Functional characterization of GYG1 variants in two patients with myopathy and glycogenin-1 deficiency.

Glycogen storage disease XV is caused by variants in the glycogenin-1 gene, GYG1, and presents as a predominant skeletal myopathy or cardiomyopathy. We describe two patients with late-onset myopathy and biallelic GYG1 variants. In patient 1, the novel c.144-2A>G splice acceptor variant and the novel frameshift variant c.631delG (p.Val211Cysfs*30) were identified, and in patient 2, the previously described c.304G>C (p.Asp102His) and c.487delG (p.Asp163Thrfs*5) variants were found. Protein analysis showed total absence of glycogenin-1 expression in patient 1, whereas in patient 2 there was reduced expression of glycogenin-1, with the residual protein being non-functional. Both patients showed glycogen and polyglucosan storage in their muscle fibers, as revealed by PAS staining and electron microscopy. Age at onset of the myopathy phenotype was 53 years and 70 years respectively, with the selective pattern of muscle involvement on MRI corroborating the pattern of weakness. Cardiac evaluation of patient 1 and 2 did not show any specific abnormalities linked to the glycogenin-1 deficiency. In patient 2, who was shown to express the p.Asp102His mutated glycogenin-1, cardiac evaluation was still normal at age 77 years. This contrasts with the association of the p.Asp102His variant in homozygosity with a severe cardiomyopathy in several cases with an onset age between 30 and 50 years. This finding might indicate that the level of p.Asp102His mutated glycogenin-1 determines if a patient will develop a cardiomyopathy.

Abnormalities of Cortical Neural Synchronization Mechanisms in Subjects with Mild Cognitive Impairment due to Alzheimer's and Parkinson's Diseases: An EEG Study.

The aim of this retrospective and exploratory study was that the cortical sources of resting state eyes-closed electroencephalographic (rsEEG) rhythms might reveal different abnormalities in cortical neural synchronization in groups of patients with mild cognitive impairment due to Alzheimer's disease (ADMCI) and Parkinson's disease (PDMCI) as compared to healthy subjects. Clinical and rsEEG data of 75 ADMCI, 75 PDMCI, and 75 cognitively normal elderly (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) was matched between the ADMCI and PDMCI 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 (ROC) classified these sources across individuals. Results showed that compared to the Nold group, the posterior alpha2 and alpha3 source activities were more abnormal in the ADMCI than the PDMCI group, while the parietal delta source activities were more abnormal in the PDMCI than the ADMCI group. The parietal delta and alpha sources correlated with MMSE score and correctly classified the Nold and diseased individuals (area under the ROC = 0.77-0.79). In conclusion, the PDMCI and ADMCI 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 these rsEEG markers for clinical applications and drug discovery.

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.

Differential diagnosis between patients with probable Alzheimer's disease, Parkinson's disease dementia, or dementia with Lewy bodies and frontotemporal dementia, behavioral variant, using quantitative electroencephalographic features.

The objective of this work was to develop and evaluate a classifier for differentiating probable Alzheimer's disease (AD) from Parkinson's disease dementia (PDD) or dementia with Lewy bodies (DLB) and from frontotemporal dementia, behavioral variant (bvFTD) based on quantitative electroencephalography (QEEG). We compared 25 QEEG features in 61 dementia patients (20 patients with probable AD, 20 patients with PDD or probable DLB (DLBPD), and 21 patients with bvFTD). Support vector machine classifiers were trained to distinguish among the three groups. Out of the 25 features, 23 turned out to be significantly different between AD and DLBPD, 17 for AD versus bvFTD, and 12 for bvFTD versus DLBPD. Using leave-one-out cross validation, the classification achieved an accuracy, sensitivity, and specificity of 100% using only the QEEG features Granger causality and the ratio of theta and beta1 band powers. These results indicate that classifiers trained with selected QEEG features can provide a valuable input in distinguishing among AD, DLB or PDD, and bvFTD patients. In this study with 61 patients, no misclassifications occurred. Therefore, further studies should investigate the potential of this method to be applied not only on group level but also in diagnostic support for individual subjects.

Quantifying synchrony patterns in the EEG of Alzheimer's patients with linear and non-linear connectivity markers.

We analyzed the relation of several synchrony markers in the electroencephalogram (EEG) and Alzheimer's disease (AD) severity as measured by Mini-Mental State Examination (MMSE) scores. The study sample consisted of 79 subjects diagnosed with probable AD. All subjects were participants in the PRODEM-Austria study. Following a homogeneous protocol, the EEG was recorded both in resting state and during a cognitive task. We employed quadratic least squares regression to describe the relation between MMSE and the EEG markers. Factor analysis was used for estimating a potentially lower number of unobserved synchrony factors. These common factors were then related to MMSE scores as well. Most markers displayed an initial increase of EEG synchrony with MMSE scores from 26 to 21 or 20, and a decrease below. This effect was most prominent during the cognitive task and may be owed to cerebral compensatory mechanisms. Factor analysis provided interesting insights in the synchrony structures and the first common factors were related to MMSE scores with coefficients of determination up to 0.433. We conclude that several of the proposed EEG markers are related to AD severity for the overall sample with a wide dispersion for individual subjects. Part of these fluctuations may be owed to fluctuations and day-to-day variability associated with MMSE measurements. Our study provides a systematic analysis of EEG synchrony based on a large and homogeneous sample. The results indicate that the individual markers capture different aspects of EEG synchrony and may reflect cerebral compensatory mechanisms in the early stages of AD.

Quantitative EEG markers relate to Alzheimer's disease severity in the Prospective Dementia Registry Austria (PRODEM).

OBJECTIVE: To investigate which single quantitative electro-encephalographic (QEEG) marker or which combination of markers correlates best with Alzheimer's disease (AD) severity as measured by the Mini-Mental State Examination (MMSE). METHODS: We compared quantitative EEG markers for slowing (relative band powers), synchrony (coherence, canonical correlation, Granger causality) and complexity (auto-mutual information, Shannon/Tsallis entropy) in 118 AD patients from the multi-centric study PRODEM Austria. Signal spectra were determined using an indirect spectral estimator. Analyses were adjusted for age, sex, duration of dementia, and level of education. RESULTS: For the whole group (39 possible, 79 probable AD cases) MMSE scores explained 33% of the variations in relative theta power during face encoding, and 31% of auto-mutual information in resting state with eyes closed. MMSE scores explained also 25% of the overall QEEG factor. This factor was thus subordinate to individual markers. In probable AD, QEEG coefficients of determination were always higher than in the whole group, where MMSE scores explained 51% of the variations in relative theta power. CONCLUSIONS: Selected QEEG markers show strong associations with AD severity. Both cognitive and resting state should be used for QEEG assessments. SIGNIFICANCE: Our data indicate theta power measured during face-name encoding to be most closely related to AD severity.

Quantitative EEG in Alzheimer's disease: cognitive state, resting state and association with disease severity.

BACKGROUND: Quantitative electroencephalogram (qEEG) recorded during cognitive tasks has been shown to differentiate between patients with Alzheimer's disease (AD) and healthy individuals. However, the association between various qEEG markers recorded during mnestic paradigms and clinical measures of AD has not been studied in detail. OBJECTIVE: To evaluate if 'cognitive' qEEG is a useful diagnostic option, particularly if memory paradigms are used as cognitive stimulators. METHODS: This study is part of the Prospective Registry on Dementia in Austria (PRODEM), a multicenter dementia research project. A cohort of 79 probable AD patients was included in a cross-sectional analysis. qEEG recordings performed in resting states were compared with recordings during cognitively active states. Cognition was evoked with a face-name paradigm and a paired-associate word list task, respectively. Relative band powers, coherence and auto-mutual information were computed as functions of MMSE scores for the memory paradigms and during rest. Analyses were adjusted for the co-variables age, sex, duration of dementia and educational level. RESULTS: MMSE scores explained 36-51% of the variances of qEEG-markers. Face-name encoding with eyes open was superior to resting state with eyes closed in relative theta and beta1 power as well as coherence, whereas relative alpha power and auto-mutual information yielded more significant results during resting state with eyes closed. The face-name task yielded stronger correlations with MMSE scores than the verbal memory task. CONCLUSION: qEEG alterations recorded during mnestic activity, particularly face-name encoding showed the highest association with the MMSE and may serve as a clinically valuable marker for disease severity.