Publicacións nas que colabora con JOSÉ LUIS AYALA RODRIGO (18)

2024

  1. A data-driven approach to complement the A/T/(N) classification system using CSF biomarkers

    CNS Neuroscience and Therapeutics, Vol. 30, Núm. 2

  2. Correction to: Genetic-based patient stratification in Alzheimer’s disease (Scientific Reports, (2024), 14, 1, (9970), 10.1038/s41598-024-60707-1)

    Scientific Reports

  3. Deep Learning For Predicting Cerebral Metabolism Changes Along The Alzheimer’s Disease Continuum

    Proceedings IMCIC - International Multi-Conference on Complexity, Informatics and Cybernetics

  4. Genetic-based patient stratification in Alzheimer’s disease

    Scientific Reports, Vol. 14, Núm. 1

  5. Predicting changes in brain metabolism and progression from mild cognitive impairment to dementia using multitask Deep Learning models and explainable AI

    NeuroImage, Vol. 297

2023

  1. Development of criteria for cognitive dysfunction in post-COVID syndrome: the IC-CoDi-COVID approach

    Psychiatry Research, Vol. 319

  2. Unraveling brain fog in post-COVID syndrome: Relationship between subjective cognitive complaints and cognitive function, fatigue, and neuropsychiatric symptoms

    European Journal of Neurology

2022

  1. Body Complexion and Circulating Lipids in the Risk of TDP-43 Related Disorders

    Frontiers in Aging Neuroscience, Vol. 14

  2. Diagnosis of Alzheimer's disease and behavioural variant frontotemporal dementia with machine learning-aided neuropsychological assessment using feature engineering and genetic algorithms

    International Journal of Geriatric Psychiatry, Vol. 37, Núm. 2

  3. GA-MADRID: design and validation of a machine learning tool for the diagnosis of Alzheimer’s disease and frontotemporal dementia using genetic algorithms

    Medical and Biological Engineering and Computing, Vol. 60, Núm. 9, pp. 2737-2756

  4. Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging

    Frontiers in Aging Neuroscience, Vol. 13

  5. Neuropsychological Predictors of Fatigue in Post-COVID Syndrome

    Journal of Clinical Medicine, Vol. 11, Núm. 13

  6. On the limits of graph neural networks for the early diagnosis of Alzheimer’s disease

    Scientific Reports, Vol. 12, Núm. 1

2021

  1. Application of machine learning to electroencephalography for the diagnosis of primary progressive aphasia: A pilot study

    Brain Sciences, Vol. 11, Núm. 10

  2. Personalized Repetitive Transcranial Magnetic Stimulation for Primary Progressive Aphasia

    Journal of Alzheimer's disease : JAD, Vol. 84, Núm. 1, pp. 151-167

2019

  1. An application of machine learning with feature selection to improve diagnosis and classification of neurodegenerative disorders

    BMC Bioinformatics, Vol. 20, Núm. 1

  2. Machine learning in the clinical and language characterisation of primary progressive aphasia variants

    Cortex, Vol. 119, pp. 312-323

2018

  1. Clustering analysis of FDG-Pet imaging in primary progressive aphasia

    Frontiers in Aging Neuroscience, Vol. 10, pp. 1-12