Electroencephalographic markers in dementia

Screening of Aβ and phosphorylated tau status in the cerebrospinal fluid through machine learning analysis of portable electroencephalography data

Diagnosing Alzheimer's disease (AD) through pathological markers is typically costly and invasive. This study aims to find a noninvasive, cost-effective method using portable electroencephalography (EEG) to detect changes in AD-related biomarkers in cerebrospinal fluid (CSF). A total of 102 patients, both with and without AD-related biomarker changes (amyloid beta and phosphorylated tau), were recorded using a 2-minute resting-state portable EEG. A machine-learning algorithm then analyzed the EEG data to identify these biomarker changes. The results showed that the machine learning model could distinguish patients with AD-related biomarker changes, achieving 68.1% accuracy (AUROC 0.75) for amyloid beta and 71.2% accuracy (AUROC 0.77) for phosphorylated tau, with gamma activities being key features. When excluding cases with idiopathic normal pressure hydrocephalus, accuracy improved to 74.1% (AUROC 0.80) for amyloid beta and 73.1% (AUROC 0.80) for phosphorylated tau. This study suggests that portable EEG combined with machine learning is a promising noninvasive and cost-effective tool for early AD-related pathological marker screening, which could enhance neurophysiological understanding and diagnostic accessibility.

Interaction of Cooking-Generated Aerosols on the Human Nervous System and the Impact of Caloric Restriction Post-Exposure

Background: The inhalation of cooking-generated aerosols could lead to translocation to the brain and impact its function; therefore, the effects of cooking-generated aerosols on healthy adults were investigated using an electroencephalograph (EEG) during the 2 h period post-exposure. Methods: To explore any changes from the impact of exposure to cooking-generated aerosols on the human brain due to the absence of food intake during exposure, we divided the study participants into three groups: (A) no food intake for 2 h (2 h-zero calorie intake), (B) non-zero calorie intake, and (C) control group (simulated cooking). Results: The ultrafine particle concentrations increased from 9.0 × 103 particles/cm3 at the background level to approximately 8.74 × 104 particles/cm3 during cooking. EEGs were recorded before cooking (step 1), 60 min after cooking (step 2), 90 min after cooking (step 3), and 120 min after cooking (step 4). Comparing the non-zero calorie group with the control group, it was concluded that exposure to cooking-generated aerosols resulted in a 12.82% increase in the alpha band two hours post-exposure, compared to pre-exposure. The results revealed that zero calorie intake after exposure mitigated the impacts of cooking-generated aerosols for the alpha, beta3, theta, and delta bands, while it exacerbated effects on the whole brain for the beta1 and beta2 bands. Conclusions: While these are short-term studies, long-term exposure to cooking-generated ultrafine particles can be established through successive short-term exposures. These results underscore the need for further research into the health impacts of cooking-generated aerosols and the importance of implementing strategies to mitigate exposure.

Data-driven retrieval of population-level EEG features and their role in neurodegenerative diseases

Electrophysiologic disturbances due to neurodegenerative disorders such as Alzheimer's disease and Lewy Body disease are detectable by scalp EEG and can serve as a functional measure of disease severity. Traditional quantitative methods of EEG analysis often require an a-priori selection of clinically meaningful EEG features and are susceptible to bias, limiting the clinical utility of routine EEGs in the diagnosis and management of neurodegenerative disorders. We present a data-driven tensor decomposition approach to extract the top 6 spectral and spatial features representing commonly known sources of EEG activity during eyes-closed wakefulness. As part of their neurologic evaluation at Mayo Clinic, 11 001 patients underwent 12 176 routine, standard 10-20 scalp EEG studies. From these raw EEGs, we developed an algorithm based on posterior alpha activity and eye movement to automatically select awake-eyes-closed epochs and estimated average spectral power density (SPD) between 1 and 45 Hz for each channel. We then created a three-dimensional (3D) tensor (record × channel × frequency) and applied a canonical polyadic decomposition to extract the top six factors. We further identified an independent cohort of patients meeting consensus criteria for mild cognitive impairment (30) or dementia (39) due to Alzheimer's disease and dementia with Lewy Bodies (31) and similarly aged cognitively normal controls (36). We evaluated the ability of the six factors in differentiating these subgroups using a Naïve Bayes classification approach and assessed for linear associations between factor loadings and Kokmen short test of mental status scores, fluorodeoxyglucose (FDG) PET uptake ratios and CSF Alzheimer's Disease biomarker measures. Factors represented biologically meaningful brain activities including posterior alpha rhythm, anterior delta/theta rhythms and centroparietal beta, which correlated with patient age and EEG dysrhythmia grade. These factors were also able to distinguish patients from controls with a moderate to high degree of accuracy (Area Under the Curve (AUC) 0.59-0.91) and Alzheimer's disease dementia from dementia with Lewy Bodies (AUC 0.61). Furthermore, relevant EEG features correlated with cognitive test performance, PET metabolism and CSF AB42 measures in the Alzheimer's subgroup. This study demonstrates that data-driven approaches can extract biologically meaningful features from population-level clinical EEGs without artefact rejection or a-priori selection of channels or frequency bands. With continued development, such data-driven methods may improve the clinical utility of EEG in memory care by assisting in early identification of mild cognitive impairment and differentiating between different neurodegenerative causes of cognitive impairment.

Subacute sclerosing panencephalitis presenting as severe depression in an adult

Subacute sclerosing panencephalitis (SSPE) is a fatal disorder that occurs as a rare complication of childhood measles. Symptoms typically manifest between the ages of 5 and 15. While the incidence of SSPE is declining globally, it is still prevalent in regions where measles remains common and vaccination rates are low due to poverty and lack of health education. Diagnosing SSPE can be challenging, particularly when patients exhibit unusual symptoms. A thorough clinical evaluation, including vaccination history, physical examination, electroencephalogram (EEG) and Cerebrospinal fluid (CSF) analysis, can help in making a diagnosis. We present the case of a young woman in her early 20s who initially experienced depressive symptoms, followed by myoclonus, dementia and visual impairment. The patient was ultimately diagnosed with SSPE based on characteristic EEG findings, neuroimaging results, CSF analysis and elevated serum measles antibody levels.

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.

Personalized modeling of Alzheimer's disease progression estimates neurodegeneration severity from EEG recordings

INTRODUCTION

Early identification of Alzheimer's disease (AD) is necessary for a timely onset of therapeutic care. However, cortical structural alterations associated with AD are difficult to discern.

METHODS

We developed a cortical model of AD-related neurodegeneration accounting for slowing of local dynamics and global connectivity degradation. In a monocentric study we collected electroencephalography (EEG) recordings at rest from participants in healthy (HC, n = 17), subjective cognitive decline (SCD, n = 58), and mild cognitive impairment (MCI, n = 44) conditions. For each patient, we estimated neurodegeneration model parameters based on individual EEG recordings.

RESULTS

Our model outperformed standard EEG analysis not only in discriminating between HC and MCI conditions (F1 score 0.95 vs 0.75) but also in identifying SCD patients with biological hallmarks of AD in the cerebrospinal fluid (recall 0.87 vs 0.50).

DISCUSSION

Personalized models could (1) support classification of MCI, (2) assess the presence of AD pathology, and (3) estimate the risk of cognitive decline progression, based only on economical and non-invasive EEG recordings.

Highlights

Personalized cortical model estimating structural alterations from EEG recordings.Discrimination of Mild Cognitive Impairment (MCI) and Healthy (HC) subjects (95%)Prediction of biological markers of Alzheimer's in Subjective Decline (SCD) Subjects (87%)Transition correctly predicted for 3/3 subjects that converted from SCD to MCI after 1y.

Multi-approach comparative study of EEG patterns associated with the most common forms of dementia

Electroencephalography's (EEG) sensitivity in discriminating dementia syndromes remains unclear. This study aimed to investigate EEG markers in patients with major cognitive disorders. The studied population included 4 groups of patients: Alzheimer's disease with associated vascular lesions, Alzheimer's disease without vascular lesions (AD-V), Lewy body disease and vascular dementia (VaD); and completed by a control group composed by cognitively unimpaired patients. EEGs were analysed quantitatively using spectral analysis, functional connectivity and micro-states. By comparison to the controls, expected slowing and alterations of functional connectivity were detected in patients with dementia. Among these patients, an overall increase in power in the alpha band was observed in the VaD group, mainly when compared to the 2 AD groups, while the Alzheimer's disease without vascular lesions group exhibited increased power in the beta-2 band and higher functional connectivity in the same frequency band. Micro-state analyses revealed differences in temporal dynamics for the VaD group. A number of EEG modifications reported as markers of some syndromes were found, but others were not reproduced.

PRedicting the EVolution of SubjectIvE Cognitive Decline to Alzheimer's Disease With machine learning: the PREVIEW study protocol

BACKGROUND

As disease-modifying therapies (DMTs) for Alzheimer's disease (AD) are becoming a reality, there is an urgent need to select cost-effective tools that can accurately identify patients in the earliest stages of the disease. Subjective Cognitive Decline (SCD) is a condition in which individuals complain of cognitive decline with normal performances on neuropsychological evaluation. Many studies demonstrated a higher prevalence of Alzheimer's pathology in patients diagnosed with SCD as compared to the general population. Consequently, SCD was suggested as an early symptomatic phase of AD. We will describe the study protocol of a prospective cohort study (PREVIEW) that aim to identify features derived from easily accessible, cost-effective and non-invasive assessment to accurately detect SCD patients who will progress to AD dementia.

METHODS

We will include patients who self-referred to our memory clinic and are diagnosed with SCD. Participants will undergo: clinical, neurologic and neuropsychological examination, estimation of cognitive reserve and depression, evaluation of personality traits, APOE and BDNF genotyping, electroencephalography and event-related potential recording, lumbar puncture for measurement of Aβ42, t-tau, and p-tau concentration and Aβ42/Aβ40 ratio. Recruited patients will have follow-up neuropsychological examinations every two years. Collected data will be used to train a machine learning algorithm to define the risk of being carriers of AD and progress to dementia in patients with SCD.

DISCUSSION

This is the first study to investigate the application of machine learning to predict AD in patients with SCD. Since all the features we will consider can be derived from non-invasive and easily accessible assessments, our expected results may provide evidence for defining cost-effective and globally scalable tools to estimate the risk of AD and address the needs of patients with memory complaints. In the era of DMTs, this will have crucial implications for the early identification of patients suitable for treatment in the initial stages of AD.

TRIAL REGISTRATION NUMBER (TRN)

NCT05569083.

Degradation of EEG microstates patterns in subjective cognitive decline and mild cognitive impairment: Early biomarkers along the Alzheimer's Disease continuum?

Alzheimer's disease (AD) pathological changes may begin up to decades earlier than the appearance of the first symptoms of cognitive decline. Subjective cognitive decline (SCD) could be the first pre-clinical sign of possible AD, which might be followed by mild cognitive impairment (MCI), the initial stage of clinical cognitive decline. However, the neural correlates of these prodromic stages are not completely clear yet. Recent studies suggest that EEG analysis tools characterizing the cortical activity as a whole, such as microstates and cortical regions connectivity, might support a characterization of SCD and MCI conditions. Here we test this approach by performing a broad set of analyses to identify the prominent EEG markers differentiating SCD (n = 57), MCI (n = 46) and healthy control subjects (HC, n = 19). We found that the salient differences were in the temporal structure of the microstates patterns, with MCI being associated with less complex sequences due to the altered transition probability, frequency and duration of canonic microstate C. Spectral content of EEG, network connectivity, and spatial arrangement of microstates were instead largely similar in the three groups. Interestingly, comparing properties of EEG microstates in different cerebrospinal fluid (CSF) biomarkers profiles, we found that canonic microstate C displayed significant differences in topography in AD-like profile. These results show that the progression of dementia might be associated with a degradation of the cortical organization captured by microstates analysis, and that this leads to altered transitions between cortical states. Overall, our approach paves the way for the use of non-invasive EEG recordings in the identification of possible biomarkers of progression to AD from its prodromal states.

Deep learning-based EEG analysis to classify normal, mild cognitive impairment, and dementia: algorithms and dataset

For automatic EEG diagnosis, this paper presents a new EEG data set with well-organized clinical annotations called Chung-Ang University Hospital EEG (CAUEEG), which has event history, patient's age, and corresponding diagnosis labels. We also designed two reliable evaluation tasks for the low-cost, non-invasive diagnosis to detect brain disorders: i) CAUEEG-Dementia with normal, mci, and dementia diagnostic labels and ii) CAUEEG-Abnormal with normal and abnormal. Based on the CAUEEG dataset, this paper proposes a new fully end-to-end deep learning model, called the CAUEEG End-to-end Deep neural Network (CEEDNet). CEEDNet pursues to bring all the functional elements for the EEG analysis in a seamless learnable fashion while restraining non-essential human intervention. Extensive experiments showed that our CEEDNet significantly improves the accuracy compared with existing methods, such as machine learning methods and Ieracitano-CNN (Ieracitano et al., 2019), due to taking full advantage of end-to-end learning. The high ROC-AUC scores of 0.9 on CAUEEG-Dementia and 0.86 on CAUEEG-Abnormal recorded by our CEEDNet models demonstrate that our method can lead potential patients to early diagnosis through automatic screening.

Applications of artificial intelligence in dementia research

More than 50 million older people worldwide are suffering from dementia, and this number is estimated to increase to 150 million by 2050. Greater caregiver burdens and financial impacts on the healthcare system are expected as we wait for an effective treatment for dementia. Researchers are constantly exploring new therapies and screening approaches for the early detection of dementia. Artificial intelligence (AI) is widely applied in dementia research, including machine learning and deep learning methods for dementia diagnosis and progression detection. Computerized apps are also convenient tools for patients and caregivers to monitor cognitive function changes. Furthermore, social robots can potentially provide daily life support or guidance for the elderly who live alone. This review aims to provide an overview of AI applications in dementia research. We divided the applications into three categories according to different stages of cognitive impairment: (1) cognitive screening and training, (2) diagnosis and prognosis for dementia, and (3) dementia care and interventions. There are numerous studies on AI applications for dementia research. However, one challenge that remains is comparing the effectiveness of different AI methods in real clinical settings.

EEG Network Analysis in Epilepsy with Generalized Tonic-Clonic Seizures Alone

Many contradictory theories regarding epileptogenesis in idiopathic generalized epilepsy have been proposed. This study aims to define the network that takes part in the formation of the spike-wave discharges in patients with generalized tonic-clonic seizures alone (GTCSa) and elucidate the network characteristics. Furthermore, we intend to define the most influential brain areas and clarify the connectivity pattern among them. The data were collected from 23 patients with GTCSa utilizing low-density electroencephalogram (EEG). The source localization of generalized spike-wave discharges (GSWDs) was conducted using the Standardized low-resolution brain electromagnetic tomography (sLORETA) methodology. Cortical connectivity was calculated utilizing the imaginary part of coherence. The network characteristics were investigated through small-world propensity and the integrated value of influence (IVI). Source localization analysis estimated that most sources of GSWDs were in the superior frontal gyrus and anterior cingulate. Graph theory analysis revealed that epileptic sources created a network that tended to be regularized during generalized spike-wave activity. The IVI analysis concluded that the most influential nodes were the left insular gyrus and the left inferior parietal gyrus at 3 and 4 Hz, respectively. In conclusion, some nodes acted mainly as generators of GSWDs and others as influential ones across the whole network.

Feasibility and potential of a bedside mini-EEG for diagnosing delirium superimposed on dementia

OBJECTIVE

Delirium superimposed on dementia (DSD) is difficult to diagnose because symptoms of delirium might be interpreted as symptoms of dementia. To improve diagnostic accuracy, we investigated the potential of a brief point-of-care EEG measurement.

METHODS

Thirty older patients were included, all with Major Neurocognitive Disorder (i.e. dementia) according to DSM-5 criteria. EEG was registered at right prefrontal and right temporal site, with eyes either open or closed for three minutes, simultaneously with the Discomfort Scale for Dementia of Alzheimer Type. The Confusion Assessment Method for the Intensive Care Unit was administered to determine the presence of symptoms of a delirium at the time of EEG administration. Video registrations were reviewed independently by two delirium experts.

RESULTS

Higher activities of delta and theta1, and lower activities of theta2, alpha, and beta activity, were found in DSD when compared to dementia only. The ratio of delta and theta power during eyes-open conditions had the highest accuracy (AUC = 0.80 [0.63-0.94]; p <.001) to distinguish DSD from dementia alone. All subjects were on benzodiazepines and half on clozapine, thus the effects of psychotropics on EEG cannot be fully excluded.

CONCLUSIONS

A brief point-of-care EEG at two sites of the head has the potential to aid in the detection of DSD.

SIGNIFICANCE

The diagnostic accuracy of EEG in recognizing or excluding delirium in patients who already have dementia is of large potential given the lack of proper diagnostic tools.

Alternations in neural oscillation related to attention network reveal influence of indoor toluene on cognition at low concentration

Despite accumulative literature reporting negative impacts of high-concentration toluene, cognitive effects of toluene at low concentration are still unclear. Twenty-two healthy college students were exposed in a closed environmental chamber to investigate the influence of indoor toluene on cognitive performance and brain activity. During each toluene exposure condition (0 ppb, 17.5 ppb, 35 ppb, and 70 ppb), attention network test and electroencephalogram (EEG) recording were synchronously performed after 4-hour toluene exposure. Characteristic neural oscillation patterns in three attention networks were compared between four groups. The statistical analyses indicated that short-term exposure to toluene had no significant impact on behavioral performance of attention network. However, there was a significant increase in the power of theta and alpha band of executive network and orienting network in the whole brain, especially in frontal region when exposed to toluene. Besides, no significant difference was observed in alerting network. The alternations in neural oscillation demonstrated that more effort was required to accomplish the same tasks when exposed to toluene. The present study revealed that short-term exposure to toluene affected brain activity of attention network even at low concentration, which provided a theoretical basis for the development of safer evaluation methods and standards in the future.

Transfreq: A Python package for computing the theta-to-alpha transition frequency from resting state electroencephalographic data

A classic approach to estimate individual theta-to-alpha transition frequency (TF) requires two electroencephalographic (EEG) recordings, one acquired in a resting state condition and one showing alpha desynchronisation due, for example, to task execution. This translates into long recording sessions that may be cumbersome in studies involving patients. Moreover, an incomplete desynchronisation of the alpha rhythm may compromise TF estimates. Here we present transfreq, a publicly available Python library that allows TF computation from resting state data by clustering the spectral profiles associated to the EEG channels based on their content in alpha and theta bands. A detailed overview of transfreq core algorithm and software architecture is provided. Its effectiveness and robustness across different experimental setups are demonstrated on a publicly available EEG data set and on in-house recordings, including scenarios where the classic approach fails to estimate TF. We conclude with a proof of concept of the predictive power of transfreq TF as a clinical marker. Specifically, we present a scenario where transfreq TF shows a stronger correlation with the mini mental state examination score than other widely used EEG features, including individual alpha peak and median/mean frequency. The documentation of transfreq and the codes for reproducing the analysis of the article with the open-source data set are available online at https://elisabettavallarino.github.io/transfreq/. Motivated by the results showed in this article, we believe our method will provide a robust tool for discovering markers of neurodegenerative diseases.

Human exposure to aerosol from indoor gas stove cooking and the resulting nervous system responses

Our knowledge of the effects of exposure to indoor ultrafine particles (sub-100 nm, #/cm³ ) on human brain activity is very limited. The effects of cooking ultrafine particles (UFP) on healthy adults were assessed using an electroencephalograph (EEGs) for brain response. Peak ultrafine particle concentrations were approximately 3 × 10⁵ particle/cm^3, and the average level was 1.64 × 10⁵ particle/cm³ . The average particle number emission rate (S) and the average number decay rate (a+k) for chicken frying in brain experiments were calculated to be 2.82 × 10¹² (SD = 1.83 × 10¹² , R²  = 0.91, p = 0.0013) particles/min, 0.47 (SD = 0.30, R²  = 0.90, p < 0.0001) min^-1 , respectively. EEGs were recorded before and during cooking (14 min) and 30 min after the cooking sessions. The brain fast-wave band (beta) decreased during exposure, similar to people with neurodegenerative diseases. It subsequently increased to its pre-exposure condition for 70% of the study participants after 30 min. The brain slow-wave band to fast-wave band ratio (theta/beta ratio) increased during and after exposure, similar to observed behavior in early-stage Alzheimer's disease (AD) patients. The brain then tended to return to its normal condition within 30 min following the exposure. This study suggests that chronically exposed people to high concentrations of cooking aerosol might progress toward AD.

Differences in Multimodal Electroencephalogram and Clinical Correlations Between Early-Onset Alzheimer's Disease and Frontotemporal Dementia

Background

Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are the two main types of dementia. We investigated the electroencephalogram (EEG) difference and clinical correlation in early-onset Alzheimer’s disease (EOAD), and FTD using multimodal EEG analyses. EOAD had more severe EEG abnormalities than late-onset AD (LOAD). Group comparisons between EOAD and LOAD were also performed.

Methods

Thirty patients diagnosed with EOAD, nine patients with LOAD, and 14 patients with FTD (≤65 y) were recruited (2008.1–2020.2), along with 24 healthy controls (≤65 y, n = 18; >65 y, n = 6). Clinical data were reviewed. Visual EEG, EEG microstate, and spectral analyses were performed.

Results

Compared to controls, markedly increased mean microstate duration, reduced mean occurrence, and reduced global field power (GFP) peaks per second were observed in EOAD and FTD. We found increased durations of class B in EOAD and class A in FTD. EOAD had reduced occurrences in classes A, B, and C, while only class C occurrence was reduced in FTD. The visual EEG results did not differ between AD and FTD. Microstate B showed correlations with activities of daily living score (r = 0.780, p = 0.008) and cerebrospinal fluid (CSF) Aβ42 (r = −0.833, p = 0.010) in EOAD. Microstate D occurrence was correlated with the CSF Aβ42 level in FTD (r = 0.786, p = 0.021). Spectral analysis revealed a general slowing EEG, which may contribute to microstate dynamic loss. Power in delta was significantly higher in EOAD than in FTD all over the head. In addition, EOAD had a marked increased duration and decreased occurrence than late-onset AD (LOAD), with no group differences in visual EEG results.

Conclusion

The current study found that EOAD and FTD had different EEG changes, and microstate had an association with clinical severity and CSF biomarkers. EEG microstate is more sensitive than visual EEG and may be useful for the differentiation between AD and FTD. The observations support that EEG can be a potential biomarker for the diagnosis and assessment of early-onset dementias.

The Significance of EEG Alpha Oscillation Spectral Power and Beta Oscillation Phase Synchronization for Diagnosing Probable Alzheimer Disease

Alzheimer disease (AD) is the most common cause of dementia in geriatric population. At present, no effective treatments exist to reverse the progress of AD, however, early diagnosis and intervention might delay its progression. The search for biomarkers with good safety, repeatable detection, reliable sensitivity and community application is necessary for AD screening and early diagnosis and timely intervention. Electroencephalogram (EEG) examination is a non-invasive, quantitative, reproducible, and cost-effective technique which is suitable for screening large population for possible AD. The power spectrum, complexity and synchronization characteristics of EEG waveforms in AD patients have distinct deviation from normal elderly, indicating these EEG features can be a promising candidate biomarker of AD. However, current reported deviation results are inconsistent, possibly due to multiple factors such as diagnostic criteria, sample sizes and the use of different computational measures. In this study, we collected two neurological tests scores (MMSE and MoCA) and the resting-state EEG of 30 normal control elderly subjects (NC group) and 30 probable AD patients confirmed by Pittsburgh compound B positron emission tomography (PiB-PET) inspection (AD group). We calculated the power spectrum, spectral entropy and phase synchronization index features of these two groups’ EEG at left/right frontal, temporal, central and occipital brain regions in 4 frequency bands: δ oscillation (1–4 Hz), θ oscillation (4–8 Hz), α oscillation (8–13 Hz), and β oscillation (13–30 Hz). In most brain areas, we found that the AD group had significant differences compared to NC group: (1) decreased α oscillation power and increased θ oscillation power; (2) decreased spectral entropy in α oscillation and elevated spectral entropy in β oscillation; and (3) decrease phase synchronization index in δ, θ, and β oscillation. We also found that α oscillation spectral power and β oscillation phase synchronization index correlated well with the MMSE/MoCA test scores in AD groups. Our study suggests that these two EEG features might be useful metrics for population screening of probable AD patients.

Diagnosis of Alzheimer's Disease by Time-Dependent Power Spectrum Descriptors and Convolutional Neural Network Using EEG Signal

Using strategies that obtain biomarkers where early symptoms coincide, the early detection of Alzheimer's disease and its complications is essential. Electroencephalogram is a technology that allows thousands of neurons with equal spatial orientation of the duration of cerebral cortex electrical activity to be registered by postsynaptic potential. Therefore, in this paper, the time-dependent power spectrum descriptors are used to diagnose the electroencephalogram signal function from three groups: mild cognitive impairment, Alzheimer's disease, and healthy control test samples. The final feature used in three modes of traditional classification methods is recorded: k-nearest neighbors, support vector machine, linear discriminant analysis approaches, and documented results. Finally, for Alzheimer's disease patient classification, the convolutional neural network architecture is presented. The results are indicated using output assessment. For the convolutional neural network approach, the accurate meaning of accuracy is 82.3%. 85% of mild cognitive impairment cases are accurately detected in-depth, but 89.1% of the Alzheimer's disease and 75% of the healthy population are correctly diagnosed. The presented convolutional neural network outperforms other approaches because performance and the k-nearest neighbors' approach is the next target. The linear discriminant analysis and support vector machine were at the low area under the curve values.

Z-score neurofeedback, heart rate variability biofeedback, and brain coaching for older adults with memory concerns

BACKGROUND

The three-month, multi-domain Memory Boot Camp program incorporates z-score neurofeedback (NFB), heart rate variability (HRV) biofeedback, and one-on-one coaching to teach memory skills and encourage behavior change in diet, sleep, physical fitness, and stress reduction.

OBJECTIVE

This prospective trial evaluates the Memory Boot Camp program for adults ages 55 to 85 with symptoms of Mild Cognitive Impairment (MCI) and subjective memory complaints.

METHODS

Participants were evaluated via the Montreal Cognitive Assessment (MoCA), NeuroTrax Global Cognitive Score, measures of anxiety, depression, sleep, quality of life, quantitative electroencephalography (QEEG), and HRV parameters at four timepoints: baseline, pre-program, post-program, and follow-up. The trial included a three-month waiting period between baseline and pre-program, such that each participant acted as their own control, and follow-up took place six months after completion of the program.

RESULTS

Participants' MoCA scores and self-reported measures of anxiety, depression, sleep quality, and quality of life improved after treatment, and these changes were maintained at follow-up. Physiological changes in HRV parameters after treatment were not significant, however, breathing rate and QEEG parameters were improved at post-program and maintained at follow-up. Finally, participants' improvement in MoCA score over the treatment period was correlated with their improvement in two brain oscillation parameters targeted by the z-score NFB protocol: relative power of delta and relative power of theta.

CONCLUSIONS

Trial results suggest that the Memory Boot Camp program is a promising treatment strategy for older adults with symptoms of MCI and subjective memory complaints.

Sporadic Creutzfeldt-Jakob disease with tau pathology mimicking new-onset refractory non-convulsive status epilepticus: Case report and review of the literature

BACKGROUND AND PURPOSE

The aim of our study is to review the relationship between NCSE and sCJD. Creutzfeldt-Jakob disease (CJD) is the most common form of human prion disease. Electroencephalography (EEG)-detected changes such as periodic sharp wave complexes, superimposable to those seen in non-convulsive epileptic status (NCSE), have only rarely been described at CJD onset, especially in sporadic CJD (sCJD) cases.

METHODS

We describe clinical, EEG, cerebrospinal fluid (CSF) and neuroimaging findings of a confirmed case of sCJD with tau pathology, initially diagnosed as NCSE. We performed a literature review in PubMed of previous publications on both sCJD and NCSE.

RESULTS

An 82-year-old woman with no medical history presented with a 2-week rapidly progressive neurological disorder, with motor aphasia, myoclonus, pyramidalism, and left posterior alien hand. EEG showed periodic sharp waves on right frontal regions, so anti-epileptic treatment was started. CSF results were normal. Brain magnetic resonance imaging demonstrated hyperintensity of the right cerebral cortex in diffusion sequences. Due to suspected new-onset refractory status epilepticus (NORSE), corticosteroid treatment was started, without clinical improvement. Necropsy results confirmed sCJD with tau pathology. The literature review identified 14 references including a total of 18 cases with NCSE as the presenting symptom of sCJD; the clinical and results in complementary tests were compiled into a table.

CONCLUSIONS

Sporadic CJD should be considered in the differential diagnosis of patients with rapid cognitive decline and EEG changes consistent with NCSE. The wide heterogeneity in the etiology of NCSE, including autoimmune disorders, especially NORSE, suggests immunotherapy should be initiated based on a good risk-benefit balance. Some cases of sCJD, such as the present case with tau pathology, may mimic this clinico-electrical course.

Linking Molecular Pathways and Large-Scale Computational Modeling to Assess Candidate Disease Mechanisms and Pharmacodynamics in Alzheimer's Disease

Introduction: While the prevalence of neurodegenerative diseases associated with dementia such as Alzheimer's disease (AD) increases, our knowledge on the underlying mechanisms, outcome predictors, or therapeutic targets is limited. In this work, we demonstrate how computational multi-scale brain modeling links phenomena of different scales and therefore identifies potential disease mechanisms leading the way to improved diagnostics and treatment. Methods: The Virtual Brain (TVB; thevirtualbrain.org) neuroinformatics platform allows standardized large-scale structural connectivity-based simulations of whole brain dynamics. We provide proof of concept for a novel approach that quantitatively links the effects of altered molecular pathways onto neuronal population dynamics. As a novelty, we connect chemical compounds measured with positron emission tomography (PET) with neural function in TVB addressing the phenomenon of hyperexcitability in AD related to the protein amyloid beta (Abeta). We construct personalized virtual brains based on an averaged healthy connectome and individual PET derived distributions of Abeta in patients with mild cognitive impairment (MCI, N = 8) and Alzheimer's Disease (AD, N = 10) and in age-matched healthy controls (HC, N = 15) using data from ADNI-3 data base (http://adni.loni.usc.edu). In the personalized virtual brains, individual Abeta burden modulates regional Excitation-Inhibition balance, leading to local hyperexcitation with high Abeta loads. We analyze simulated regional neural activity and electroencephalograms (EEG). Results: Known empirical alterations of EEG in patients with AD compared to HCs were reproduced by simulations. The virtual AD group showed slower frequencies in simulated local field potentials and EEG compared to MCI and HC groups. The heterogeneity of the Abeta load is crucial for the virtual EEG slowing which is absent for control models with homogeneous Abeta distributions. Slowing phenomena primarily affect the network hubs, independent of the spatial distribution of Abeta. Modeling the N-methyl-D-aspartate (NMDA) receptor antagonism of memantine in local population models, reveals potential functional reversibility of the observed large-scale alterations (reflected by EEG slowing) in virtual AD brains. Discussion: We demonstrate how TVB enables the simulation of systems effects caused by pathogenetic molecular candidate mechanisms in human virtual brains.

Subacute sclerosing panencephalitis

Subacute sclerosing panencephalitis (SSPE) is a slowly progressive brain disorder caused by mutant measles virus. SSPE affects younger age groups. SSPE incidence is proportional to that of measles. High-income countries have seen substantial decline in SSPE incidence following universal vaccination against measles. SSPE virus differs from wild measles virus. Measles virus genome recovered from the autopsied brain tissues demonstrates clustered mutations in virus genome particularly in the M gene. These mutations destroy the structure and functioning of the encoded proteins. Complete infectious virus particle has rarely been recovered from the brain. Human neurons lack required receptor for entry of measles virus inside the neurons. Recent in vitro studies suggest that mutations in F protein confer hyperfusogenic properties to measles virus facilitating transneuronal viral spread. The inflammatory response in the brain leads to extensive tissue damage. Clinically, SSPE is characterized by florid panencephalitis. Clinically, SSPE is characterized by cognitive decline, periodic myoclonus, gait abnormalities, vision loss, and ultimately to a vegetative state. Chorioretinitis is a common ocular abnormality. Electroencephalography (EEG) shows characteristic periodic discharges. Neuroimaging demonstrates periventricular white matter signal abnormalities. In advanced stages, there is marked cerebral atrophy. Definitive diagnosis requires demonstration of elevated measles antibody titers in cerebrospinal fluid (CSF). Many drugs have been used to stabilize the course of the disease but without evidence from randomized clinical trials. Six percent of patients may experience prolonged spontaneous remission. Fusion inhibitor peptide may, in the future, be exploited to treat SSPE. A universal vaccination against measles is the only proven way to tackle this menace currently.

Systematic Review on Resting-State EEG for Alzheimer's Disease Diagnosis and Progression Assessment

Alzheimer's disease (AD) is a neurodegenerative disorder that accounts for nearly 70% of the more than 46 million dementia cases estimated worldwide. Although there is no cure for AD, early diagnosis and an accurate characterization of the disease progression can improve the quality of life of AD patients and their caregivers. Currently, AD diagnosis is carried out using standardized mental status examinations, which are commonly assisted by expensive neuroimaging scans and invasive laboratory tests, thus rendering the diagnosis time consuming and costly. Notwithstanding, over the last decade, electroencephalography (EEG) has emerged as a noninvasive alternative technique for the study of AD, competing with more expensive neuroimaging tools, such as MRI and PET. This paper reports on the results of a systematic review on the utilization of resting-state EEG signals for AD diagnosis and progression assessment. Recent journal articles obtained from four major bibliographic databases were analyzed. A total of 112 journal articles published from January 2010 to February 2018 were meticulously reviewed, and relevant aspects of these papers were compared across articles to provide a general overview of the research on this noninvasive AD diagnosis technique. Finally, recommendations for future studies with resting-state EEG were presented to improve and facilitate the knowledge transfer among research groups.

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

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

Wernicke-Korsakoff syndrome as a rare phenotype of sporadic Creutzfeldt-Jakob disease

We reported the case of a patient with Wernicke-Korsakoff syndrome (WKs) as an early clinical manifestation of sporadic Creutzfeld-Jakob disease (sCJD). The 66-year-old female complained of dizziness and imbalance which mostly occurred while walking. A neurological examination revealed a triad of symptoms characteristic for WKs such as gaze paresis, ataxia of limbs and trunk as well as memory disturbances with confabulations. The disturbances increased during the course of the disease, which led to the death of the patient four months after the appearance of the signs. The patient was finally diagnosed with sCJD disease. The most useful ancillary examination results supporting sCJD diagnosis were brain diffusion DWI MRI (diffusion weighted magnetic resonance imaging) and the presence of 14-3-3 protein in CSF (cerebrospinal fluid). Since that manifestation of sCJD is very unique other causes should be taken into consideration while making a final diagnosis.