ArticlePsychiatry investigation2015
Automated Classification to Predict the Progression of Alzheimer's Disease Using Whole-Brain Volumetry and DTI.
Article in Psychiatry investigation, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- White matter changes from mild cognitive impairment to Alzheimer's disease: a meta-analysis.Acta neurologica Belgica · 2021Pooled it
- Reproducible Evaluation of Diffusion MRI Features for Automatic Classification of Patients with Alzheimer's Disease.Neuroinformatics · 2021Pooled it
- Review
- Exploiting macro- and micro-structural brain changes for improved Parkinson's disease classification from MRI data.NPJ Parkinson's disease · 2024Article
- Microstructural mapping of dentate gyrus pathology in Alzheimer's disease: A 16.4 Tesla MRI study.NeuroImage. Clinical · 2023Article
- In search of multimodal brain alterations in Alzheimer's and Binswanger's disease.NeuroImage. Clinical · 2020Article
- White matter and its relationship with cognition in subjective cognitive decline.Alzheimer's & dementia (Amsterdam, Netherlands) · 2019Article
- Functional connectivity between white matter and gray matter based on fMRI for Alzheimer's disease classification.Brain and behavior · 2019Article
- White Matter Microstructural Damage as an Early Sign of Subjective Cognitive Decline.Frontiers in aging neuroscience · 2019Article
- The anteroposterior and primary-to-posterior limbic ratios as MRI-derived volumetric markers of Alzheimer's disease.Journal of the neurological sciences · 2017Article
- Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.NeuroImage · 2017Review
- Changes of Functional and Directed Resting-State Connectivity Are Associated with Neuronal Oscillations, ApoE Genotype and Amyloid Deposition in Mild Cognitive Impairment.Frontiers in aging neuroscience · 2017Article
- In Vivo Detection of Gray Matter Neuropathology in the 3xTg Mouse Model of Alzheimer's Disease with Diffusion Tensor Imaging.Journal of Alzheimer's disease : JAD · 2017Article
- Classification of brain disease in magnetic resonance images using two-stage local feature fusion.PloS one · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study proposes an automated diagnostic method to classify patients with Alzheimer's disease (AD) of degenerative etiology using magnetic resonance imaging (MRI) markers.
methodsTwenty-seven patients with subjective memory impairment (SMI), 18 patients with mild cognitive impairment (MCI), and 27 patients with AD participated. MRI protocols included three dimensional brain structural imaging and diffusion tensor imaging to assess the cortical thickness, subcortical volume and white matter integrity. Recursive feature elimination based on support vector machine (SVM) was conducted to determine the most relevant features for classifying abnormal regions and imaging parameters, and then a factor analysis for the top-ranked factors was performed. Subjects were classified using nonlinear SVM.
resultsMedial temporal regions in AD patients were dominantly detected with cortical thinning and volume atrophy compared with SMI and MCI patients. Damage to white matter integrity was also accredited with decreased fractional anisotropy and increased mean diffusivity (MD) across the three groups. The microscopic damage in the subcortical gray matter was reflected in increased MD. Classification accuracy between pairs of groups (SMI vs. MCI, MCI vs. AD, SMI vs. AD) and among all three groups were 84.4% (±13.8), 86.9% (±10.5), 96.3% (±4.6), and 70.5% (±11.5), respectively.
conclusionThis proposed method may be a potential tool to diagnose AD pathology with the current clinical criteria.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.