whether apolipoprotein B (ApoB), apolipoprotein A‐1 (ApoA1), or their ratio (ApoB/A1) were associated with early changes in cerebrospinal fluid (CSF) biomarkers of Alzheimer’s disease (AD) pathology in elderly adults with subjective cognitive decline (SCD).

Methods

This study included 507 objective cognitive normal participants from the Chinese Alzheimer’s Biomarker and LifestylE (CABLE) database including 288 cognitive normal participants (CN) and 219 SCD. Multiple linear regression models were used to examine the associations of apolipoproteins with CSF AD biomarkers.

Results

Compared with control group, SCD participants with significant AD biological characteristics had lower ApoB levels (P = 0.0461). In total participants, lower level of serum ApoB was associated with decreases in CSF Aβ42 (P = 0.0015) and Aβ42/40 (P = 0.0081) as well as increases in CSF p‐tau/Aβ42 (P < 0.0001) and t‐tau/Aβ42 (P = 0.0013), independent of APOEɛ4 status. In further subgroup analysis, these associations were more significant in SCD participants (ApoB × Diagnose: P < 0.05). In addition, lower levels of ApoB were also found associated with increases in p‐tau in the SCD subgroup (P = 0.0263). Furthermore, these protective associations were more significant in the overweight participants (ApoB × weight: P < 0.05). Results showed no association between ApoA1 and CSF biomarkers.

Interpretation

This study is the first to find protective associations of serum ApoB with CSF AD core biomarkers, especially in SCD individuals. It indicated that ApoB may be a potential biomarker for preclinical AD and may play different roles in different stages of AD.

Waking EEG functional connectivity in middle-aged and older adults with obstructive sleep apnea

OBJECTIVES

The present study aimed at investigating changes in waking electroencephalography (EEG), most specifically regarding spectral power and functional connectivity, in middle-aged and older adults with obstructive sleep apnea (OSA). We also explored whether changes in spectral power or functional connectivity are associated with polysomnographic characteristics and/or neuropsychological performance.

METHODS

In sum, 19 OSA subjects (apnea-hypopnea index ≥ 20, age: 63.6 ± 6.4) and 22 controls (apnea-hypopnea index ≤ 10, age: 63.6 ± 6.7) underwent a full night of in-laboratory polysomnography (PSG) followed by a waking EEG and a neuropsychological assessment. Waking EEG spectral power and imaginary coherence were compared between groups for all EEG frequency bands and scalp regions. Correlation analyses were performed between selected waking EEG variables, polysomnographic parameters and neuropsychological performance.

RESULTS

No group difference was observed for EEG spectral power for any frequency band. Regarding the imaginary coherence, when compared to controls, OSA subjects showed decreased EEG connectivity between frontal and temporal regions in theta and alpha bands as well as increased connectivity between frontal and parietal regions in delta and beta 1 bands. In the OSA group, these changes in connectivity correlated with lower sleep efficiency, lower total sleep time and higher apnea-hypopnea index. No relationship was found with neuropsychological performance.

CONCLUSIONS

Contrary to spectral power, imaginary coherence was sensitive enough to detect changes in brain function in middle-aged and older subjects with OSA when compared to controls. Whether these changes in cerebral connectivity predict cognitive decline needs to be investigated longitudinally.

Determining the effects of LLD and MCI on brain decline according to machine learning and a structural covariance network analysis

BACKGROUND

Late-life depression (LLD) and mild cognitive impairment (MCI) are risk factors for Alzheimer disease (AD). However, the interactive effect between LLD and MCI in the progression to AD remains unknown. The purpose of this research is to clarify whether this interaction exists and determined the characteristics of the structural change patterns in LLD and MCI.

METHOD

To address this question, a total 225 participants (91 with intact cognitive function (IC), 34 with MCI, 35 with LLD-IC, 47 with LLD-MCI and 18 with AD) were recruited for the current study and their T1 scanning were acquired. Machine learning was applied to estimate the brain's age gap according to grey matter information (thickness and volume was calculated based on the Human Connectome Project Multi-Modal Parcellation version 1.0 and the Desikan atlas). A structural covariance network (SCN) was constructed based on grey matter volume. Rich-club analysis, global network properties and the Jaccard distance were utilized to describe the topological features in each cohort. Their cognitive functions (executive function, processing speed and memory) were evaluated by a full-scale battery of neuropsychological tests.

RESULT

The interactive effect between LLD and MCI was detected through the brain age gap. The estimated age was positively correlated with processing speed and memory in LLD and non-LLD subjects. In the SCN analysis, the rich-club coefficient and global network properties were disrupted in the MCI group, but remained normal in the LLD-IC, LLD-MCI and AD groups. There was a significant discrepancy in brain structural change patterns between the AD and other cohorts by the Jaccard distance.

CONCLUSION

The application of machine learning reflects that synergies between LLD and MCI could increase the risk of developing AD. According to the SCN, the structural coordination was disrupted in MCI and was kept normal in the other cohorts, while the discrepancies in brain structural change patterns appeared in AD. Overall, the brain age gap could be a potential predictor of AD, and the Jaccard distance has the potential to be a new type of SCN analysis indicator.