ArticleFrontiers in neuroscience2017
Ensemble Classification of Alzheimer's Disease and Mild Cognitive Impairment Based on Complex Graph Measures from Diffusion Tensor Images.
Article in Frontiers in neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.
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Who cites it
33 citing papers in PubMed, 2 syntheses or guidelines pooled it, 70 citations in OpenAlex.
- Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer's Disease Not Using Neuroimaging Biomarkers: A Systematic Review.Journal of Alzheimer's disease : JAD · 2024Pooled it
- Reproducible Evaluation of Diffusion MRI Features for Automatic Classification of Patients with Alzheimer's Disease.Neuroinformatics · 2021Pooled it
- A systematic study on the integration of MRI connectivity metrics for Alzheimer's diagnosis, staging, and cognitive decline prediction.Frontiers in neuroimaging · 2026Article
- Domain-Specific Prediction of Clinical Progression in Parkinson's Disease Using the Mosaic Approach.Brain and behavior · 2025Article
- Learning disentangled representations to harmonize connectome network measures.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Reorganization of brain connectivity across the spectrum of clinical cognitive decline.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024Article
- Non-local diffusion-based biomarkers in patients with cocaine use disorder.Neuroimage. Reports · 2024Article
- The decreased astrocyte-microglia interaction reflects the early characteristics of Alzheimer's disease.iScience · 2024Article
- Characterization of gill bacterial microbiota in wild Arctic char (Microbiology spectrum · 2024Article
- Learning site-invariant features of connectomes to harmonize complex network measures.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Exploring Alzheimer's disease: a comprehensive brain connectome-based survey.Psychoradiology · 2024Review
- Computer aided progression detection model based on optimized deep LSTM ensemble model and the fusion of multivariate time series data.Scientific reports · 2023Article
- A correlational study between microstructural, macrostructural and functional age-related changes in the human visual cortex.PloS one · 2023Article
- Tau deposition and structural connectivity demonstrate differential association patterns with neurocognitive tests.Brain imaging and behavior · 2022Article
- Neuroimaging of Mouse Models of Alzheimer's Disease.Biomedicines · 2022Review
- Chronic Ethanol Causes Persistent Increases in Alzheimer's Tau Pathology in Female 3xTg-AD Mice: A Potential Role for Lysosomal Impairment.Frontiers in behavioral neuroscience · 2022Article
- Article
- Abnormal Dynamic Functional Networks in Subjective Cognitive Decline and Alzheimer's Disease.Frontiers in computational neuroscience · 2022Article
- Noradrenergic Modulation of the Piriform Cortex: A Possible Avenue for Understanding Pre-Clinical Alzheimer's Disease Pathogenesis.Frontiers in cellular neuroscience · 2022Review
- Automated Classification of Mild Cognitive Impairment by Machine Learning With Hippocampus-Related White Matter Network.Frontiers in aging neuroscience · 2022Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 4 countries.
Funding
Abstract
The human brain is a complex network of interacting regions. The gray matter regions of brain are interconnected by white matter tracts, together forming one integrative complex network. In this article, we report our investigation about the potential of applying brain connectivity patterns as an aid in diagnosing Alzheimer's disease and Mild Cognitive Impairment (MCI). We performed pattern analysis of graph theoretical measures derived from Diffusion Tensor Imaging (DTI) data representing structural brain networks of 45 subjects, consisting of 15 patients of Alzheimer's disease (AD), 15 patients of MCI, and 15 healthy subjects (CT). We considered pair-wise class combinations of subjects, defining three separate classification tasks, i.e., AD-CT, AD-MCI, and CT-MCI, and used an ensemble classification module to perform the classification tasks. Our ensemble framework with feature selection shows a promising performance with classification accuracy of 83.3% for AD vs. MCI, 80% for AD vs. CT, and 70% for MCI vs. CT. Moreover, our findings suggest that AD can be related to graph measures abnormalities at Brodmann areas in the sensorimotor cortex and piriform cortex. In this way, node redundancy coefficient and load centrality in the primary motor cortex were recognized as good indicators of AD in contrast to MCI. In general, load centrality, betweenness centrality, and closeness centrality were found to be the most relevant network measures, as they were the top identified features at different nodes. The present study can be regarded as a "proof of concept" about a procedure for the classification of MRI markers between AD dementia, MCI, and normal old individuals, due to the small and not well-defined groups of AD and MCI patients. Future studies with larger samples of subjects and more sophisticated patient exclusion criteria are necessary toward the development of a more precise technique for clinical diagnosis.
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