ArticleBrain sciences2020
Association between Structural Connectivity and Generalized Cognitive Spectrum in Alzheimer's Disease.
Article in Brain sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Article
- Detecting label noise in longitudinal Alzheimer's data with explainable artificial intelligence.Brain informatics · 2025Article
- Impact of white matter hyperintensities on structural connectivity and cognition in cognitively intact ADNI participants.Neurobiology of aging · 2024Article
- Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing.Frontiers in human neuroscience · 2024Review
- Markov model combined with MR diffusion tensor imaging for predicting the onset of Alzheimer's disease.Open life sciences · 2023Article
- A robust framework to investigate the reliability and stability of explainable artificial intelligence markers of Mild Cognitive Impairment and Alzheimer's Disease.Brain informatics · 2022Article
- Plasma phosphorylated-tau181 levels reflect white matter microstructural changes across Alzheimer's disease progression.Metabolic brain disease · 2022Article
- The Road to Personalized Medicine in Alzheimer's Disease: The Use of Artificial Intelligence.Biomedicines · 2022Review
- Explainable Deep Learning for Personalized Age Prediction With Brain Morphology.Frontiers in neuroscience · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Modeling disease progression through the cognitive scores has become an attractive challenge in the field of computational neuroscience due to its importance for early diagnosis of Alzheimer's disease (AD). Several scores such as Alzheimer's Disease Assessment Scale cognitive total score, Mini Mental State Exam score and Rey Auditory Verbal Learning Test provide a quantitative assessment of the cognitive conditions of the patients and are commonly used as objective criteria for clinical diagnosis of dementia and mild cognitive impairment (MCI). On the other hand, connectivity patterns extracted from diffusion tensor imaging (DTI) have been successfully used to classify AD and MCI subjects with machine learning algorithms proving their potential application in the clinical setting. In this work, we carried out a pilot study to investigate the strength of association between DTI structural connectivity of a mixed ADNI cohort and cognitive spectrum in AD. We developed a machine learning framework to find a generalized cognitive score that summarizes the different functional domains reflected by each cognitive clinical index and to identify the connectivity biomarkers more significantly associated with the score. The results indicate that the efficiency and the centrality of some regions can effectively track cognitive impairment in AD showing a significant correlation with the generalized cognitive score (
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Registered trials
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