Search

Background: In patients with Alzheimer's disease (AD) without clinical seizures, up to half have epileptiform discharges on long-term in-patient electroencephalography (EEG) recordings. Long-term in-patient monitoring is obtrusive, and expensive as compared to outpatient monitoring. No studies have so far investigated if long-term outpatient EEG monitoring is able to identify epileptiform discharges in AD. Our aim is to investigate if epileptiform discharges as measured with ear-EEG are more common in patients with AD compared to healthy elderly controls (HC). Methods: In this longitudinal observational study, 24 patients with mild to moderate AD and 15 age-matched HC were included in the analysis. Patients with AD underwent up to three ear-EEG recordings, each lasting up to two days, within 6 months. Results: The first recording was defined as the baseline recording. At baseline, epileptiform discharges were detected in 75.0% of patients with AD and in 46.7% of HC (p-value = 0.073). The spike frequency (spikes or sharp waves/24 h) was significantly higher in patients with AD as compared to HC with a risk ratio of 2.90 (CI: 1.77–5.01, p

Summary: Lecanemab, an anti-amyloid antibody with effects on biomarker and clinical endpoints in early Alzheimer's Disease (AD), was granted accelerated approval by the FDA in 2023 and regulatory review in Europe is ongoing. We estimate the population potentially eligible for treatment with lecanemab in the 27 EU countries to 5.4 million individuals. Treatment costs would exceed 133 billion EUR per year if the drug is priced similarly as in the United States, amounting to over half of the total pharmaceutical expenditures in the EU. This pricing would be unsustainable; the ability to pay for high-priced therapies varies substantially across countries. Pricing similarly to what has been announced for the United States may place the drug out of reach for patients in some European countries. Disparities in access to novel amyloid-targeting agents may further deepen the inequalities across Europe in health outcomes. As representatives of the European Alzheimer's Disease Consortium Executive Committee, we call for pricing policies that allow eligible patients across Europe to access important innovations, but also continued investments in research and development. Infrastructure to follow up the usage of new therapies in routine care and new payment models may be needed to address affordability and inequalities in patient access.

Little evidence from the Arab region is available on dementia and its associated risk factors. This study is the first in Lebanon to examine the association between community older adults' socio-demographics and cardiovascular disease risk factors (CVDRF) and dementia in the aim of closing the knowledge gap. A cross-sectional household survey was conducted in 2013 in Beirut and two districts of Mount Lebanon with 502 older adults (65years and above) and their informants. Data was collected on CVDRF and socio-demographics using structured questionnaires and dementia was assessed using the 10/66 Dementia Research Group validated tools. Multivariable analysis was done using a generalized estimating equation to account for cluster effect. Being older and perceiving personal income as insufficient significantly increased the odds of dementia [OR75–84years=4.00 (95%CI=1.46, 10.95); OR85+years=7.07 (1.84, 27.03); ORinsufficient income=3.90 (1.58, 9.60)]. Having uncontrolled hypertension (versus no hypertension) was the only significant CVDRF that increased the odds of dementia [OR=6.35 (1.60, 25.10)]. Interventions targeting uncontrolled hypertension that aim to increase awareness about proper management of this chronic condition would contribute to the needed preventive efforts against CVDRFs in response to dementia risk. Further research on the association between income sufficiency – one indicator of low socio-economic status – and dementia is warranted. Keywords: Dementia, CVD risk factors, Uncontrolled hypertension, Income, Lebanon

Differentiating between different types of neurodegenerative diseases is not only crucial in clinical practice when treatment decisions have to be made, but also has a significant potential for the enrichment of clinical trials. The purpose of this study is to develop a classification framework for distinguishing the four most common neurodegenerative diseases, including Alzheimer's disease, frontotemporal lobe degeneration, Dementia with Lewy bodies and vascular dementia, as well as patients with subjective memory complaints. Different biomarkers including features from images (volume features, region-wise grading features) and non-imaging features (CSF measures) were extracted for each subject. In clinical practice, the prevalence of different dementia types is imbalanced, posing challenges for learning an effective classification model. Therefore, we propose the use of the RUSBoost algorithm in order to train classifiers and to handle the class imbalance training problem. Furthermore, a multi-class feature selection method based on sparsity is integrated into the proposed framework to improve the classification performance. It also provides a way for investigating the importance of different features and regions. Using a dataset of 500 subjects, the proposed framework achieved a high accuracy of 75.2% with a balanced accuracy of 69.3% for the five-class classification using ten-fold cross validation, which is significantly better than the results using support vector machine or random forest, demonstrating the feasibility of the proposed framework to support clinical decision making. Keywords: Neurodegenerative diseases, Differential diagnosis, MRI, Dementia, Imbalance learning, Multi-class feature selection

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease causing neural cell degeneration and brain atrophy and is considered to be the most common form of dementia. We previously generated an induced pluripotent stem cell (iPSC) line from an AD patient carrying an A79V mutation in PSEN1 as an in vitro disease model. Here we generated a gene-corrected version from this hiPSC line by substituting the point mutation with the wild-type sequence. The reported A79V-GC-iPSCs line is a very useful resource in combination with the A79V-iPSC line in order to study pathological cellular phenotypes related to this particular mutation.

Skin fibroblasts were obtained from a 46-year-old symptomatic man carrying a M146I mutation in the presenilin 1 gene (PSEN1), responsible for causing Alzheimer's disease (AD). Induced pluripotent stem cells (iPSCs) were derived via transfection with episomal vectors carrying hOCT4, hSOX2, hKLF2, hL-MYC, hLIN28 and shTP53 genes. M146I-iPSCs were free of genomically integrated reprogramming genes, had the specific mutation but no additional genomic aberrancies, expressed the expected pluripotency markers and displayed in vitro differentiation potential to the three germ layers. The reported M146I-iPSCs line may be a useful resource for in vitro modeling of familial AD.

Skin fibroblasts were obtained from a 48-year-old presymptomatic woman carrying a A79V mutation in the presenilin 1 gene (PSEN1), causing Alzheimer's disease (AD). Induced pluripotent stem cell (iPSCs) were derived via transfection with episomal vectors carrying hOCT4, hSOX2, hKLF2, hL-MYC, hLIN28 and shTP53 genes. A79V-iPSCs were free of genomically integrated reprogramming genes, had the specific mutation but no additional genomic aberrancies, expressed the expected pluripotency markers and displayed in vitro differentiation potential to the three germ layers. The reported A79V-iPSCs line may be a useful resource for in vitro modeling of familial AD.

Background: Diagnosing frontotemporal dementia may be challenging. New methods for analysis of regional brain atrophy patterns on magnetic resonance imaging (MRI) could add to the diagnostic assessment. Therefore, we aimed to develop automated imaging biomarkers for differentiating frontotemporal dementia subtypes from other diagnostic groups, and from one another. Methods: In this retrospective multicenter cohort study, we included 1213 patients (age 67 ± 9, 48% females) from two memory clinic cohorts: 116 frontotemporal dementia, 341 Alzheimer's disease, 66 Dementia with Lewy bodies, 40 vascular dementia, 104 other dementias, 229 mild cognitive impairment, and 317 subjective cognitive decline. Three MRI atrophy biomarkers were derived from the normalized volumes of automatically segmented cortical regions: 1) the anterior vs. posterior index, 2) the asymmetry index, and 3) the temporal pole left index. We used the following performance metrics: area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. To account for the low prevalence of frontotemporal dementia we pursued a high specificity of 95%. Cross-validation was used in assessing the performance. The generalizability was assessed in an independent cohort (n = 200). Results: The anterior vs. posterior index performed with an AUC of 83% for differentiation of frontotemporal dementia from all other diagnostic groups (Sensitivity = 59%, Specificity = 95%, positive likelihood ratio = 11.8, negative likelihood ratio = 0.4). The asymmetry index showed highest performance for separation of primary progressive aphasia and behavioral variant frontotemporal dementia (AUC = 85%, Sensitivity = 79%, Specificity = 92%, positive likelihood ratio = 9.9, negative likelihood ratio = 0.2), whereas the temporal pole left index was specific for detection of semantic variant primary progressive aphasia (AUC = 85%, Sensitivity = 82%, Specificity = 80%, positive likelihood ratio = 4.1, negative likelihood ratio = 0.2). The validation cohort provided corresponding results for the anterior vs. posterior index and temporal pole left index. Conclusion: This study presents three quantitative MRI biomarkers, which could provide additional information to the diagnostic assessment and assist clinicians in diagnosing frontotemporal dementia. Keywords: Dementia, Frontotemporal lobar degeneration, Differential diagnosis, Behavioral variant frontotemporal dementia, Primary progressive aphasia, MRI

Different neurodegenerative diseases can cause memory disorders and other cognitive impairments. The early detection and the stratification of patients according to the underlying disease are essential for an efficient approach to this healthcare challenge. This emphasizes the importance of differential diagnostics. Most studies compare patients and controls, or Alzheimer's disease with one other type of dementia. Such a bilateral comparison does not resemble clinical practice, where a clinician is faced with a number of different possible types of dementia. Here we studied which features in structural magnetic resonance imaging (MRI) scans could best distinguish four types of dementia, Alzheimer's disease, frontotemporal dementia, vascular dementia, and dementia with Lewy bodies, and control subjects. We extracted an extensive set of features quantifying volumetric and morphometric characteristics from T1 images, and vascular characteristics from FLAIR images. Classification was performed using a multi-class classifier based on Disease State Index methodology. The classifier provided continuous probability indices for each disease to support clinical decision making. A dataset of 504 individuals was used for evaluation. The cross-validated classification accuracy was 70.6% and balanced accuracy was 69.1% for the five disease groups using only automatically determined MRI features. Vascular dementia patients could be detected with high sensitivity (96%) using features from FLAIR images. Controls (sensitivity 82%) and Alzheimer's disease patients (sensitivity 74%) could be accurately classified using T1-based features, whereas the most difficult group was the dementia with Lewy bodies (sensitivity 32%). These results were notable better than the classification accuracies obtained with visual MRI ratings (accuracy 44.6%, balanced accuracy 51.6%). Different quantification methods provided complementary information, and consequently, the best results were obtained by utilizing several quantification methods. The results prove that automatic quantification methods and computerized decision support methods are feasible for clinical practice and provide comprehensive information that may help clinicians in the diagnosis making.