The EEG as a diagnostic tool in distinguishing between dementia with Lewy bodies and Alzheimer’s disease

Visual Electroencephalography Assessment in the Diagnosis and Prognosis of Cognitive Disorders

PURPOSE

Electroencephalography (EEG) is a noninvasive diagnostic tool that can be of diagnostic value in patients with cognitive disorders. In recent years, increasing emphasis has been on quantitative EEG analysis, which is not easily accessible in clinical practice. The aim of this study was to assess the diagnostic and prognostic value of visual EEG assessment to distinguish different causes of cognitive disorders.

METHODS

Patients with cognitive disorders from a specialized memory clinic cohort underwent routine workup including EEG, neuropsychological testing and brain imaging. Electroencephalography parameters including posterior dominant rhythm, background activity, and response to photic stimulation (intermittent photic stimulation) were visually scored. Final diagnosis was made by an expert panel.

RESULTS

A total of 501 patients were included and underwent full diagnostic workup. One hundred eighty-three patients had dementia (111 Alzheimer disease, 30 vascular dementia, 15 frontotemporal dementia, and 9 dementia with Lewy bodies), 66 patients were classified as mild cognitive impairment, and in 176, no neurologic diagnosis was made. Electroencephalography was abnormal in 60% to 90% of patients with mild cognitive impairment and dementia, most profoundly in dementia with Lewy bodies and Alzheimer disease, while frontotemporal dementia had normal EEG relatively often. Only 30% of those without neurologic diagnosis had EEG abnormalities, mainly a diminished intermittent photic stimulation response. Odds ratio of conversion to dementia was 6.1 [1.5-24.7] for patients with mild cognitive impairment with abnormal background activity, compared with those with normal EEG.

CONCLUSIONS

Visual EEG assessment has diagnostic and prognostic value in clinical practice to distinguish patients with memory complaints without underlying neurologic disorder from patients with mild cognitive impairment or dementia.

Qualitative versus quantitative assessment of electroencephalography in cognitive decline: Comparison in a clinical population

This study aimed to compare the diagnostic performance of visual assessment of electroencephalography (EEG) using the Grand Total EEG (GTE) score and quantitative EEG (QEEG) using spectral analysis in the context of cognitive impairment. This was a retrospective study of patients with mild cognitive impairment, with (MCI+V) or without (MCI) vascular dysfunction, and patients with dementia including Alzheimer's disease, Lewy Body Dementia and vascular dementia. The results showed that the GTE is a simple scoring system with some potential applications, but limited ability to distinguish between dementia subtypes, while spectral analysis appeared to be a powerful tool, but its clinical development requires the use of artificial intelligence tools.

Differentiating Prodromal Dementia with Lewy Bodies from Prodromal Alzheimer's Disease: A Pragmatic Review for Clinicians

This pragmatic review synthesises the current understanding of prodromal dementia with Lewy bodies (pDLB) and prodromal Alzheimer's disease (pAD), including clinical presentations, neuropsychological profiles, neuropsychiatric symptoms, biomarkers, and indications for disease management. The core clinical features of dementia with Lewy bodies (DLB)-parkinsonism, complex visual hallucinations, cognitive fluctuations, and REM sleep behaviour disorder are common prodromal symptoms. Supportive clinical features of pDLB include severe neuroleptic sensitivity, as well as autonomic and neuropsychiatric symptoms. The neuropsychological profile in mild cognitive impairment attributable to Lewy body pathology (MCI-LB) tends to include impairment in visuospatial skills and executive functioning, distinguishing it from MCI due to AD, which typically presents with impairment in memory. pDLB may present with cognitive impairment, psychiatric symptoms, and/or recurrent episodes of delirium, indicating that it is not necessarily synonymous with MCI-LB. Imaging, fluid and other biomarkers may play a crucial role in differentiating pDLB from pAD. The current MCI-LB criteria recognise low dopamine transporter uptake using positron emission tomography or single photon emission computed tomography (SPECT), loss of REM atonia on polysomnography, and sympathetic cardiac denervation using meta-iodobenzylguanidine SPECT as indicative biomarkers with slowing of dominant frequency on EEG among others as supportive biomarkers. This review also highlights the emergence of fluid and skin-based biomarkers. There is little research evidence for the treatment of pDLB, but pharmacological and non-pharmacological treatments for DLB may be discussed with patients. Non-pharmacological interventions such as diet, exercise, and cognitive stimulation may provide benefit, while evaluation and management of contributing factors like medications and sleep disturbances are vital. There is a need to expand research across diverse patient populations to address existing disparities in clinical trial participation. In conclusion, an early and accurate diagnosis of pDLB or pAD presents an opportunity for tailored interventions, improved healthcare outcomes, and enhanced quality of life for patients and care partners.

Natural history of epilepsy in argininosuccinic aciduria provides new insights into pathophysiology: A retrospective international study

OBJECTIVE

Argininosuccinate lyase (ASL) is integral to the urea cycle, which enables nitrogen wasting and biosynthesis of arginine, a precursor of nitric oxide. Inherited ASL deficiency causes argininosuccinic aciduria, the second most common urea cycle defect and an inherited model of systemic nitric oxide deficiency. Patients present with developmental delay, epilepsy, and movement disorder. Here we aim to characterize epilepsy, a common and neurodebilitating comorbidity in argininosuccinic aciduria.

METHODS

We conducted a retrospective study in seven tertiary metabolic centers in the UK, Italy, and Canada from 2020 to 2022, to assess the phenotype of epilepsy in argininosuccinic aciduria and correlate it with clinical, biochemical, radiological, and electroencephalographic data.

RESULTS

Thirty-seven patients, 1-31 years of age, were included. Twenty-two patients (60%) presented with epilepsy. The median age at epilepsy onset was 24 months. Generalized tonic-clonic and focal seizures were most common in early-onset patients, whereas atypical absences were predominant in late-onset patients. Seventeen patients (77%) required antiseizure medications and six (27%) had pharmacoresistant epilepsy. Patients with epilepsy presented with a severe neurodebilitating disease with higher rates of speech delay (p = .04) and autism spectrum disorders (p = .01) and more frequent arginine supplementation (p = .01) compared to patients without epilepsy. Neonatal seizures were not associated with a higher risk of developing epilepsy. Biomarkers of ureagenesis did not differ between epileptic and non-epileptic patients. Epilepsy onset in early infancy (p = .05) and electroencephalographic background asymmetry (p = .0007) were significant predictors of partially controlled or refractory epilepsy.

SIGNIFICANCE

Epilepsy in argininosuccinic aciduria is frequent, polymorphic, and associated with more frequent neurodevelopmental comorbidities. We identified prognostic factors for pharmacoresistance in epilepsy. This study does not support defective ureagenesis as prominent in the pathophysiology of epilepsy but suggests a role of central dopamine deficiency. A role of arginine in epileptogenesis was not supported and warrants further studies to assess the potential arginine neurotoxicity in argininosuccinic aciduria.

Regional spectral ratios as potential neural markers to identify mild cognitive impairment related to Alzheimer's disease

OBJECTIVE

Alzheimer's disease (AD) has prolonged asymptomatic or mild symptomatic periods. Given that there is an increase in treatment options and that early intervention could modify the disease course, it is desirable to devise biological indices that may differentiate AD and nonAD at mild cognitive impairment (MCI) stage.

METHODS

Based on two well-acknowledged observations of background slowing (attenuation in alpha power and enhancement in theta and delta powers) and early involvement of posterior cingulate cortex (PCC, a neural hub of default-mode network), this study devised novel neural markers, namely, spectral ratios of alpha1 to delta and alpha1 to theta in the PCC.

RESULTS

We analysed 46 MCI patients, with 22 ADMCI and 24 nonADMCI who were matched in age, education, and global cognitive capability. Concordant with the prediction, the regional spectral ratios were lower in the ADMCI group, suggesting its clinical application potential.

CONCLUSION

Previous research has verified that neural markers derived from clinical electroencephalography may be informative in differentiating AD from other neurological conditions. We believe that the spectral ratios in the neural hubs that show early pathological changes can enrich the instrumental assessment of brain dysfunctions at the MCI (or pre-clinical) stage.

Information Contained in EEG Allows Characterization of Cognitive Decline in Neurodegenerative Disorders

Over the last few decades, electroencephalography (EEG) has evolved from being a method that purely relies on visual inspection into a quantitative method. Quantitative EEG, or QEEG, enables the assessment of neurological disorders based on spectral features, dynamic characterizations of EEG resting-state activity, brain connectivity analyzes or quantification of EEG signal complexity. The information contained in EEG is multidimensional: Electrodes, positioned at different scalp locations, provide a spatial dimension to the analysis of EEG while time provides a dynamic dimension: This multidimensional property of EEG makes its quantification a challenging task. In this narrative review we present quantitative models focused on different aspects of EEG: While microstate models focus more on the quantification of the dynamic aspects of EEG, spectral methods, connectivity analysis and entropy based models are more concerned with its spatial aspects. Nevertheless, these diverse approaches have provided neurophysiology based biomarkers, especially for monitoring and predicting the course of various neurodegenerative disorders. However, their translation into clinical practice crucially depends on the ability to automate the analysis of EEG in a user-friendly manner, without compromising on the validity of the provided results. Once this has been accomplished, EEG would provide an inexpensive and widely available method for monitoring disease progression, identifying patients at risk of neurodegeneration-especially before the onset of clinical symptoms, and predicting future cognition. For stratification of patients to clinical trials, EEG would allow shortening the trial duration and lowering the number of necessary participants by identifying patients at risk of fast cognitive decline.

Generalized Rhythmic Delta Activity Frontally Predominant Differentiates Dementia with Lewy Bodies From Alzheimer's Disease and Parkinson's Disease Dementia: A Conventional Electroencephalography Visual Analysis

Introduction. An easily accessible and inexpensive neurophysiological technique such as conventional electroencephalography may provide an accurate and generally applicable biomarker capable of differentiating dementia with Lewy bodies (DLB) from Alzheimer's disease (AD) and Parkinson's disease-associated dementia (PDD). Method. We carried out a retrospective visual analysis of resting-state electroencephalography (EEG) recording of 22 patients with a clinical diagnosis of 19 probable and 3 possible DLB, 22 patients with probable AD and 21 with PDD, matched for age, duration, and severity of cognitive impairment. Results. By using the grand total EEG scoring method, the total score and generalized rhythmic delta activity frontally predominant (GRDAfp) alone or, even better, coupled with a slowing of frequency of background activity (FBA) and its reduced reactivity differentiated DLB from AD at an individual level with an high accuracy similar to that obtained with quantitative EEG (qEEG). GRDAfp alone could also differentiate DLB from PDD with a similar level of diagnostic accuracy. AD differed from PDD only for a slowing of FBA. The duration and severity of cognitive impairment did not differ between DLB patients with and without GRDAfp, indicating that this abnormal EEG pattern should not be regarded as a disease progression marker. Conclusions. The findings of this investigation revalorize the role of conventional EEG in the diagnostic workup of degenerative dementias suggesting the potential inclusion of GRDAfp alone or better coupled with the slowing of FBA and its reduced reactivity, in the list of supportive diagnostic biomarkers of DLB.

A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer's Disease Using EEG Signals

This study will concentrate on recent research on EEG signals for Alzheimer's diagnosis, identifying and comparing key steps of EEG-based Alzheimer's disease (AD) detection, such as EEG signal acquisition, preprocessing function extraction, and classification methods. Furthermore, highlighting general approaches, variations, and agreement in the use of EEG identified shortcomings and guidelines for multiple experimental stages ranging from demographic characteristics to outcomes monitoring for future research. Two main targets have been defined based on the article's purpose: (1) discriminative (or detection), i.e., look for differences in EEG-based features across groups, such as MCI, moderate Alzheimer's disease, extreme Alzheimer's disease, other forms of dementia, and stable normal elderly controls; and (2) progression determination, i.e., look for correlations between EEG-based features and clinical markers linked to MCI-to-AD conversion and Alzheimer's disease intensity progression. Limitations mentioned in the reviewed papers were also gathered and explored in this study, with the goal of gaining a better understanding of the problems that need to be addressed in order to advance the use of EEG in Alzheimer's disease science.

Routine magnetoencephalography in memory clinic patients: A machine learning approach

INTRODUCTION

We report the routine application of magnetoencephalography (MEG) in a memory clinic, and its value in the discrimination of patients with Alzheimer's disease (AD) dementia from controls.

METHODS

Three hundred sixty-six patients visiting our memory clinic underwent MEG recording. Source-reconstructed MEG data were visually assessed and evaluated in the context of clinical findings and other diagnostic markers. We analyzed the diagnostic accuracy of MEG spectral measures in the discrimination of individual AD dementia patients (n = 40) from subjective cognitive decline (SCD) patients (n = 40) using random forest models.

RESULTS

Best discrimination was obtained using a combination of relative theta and delta power (accuracy 0.846, sensitivity 0.855, specificity 0.837). The results were validated in an independent cohort. Hippocampal and thalamic regions, besides temporal-occipital lobes, contributed considerably to the model.

DISCUSSION

MEG has been implemented successfully in the workup of memory clinic patients and has value in diagnostic decision-making.

Electroencephalogram in dementia with Lewy bodies: a systematic review

Dementia with Lewy bodies (DLB) belongs to the spectrum of Lewy body dementia (LBD) that also encompasses Parkinson's disease dementia (PDD). It is a common neurodegenerative disorder characterized by memory decline, cognitive fluctuations, visual hallucinations, autonomic nervous system disturbance, REM sleep behavior disorder, and parkinsonism. Definite diagnosis can be established only through neuropathological confirmation of Lewy bodies' presence in brain tissue. Probable or possible diagnosis relies upon clinical features, imaging, polysomnography, and electroencephalogram (EEG) findings. Potential neurophysiological biomarkers for the diagnosis, management, and evaluation of treatment-response in DLB should be affordable and widely available outside academic centers. Increasing evidence supports the use of quantitative EEG (qEEG) as a potential DLB biomarker, with promising results in discriminating DLB from other dementias and in identifying subjects who are on the trajectory to develop DLB. Several studies evaluated the diagnostic value of EEG in DLB. Visual analysis and qEEG techniques have been implemented, showing a superiority of the last in terms of sensitivity and objectivity. In this systematic review, we attempt to provide a general synthesis of the current knowledge on EEG application in DLB. We review the findings from original studies and address the issues remaining to be further clarified.

Clinical Correlates of Frontal Intermittent Rhythmic Delta Activity Without Structural Brain Lesion

Frontal intermittent rhythmic delta activity (FIRDA), rhythmic slow wave pattern lasting several seconds over the anterior leads of electroencephalography (EEG), has been reported in a wide variety of clinical conditions. We investigated the clinical significance of FIRDA without structural brain lesions. We reviewed 7689 EEGs between October 2017 and September 2019 at a university hospital. Patients (age >18 years) who were confirmed to have "nonsignificant neuroimaging" were examined. Clinical data were retrospectively collected, and the estimated cause was carefully decided. We found 83 FIRDA among 7689 EEGs (1.08%). After patients with any structural lesion identified on neuroimaging were excluded, 37 FIRDAs were reviewed. There were 20 (51.35%) patients of metabolic encephalopathy. Six patients showed FIRDA due to neurodegenerative disease (16.21%). In addition, we found 6 (16.21%) of neurodegenerative disease and 5 (13.51%) of hypoxic encephalopathy (cardiac arrest). Four (16.21%) patients were related to systemic infection (10.81%), whereas 2 were related to encephalitis (5.40%). We demonstrated several potential etiologies, including metabolic encephalopathy, neurodegenerative disease, hypoxic encephalopathy, and infections, which should be considered in the case of FIRDA without structural brain lesions.

The Role of EEG in the Diagnosis, Prognosis and Clinical Correlations of Dementia with Lewy Bodies-A Systematic Review

Despite improvements in diagnostic criteria for dementia with Lewy bodies (DLB), the ability to discriminate DLB from Alzheimer’s disease (AD) and other dementias remains suboptimal. Electroencephalography (EEG) is currently a supportive biomarker in the diagnosis of DLB. We performed a systematic review to better clarify the diagnostic and prognostic role of EEG in DLB and define the clinical correlates of various EEG features described in DLB. MEDLINE, EMBASE, and PsycINFO were searched using search strategies for relevant articles up to 6 August 2020. We included 43 studies comparing EEG in DLB with other diagnoses, 42 of them included a comparison of DLB with AD, 10 studies compared DLB with Parkinson’s disease dementia, and 6 studies compared DLB with other dementias. The studies were visual EEG assessment (6), quantitative EEG (35) and event-related potential studies (2). The most consistent observation was the slowing of the dominant EEG rhythm (<8 Hz) assessed visually or through quantitative EEG, which was observed in ~90% of patients with DLB and only ~10% of patients with AD. Other findings based on qualitative rating, spectral power analyses, connectivity, microstate and machine learning algorithms were largely heterogenous due to differences in study design, EEG acquisition, preprocessing and analysis. EEG protocols should be standardized to allow replication and validation of promising EEG features as potential biomarkers in DLB.

Diagnostic and prognostic value of EEG in prodromal dementia with Lewy bodies

OBJECTIVE

Early biomarkers for dementia with Lewy bodies (DLB) are lacking. To determine whether EEG differentiates the prodromal phase of DLB from other causes of mild cognitive impairment (MCI) and whether EEG is predictive for time to conversion from MCI to DLB, we compared EEGs and clinical follow-up of patients with MCI due to DLB with those of patients with MCI due to Alzheimer disease (MCI-AD).

METHODS

We compared 37 patients with MCI who developed DLB during follow-up or had an abnormal ¹²³I-PF-CIT SPECT scan (MCI-DLB) with 67 age-matched patients with MCI-AD. EEGs were assessed visually with a score of increasing abnormality (range 1-5). We performed fast Fourier transform to analyze the power spectrum. With survival analyses, EEG characteristics were related to time to progression to dementia.

RESULTS

The visual EEG score was higher in MCI-DLB (score >2 in 60%) compared to MCI-AD (score >2 in 8%, p < 0.001). We found frontal intermittent delta activity in 22% of MCI-DLB, not in MCI-AD. Patients with MCI-DLB had a lower peak frequency (7.5 [6.0-9.9] Hz vs 8.8 [6.8-10.2] in MCI-AD, p < 0.001) and more slow-wave activity. Several individual EEG measures showed good performance to discriminate MCI-DLB from MCI-AD (areas under the curve up to 0.94). In MCI-DLB, high visual EEG score, diffuse abnormalities, and low α2 power were related to time to progression to dementia (hazard ratios 4.1, 9.9, 5.1, respectively).

CONCLUSIONS

Profound EEG abnormalities are already present in the prodromal stage of DLB and have diagnostic and prognostic value.

CLASSIFICATION OF EVIDENCE

This study provides Class III evidence that EEG abnormalities are more common in MCI-DLB than MCI-AD.

Clinical correlates and electroencephalographic features of FIRDA in a tertiary center

OBJECTIVES

We aimed to explore the diagnostic value, clinical correlates and electroencephalographic features of FIRDA (Frontal intermittent rhythmic delta activity).

MATERIALS AND METHODS

We retrospectively reviewed reports from EEG studies done in adults at our tertiary center between January 2015 and May 2018. For cases demonstrating FIRDA, medical files were reviewed and each case was given a diagnostic category. EEG recordings were reviewed and electrophysiologic data were extracted including FIRDA characteristics (frequency, location, duration, and symmetry). Then, a statistical analysis was done to evaluate the relationship between the diagnostic categories and EEG variables.

RESULTS

Ninety-four cases of FIRDA were found, with a frequency of 1.6% among inpatients. EEG recordings were available for review in 84 cases. FIRDA was asymmetric in 43 of these cases (49%), usually more prominent on the left (36/43, 84%). The diagnostic category groups included epilepsy (n = 39, 41%), other central nervous system (CNS) disease (n = 33, 35%), and systemic illness (n = 22, 23%). A significant difference in FIRDA location was found, as patients with epilepsy or other CNS disease, had a significantly higher probability for the delta activity to involve the temporal areas (frontotemporal location in 27/64 in these groups compared with 3/20 in the systemic illness group, P-value = .033).

CONCLUSIONS

This study provides insights to the diagnosis underlying FIRDA, especially the high rate of epilepsy patients, and calls for further neurologic investigation of cases in which FIRDA involves the temporal areas since most of these cases were due to epilepsy or other CNS disease and not a systemic illness.

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

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

An NMR-based lipidomic approach to identify Parkinson's disease-stage specific lipoprotein-lipid signatures in plasma

Disturbances in lipid composition and lipoproteins metabolism can play a crucial role in the pathogenesis of Parkinson's disease (PD) and other neurodegenerative diseases. The lipidomic strategy proposed here involves lipoprotein profiling using NMR spectroscopy and multivariate data pre-processing and analysis tools on 94 plasma samples (belonging to 38 early-stage PD patients, 10 PD-related dementia patients, 23 persons with Alzheimer's dementia, and 23 healthy control subjects) to firstly differentiate PD patients (irrespective of the stage of the disease) from persons with Alzheimer's disease (AD) as well as from controls, and then to discriminate among PD patients according to disease severity. The whole data set was subdivided into 86 training and 8 external test samples for validation purposes. A two-step classification scheme, based on linear discriminant analysis with variable selection accomplished by a stepwise orthogonalisation procedure, was proposed to optimise classification performance. Careful pre-processing of NMR signals was crucial to ensure data set quality. A total of 30 chemical shift buckets enabled differentiation between PD patients (regardless of disease severity), AD and control subjects, providing classification, cross-validation and external prediction rates of 100% in all cases. Only 15 variables were required to further discriminate between early-stage PD and PD-related dementia, again with 100% correct classifications, and internal/external predictions. The simplicity and effectiveness of the classification methodology proposed support the use of NMR spectroscopy, in combination with chemometrics, as a viable alternative diagnostic tool to conventional PD clinical diagnosis.

Grand Total EEG Score Can Differentiate Parkinson's Disease From Parkinson-Related Disorders

Background: Semi-quantitative electroencephalogram (EEG) analysis is easy to perform and has been used to differentiate dementias, as well as idiopathic and vascular Parkinson's disease.

Purpose: To study whether a semi-quantitative EEG analysis can aid in distinguishing idiopathic Parkinson's disease (IPD) from atypical parkinsonian disorders (APDs), and furthermore, whether it can help to distinguish between APDs.

Materials and Methods: A comprehensive retrospective review of charts was performed to include patients with parkinsonian disorders who had at least one EEG recording available. A modified grand total EEG (GTE) score evaluating the posterior background activity, and diffuse and focal slow wave activities was used in further analyses.

Results: We analyzed data from 76 patients with a final diagnosis of either IPD, probable corticobasal degeneration (CBD), multiple system atrophy (MSA), or progressive supra-nuclear palsy (PSP). IPD patients had the lowest mean GTE score, followed those with CBD or MSA, while PSP patients scored the highest. However, none of these differences were statistically significant. A GTE score of ≤9 distinguished IPD patients from those with APD (p < 0.01) with a sensitivity of 100% and a specificity of 33.3%.

Conclusion: The modified GTE score can distinguish patients with IPD from those with CBD, PSP or MSA at a cut-off score of 9 with excellent sensitivity but poor specificity. However, this score is not able to distinguish a particular form of APD from other forms of the disorder.

Abnormalities of Resting State Cortical EEG Rhythms in Subjects with Mild Cognitive Impairment Due to Alzheimer's and Lewy Body Diseases

The present study tested the hypothesis that cortical sources of resting state eyes-closed electroencephalographic (rsEEG) rhythms reveal different abnormalities in cortical neural synchronization in groups of patients with mild cognitive impairment due to Alzheimer's disease (ADMCI) and dementia with Lewy bodies (DLBMCI) as compared to cognitively normal elderly (Nold) subjects. Clinical and rsEEG data in 30 ADMCI, 23 DLBMCI, and 30 Nold subjects were available in an international archive. Age, gender, and education were carefully matched in the three groups. The Mini-Mental State Evaluation (MMSE) score was matched between the ADMCI and DLBMCI groups. Individual alpha frequency peak (IAF) was used to determine the delta, theta, alpha1, alpha2, and alpha3 frequency band ranges. Fixed beta1, beta2, and gamma bands were also considered. eLORETA estimated the rsEEG cortical sources. Receiver operating characteristic curve (ROCC) classified these sources across individuals. Compared to Nold, IAF showed marked slowing in DLBMCI and moderate in ADMCI. Furthermore, the posterior alpha 2 and alpha 3 source activities were more abnormal in the ADMCI than the DLBMCI group, while widespread delta source activities were more abnormal in the DLBMCI than the ADMCI group. The posterior delta and alpha sources correlated with the MMSE score and correctly classified the Nold and MCI individuals (area under the ROCC >0.85). In conclusion, the ADMCI and DLBMCI patients showed different features of cortical neural synchronization at delta and alpha frequencies underpinning brain arousal and vigilance in the quiet wakefulness. Future prospective cross-validation studies will have to test the clinical validity of these rsEEG markers.

Relevance of electroencephalogram assessment in amyloid and tau pathology in rat

In order to contribute to a better knowledge on the relationship between amyloid and tau pathology, and electroencephalography (EEG) disturbances, the aim of this study was to evaluate the effects of injection of beta amyloid Abeta(1-42) peptide, tau (a recombinant AAV (Adeno-Associated Virus) containing the human transgene tau with the P301 L mutation on rats and the combination of both, on the power of brain's rhythm (delta, theta, alpha, beta and gamma waves) during the different sleep/wake states of animals by EEG recording. Currently, no preclinical studies explore the effect of the tau pathology on EEG. The experimentations were performed 3 weeks and 3 months post injections. Beta amyloid deposits and hyperphosphorylated Tau are observed by immunohistofluorescence, only in the hippocampus. Furthermore, using a radial arm water maze, the main effect was observed on working memory which was significantly impaired in Abeta-Tau group only 3 months post injections. However, on EEG, as early as the 3^rd week, an overall decrease of the EEG bands power was observed in the treated groups, particularly the theta waves during the rapid eye movement (REM) sleep. Beta amyloid was mainly involved in these perturbations. Obviously, EEG seems to be an interesting tool in the early diagnostic of amyloid and tau pathologies, with a good sensitivity and the possibility to perform a follow up during a large period.

Generalized and Lateralized Rhythmic Patterns

The relationship between generalized and lateralized rhythmic delta activity (RDA) and seizures is more ambiguous than the relationship between periodic discharges and seizures. Although frontally predominant generalized RDA is not associated with seizures, occipitally predominant RDA may be associated with the absence of seizures. Lateralized RDA seems to be more strongly associated with the presence of seizure activity. Appropriate recognition of generalized RDA and lateralized rhythmic delta activity may be confounded by benign etiologies of RDA, such as phi rhythm, slow alpha variant, subclinical rhythmic electrographic discharges of adults, or hyperventilation-induced high-amplitude rhythmic slowing. Angelman syndrome and NMDA-receptor antibody encephalitis can also produce morphologically distinct patterns of RDA.

EEG Characteristics of Dementia With Lewy Bodies, Alzheimer's Disease and Mixed Pathology

Introduction: Previous studies on electroencephalography (EEG) to discriminate between dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) have been promising. These studies did not consider the pathological overlap of the two diseases. DLB-patients with concomitant AD pathology (DLB/AD+) have a more severe disease manifestation. The EEG may also be influenced by a synergistic effect of the two pathologies. We aimed to compare EEG characteristics between DLB/AD+, "pure" DLB (DLB/AD-) and AD. Methods: We selected probable DLB patients who had an EEG and cerebrospinal fluid (CSF) available, from the Amsterdam Dementia Cohort (ADC). Concomitant AD-pathology was defined as a CSF tau/Aβ-42 ratio > 0.52. Forty-one DLB/AD+ cases were matched for age (mean 70 (range 53-85)) and sex (85% male) 1:1 to DLB/AD- and AD-patients. EEGs were assessed visually, with Fast Fourier Transform (FFT), network- and connectivity measures. Results: EEG visual severity score (range 1-5) did not differ between DLB/AD- and DLB/AD+ (2.7 in both groups) and was higher compared to AD (1.9, p < 0.01). Both DLB groups had a lower peak frequency (7.0 Hz and 6.9 Hz in DLB vs. 8.2 in AD, p < 0.05), more slow-wave activity and more prominent disruptions of connectivity and networks, compared to AD. No significant differences were found between DLB/AD+ and DLB/AD-. Discussion: EEG abnormalities are more pronounced in DLB, regardless of AD co-pathology. This emphasizes the valuable role of EEG in discriminating between DLB and AD. It suggests that EEG slowing in DLB is influenced more by the α-synucleinopathy, or the associated cholinergic deficit, than by amyloid and tau pathology.

Predictive values and specificity of electroencephalographic findings in autoimmune encephalitis diagnosis

OBJECTIVE

Early diagnosis of autoimmune encephalitis (AE) to not delay treatment is challenging but needed in practice. Most previous evidences of electroencephalographic (EEG) findings in AE were derived from descriptive studies. Given paucity of evidence of specific EEG findings to help with early diagnosis of AE, this study aimed to ascertain specific EEG findings and assess their predictive values in diagnosis of AE.

METHODS

We included all cases with AE in our institution from January 2013 to June 2017. Cases were matched with controls by age and level of consciousness (1:2 ratio). Potential confounders for EEG findings collected as baseline characteristics were compared. Two epileptologists independently reviewed EEGs. Standardized terminology, definitions, and scoring system of EEG findings were employed. Logistic regression analysis was performed, and diagnostic performance of significant EEG features was assessed.

RESULTS

Twenty cases and 40 controls were included in this study. Poorly sustained posterior dominant rhythm (PDR) was significantly associated with AE (p = 0.007) and even more predictive in anti-N-methyl-d-aspartate (NMDA) encephalitis. Inter-rater agreement (kappa) was 0.714. None of the cases had normal EEG nor Grand Total EEG (GTE) score < 4 (negative predictive value (NPV) of 100%). Specificity of well sustained PDR to exclude the diagnosis of anti-NMDA encephalitis was high (91.67%).

CONCLUSIONS

Simple EEG assessment can be used to help exclude AE. When AE is suspected, careful assessment of the sustainment of the PDR is warranted. The NPV of GTE score < 4 and specificity of well sustained PDR can be simply used to differentiate many conditions from AE.

Abnormalities of cortical neural synchronization mechanisms in patients with dementia due to Alzheimer's and Lewy body diseases: an EEG study

The aim of this retrospective exploratory study was that resting state eyes-closed electroencephalographic (rsEEG) rhythms might reflect brain arousal in patients with dementia due to Alzheimer's disease dementia (ADD), Parkinson's disease dementia (PDD), and dementia with Lewy body (DLB). Clinical and rsEEG data of 42 ADD, 42 PDD, 34 DLB, and 40 healthy elderly (Nold) subjects were available in an international archive. Demography, education, and Mini-Mental State Evaluation score were not different between the patient groups. Individual alpha frequency peak (IAF) determined the delta, theta, alpha 1, alpha 2, and alpha 3 frequency bands. Fixed beta 1, beta 2, and gamma bands were also considered. rsEEG cortical sources were estimated by means of the exact low-resolution brain electromagnetic source tomography and were then classified across individuals, on the basis of the receiver operating characteristic curves. Compared to Nold, IAF showed marked slowing in PDD and DLB and moderate slowing in ADD. Furthermore, all patient groups showed lower posterior alpha 2 source activities. This effect was dramatic in ADD, marked in DLB, and moderate in PDD. These groups also showed higher occipital delta source activities, but this effect was dramatic in PDD, marked in DLB, and moderate in ADD. The posterior delta and alpha sources allowed good classification accuracy (approximately 0.85-0.90) between the Nold subjects and patients, and between ADD and PDD patients. In quiet wakefulness, delta and alpha sources unveiled different spatial and frequency features of the cortical neural synchronization underpinning brain arousal in ADD, PDD, and DLB patients. Future prospective cross-validation studies should test these rsEEG markers for clinical applications and drug discovery.

Random forest to differentiate dementia with Lewy bodies from Alzheimer's disease

Introduction

The aim of this study was to build a random forest classifier to improve the diagnostic accuracy in differentiating dementia with Lewy bodies (DLB) from Alzheimer's disease (AD) and to quantify the relevance of multimodal diagnostic measures, with a focus on electroencephalography (EEG).

Methods

A total of 66 DLB, 66 AD patients, and 66 controls were selected from the Amsterdam Dementia Cohort. Quantitative EEG (qEEG) measures were combined with clinical, neuropsychological, visual EEG, neuroimaging, and cerebrospinal fluid data. Variable importance scores were calculated per diagnostic variable.

Results

For discrimination between DLB and AD, the diagnostic accuracy of the classifier was 87%. Beta power was identified as the single-most important discriminating variable. qEEG increased the accuracy of the other multimodal diagnostic data with almost 10%.

Discussion

Quantitative EEG has a higher discriminating value than the combination of the other multimodal variables in the differentiation between DLB and AD.

Multimodal EEG-MRI in the differential diagnosis of Alzheimer's disease and dementia with Lewy bodies

Differential diagnosis of Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) remains challenging; currently the best discriminator is striatal dopaminergic imaging. However this modality fails to identify 15-20% of DLB cases and thus other biomarkers may be useful. It is recognised electroencephalography (EEG) slowing and relative medial temporal lobe preservation are supportive features of DLB, although individually they lack diagnostic accuracy. Therefore, we investigated whether combined EEG and MRI indices could assist in the differential diagnosis of AD and DLB. Seventy two participants (21 Controls, 30 AD, 21 DLB) underwent resting EEG and 3 T MR imaging. Six EEG classifiers previously generated using support vector machine algorithms were applied to the present dataset. MRI index was derived from medial temporal atrophy (MTA) ratings. Logistic regression analysis identified EEG predictors of AD and DLB. A combined EEG-MRI model was then generated to examine whether there was an improvement in classification compared to individual modalities. For EEG, two classifiers predicted AD and DLB (model: χ(2) = 22.1, df = 2, p < 0.001, Nagelkerke R(2) = 0.47, classification = 77% (AD 87%, DLB 62%)). For MRI, MTA also predicted AD and DLB (model: χ(2) = 6.5, df = 1, p = 0.01, Nagelkerke R(2) = 0.16, classification = 67% (77% AD, 52% DLB). However, a combined EEG-MRI model showed greater prediction in AD and DLB (model: χ(2) = 31.1, df = 3, p < 0.001, Nagelkerke R(2) = 0.62, classification = 90% (93% AD, 86% DLB)). While suggestive and requiring validation, diagnostic performance could be improved by combining EEG and MRI, and may represent an alternative to dopaminergic imaging.

[Significance of clinical electroencephalogram in psychiatry]

The generation of an electroencephalogram (EEG) provides a sensitive, non-invasive and inexpensive method for the investigation of brain function. This article critically reviews the significance of EEG examinations in clinical psychiatric practice and describes relevant applications and limitations. A summary of the basic principles of the production and interpretation of an EEG is followed by a survey of typical EEG patterns in healthy subjects and pathological alterations of EEG patterns. The importance of the EEG for the clinical diagnostics of Alzheimer's disease and acute delirium as well as the differentiation between psychiatric syndromes and non-convulsive status epilepticus is reviewed. Moreover, the usefulness of the EEG is highlighted with respect to the diagnostics and monitoring of the course of lithium intoxication. Finally, this article gives a brief insight into promising research approaches that are currently being followed in modern psychiatry using an EEG.

Neurophysiological biomarkers for Lewy body dementias

•

Biomarkers are needed to improve Lewy body dementia (LBD) diagnosis and measure treatment response.

•

There is substantial heterogeneity in neurophysiology biomarker methodologies limiting comparison.

•

However, there is tentative evidence to suggest neurophysiological approaches may show promise as potential biomarkers of LBD.

Objective

Lewy body dementias (LBD) include both dementia with Lewy bodies (DLB) and Parkinson’s disease with dementia (PDD), and the differentiation of LBD from other neurodegenerative dementias can be difficult. Currently, there are few biomarkers which might assist early diagnosis, map onto LBD symptom severity, and provide metrics of treatment response. Traditionally, biomarkers in LBD have focussed on neuroimaging modalities; however, as biomarkers need to be simple, inexpensive and non-invasive, neurophysiological approaches might also be useful as LBD biomarkers.

Methods

In this review, we searched PubMED and PsycINFO databases in a semi-systematic manner in order to identify potential neurophysiological biomarkers in the LBDs.

Results

We identified 1491 studies; of these, 37 studies specifically examined neurophysiological biomarkers in LBD patients. We found that there was substantial heterogeneity with respect to methodologies and patient cohorts.

Conclusion

Generally, many of the findings have yet to be replicated, although preliminary findings reinforce the potential utility of approaches such as quantitative electroencephalography and motor cortical stimulation paradigms.

Significance

Various neurophysiological techniques have the potential to be useful biomarkers in the LBDs. We recommend that future studies focus on maximising the diagnostic specificity and sensitivity of the most promising neurophysiological biomarkers.