🧠 Category

neurology

228 papers

Neurology explores the intricate workings of the brain and nervous system, tackling everything from memory and movement to complex conditions like epilepsy and Alzheimer's. This field seeks to understand how our minds function and what happens when that delicate machinery falters, aiming to improve lives through better diagnosis and treatment.

At Gist.Science, we make the latest research from medRxiv accessible to everyone. We process every new preprint in this category, offering both straightforward plain-language explanations and detailed technical summaries so you can grasp the science at your own pace. Below are the latest papers in neurology, ready for you to explore.

🧠 neurology

Exploring Attitudes and Acceptance of Artificial Intelligence in Multiple Sclerosis from the Patient Perspective

A survey of 241 people with multiple sclerosis reveals that acceptance of artificial intelligence in healthcare is moderate and context-dependent, with patients most comfortable using AI for supportive, low-risk tasks like symptom screening while strongly preferring clinician-led decision-making for diagnosis and treatment, a stance driven more by prior familiarity with AI than by disease severity.

Inojosa, H., Masanneck, L., Voigt, I., Schriefer, D., von Horsten, N., Wenk, J., Gasparovic-Curtini, I., Haase, R., Meut (…)2026-01-30
🧠 neurology

Time's up: Using data-driven phenotype-severity metrics not time to map progression in the dementias

This study proposes a data-driven, transdiagnostic phenotype-severity metric derived from neuropsychological scores that outperforms traditional time-based measures in accurately mapping dementia progression and distinguishing disease stages from clinical heterogeneity.

Smith, V., Schumacher, R., Ramanan, S., Bouzigues, A., Russell, L. L., Foster, P. H., Ferry-Bolder, E., van Swieten, J. (…)2026-01-30
🧠 neurology

Episodic memory retrieval with increasing task demand: Associations with age, APOE4 genotype, and Alzheimer's disease pathology

This study demonstrates that a multi-level episodic memory task reveals age- and APOE4-related declines in retrieval accuracy and confidence under increasing cognitive demands, offering a sensitive approach to detecting subtle deficits in cognitively unimpaired older adults that single-level paradigms might miss.

Askevold, F., Schumann-Werner, B., Behrenbruch, N., Schwarck, S., Molloy, E. N., Peelle, J. E., O Leary, R. M., Wingfiel (…)2026-01-30
🧠 neurology

Feature Integration of FDG PET Brain Imaging Using Deep Learning for Sensitive Cognitive Decline Detection

This study proposes a multi-representational deep learning framework that integrates voxel-level PET imaging features with region-level quantification to significantly improve the sensitivity and accuracy of detecting cognitive decline compared to single-feature models and standard clinical assessments.

Lee, Y., Kim, S., Kim, S., Kang, Y., Alzheimer's Disease Neuroimaging Initiative,2026-01-28
🧠 neurology

Telemedical communication patterns in myasthenia gravis in a remote monitoring study

In a randomized controlled trial of 45 myasthenia gravis patients, telemedical monitoring via a mobile app significantly increased perceived specialist accessibility and facilitated frequent, timely communication regarding medical and technical issues, highlighting the potential of such solutions to address the demand for remote advice in managing this rare disease.

Herdick, M. L., Lehnerer, S., Moench, M., Gerischer, L., Legg, D., Meisel, A., Sun, H., Narayanaswami, P., Stein, M.2026-01-22
🧠 neurology

In vivo Assessment of Brain Microstructure in Patients with Huntington's Disease Using Quantitative MRI

This study demonstrates that quantitative multiparametric MRI (MPM) effectively detects stage-specific microstructural alterations, such as early demyelination in premanifest and widespread neuronal loss in manifest Huntington's disease, establishing MPM as a valuable non-invasive biomarker for tracking neurodegeneration and correlating with clinical severity.

Pokotylo, M. M., Assmann, J.-L., Koedderitzsch Mertins, M. G., Henkel, J., Uter, J., van Well, L., Muenchau, A., Loens (…)2026-01-21
🧠 neurology

ATN Classification and Machine-Learned Plasma Biomarker Phenotypes Reveal Distinct Alzheimer's Pathology in a Population-Based Cohort

In a large population-based cohort, this study demonstrates that while theory-driven ATN classification and data-driven machine learning phenotypes show only modest concordance—largely driven by GFAP rather than shared amyloid, tau, and neurodegeneration biomarkers—both frameworks effectively predict longitudinal cognitive decline, suggesting that integrating these complementary approaches offers a more comprehensive characterization of Alzheimer's pathology.

Chea, E. F.2026-01-15