ArticleNeuroImage. Clinical2019
Detecting frontotemporal dementia syndromes using MRI biomarkers.
Article in NeuroImage. Clinical, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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36 citing papers in PubMed, 77 citations in OpenAlex.
- Deep Learning-Based Alzheimer's Disease Detection from Multi-Channel EEG Using Fused Time-Frequency Image Grids.Diagnostics (Basel, Switzerland) · 2026Article
- Distinct and shared multimodal neuroimaging patterns in clinical frontotemporal dementia syndromes.Brain communications · 2026Article
- Mapping the Cross-species Brain Connectivity Atlas and Hemispheric Asymmetry of the Temporal Pole in Humans and Macaques.Neuroscience bulletin · 2026Article
- Evaluating the correspondence of neuropsychological profiles with clinical diagnostic criteria for frontotemporal lobar degeneration and Alzheimer's disease: insights fromFrontiers in dementia · 2026Article
- A computational ontology framework for the synthesis of multi-level pathology reports from brain MRI scans.Journal of Alzheimer's disease : JAD · 2025Article
- The Cortical Asymmetry Index for subtyping dementia patients.European radiology · 2025Article
- Interhemispheric cortical thickness asymmetry is an imaging biomarker of dementia type.European radiology · 2025Article
- Imaging Interstitial Fluids and Extracellular Matrix in Cerebrovascular Disorders: Current Perspectives and Clinical Applications.Neuroimaging clinics of North America · 2025Review
- Multi-target approach to Alzheimer's disease prevention and treatment: antioxidant, anti-inflammatory, and amyloid- modulating mechanisms.Neurogenetics · 2025Review
- Advanced Magnetic Resonance Imaging for Early Diagnosis and Monitoring of Movement Disorders.Brain sciences · 2025Review
- Sink-index: a network-based EEG marker for frontotemporal dementia and Alzheimer's disease.Brain communications · 2025Article
- The use of low-density EEG for the classification of PPA and MCI.Frontiers in human neuroscience · 2025Article
- Computerized decision support to optimally funnel patients through the diagnostic pathway for dementia.Alzheimer's research & therapy · 2024Article
- Automated Volumetric Software in Dementia: Help or Hindrance to the Neuroradiologist?AJNR. American journal of neuroradiology · 2024Article
- Automated magnetic resonance imaging quantification of cerebral parenchymal and ventricular volume following subarachnoid hemorrhage: associations with cognition.Brain imaging and behavior · 2024Article
- Computerized decision support is an effective approach to select memory clinic patients for amyloid-PET.PloS one · 2024Article
- Genetic overlap between cortical brain morphometry and frontotemporal dementia risk.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- MicroRNA biomarkers in frontotemporal dementia and to distinguish from Alzheimer's disease and amyotrophic lateral sclerosis.Neural regeneration research · 2022Review
- Machine learning approaches for biomolecular, biophysical, and biomaterials research.Biophysics reviews · 2022Review
- Deep Learning for Smart Healthcare-A Survey on Brain Tumor Detection from Medical Imaging.Sensors (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
17 authors at 7 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDiagnosing frontotemporal dementia may be challenging. New methods for analysis of regional brain atrophy patterns on magnetic resonance imaging (MRI) could add to the diagnostic assessment. Therefore, we aimed to develop automated imaging biomarkers for differentiating frontotemporal dementia subtypes from other diagnostic groups, and from one another.
methodsIn this retrospective multicenter cohort study, we included 1213 patients (age 67 ± 9, 48% females) from two memory clinic cohorts: 116 frontotemporal dementia, 341 Alzheimer's disease, 66 Dementia with Lewy bodies, 40 vascular dementia, 104 other dementias, 229 mild cognitive impairment, and 317 subjective cognitive decline. Three MRI atrophy biomarkers were derived from the normalized volumes of automatically segmented cortical regions: 1) the anterior vs. posterior index, 2) the asymmetry index, and 3) the temporal pole left index. We used the following performance metrics: area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. To account for the low prevalence of frontotemporal dementia we pursued a high specificity of 95%. Cross-validation was used in assessing the performance. The generalizability was assessed in an independent cohort (n = 200).
resultsThe anterior vs. posterior index performed with an AUC of 83% for differentiation of frontotemporal dementia from all other diagnostic groups (Sensitivity = 59%, Specificity = 95%, positive likelihood ratio = 11.8, negative likelihood ratio = 0.4). The asymmetry index showed highest performance for separation of primary progressive aphasia and behavioral variant frontotemporal dementia (AUC = 85%, Sensitivity = 79%, Specificity = 92%, positive likelihood ratio = 9.9, negative likelihood ratio = 0.2), whereas the temporal pole left index was specific for detection of semantic variant primary progressive aphasia (AUC = 85%, Sensitivity = 82%, Specificity = 80%, positive likelihood ratio = 4.1, negative likelihood ratio = 0.2). The validation cohort provided corresponding results for the anterior vs. posterior index and temporal pole left index.
conclusionThis study presents three quantitative MRI biomarkers, which could provide additional information to the diagnostic assessment and assist clinicians in diagnosing frontotemporal dementia.
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