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Accumulating evidence suggests that Alzheimer’s disease (AD) is heterogenous and can be classified into several subtypes. Here, we propose a robust subtyping method for AD based on cortical atrophy patterns and graph theory. We calculated similarities between subjects in their atrophy patterns throughout the whole brain, and clustered subjects with similar atrophy patterns using the Louvain method for modular organization extraction. We applied our method to AD patients recruited at Samsung Medical Center and externally validated our method by using the AD Neuroimaging Initiative ( ADNI ) dataset. Our method categorized very mild AD into three clinically distinct subtypes with high reproducibility (>90%); the parietal-predominant (P), medial temporal-predominant (MT), and diffuse (D) atrophy subtype. The P subtype showed the worst clinical presentation throughout the cognitive domains, while the MT and D subtypes exhibited relatively mild presentation. The MT subtype revealed more impaired language and executive function compared to the D subtype.

Although treatments for Alzheimer's disease sometimes improve cognition, functional ability, or behaviour compared with baseline levels, such improvements are inconsistent across studies and measures, and effects diminish over time. More effective treatments are needed. We assessed the safety, tolerability, and efficacy of dimebon in the treatment of patients with mild-to-moderate Alzheimer's disease. We enrolled 183 patients with mild-to-moderate Alzheimer's disease (mini-mental state examination [MMSE] scores 10–24) at 11 sites in Russia. Patients were randomly assigned by a computer-generated randomisation scheme to receive oral dimebon, 20 mg three times a day (60 mg/day [n=89]), or matched placebo (n=94). Other antidementia drugs were not allowed. The primary outcome measure assessed cognition, the difference in mean change from baseline to week 26, or last completed observation on the cognitive subscale of the Alzheimer's disease assessment scale (ADAS-cog). All patients and study personnel were blinded throughout the study. We compared dimebon with placebo with an intention-to-treat analysis, with last observation carried forward (ITT-LOCF) imputation. Analyses were repeated on the fully evaluable population, defined as all patients in the intention-to-treat population who had an ADAS-cog at week 26 and at least 80% compliance. 134 patients (68 in dimebon group, 66 in placebo group) enrolled in the 6-month blinded extension phase of the study. This trial is registered with Clinicaltrials.gov, number NCT00377715. 155 (85%) patients completed the trial (78 [88%] in dimebon group, 77 [82%] in placebo group). Treatment with dimebon resulted in significant benefits in ADAS-cog compared with placebo (ITT-LOCF) at week 26 (mean drug-placebo difference −4·0 [95% CI −5·73 to −2·28]; p<0·0001). Results of the ITT-LOCF and the evaluable population analyses were much the same for all measures. Patients given dimebon were significantly improved over baseline for ADAS-cog (mean difference −1·9 [−2·92 to −0·85]; p=0·0005). Dimebon was well tolerated: dry mouth and depressed mood or depression were the most common adverse events associated with dimebon (12 [14%] patients for each symptom by week 26). The percentage of patients who had adverse events in the two groups did not differ. Dimebon was safe, well tolerated, and significantly improved the clinical course of patients with mild-to-moderate Alzheimer's disease. Medivation (USA).

Overstimulation of the N -methyl-D-aspartate (NMDA) receptor by glutamate is implicated in neurodegenerative disorders. This 28-week study compared memantine, an NMDA-receptor antagonist, with placebo in persons with moderate-to-severe Alzheimer's disease. Among the patients who completed the study, memantine appeared to confer benefit in terms of activities of daily living and other measures; analysis of the last observation carried forward for the whole group supported this conclusion. Antiglutamatergic therapy may help in moderate-to-severe disease. Alzheimer's disease affects at least 15 million persons throughout the world. 1 , 2 The number of persons with Alzheimer's disease is increasing substantially as populations age. 3 As Alzheimer's disease advances, patients become progressively impaired in both cognitive and functional capacities, 2 , 4 and the burden on caregivers increases. Pharmacologic treatments are currently approved for treating mild-to-moderate Alzheimer's disease. 5 However, there are no treatments for the more advanced stages of Alzheimer's disease. Glutamate is the principal excitatory neurotransmitter in the brain. 6 , 7 Glutamatergic overstimulation may result in neuronal damage, a phenomenon that has been termed excitotoxicity. Such excitotoxicity ultimately leads to neuronal calcium . . .

Objective To develop and validate a comprehensive and interpretable framework for multi-class classification of Alzheimer’s disease (AD) progression stages based on hippocampal MRI, integrating radiomic, deep, and clinical features. Materials and methods This retrospective multi-center study included 2956 patients across four AD stages (Non-Demented, Very Mild Demented, Mild Demented, Moderate Demented). T1-weighted MRI scans were processed through a standardized pipeline involving hippocampal segmentation using four models (U-Net, nnU-Net, Swin-UNet, MedT). Radiomic features ( n  = 215) were extracted using the SERA platform, and deep features ( n  = 256) were learned using an LSTM network with attention applied to hippocampal slices. Fused features were harmonized with ComBat and filtered by ICC (≥ 0.75), followed by LASSO-based feature selection. Classification was performed using five machine learning models, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron (MLP), and eXtreme Gradient Boosting (XGBoost). Model interpretability was addressed using SHAP, and a nomogram and decision curve analysis (DCA) were developed. Additionally, an end-to-end 3D CNN-LSTM model and two transformer-based benchmarks (Vision Transformer, Swin Transformer) were trained for comparative evaluation. Results MedT achieved the best hippocampal segmentation (Dice = 92.03% external). Fused features yielded the highest classification performance with XGBoost (external accuracy = 92.8%, AUC = 94.2%). SHAP identified MMSE, hippocampal volume, and APOE ε4 as top contributors. The nomogram accurately predicted early-stage AD with clinical utility confirmed by DCA. The end-to-end model performed acceptably (AUC = 84.0%) but lagged behind the fused pipeline. Statistical tests confirmed significant performance advantages for feature fusion and MedT-based segmentation. Conclusions This study demonstrates that integrating radiomics, deep learning, and clinical data from hippocampal MRI enables accurate and interpretable classification of AD stages. The proposed framework is robust, generalizable, and clinically actionable, representing a scalable solution for AD diagnostics.

The cholinesterase inhibitor donepezil is used to treat mild-to-moderate Alzheimer's disease. Its efficacy in severe dementia has not been assessed and is controversial. Our aim was to ascertain the effectiveness of donepezil in patients with severe Alzheimer's disease, by focusing primarily on cognition and activities of daily living. We did a 6-month, double-blind, parallel-group, placebo-controlled study in 248 patients with severe Alzheimer's disease (mini mental state examination score 1–10) who were living in assisted care nursing homes ran by trained staff in Sweden. We assigned patients oral donepezil (5 mg per day for 30 days then up to 10 mg per day thereafter, n=128) or matched placebo (n=120). Our primary endpoints were change from baseline to month 6 in the severe impairment battery (SIB) and modified Alzheimer's Disease Cooperative Study activities of daily living inventory for severe Alzheimer's disease (ADCS-ADL-severe). We analysed outcomes for patients with data at baseline and at one or more other timepoints (modified intent-to-treat population) with last observation carried forward used to replace missing data. 95 patients assigned donepezil and 99 patients assigned placebo completed the study. Patients treated with donepezil improved more in SIB scores and declined less in ADCS-ADL-severe scores at 6 months after initiation of treatment compared with baseline than did controls (least squares [LS] mean difference, 5·7, 95% CI 1·5–9·8; p=0·008, and 1·7, 0·2–3·2; p=0·03, respectively). The incidence of adverse events was comparable between groups (donepezil 82% [n=105] vs placebo 76% [n=91]), with most being transient and mild or moderate in severity. More patients discontinued treatment because of adverse events in the donepezil group (n=20) than in the placebo group (n=8). Donepezil improves cognition and preserves function in individuals with severe Alzheimer's disease who live in nursing homes.

Most patients with Alzheimer's disease present with amnestic problems; however, a substantial proportion, over-represented in young-onset cases, have atypical phenotypes including predominant visual, language, executive, behavioural, or motor dysfunction. In the past, these individuals often received a late diagnosis; however, availability of CSF and PET biomarkers of Alzheimer's disease pathologies and incorporation of atypical forms of Alzheimer's disease into new diagnostic criteria increasingly allows them to be more confidently diagnosed early in their illness. This early diagnosis in turn allows patients to be offered tailored information, appropriate care and support, and individualised treatment plans. These advances will provide improved access to clinical trials, which often exclude atypical phenotypes. Research into atypical Alzheimer's disease has revealed previously unrecognised neuropathological heterogeneity across the Alzheimer's disease spectrum. Neuroimaging, genetic, biomarker, and basic science studies are providing key insights into the factors that might drive selective vulnerability of differing brain networks, with potential mechanistic implications for understanding typical late-onset Alzheimer's disease.

Breakthroughs in the development of highly accurate fluid and neuroimaging biomarkers have catalysed the conceptual transformation of Alzheimer disease (AD) from the traditional clinical symptom-based definition to a clinical–biological construct along a temporal continuum. The AT(N) system is a symptom-agnostic classification scheme that categorizes individuals using biomarkers that chart core AD pathophysiological features, namely the amyloid-β (Aβ) pathway (A), tau-mediated pathophysiology (T) and neurodegeneration (N). This biomarker matrix is now expanding towards an ATX(N) system, where X represents novel candidate biomarkers for additional pathophysiological mechanisms such as neuroimmune dysregulation, synaptic dysfunction and blood–brain barrier alterations. In this Perspective, we describe the conceptual framework and clinical importance of the existing AT(N) system and the evolving ATX(N) system. We provide a state-of-the-art summary of the potential contexts of use of these systems in AD clinical trials and future clinical practice. We also discuss current challenges related to the validation, standardization and qualification process and provide an outlook on the real-world application of the AT(N) system.The AT(N) system is a classification scheme based on biomarkers that reflect the core pathophysiological features of Alzheimer disease. This Perspective outlines the conceptual framework and clinical importance of the AT(N) system and considers its potential expansion to incorporate biomarkers for additional pathophysiological mechanisms.

Neuroimaging has made it possible to measure pathological brain changes associated with Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been increasingly integrated into imaging signatures of AD by means of classification frameworks, offering promising tools for individualized diagnosis and prognosis. We reviewed neuroimaging-based studies for AD and mild cognitive impairment classification, selected after online database searches in Google Scholar and PubMed (January, 1985–June, 2016). We categorized these studies based on the following neuroimaging modalities (and sub-categorized based on features extracted as a post-processing step from these modalities): i) structural magnetic resonance imaging [MRI] (tissue density, cortical surface, and hippocampal measurements), ii) functional MRI (functional coherence of different brain regions, and the strength of the functional connectivity), iii) diffusion tensor imaging (patterns along the white matter fibers), iv) fluorodeoxyglucose positron emission tomography (FDG-PET) (metabolic rate of cerebral glucose), and v) amyloid-PET (amyloid burden). The studies reviewed indicate that the classification frameworks formulated on the basis of these features show promise for individualized diagnosis and prediction of clinical progression. Finally, we provided a detailed account of AD classification challenges and addressed some future research directions. •We reviewed Alzheimer’s disease neuroimaging-based classification studies.•We covered structural MRI, fMRI, DTI, amyloid-PET, FDG-PET, and multimodalities.•The reported studies were validated through appropriate cross-validation.•We categorized the studies based on feature extraction methods.•We discussed challenges, disparities in experimental conditions and future directions.

Alzheimer's disease (AD) is classically defined as a dual clinicopathological entity. The recent advances in use of reliable biomarkers of AD that provide in-vivo evidence of the disease has stimulated the development of new research criteria that reconceptualise the diagnosis around both a specific pattern of cognitive changes and structural/biological evidence of Alzheimer's pathology. This new diagnostic framework has stimulated debate about the definition of AD and related conditions. The potential for drugs to intercede in the pathogenic cascade of the disease adds some urgency to this debate. This paper by the International Working Group for New Research Criteria for the Diagnosis of AD aims to advance the scientific discussion by providing broader diagnostic coverage of the AD clinical spectrum and by proposing a common lexicon as a point of reference for the clinical and research communities. The cornerstone of this lexicon is to consider AD solely as a clinical and symptomatic entity that encompasses both predementia and dementia phases.

The molecular architecture of amyloids formed in vivo can be interrogated using luminescent conjugated oligothiophenes (LCOs), a unique class of amyloid dyes. When bound to amyloid, LCOs yield fluorescence emission spectra that reflect the 3D structure of the protein aggregates. Given that synthetic amyloid-β peptide (Aβ) has been shown to adopt distinct structural conformations with different biological activities, we asked whether Aβ can assume structurally and functionally distinct conformations within the brain. To this end, we analyzed the LCO-stained cores of β-amyloid plaques in postmortem tissue sections from frontal, temporal, and occipital neocortices in 40 cases of familial Alzheimer’s disease (AD) or sporadic (idiopathic) AD (sAD). The spectral attributes of LCO-bound plaques varied markedly in the brain, but the mean spectral properties of the amyloid cores were generally similar in all three cortical regions of individual patients. Remarkably, the LCO amyloid spectra differed significantly among some of the familial and sAD subtypes, and between typical patients with sAD and those with posterior cortical atrophy AD. Neither the amount of Aβ nor its protease resistance correlated with LCO spectral properties. LCO spectral amyloid phenotypes could be partially conveyed to Aβ plaques induced by experimental transmission in a mouse model. These findings indicate that polymorphic Aβ-amyloid deposits within the brain cluster as clouds of conformational variants in different AD cases. Heterogeneity in the molecular architecture of pathogenic Aβ among individuals and in etiologically distinct subtypes of AD justifies further studies to assess putative links between Aβ conformation and clinical phenotype.