Diagnosis Of Neurodegenerative Diseases Research Articles

Glial fibrillary acidic protein in Alzheimer’s disease: a narrative review

Abstract Astrocytes are fundamental in neural functioning and homeostasis in the Central Nervous System. These cells respond to injuries and pathological conditions through astrogliosis, a reactive process associated with neurodegenerative diseases such as Alzheimer’s disease. This process is thought to begin in the early stages of these conditions. Glial Fibrillary Acidic Protein (GFAP), a type III intermediate filament protein predominantly expressed in astrocytes, has emerged as a key biomarker for monitoring this response. During astrogliosis, GFAP is released into biofluids, making it a candidate for non-invasive diagnosis and tracking of neurodegenerative diseases. Growing evidence positions GFAP as a biomarker for Alzheimer’s disease with specificity and disease-correlation characteristics comparable to established clinical markers, such as Aβ peptides and phosphorylated tau protein. To improve diagnostic accuracy, particularly in the presence of confounders and comorbidities, incorporating a panel of biomarkers may be advantageous. This review will explore the potential of GFAP within such a panel, examining its role in early diagnosis, disease progression monitoring, and its integration into clinical practice for Alzheimer’s disease management.

Characteristics of Severe Late-Life Depression in the Prodromal Phase of Neurodegenerative Dementia

ObjectiveLate-life depression (LLD) is associated with neurodegenerative disease. The clinical characteristics of patients with LLD who develop neurodegenerative dementia or mild cognitive impairment (MCI) have not been thoroughly investigated. There has also been evidence that the severity of depression affects the transition to dementia. However, the clinical characteristics of the most severely depressed patients who receive the electroconvulsive therapy (ECT) and subsequently transition to a diagnosis of neurodegenerative disease remain unclear. MethodWe conducted a retrospective chart review of patients who received ECT between April 2012 and September 2022 at our academic hospital. We compared the clinical characteristics of individuals who transitioned to a diagnosis of neurodegenerative diseases and those whose diagnoses remained stable over a follow-up period of more than one year. ResultsOf the 94 patients who underwent ECT, 12 (13%) were diagnosed with neurodegenerative disease 20 months after their last ECT session. These patients had an older age of onset (p = 0.01) , fewer melancholic features (p = 0.03) , and fewer family histories of psychiatric disorders (p = 0.01) than those whose diagnoses did not change. ConclusionsIt is clinically important to consider the potential transition of older individuals with severe depression to neurodegenerative dementia or MCI, especially in cases characterized by an older age of onset, lack of melancholic features, or absence of a family history of psychiatric disorders.

RGO-g-C3N4-Co3O4 Composite Modified Platinum Electrode for Electrochemical Detection of Dopamine

Abstract A novel RGO-g-C3N4-Co3O4 composite modified platinum electrode with significant sensing performance for dopamine was fabricated. Herein, RGO-g-C3N4-Co3O4 hybrid nanostructure could boost the electrocatalytic performance of nanoparticles by avoiding the clustering of nanoparticles. These spinel-based composites are stable and affordable materials. Electrochemical impedance spectroscopy revealed enhanced electron transfer at the modified electrode, as evidenced by the lowest charge transfer resistance (Rct) for the RGO-g-C₃N₄-Co₃O₄/Pt electrode. An increased electroactive surface area compared to bare and other modified Pt electrodes was obtained. Several experimental parameters were optimized to maximize sensitivity, including the choice of supporting electrolyte and pH. Cyclic voltammetry conducted at varying scan rates confirmed that the oxidation of dopamine followed a diffusion-controlled process. The modified electrode exhibits outstanding electrocatalytic activity, with a detection limit as low as 8.10×10−7M, demonstrating a wide linear range between 2.00×10−6 M to 4×10−4 M. Selectivity tests indicated that the sensor could reliably detect dopamine in the presence of common interfering substances such as NaCl, KCl, glucose and urea, ascorbic acid, uric acid and L-dopa. This enhanced sensitivity and selectivity were validated in both synthetic blood and urine samples, providing the electrode’s potential for real-world applications in the diagnosis of neurodegenerative diseases.

Comprehensive mapping of synaptic vesicle protein 2A (SV2A) in health and neurodegenerative diseases: a comparative analysis with synaptophysin and ground truth for PET-imaging interpretation.

Synaptic dysfunction and loss are central to neurodegenerative diseases and correlate with cognitive decline. Synaptic Vesicle Protein 2A (SV2A) is a promising PET-imaging target for assessing synaptic density in vivo, but comprehensive mapping in the human brain is needed to validate its biomarker potential. This study used quantitative immunohistochemistry and Western blotting to map SV2A and synaptophysin (SYP) densities across six cortical regions in healthy controls and patients with early-onset Alzheimer's disease (EOAD), late-onset Alzheimer's disease (LOAD), progressive supranuclear palsy (PSP), and frontotemporal lobar degeneration with TDP-43 inclusions (FTLD-GRN). We identified region in SV2A density among controls and observed disease- and region-specific reductions, with the most severe in FTLD-GRN (up to 59.5%) and EOAD. EOAD showed a 49% reduction in the middle frontal gyrus (MFG), while LOAD had over 30% declines in the inferior frontal gyrus (IFG) and hippocampus (CA1). In PSP, smaller but significant reductions were noted in the hippocampal formation, with the inferior temporal gyrus (ITG) relatively unaffected. A strong positive correlation between SV2A and SYP densities confirmed SV2A's reliability as a synaptic integrity marker. This study supports the use of SV2A PET imaging for early diagnosis and monitoring of neurodegenerative diseases, providing essential data for interpreting in vivo PET results. Further research should explore SV2A as a therapeutic target and validate these findings in larger, longitudinal studies.

Spectral graph convolutional neural network for Alzheimer's disease diagnosis and multi-disease categorization from functional brain changes in magnetic resonance images

Alzheimer's disease (AD) is a progressive neurological disorder characterized by the gradual deterioration of cognitive functions, leading to dementia and significantly impacting the quality of life for millions of people worldwide. Early and accurate diagnosis is crucial for the effective management and treatment of this debilitating condition. This study introduces a novel framework based on Spectral Graph Convolutional Neural Networks (SGCNN) for diagnosing AD and categorizing multiple diseases through the analysis of functional changes in brain structures captured via magnetic resonance imaging (MRI). To assess the effectiveness of our approach, we systematically analyze structural modifications to the SGCNN model through comprehensive ablation studies. The performance of various Convolutional Neural Networks (CNNs) is also evaluated, including SGCNN variants, Base CNN, Lean CNN, and Deep CNN. We begin with the original SGCNN model, which serves as our baseline and achieves a commendable classification accuracy of 93%. In our investigation, we perform two distinct ablation studies on the SGCNN model to examine how specific structural changes impact its performance. The results reveal that Ablation Model 1 significantly enhances accuracy, achieving an impressive 95%, while Ablation Model 2 maintains the baseline accuracy of 93%. Additionally, the Base CNN model demonstrates strong performance with a classification accuracy of 93%, whereas both the Lean CNN and Deep CNN models achieve 94% accuracy, indicating their competitive capabilities. To validate the models' effectiveness, we utilize multiple evaluation metrics, including accuracy, precision, recall, and F1-score, ensuring a thorough assessment of their performance. Our findings underscore that Ablation Model 1 (SGCNN Model 1) delivers the highest predictive accuracy among the tested models, highlighting its potential as a robust approach for Alzheimer's image classification. Ultimately, this research aims to facilitate early diagnosis and treatment of AD, contributing to improved patient outcomes and advancing the field of neurodegenerative disease diagnosis.

Identification of mild cognitive impairment using multimodal 3D imaging data and graph convolutional networks

Abstract Objective. Mild cognitive impairment (MCI) is a precursor stage of dementia characterized by mild cognitive decline in one or more cognitive domains, without meeting the criteria for dementia. MCI is considered a prodromal form of Alzheimer's disease (AD). Early identification of MCI is crucial for both intervention and prevention of AD. To accurately identify MCI, a novel multimodal 3D imaging data integration graph convolutional network model is designed in this paper. Approach. The proposed model utilizes 3D-VGGNet to extract three-dimensional features from multimodal imaging data (such as sMRI and FDG-PET), which are then fused into feature vectors as the node features of a population graph. Non-imaging features of participants are combined with the multimodal imaging data to construct a population sparse graph. Additionally, in order to optimize the connectivity of the graph, we employed the Pairwise Attribute Estimation (PAE) method to compute the edge weights based on non-imaging data, thereby enhancing the effectiveness of the graph structure. Subsequently, a population-based graph convolutional network (GCN) integrates the structural and functional features of different modal images into the features of each participant for MCI classification. Main results. Experiments on the ADNI demonstrated accuracies of 98.57%, 96.03%, and 96.83% for the NC-EMCI, NC-LMCI, and EMCI-LMCI classification tasks, respectively. The AUC, specificity, sensitivity, and F1-score are also superior to state-of-the-art models, demonstrating the effectiveness of the proposed model. Furthermore, the proposed model is applied to the ABIDE dataset for autism diagnosis, achieving an accuracy of 91.43% and outperforming the state-of-the-art models, indicating excellent generalization capabilities of the proposed model. Significance. This study demonstrate the proposed model’s ability to integrate multimodal imaging data and its excellent ability to recognize MCI. This will help achieve early warning for AD and intelligent diagnosis of other brain neurodegenerative diseases.

Omnidirectional improvement of mitochondrial health in Alzheimer's disease by multi-targeting engineered activated neutrophil exosomes

Alzheimer's disease (AD) is one kind of devasting neurodegenerative disorders affecting over 50 million people worldwide. Multi-targeted therapy has emerged as a new treatment for diagnosing and alleviating the pathogenesis process of AD; however, the current strategy is limited by its unsatisfactory efficiency. In our study, engineered activated neutrophil-derived exosomes (MP@Cur-MExo) were developed to improve the mitochondrial function in neurons by targeting and alleviating Aβ-induced neurotoxicity. MP@Cur-MExo are exosomes derived from IL-8-stimulated neutrophils decorated with mitochondria targeting ligand and Aβ targeted ligand modified SPION. Engineered exosomes can be cleaved by matrix metallopeptidase-2, which is overexpressed in the AD brain. Consequently, the released SPION and Curcumin-loaded engineered exosomes collaboratively protected neuron cells against Aβ-induced mitochondrial deficiency. In addition, MP@Cur-MExo effectively accumulated in the inflamed region of AD brain at an early stage, allowing early diagnosis of AD through bimodal (MRI/IVIS) imaging. Importantly, in a mouse model at an early stage of AD, intravenously injected MP@Cur-MExo restored mitochondrial function and reduced Aβ-induced mitochondrial damage, thereby attenuating AD progression. In conclusion, our designed engineered exosomes demonstrated that omnidirectional improvement of mitochondrial function can serve as a novel and practical approach for the diagnosis and treatment of neurodegenerative diseases. This study also reveals a promising therapeutic agent for impeding AD progression for future clinical applications.

Algorithm development and metal oxide nanoparticle analysis in magnetic resonance imaging: Advancing neurodegenerative disease diagnostics

Magnetic resonance imaging (MRI) is critical in the diagnosis of neurodegenerative diseases, enabling the detection of brain lesions. Recent research has examined metallic nanoparticles (NPs) as MRI contrast agents (CAs) that can enhance lesion visibility by altering relaxation times. This study investigates the effects of metal oxide NPs on MRI relaxation times and brain lesion signals and proposes an algorithm for automated relaxation time determination using these NPs. The utilized NPs were synthesized using the sol‒gel method and characterized using Fourier-transform infrared spectroscopy and X-ray diffraction. MRI scans were performed on a phantom infused with varying concentrations of each metal oxide NP to assess changes in pixel signal intensities and relaxation rates. Our analysis involved segmenting the MRI images to focus on regions with different NP concentrations. The algorithm computed the longitudinal relaxation time for each region, revealing that Fe2O3 NPs exhibited the most substantial effect on signal intensity and relaxation time. The results indicated a high correlation (r = 0.9977), demonstrating strong agreement and confirming the reliability of our method. Our findings suggest that metallic oxide NPs, particularly Fe2O3, can considerably alter magnetization and act as effective negative CAs in MRI. These capabilities can improve the monitoring and treatment efficacy of neurodegenerative diseases. Our method for quantifying longitudinal relaxation times can potentially enhance routine clinical MRI assessments, offering a promising tool for future clinical applications.

A-103 Analytical Performance Evaluation of Plasma NfL in a Clinical Diagnostic Laboratory

Abstract Background Neurofilament light chain (NfL) is a neuron-specific intermediate filament that is released at a consistent low level in normal individuals and increases with age and axonal injuries. NfL is a neurodegeneration biomarker in Alzheimer’s disease (AD), multiple sclerosis, stroke, traumatic brain injury and other neurodegenerative diseases (NDD). Here, we report the analytical performance of plasma NfL using high-sensitivity methodology in a clinical diagnostic laboratory. Methods The analytical performance, encompassing precision, detection limits, interference, and linearity, was evaluated. Plasma NfL levels were measured between July and October 2023 at Neurocode USA in Bellingham, WA, using a single Lumipulse G1200 instrument following CLSI guidelines, particularly for the utilization of the Fujirebio NfL Blood RUO assay as a laboratory-developed test for clinical analysis. A total of 100 healthy control samples, near the age of AD onset, were used to validate the reference ranges. Results The average plasma NfL concentrations varied from ≤ 8.4 to ≤ 37.9 ng/L among healthy individuals aged 20 to 80 years. The Plasma NfL assay exhibits analytical measurements, including a limit of blank (LoB) of 0.75 ng/L, a limit of detection (LoD) of 1.99 ng/ml, and a limit of quantification (LoQ) of 3.96 ng/L. Its linearity ranges from 11.08 to 741.58 ng/L, with intra-laboratory precision ≤ 9% CV. Stability is maintained at 4⁰C for ≤ 72 hours, at room temperature for ≤ 4 hours, at -20⁰C for ≤ 4 weeks, and at -80⁰C for ≤ 7 days, with freeze/thaw cycles tolerated within the specified time frame. Additionally, no interference was observed at the maximum concentrations tested for bilirubin, hemoglobin, and intralipid. Conclusions With a reference range between ≤ 8.4 to ≤ 37.9 ng/L and exceptional analytical performance, the measurement of plasma NfL on a state-of-the-art instrument using CLSI guidelines, can be useful in the diagnosis and monitoring of AD and other NDD.

DEVELOPMENT AND DATA ANALYSIS OF A ROBO-PEN FOR ALZHEIMER’S DISEASE DIAGNOSIS: PRELIMINARY RESULTS

Alzheimer’s Disease (AD) poses a significant challenge in contemporary medicine, necessitating early and accurate diagnostic methods to manage its progression effectively. This study explores the development and application of the Robo-pen, an innovative diagnostic tool designed to detect early signs of cognitive decline through detailed handwriting analysis. The Robo-pen, equipped with an MPU-9250 sensor, captures three-dimensional coordinates, velocity, and acceleration of handwriting movements, crucial for assessing spatial control, movement consistency, speed variations, and the ability to modulate movement speed and force–parameters often disrupted in cognitive impairments like AD. Participants included 20 patients diagnosed with AD and 18 healthy controls, matched in age and educational levels. Data collection involved tasks such as sentence rewriting, figure redrawing, and digit rewriting, processed using CoolTerm software at a sampling rate of 18 Hz. Descriptive statistics revealed that the AD group exhibited lower mean values for gyroscope and acceleration data, indicating slower and less variable movements compared to the control group. T-tests confirmed significant differences (p < 0.001) across all measured parameters between the AD and control groups. The results support the potential of the Robo-pen as a non-invasive, cost-effective diagnostic tool for early detection of AD. By capturing subtle neuromotor changes, the Robo-pen facilitates earlier diagnosis and timely intervention, potentially altering the disease trajectory and improving patient outcomes. This study marks a significant advancement in the early detection of AD, highlighting the Robo-pen’s promise as a transformative tool in neurodegenerative disease diagnosis and management.

Designing and Formulation of Nanocarriers for "Alzheimer's and Parkinson's" Early Detection and Therapy.

The potential of nanotechnology in advancing the diagnosis and treatment of neurodegenerative diseases is explored in this comprehensive literature review. The findings of these studies suggest that nanotechnology has the capacity to improve existing therapeutic approaches, create novel and safe compounds, and develop more precise imaging techniques and diagnostic methods for neurodegenerative diseases. With the emergence of the nanomedicine era, a new and innovative approach of diagnosing and treating these conditions has been introduced. Notably, the researchers' development of a nanocarrier drug delivery tool demonstrates immense potential compared to conventional therapy, as it maximizes therapeutic efficacy and minimizes undesirable as side effects.

CNN-Based Neurodegenerative Disease Classification Using QR-Represented Gait Data.

The primary aim of this study is to develop an effective and reliable diagnostic system for neurodegenerative diseases by utilizing gait data transformed into QR codes and classified using convolutional neural networks (CNNs). The objective of this method is to enhance the precision of diagnosing neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), and Huntington's disease (HD), through the introduction of a novel approach to analyze gait patterns. The research evaluates the CNN-based classification approach using QR-represented gait data to address the diagnostic challenges associated with neurodegenerative diseases. The gait data of subjects were converted into QR codes, which were then classified using a CNN deep learning model. The dataset includes recordings from patients with Parkinson's disease (n = 15), Huntington's disease (n = 20), and amyotrophic lateral sclerosis (n = 13), and from 16 healthy controls. The accuracy rates obtained through 10-fold cross-validation were as follows: 94.86% for NDD versus control, 95.81% for PD versus control, 93.56% for HD versus control, 97.65% for ALS versus control, and 84.65% for PD versus HD versus ALS versus control. These results demonstrate the potential of the proposed system in distinguishing between different neurodegenerative diseases and control groups. The results indicate that the designed system may serve as a complementary tool for the diagnosis of neurodegenerative diseases, particularly in individuals who already present with varying degrees of motor impairment. Further validation and research are needed to establish its wider applicability.

Plasma extracellular vesicle: a novel biomarker for neurodegenerative disease diagnosis

Extracellular vesicles (EVs) are membrane-bound structures that carry proteins, lipids, RNA, and DNA, playing key roles in cell communication and material transport. Recent research highlights their potential as disease biomarkers due to their stability in bodily fluids. This study explores using tau and TDP-43 proteins in plasma EVs as diagnostic biomarkers for frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS). Analyzing plasma EVs from clinical cohorts, the study found that the 3R/4R tau ratio and TDP-43 levels effectively differentiate between diagnostic groups with high accuracy. Notably, plasma EV biomarkers demonstrate higher diagnostic accuracy and stability compared to direct plasma testing, providing new insights and approaches for future research and clinical practice. Further research is needed to validate these biomarkers in diverse populations and to establish standardized protocols. Future studies should continue to explore the potential of EV biomarkers in a broader range of neurodegenerative diseases and delve deeper into the mechanisms of EV secretion and sorting to enhance their diagnostic utility.

Evaluation of serum NFL, T-tau, p-tau181, p-tau217, Aβ40 and Aβ42 for the diagnosis of neurodegenerative diseases

Abstract Objectives To assess the variations and diagnostic performance of serum biomarkers of neurodegenerative diseases. Methods In this monocentric prospective study, neurofilament light (NFL), T-tau, p-tau181, p-tau217, Aβ40, and Aβ42 were measured in serum collected from orthopedic patients (control group, n=114) and patients in the neurology department (n=69) previously diagnosed with Alzheimer’s disease (AD, n=52), parkinsonian syndromes (n=10), and other etiologies of neurodegeneration (non-AD, n=7). Results In the control group, serum NFL, T-tau, p-tau181, p-tau217, and Aβ40 significantly increased with age, independently of sex. NFL (p=0.0078), p-tau217 (p<0.001) were significantly increased with neurodegeneration when compared to controls, with only p-tau217 significant in the multivariate analysis (p<0.001). Multivariate regression analysis accounting for age highlighted a significant increase of p-tau217 (p<0.001) in the AD subgroup. NFL was significantly increased in the non-AD patients (p<0.001), and in the parkinsonian syndromes subgroup (p=0.016) when compared to negative controls. Serum p-tau181 and p-tau217 were significantly correlated with CSF p-tau181 (Spearman’s coefficients of 0.43 and 0.48 respectively, n=40). Areas under the ROC curves for the identification of patients with neurodegenerative diseases were 0.62 (0.54–0.70) for NFL, 0.62 (0.54–0.71) for T-tau, 0.83 (0.76–0.89) for p-tau217, and 0.66 (0.58–0.74) for Aβ40. Conclusions Serum biomarkers can help identify patients with neurodegenerative disease and may be a valuable tool for care and orientation. Phosphorylated tau p-tau217 is a promising blood biomarker for AD and NFL for other etiologies.

The ROSMAP project: aging and neurodegenerative diseases through omic sciences.

The Religious Order Study and Memory and Aging Project (ROSMAP) is an initiative that integrates two longitudinal cohort studies, which have been collecting clinicopathological and molecular data since the early 1990s. This extensive dataset includes a wide array of omic data, revealing the complex interactions between molecular levels in neurodegenerative diseases (ND) and aging. Neurodegenerative diseases (ND) are frequently associated with morbidity and cognitive decline in older adults. Omics research, in conjunction with clinical variables, is crucial for advancing our understanding of the diagnosis and treatment of neurodegenerative diseases. This summary reviews the extensive omics research-encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, and multiomics-conducted through the ROSMAP study. It highlights the significant advancements in understanding the mechanisms underlying neurodegenerative diseases, with a particular focus on Alzheimer's disease.

Flexible electrospun test strips functionalized with a graphene oxide/doxorubicin complex for dopamine detection

A novel flexible test strip has been developed for the selective and sensitive detection of dopamine (DA) using an electrospun polyacrylonitrile nanofiber membrane as a substrate and doxorubicin (DOX)-functionalized graphene oxide (GO) as a fluorescent ligand. Employing the Langmuir-Blodgett technique, the fluorescent probe of GO/DOX was controllably and flatly applied onto the surface of nanofiber membranes. The adsorption behavior of DA and DOX on GO in solution and on the surface of solid test strips has been investigated, respectively. The results showed that DA exhibited a higher affinity for GO, both in solution and on the surface of test strips, compared to DOX. Due to the competitive adsorption of DOX and DA onto GO, the addition of DA to the GO/DOX complex led to a significant fluorescence enhancement, attributed to the replacement of the GO-supported DOX with DA of a higher adsorption affinity to GO, enabling the detection of DA. This nanofiber-based method proves to be simple, sensitive, selective, and effectively minimizes interference from common biomolecules and small molecules in serum. Thus, this portable, cost-effective, and selective nanofiber strip holds great promise for early diagnosis of neurodegenerative diseases by measuring DA levels.

Research progress on exosomes and their non-coding RNAs in the diagnosis of neurodegenerative diseases

Neurodegenerative diseases, originating from irreversible progressive loss of neuronal structure or function, are difficult to diagnose and treat. They vary widely in scope and have poor prevention and prognosis. Therefore, research on their early diagnosis is particularly important. Exosomes are small vesicles of cellular origin that contain various bioactive small molecules, such as proteins, RNAs, and DNAs, and play important roles in intercellular communication. Recent studies have shown that exosomes and their non-coding RNAs are key factors in the pathogenesis of various neurodegenerative diseases. Therefore, exosomes and their non-coding RNAs may provide a breakthrough for the early diagnosis of neurodegenerative diseases. This review summarizes the biology of exosomes and the current research progress of exosomes and their non-coding RNAs in diagnosing neurodegenerative diseases and further explores the challenges and prospects they face.

Causal relationship between imaging-derived phenotypes and neurodegenerative diseases: a Mendelian randomization study.

Neurodegenerative diseases are incurable conditions that lead to gradual and progressive deterioration of brain function in patients. With the aging population, the prevalence of these diseases is expected to increase, posing a significant economic burden on society. Imaging techniques play a crucial role in the diagnosis and monitoring of neurodegenerative diseases. This study utilized a two-sample Mendelian randomization (MR) analysis to assess the causal relationship between different imaging-derived phenotypes (IDP) in the brain and neurodegenerative diseases. Multiple MR methods were employed to minimize bias and obtain reliable estimates of the potential causal relationship between the variable exposures of interest and the outcomes. The study found potential causal relationships between different IDPs and Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS), and frontotemporal dementia (FTD). Specifically, the study identified potential causal relationships between 2 different types of IDPs and AD, 8 different types of IDPs and PD, 11 different types of imaging-derived phenotypes and ALS, 1 type of IDP and MS, and 1 type of IDP and FTD. This study provides new insights for the prevention, diagnosis, and treatment of neurodegenerative diseases, offering important clues for understanding the pathogenesis of these diseases and developing relevant intervention strategies.

Presumed aetiologies and clinical outcomes of non-lesional late-onset epilepsy.

Our objective was to define phenotypes of non-lesional late-onset epilepsy (NLLOE) depending on its presumed aetiology and to determine their seizure and cognitive outcomes at 12 months. In all, 146 newly diagnosed NLLOE patients, >50 years old, were prospectively included and categorized by four presumed aetiological subtypes: neurodegenerative subtype (patients with a diagnosis of neurodegenerative disease) (n = 31), microvascular subtype (patients with three or more cardiovascular risk factors and two or more vascular lesions on MRI) (n = 39), inflammatory subtype (patient meeting international criteria for encephalitis) (n = 9) and unlabelled subtype (all individuals who did not meet the criteria for other subtypes) (n = 67). Cognitive outcome was determined by comparing for each patient the proportion of preserved/altered scores between initial and second neuropsychological assessment. The neurodegenerative subtype had the most severe cognitive profile at diagnosis with cognitive complaint dating back several years. The microvascular subtype was mainly evaluated through the neurovascular emergency pathway. Their seizures were characterized by transient phasic disorders. Inflammatory subtype patients were the youngest. They presented an acute epilepsy onset with high rate of focal status epilepticus. The unlabelled subtype presented fewer comorbidities with fewer lesions on brain imaging. The neurodegenerative subtype had the worst seizure and cognitive outcomes. In other groups, seizure control was good under antiseizure medication (94.7% seizure-free) and cognitive performance was stabilized or even improved. This new characterization of NLLOE phenotypes raises questions regarding the current International League Against Epilepsy aetiological classification which does not individualize neurodegenerative and microvascular aetiology per se.

Deep learning-based quantification of brain atrophy using 2D T1-weighted MRI for Alzheimer's disease classification.

Determining brain atrophy is crucial for the diagnosis of neurodegenerative diseases. Despite detailed brain atrophy assessments using three-dimensional (3D) T1-weighted magnetic resonance imaging, their practical utility is limited by cost and time. This study introduces deep learning algorithms for quantifying brain atrophy using a more accessible two-dimensional (2D) T1, aiming to achieve cost-effective differentiation of dementia of the Alzheimer's type (DAT) from cognitively unimpaired (CU), while maintaining or exceeding the performance obtained with T1-3D individuals and to accurately predict AD-specific atrophy similarity and atrophic changes [W-scores and Brain Age Index (BAI)]. Involving 924 participants (478 CU and 446 DAT), our deep learning models were trained on cerebrospinal fluid (CSF) volumes from 2D T1 images and compared with 3D T1 images. The performance of the models in differentiating DAT from CU was assessed using receiver operating characteristic analysis. Pearson's correlation analyses were used to evaluate the relations between 3D T1 and 2D T1 measurements of cortical thickness and CSF volumes, AD-specific atrophy similarity, W-scores, and BAIs. Our deep learning models demonstrated strong correlations between 2D and 3D T1-derived CSF volumes, with correlation coefficients r ranging from 0.805 to 0.971. The algorithms based on 2D T1 accurately distinguished DAT from CU with high accuracy (area under the curve values of 0.873), which were comparable to those of algorithms based on 3D T1. Algorithms based on 2D T1 image-derived CSF volumes showed high correlations in AD-specific atrophy similarity (r = 0.915), W-scores for brain atrophy (0.732 ≤ r ≤ 0.976), and BAIs (r = 0.821) compared with those based on 3D T1 images. Deep learning-based analysis of 2D T1 images is a feasible and accurate alternative for assessing brain atrophy, offering diagnostic precision comparable to that of 3D T1 imaging. This approach offers the advantage of the availability of T1-2D imaging, as well as reduced time and cost, while maintaining diagnostic precision comparable to T1-3D.