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Computerized cognitive testing to capture cognitive decline in Alzheimer's disease: Longitudinal findings from the ARMADA study.
Alzheimers Dement (Amst)
January 2025
Department of Neurology Massachusetts General Hospital, Harvard Medical School Boston Massachusetts USA.
Introduction: Timely detection and tracking of Alzheimer's disease (AD) -related cognitive decline has become a public health priority. We investigated whether the NIH Toolbox for Assessment of Neurological and Behavioral Function-Cognition Battery (NIHTB-CB) detects AD-related cognitive decline.
Methods: = 171 participants (age 76.
Decoding brain structure to stage Alzheimer's disease pathology in Down syndrome.
Alzheimers Dement
January 2025
Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA.
Introduction: Alzheimer's disease (AD) in Down syndrome (DS) is associated with changes in brain structure. It is unknown if thickness and volumetric changes can identify AD stages and if they are similar to other genetic forms of AD.
Methods: Magnetic resonance imaging scans were collected for 178 DS adults (106 nonclinical, 45 preclinical, and 27 symptomatic).
Detecting early cognitive deficits in preclinical Alzheimer's disease using a remote digital multi-day learning paradigm.
NPJ Digit Med
January 2025
Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02129, USA.
Remote, digital cognitive testing on an individual's own device provides the opportunity to deploy previously understudied but promising cognitive paradigms in preclinical Alzheimer's disease (AD). The Boston Remote Assessment for NeuroCognitive Health (BRANCH) captures a personalized learning curve for the same information presented over seven consecutive days. Here, we examined BRANCH multi-day learning curves (MDLCs) in 167 cognitively unimpaired older adults (age = 74.
View Article and Find Full Text PDFContributions of distractor dwelling, skipping, and revisiting to age differences in visual search.
Sci Rep
January 2025
Department of Psychology, Bielefeld University, 33501, Bielefeld, Germany.
Visual search becomes slower with aging, particularly when targets are difficult to discriminate from distractors. Multiple distractor rejection processes may contribute independently to slower search times: dwelling on, skipping of, and revisiting of distractors, measurable by eye-tracking. The present study investigated how age affects each of the distractor rejection processes, and how these contribute to the final search times in difficult (inefficient) visual search.
View Article and Find Full Text PDFEnhanced neurological anomaly detection in MRI images using deep convolutional neural networks.
Front Med (Lausanne)
December 2024
Software Engineering Department, LUT University, Lahti, Finland.
Introduction: Neurodegenerative diseases, including Parkinson's, Alzheimer's, and epilepsy, pose significant diagnostic and treatment challenges due to their complexity and the gradual degeneration of central nervous system structures. This study introduces a deep learning framework designed to automate neuro-diagnostics, addressing the limitations of current manual interpretation methods, which are often time-consuming and prone to variability.
Methods: We propose a specialized deep convolutional neural network (DCNN) framework aimed at detecting and classifying neurological anomalies in MRI data.
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