Dominant Frequency Variability Research Articles

Thalamocortical dysrhythmia in patients with delirium: an EEG possible marker of prodromal dementia with Lewy bodies

AbstractBackgroundEEG in DLB is characterized by pre‐alpha dominant frequency (DF) [6–7.5] Hz. The appearance of prealpha rhythm in the cortex is linked to a dysfunctional thalamocortical connection, the thalamocortical dysrhythmia (TCD). The hypothesis that TCD is implicated in DLB has been demonstrated by several structural, functional, and molecular imaging studies. TCD has been implicated in the appearance of altered conscious states, including fluctuating cognition and delirium, two clinical core features of DLB. Delirium can represent the onset of DLB and is listed among the symptoms of prodromal DLB. We aimed to assess specific QEEG abnormalities akin to those found in DLB, also in delirium, to detect a prodromal stage of DLB.MethodCross‐sectional multicentre cohort study and longitudinal prospective study, nested in the multicentre. rsEEG activity was compared in hospitalized patients with delirium vs. patients without delirium. In a subset of 23 patients, rsEEG was also recorded in patients with delirium at resolution of symptom and and at 1‐month follow‐up. DF and DF variability (DFV) were analysed. ResultCross‐sectional multicentre study: 65 subjects with acute delirium were matched for age/sex with 41 controls. Abnormal EEGs were found in 100% delirium patients vs. 34% no‐delirium patients. DF was lower in delirium group (p<0.001), which showed pre‐alpha DF; no‐delirium group had alpha DF. DFV was higher in delirium group (p<0.001). Single‐centre longitudinal study: After delirium resolution, 21 patients had EEG normalization and were cognitively unimpaired. 2 patients had abnormal EEG and were respectively diagnosed as DLB and MCI.ConclusionAcute delirium was characterized by pre‐alpha rhythm, expression of TCD. In most patients EEG normalized after resolution, making EEG a state marker of acute delirium, when the thalamocortical system becomes unstable, but it is still able to recover. TCD may become unrecoverable when the neurodegenerative process of DLB progresses.

Spectral features of resting-state EEG in Parkinson's Disease: A multicenter study using functional data analysis

ObjectiveThis study aims 1) To analyse differences in resting-state electroencephalogram (rs-EEG) spectral features of Parkinson's Disease (PD) and healthy subjects (non-PD) using Functional Data Analysis (FDA) and 2) To explore, in four independent cohorts, the external validity and reproducibility of the findings using both epoch-to-epoch FDA and averaged-epochs approach. MethodsWe included 169 subjects (85 non-PD; 84 PD) from four centres. Rs-EEG signals were preprocessed with a combination of automated pipelines. Sensor-level relative power spectral density (PSD), dominant frequency (DF), and DF variability (DFV) features were extracted. Differences in each feature were compared between PD and non-PD on averaged epochs and using FDA to model the epoch-to-epoch change of each feature. ResultsFor averaged epochs, significantly higher theta relative PSD in PD was found across all datasets. Also, higher pre-alpha relative PSD was observed in three of four datasets in PD patients. For FDA, similar findings were achieved in theta, but all datasets showed consistently significant posterior pre-alpha differences across multiple epochs. ConclusionsIncreased generalised theta, with posterior pre-alpha relative PSD, was the most reproducible finding in PD. SignificanceRs-EEG theta and pre-alpha findings are generalisable in PD.FDA constitutes a reliable and powerful tool to analyse epoch-to-epoch the rs-EEG.

One Minute of EEG Data Provides Sufficient and Reliable Data for Identifying Lewy Body Dementia.

To determine the minimum duration of electroencephalography (EEG) data necessary to differentiate EEG features of Lewy body dementia (LBD), that is, dementia with Lewy bodies and Parkinson disease dementia, from non-LBD patients, that is, Alzheimer disease and Parkinson disease. We performed quantitative EEG analysis for 16 LBD and 14 non-LBD patients. After artifact removal, a fast Fourier transform was performed on 90, 60, and thirty 2-second epochs to derive dominant frequency; dominant frequency variability; and dominant frequency prevalence. In LBD patients, there were no significant differences in EEG features derived from 90, 60, and thirty 2-second epochs (all P >0.05). There were no significant differences in EEG features derived from 3 different groups of thirty 2-second epochs (all P >0.05). When analyzing EEG features derived from ninety 2-second epochs, we found that LBD had significantly reduced dominant frequency, reduced dominant frequency variability, and reduced dominant frequency prevalence alpha compared with the non-LBD group (all P <0.05). These same differences were observed between the LBD and non-LBD groups when analyzing thirty 2-second epochs. There were no differences in EEG features derived from 1 minute versus 3 minutes of EEG data, and both durations of EEG data equally differentiated LBD from non-LBD.

EEG Abnormalities During Delirium as a Prodromal Feature of Dementia with Lewy Bodies: A Case Report

Background:A 79-year-old woman was admitted to the Neurology Clinic of the University of Chieti-Pescara for a syncope. At admission, the occurrence of an acute stroke was ruled out. Her cognitive status was unimpaired. After three days from the hospitalization, the patient experienced an episode of mixed delirium.Objective:The present case report shows a case of delirium-onset dementia with Lewy bodies (DLB) with a specific electroencephalographic (EEG) pattern from its prodromal stage.Methods:Delirium was assessed by 4AT test. During the hospitalization, the patient underwent a quantitative EEG (QEEG) with spectral analysis. At six months from the episode of delirium, she was tested by neuropsychological evaluation, QEEG, and 18F-fluorodeoxyglucose PET/CT to assess the onset of a possible cognitive decline.Results:At baseline, the QEEG exam showed a dominant frequency (DF) in the pre-alpha band (7.5 Hz) with a dominant frequency variability (DFV) of 2 Hz. This pattern is typical of DLB at early stage. After six months, she reported attention deficits in association with cognitive fluctuation and REM sleep behavior disorder. The neurological examination revealed signs of parkinsonism. Cognitive status resulted to be impaired (MoCA = 15/30). QEEG recording confirmed the presence of a DLB-typical pattern (DF = 7.5 Hz, DFV = 2.5 Hz). The 18F-FDG-PET/CT showed a moderate bilateral posterior hypometabolism (occipital and temporal cortex), with relative sparing of the posterior cingulate cortex compared to cuneus/precuneus (Cingulate Island sign), and mild bilateral hypometabolism in frontal regions (suggestive of a DLB diagnosis).Conclusion:EEGs may represent supportive and validated biomarkers for delirium-onset prodromal DLB.

558 - Multimodal EEG-MRI in the diagnosis of mild cognitive impairment with lewybodies

Background:Differentiating mild cognitive impairment with Lewy bodies (MCI-LB) from mild cognitive impairment due to Alzheimer’s disease (MCI-AD) is challenging due to an overlap of symptoms. Quantitative EEG analyses have shown varying levels of diagnostic accuracy, while visual assessment of EEG may be a promising diagnostic method. Additionally, a multimodal EEG-MRI approach may have greater diagnostic utility than individual modalities alone.Research Objective:To evaluate the utility of (1) a structured visual EEG assessment and (2) a machine learning multimodal EEG-MRI approach to differentiate MCI-LB from MCI-AD.Method:300 seconds of eyes-closed, resting-state EEG from 37 MCI-LB and 36 MCI-AD patients were analysed. EEGs were visually assessed for the presence of diffuse, focal, and epileptiform abnormalities, overall grade of abnormalities and focal rhythmic delta activity (FIRDA). Random forest classifiers to discriminate MCI-LB from MCI-AD were trained on combinations of visual EEG, quantitative EEG and structural MRI features. Quantitative EEG features (dominant frequency, dominant frequency variability, theta/alpha ratio and measures of spectral power in the delta, theta, prealpha, alpha and beta bands) and structural MRI features (hippocampal and insular volumes) were obtained from previous analyses of our dataset.Results:Most patients had abnormal EEGs on visual assessment (MCI-LB = 91.9%, MCI-AD = 77.8%). Overall grade (Χ2 (73, 2) = 4.416, p = 0.110), diffuse abnormalities Χ2(73,1) = 3.790, p = 0.052, focal abnormalities Χ2 (73,1) = 3.113, p = 0.077 and FIRDA Χ2(73,1) = 0.862, p = 0.353 did not differ between groups. All multimodal classifiers had similar diagnostic accuracy (area underthe curve, AUC = 0.681 - 0.686) to a classifier that used quantitative EEG features only (AUC =0.668). The feature ‘beta power’ had the highest predictive power in all classifiers.Conclusion:Visual EEG assessment was unable to discriminate between MCI-LB and MCI-AD. However, future work with a more sensitive visual assessment score may yield more promising results.A multimodal EEG-MRI approach does not enhance the diagnostic value of quantitative EEG alone in diagnosing MCI-LB.(326 words)

Classification of Dementia in EEG with a Two-Layered Feed Forward Artificial Neural Network

Dementia has become a global public health issue. The current study is focused on diagnosing dementia with Electro Encephalography (EEG). The detection of the advancement of the disease is carried out by detecting the abnormal behavior in EEG measurements. Assessment and evaluation of EEG abnormalities is conducted for all the subjects in order to detect dementia. EEG feature analysis, namely dominant frequency, dominant frequency variability, and frequency prevalence, is done for abnormal and normal subjects and the results are compared. For dementia with Lewy bodies, in 85% of the epochs, the dominant frequency is present in the delta range whereas for normal subjects it lies in the alpha range. The dominant frequency variability in 75% of the epochs is above 4Hz for dementia with Lewy bodies, and in normal subjects at 72% of the epochs, the dominant frequency variability is less than 2Hz. It is observed that these features are sufficient to diagnose dementia with Lewy bodies. The classification of Lewy body dementia is done by using a feed-forward artificial neural network wich proved to have a 94.4% classification accuracy. The classification with the proposed feed-forward neural network has better accuracy, sensitivity, and specificity than the already known methods.

Quantitative EEG as a biomarker in mild cognitive impairment with Lewy bodies

ObjectivesTo investigate using quantitative EEG the (1) differences between patients with mild cognitive impairment with Lewy bodies (MCI-LB) and MCI with Alzheimer’s disease (MCI-AD) and (2) its utility as a potential biomarker for early differential diagnosis.MethodsWe analyzed eyes-closed, resting-state, high-density EEG data from highly phenotyped participants (39 MCI-LB, 36 MCI-AD, and 31 healthy controls). EEG measures included spectral power in different frequency bands (delta, theta, pre-alpha, alpha, and beta), theta/alpha ratio, dominant frequency, and dominant frequency variability. Receiver operating characteristic (ROC) analyses were performed to assess diagnostic accuracy.ResultsThere was a shift in power from beta and alpha frequency bands towards slower frequencies in the pre-alpha and theta range in MCI-LB compared to healthy controls. Additionally, the dominant frequency was slower in MCI-LB compared to controls. We found significantly increased pre-alpha power, decreased beta power, and slower dominant frequency in MCI-LB compared to MCI-AD. EEG abnormalities were more apparent in MCI-LB cases with more diagnostic features. There were no significant differences between MCI-AD and controls. In the ROC analysis to distinguish MCI-LB from MCI-AD, beta power and dominant frequency showed the highest area under the curve values of 0.71 and 0.70, respectively. While specificity was high for some measures (up to 0.97 for alpha power and 0.94 for theta/alpha ratio), sensitivity was generally much lower.ConclusionsEarly EEG slowing is a specific feature of MCI-LB compared to MCI-AD. However, there is an overlap between the two MCI groups which makes it difficult to distinguish between them based on EEG alone.

P09-F Anterior EEG slowing in Dementia with Lewy Bodies: A multicenter European cohort study

Background EEG slowing with pre-alpha dominant frequency in posterior derivations is a supportive biomarker for Dementia with Lewy Bodies diagnosis, with high predictive value for the differential diagnosis with Alzheimer’s Disease. The appearance of a pre-alpha rhythm is considered to be linked to a dysfunctional thalamo-cortical connection. We hypothesize that the intra-subject evaluation would give information on possible different involvement of anterior and posterior cortical areas in Dementia with Lewy Bodies. Objectives Our aim was to analyse the anterior-posterior scalp distribution of dominant frequencies with an intra-subject evaluation in a dementia with Lewy bodies patient group. Methods We quantitatively analyzed EEGs of 122 patients from 5 Centers of the European dementia with Lewy bodies Consortium. EEG spectra values, divided into delta, theta, pre-alpha, alpha frequency bands were described by dominant frequency, dominant frequency variability, frequency prevalence. Results Dominant frequency in anterior derivations was lower than in posterior derivations (p = 2 · 10–4). Dominant frequency variability, frequency prevalence in delta and theta was higher in anterior derivations (3 · 10–7; 1 · 10–16; 1 · 10–5). As a consequence, frequency prevalence in pre-alpha and alpha was lower in anterior than posterior derivations (4 · 10–3; 1 · 10–7), due to the prevalence of delta/theta in anterior derivations. In 11% of patients, pre-alpha was dominant in anterior leads while alpha rhythm was still dominant in posterior derivations. Conclusions EEG slowing was more severe in anterior derivations. A future longitudinal study should address the possibility that the anterior EEG abnormalities appear earlier in the disease course.

Loss-of-function mutation in inositol monophosphatase 1 (IMPA1) results in abnormal synchrony in resting-state EEG

BackgroundDysregulation of the inositol cycle is implicated in a wide variety of human diseases, including developmental defects and neurological diseases. A homozygous frameshift mutation in IMPA1, coding for the enzyme inositol monophosphatase 1 (IMPase), has recently been associated with severe intellectual disability (ID) in a geographically isolated consanguineous family in Northeastern Brazil (Figueredo et al., 2016). However, the neurophysiologic mechanisms that mediate the IMPA1 mutation and associated ID phenotype have not been characterized. To this end, resting EEG (eyes-open and eyes-closed) was collected from the Figueredo et al. pedigree. Quantitative EEG measures, including mean power, dominant frequency and dominant frequency variability, were investigated for allelic associations using multivariate family-based association test using generalized estimating equations.ResultsWe found that the IMPA1 mutation was associated with relative decreases in frontal theta band power as well as altered alpha-band variability with no regional specificity during the eyes-open condition. For the eyes-closed condition, there was altered dominant theta frequency variability in the central and parietal regions.ConclusionsThese findings represent the first human in vivo phenotypic assessment of brain function disturbances associated with a loss-of-function IMPA1 mutation, and thus an important first step towards an understanding the pathophysiologic mechanisms of intellectual disability associated with the mutation that affects this critical metabolic pathway.

Quantitative electroencephalography as a marker of cognitive fluctuations in dementia with Lewy bodies and an aid to differential diagnosis

ObjectiveWe investigated for quantitative EEG (QEEG) differences between Alzheimer’s disease (AD), dementia with Lewy bodies (DLB) and Parkinson’s disease dementia (PDD) patients and healthy controls, and for QEEG signatures of cognitive fluctuations (CFs) in DLB. MethodsWe analysed eyes-closed, resting state EEGs from 18 AD, 17 DLB and 17 PDD patients with mild dementia, and 21 age-matched controls. Measures included spectral power, dominant frequency (DF), frequency prevalence (FP), and temporal DF variability (DFV), within defined EEG frequency bands and cortical regions. ResultsDLB and PDD patients showed a leftward shift in the power spectrum and DF. AD patients showed greater DFV compared to the other groups. In DLB patients only, greater DFV and EEG slowing were correlated with CFs, measured by the clinician assessment of fluctuations (CAF) scale. The diagnostic accuracy of the QEEG measures was 94% (90.4–97.9%), with 92.26% (80.4–100%) sensitivity and 83.3% (73.6–93%) specificity. ConclusionAlthough greater DFV was only shown in the AD group, within the DLB group a positive DFV – CF correlation was found. QEEG measures could classify DLB and AD patients with high sensitivity and specificity. SignificanceThe findings add to an expanding literature suggesting that EEG is a viable diagnostic and symptom biomarker in dementia, particularly DLB.

EEG Markers of Dementia with Lewy Bodies: A Multicenter Cohort Study.

Quantitative EEG (QEEG) has demonstrated good discriminative capacity for dementia with Lewy bodies (DLB) diagnosis as compared to Alzheimer's disease (AD) with a predictive value of 100% in a single cohort study. EEG in DLB was characterized by a dominant frequency (DF) in pre-alpha (5.5-7.5 Hz), theta, or delta bands and DF variability (DFV) >1.2 Hz, frequency prevalence (FP) pre-alpha in >40% and FP alpha in <32% of the epochs. To validate the aforementioned QEEG findings in independent cohorts of clinically diagnosed DLB versus AD patients, we analyzed EEG traces of 79 DLB and 133 AD patients (MMSE >20) collected from four European Centers. EEG traces from 19 scalp derivations were acquired as at least 10 min continuous signals and epoched in off-setting as series of 2-second-long epochs, subsequently processed by Fast Fourier Transform (frequency resolution 0.5 Hz). DLB patients showed EEG specific abnormalities in posterior derivations characterized by DF <8 Hz FP pre-alpha >50%, FP alpha <25%. DFV was >0.5 Hz. AD patients displayed stable alpha DF, DFV <0.5 Hz, FP pre-alpha <30%, and FP alpha >55%. DLB and AD differed for DF (p < 10-6), DFV (p < 0.05), FP pre-alpha (p < 10-12) and FP alpha (p < 10-12). Discriminant analysis detected specific cut-offs for every EEG mathematical descriptor; DF = 8, DFV = 2.2 Hz, FP pre-alpha=33%, FP alpha = 41% for posterior derivations. If at least one of the cut-off values was met, the percentage of DLB and AD patients correctly classified was 90% and 64%, respectively. The findings in this multicenter study support the validity of QEEG analysis as a tool for diagnosis in DLB patients.

Long recording sequences: how to track the intra-individual variability of acoustic signals.

Recently developed acoustic technologies - like automatic recording units - allow the recording of long sequences in natural environments. These devices are used for biodiversity survey but they could also help researchers to estimate global signal variability at various (individual, population, species) scales. While sexually-selected signals are expected to show a low intra-individual variability at relatively short time scale, this variability has never been estimated so far. Yet, measuring signal variability in controlled conditions should prove useful to understand sexual selection processes and should help design acoustic sampling schedules and to analyse long call recordings. We here use the overall call production of 36 male treefrogs (Hyla arborea) during one night to evaluate within-individual variability in call dominant frequency and to test the efficiency of different sampling methods at capturing such variability. Our results confirm that using low number of calls underestimates call dominant frequency variation of about 35% in the tree frog and suggest that the assessment of this variability is better by using 2 or 3 short and well-distributed records than by using samples made of consecutive calls. Hence, 3 well-distributed 2-minutes records (beginning, middle and end of the calling period) are sufficient to capture on average all the nightly variability, whereas a sample of 10 000 consecutive calls captures only 86% of it. From a biological point of view, the call dominant frequency variability observed in H. arborea (116Hz on average but up to 470 Hz of variability during the course of the night for one male) challenge about its reliability in mate quality assessment. Automatic acoustic recording units will provide long call sequences in the near future and it will be then possible to confirm such results on large samples recorded in more complex field conditions.

Quantitative electroencephalogram utility in predicting conversion of mild cognitive impairment to dementia with Lewy bodies

Mild cognitive impairment (MCI) as a precursor of dementia with Lewy bodies (DLB) is the focus of recent research, trying to explore the early mechanisms and possible biomarkers of DLB. Quantitative electroencephalogram (QEEG) methods are able to differentiate early DLB from Alzheimer's disease (AD). The aim of the present study was to assess whether QEEG abnormalities, characterized by dominant frequency <8 Hz and dominant frequency variability >1.5 Hz, typical of early DLB, are already present at the stage of MCI and to evaluate whether EEG abnormalities can predict the development of DLB. Forty-seven MCI subjects were followed for 3 years. EEG recordings were obtained at admission and at the end of the study. At the end of follow-up, 20 subjects had developed probable DLB (MCI-DLB), 14 had probable AD (MCI-AD), 8 did not convert to dementia, 5 developed a non-AD/DLB dementia. One hundred percent of MCI-DLB showed EEG abnormalities at admission. Ninety three percent of MCI-AD maintained a normal EEG throughout the study. QEEG may represent a powerful tool to predict the progression from MCI to DLB with a sensitivity and specificity close to 100%.

Organizational Index Mapping to Identify Focal Sources During Persistent Atrial Fibrillation

Localized rotors have been implicated in the mechanism of persistent atrial fibrillation (AF). Although regions of highest dominant frequency (DF) on spectral analysis of the left atrium (LA) have been said to identify rotors, other mechanisms such as wavefront collisions will sporadically also generate an inconsistent distribution of high DF. We hypothesized that if drivers of AF were present, their distinctive spectral characteristics would result more from their temporal stability than their high frequency. Ten patients with persistent AF underwent LA noncontact mapping. Following subtraction of far-field ventricular components, noncontact electrograms at 256 sites underwent fast Fourier transform. Mean absolute difference in DF between 5 sequential 7-second segments of AF was defined as the DF variability (DFV) at each site. Mean ratio of the DF and its harmonics to the total power of the spectrum was defined as the organizational index (OI). Mean DFV was significantly lower in organized areas (OI > 1 SD above mean) than at all sites (0.34 ± 0.04 vs 0.46 ± 0.04 Hz; P < 0.001). When organized areas were ablated during wide-area circumferential ablation, AF organized in remote regions (LA appendage ΔOI ablated vs unablated: +0.21 [0.06-0.41] vs -0.04 [-0.14-0.05]; P = 0.005). At sites of organized activation, the activation frequency was also significantly more stable over time. This observation is consistent with the existence of focal sources, and inconsistent with a purely random activation pattern. Ablation of such regions is technically feasible, and was associated with organization of AF in remote atrial regions.

HHT based cardiopulmonary coupling analysis for sleep apnea detection

To validate the feasibility of the Hilbert-Huang transform (HHT) based cardiopulmonary coupling (CPC) technique in respiratory events detection and estimation of the severity of apnea/hypopnea. The HHT-CPC sleep spectrogram technique was applied to a total of 69 single-lead ECG signals downloaded from the Physionet Sleep Apnea Database. Sleep spectrograms generated by both the original and the improved CPC method were compared on the structure distribution and time-frequency resolution. The performance of respiratory events detection by using the power of low frequency coupling (pLFC) in the new method was estimated by receiver operating characteristic analysis. Furthermore, correlation between HHT-CPC index (temporal Variability of Dominant Frequency, TVDF) and conventional OSAHS scoring was computed. The HHT-CPC spectrum provides much finer temporal resolution and frequency resolution (8 s and 0.001 Hz) compared with the original CPC (8.5 min and 0.004 Hz). The area under the ROC curve of pLFC was 0.79 in distinguishing respiratory events from normal breathing. Significant differences were found in TVDF among groups with different severities of OSAHS (normal, mild, moderate, and severe, p<0.001). TVDF has a strong negative correlation with the apnea/hypopnea index (AHI, correlation coefficient -0.71). The HHT-CPC spectrum could exhibit more detailed temporal-frequency information about cardiopulmonary coupling during sleep. As two spectrographic markers, pLFC and TVDF can be used to identify respiratory events and represent the disruption extent of sleep architecture in patients with sleep apnea/hypopnea, respectively. The proposed technique might serve as a complementary approach to enhance diagnostic efforts.

The temporal variability of dominant frequency and complex fractionated atrial electrograms constrains the validity of sequential mapping in human atrial fibrillation

It has been proposed that sequential mapping of dominant frequency (DF) and complex fractionated atrial electrograms (CFAE) can identify target sites for ablation of atrial fibrillation (AF). These mapping strategies are valid only if DF and CFAE are temporally stable on the timescale of the mapping procedure. We postulate that DF and CFAE are temporally variable; consequently, sequential mapping can be misleading. To make prolonged spatially stable multielectrode recordings to assess the temporal stability of DF and CFAE. We recorded electrical activity for 5 minutes with the use of a 64-electrode basket catheter placed in the left atrium of 18 patients presenting for AF ablation. DF and CFAE were determined off-line, and their temporal variability was quantified. Maps created from simultaneous versus sequentially acquired data were compared. DF was temporally variable: the average temporal coefficient of variation was 22.7% +/- 5.4%. DF sites were transient, meeting criteria for only 22.1 seconds out of 5 minutes. Similarly, CFAEs were transient (average duration of CFAE 8.8 +/- 11.3 seconds). DF and CFAE sequential maps failed to identify 93.0% +/- 12.4% and 35.9% +/- 14.9% of DF and CFAE sites, respectively. Because of temporal variability, sequential DF and CFAE maps do not accurately reflect the spatial distribution of excitation frequency during any given sampling interval. The spatial distribution of DF and CFAE sites on maps created with sequential point acquisition depends upon the time at which each site is sampled.

Differences in atrial fibrillation properties under vagal nerve stimulation versus atrial tachycardia remodeling

There are many similarities between atrial effects of atrial tachycardia remodeling (ATR) and vagal nerve stimulation (VS): both promote atrial fibrillation (AF), reduce atrial effective refractory period (AERP) and AERP rate accommodation, enhance AERP heterogeneity, and increase inward-rectifier K+ current. This study sought to compare the consequences of ATR and VS at similar levels of AERP abbreviation in dogs. ATR dogs (n = 6) were subjected to 7-day atrial tachypacing at 400 beats/min, with radiofrequency-induced atrioventricular block and ventricular demand pacing (80 beats/min) to control ventricular response. VS was applied in 6 matched dogs with stimulation parameters selected to produce similar mean AERP values to ATR dogs. ATR and VS produced similarly short AERPs (79 +/- 12 and 80 +/- 12 ms, respectively), AERP rate-adaptation loss, and AERP heterogeneity increases. Although both ATR and VS increased AF duration, VS was significantly more effective in AF promotion, with mean AF duration of 992 +/- 134 seconds, versus 440 +/- 240 seconds (P <.05) under ATR. The greater AF-promoting effect of VS was associated with greater mean dominant frequency values during AF (11.7 +/- 1.8 versus 10.0 +/- 1.3 Hz ATR, P <.05). VS greatly enhanced the spatial dominant frequency variability, increasing the coefficient of variation to 15.2 +/- 1.9 Hz, versus 8.9 +/- 1.5 Hz for ATR (P <.05), primarily by increasing the per-dog maximum dominant frequency (15.4 +/- 0.6 Hz versus 12.5 +/- 0.6 for ATR, P <.01). For matched AERP values, VS promotes AF more strongly than ATR. Despite similar AERP changes, VS produces considerably greater increases in dominant frequencies, particularly maximum values, consistent with previous suggestions that inward-rectifier K+ current enhancement is particularly effective at accelerating and stabilizing spiral wave rotors that maintain AF.

Spatial and temporal organization of the dominant frequencies in the fibrillating heart: body surface potential mapping in a rare case of sustained human ventricular fibrillation

The aim of this study was to assess the spatial and temporal variability of dominant fibrillation frequencies in a rare case of sustained human ventricular fibrillation (VF). Body surface potential mapping was performed in a patient with sustained VF and who was supported by a biventricular assist device. Dominant frequencies at 54 body sites were calculated from two recordings obtained 38 days apart. Variability of dominant frequencies between recordings and across body sites was quantified. Median dominant frequencies within recordings varied between 6.1 and 7.2 Hz in recording 1 and 5.6 and 6.6 Hz in recording 2, indicating a significant reduction in dominant frequencies between the recordings (P < 0.0001). Dominant frequencies differed across body sites by a mean (range) of 1.7 (0.4-2.8) Hz. In this rare case of sustained VF, there was significant spatial and temporal variability of VF dominant frequencies. These findings should be considered in future ECG studies on VF where the spatial variability of dominant frequency might not otherwise have been considered.

EEG comparisons in early Alzheimer's disease, dementia with Lewy bodies and Parkinson's disease with dementia patients with a 2-year follow-up

EEG abnormalities have been reported for both dementia with Lewy bodies (DLB) and Alzheimer's disease (AD). Although it has been suggested that variations in mean EEG frequency are greater in the former, the existence of meaningful differences remains controversial. No evidence is as yet available for Parkinson's disease with dementia (PDD). The aim of this study was to evaluate whether EEG abnormalities can discriminate between DLB, AD and PDD in the earliest stages of dementia and to do this 50 DLB, 50 AD and 40 PDD patients with slight cognitive impairment at first visit (MMSE > or = 20) were studied. To improve clinical diagnostic accuracy, special emphasis was placed on identifying cognitive fluctuations and REM-sleep behaviour disorder. EEG variability was assessed by mean frequency analysis and compressed spectral arrays (CSA) in order to detect changes over time from different scalp derivations. Patients' initial diagnoses were revised at a 2-year follow-up visit with neuroimaging evaluation. Initial diagnoses were confirmed in 36 DLB, 40 AD and 35 PDD patients. The most relevant group differences were observed between the AD and DLB patients in EEGs from posterior derivations (P<0.001). Dominant frequencies were 8.3 +/- 0.6 Hz for the AD group and 7.4 +/- 1.6 Hz for the DLB group, in which most of the patients (88%) exhibited a frequency band of 5.6-7.9 Hz. Dominant frequency variability also differed between the AD (1.1 +/- 0.4 Hz) and DLB groups (1.8 +/- 1.2 Hz, P<0.001). Of note, less than a half (46%) of the patients with PDD exhibited the EEG abnormalities seen in those with DLB. Graded according to the presence of alpha activity, five different patterns were identified on EEG CSA from posterior derivations. A pattern with dominant alpha bands was observed in patients with AD alone while, in those with DLB and PDD, the degree to which residual alpha and 5.6-7.9 bands appeared was related to the presence and severity of cognitive fluctuations. At follow-up, EEG abnormalities from posterior leads were seen in all subjects with DLB and in three-quarters of those with PDD. Of interest, in four patients initially labelled as having AD, in whom the occurrence of fluctuations and/or REM-sleep behaviour disorder during the 2-year follow-up had made the diagnosis of AD questionable, the initial EEG was characterized by the features observed in the DLB group. If revised consensus criteria for DLB diagnosis are properly applied (i.e. emphasizing the diagnostic weight of fluctuations and REM sleep behaviour disorder), EEG recording may act to support discrimination between AD and DLB at the earliest stages of dementia, since characteristic abnormalities may even precede the appearance of distinctive clinical features.