2025-11-06 09:30 |
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2025-11-06 09:26 |
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2025-11-06 09:24 |
[DZNE-2025-01235]
Journal Article (Editorial)
Teipel, S. ; Singh, D. ; Dubbelman, M. A. ; et al
Early detection of Alzheimer's disease: From multiplex assays and imaging to point of care devices and AI-based functional monitoring.
The advent of disease-modifying treatments and risk-reduction strategies in the clinic have increased the demand for biomarkers for early disease detection, prediction of clinical course of disease, individual risk prediction and monitoring of treatment effects. The studies in this special issue span ultra-sensitive fluid assays and automated laboratory platforms, structural and molecular neuroimaging, electrophysiology, digital cognitive and behavioral monitoring, multi-omics, neuromodulation, and the computational frameworks necessary to integrate these diverse data..
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2025-11-06 09:22 |
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2025-11-06 09:20 |
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2025-11-05 17:11 |
[DZNE-2025-01232]
Journal Article
Alexopoulou, Z.-S. ; Köhler, S. ; Mallick, E. ; et al
Speech-based digital cognitive assessments for detection of mild cognitive impairment: Validation against paper-based neurocognitive assessment scores.
BackgroundCognitive decline in Alzheimer's disease (AD) often includes speech impairments, where subtle changes may precede clinical dementia onset. As clinical trials focus on early identification of patients for disease-modifying treatments, digital speech-based assessments for scalable screening have become crucial.ObjectiveThis study aimed to validate a remote, speech-based digital cognitive assessment for mild cognitive impairment (MCI) detection through the comparison with gold-standard paper-based neurocognitive assessments.MethodsWithin the PROSPECT-AD project, speech and clinical data were obtained from the German DELCODE and DESCRIBE cohorts, including 21 healthy controls (HC), 110 participants with subjective cognitive decline (SCD), and 59 with MCI. [...]
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2025-11-05 17:01 |
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2025-11-05 16:59 |
[DZNE-2025-01230]
Journal Article
Singh, D. ; Grazia, A. ; Reiz, A. ; et al
A computational ontology framework for the synthesis of multi-level pathology reports from brain MRI scans.
BackgroundConvolutional neural network (CNN) based volumetry of MRI data can help differentiate Alzheimer's disease (AD) and the behavioral variant of frontotemporal dementia (bvFTD) as causes of cognitive decline and dementia. However, existing CNN-based MRI volumetry tools lack a structured hierarchical representation of brain anatomy, which would allow for aggregating regional pathological information and automated computational inference.ObjectiveDevelop a computational ontology pipeline for quantifying hierarchical pathological abnormalities and visualize summary charts for brain atrophy findings, aiding differential diagnosis.MethodsUsing FastSurfer, we segmented brain regions and measured volume and cortical thickness from MRI scans pooled across multiple cohorts (N = 3433; ADNI, AIBL, DELCODE, DESCRIBE, EDSD, and NIFD), including healthy controls, prodromal and clinical AD cases, and bvFTD cases. [...]
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2025-11-05 16:47 |
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2025-11-05 16:45 |
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