ArticleNeuroImage. Clinical2017
Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data.
Article in NeuroImage. Clinical, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 2 syntheses or guidelines pooled it, 84 citations in OpenAlex.
- Atrophy in midbrain & cerebral/cerebellar pedunculi is characteristic for progressive supranuclear palsy - A double-validation whole-brain meta-analysis.NeuroImage. Clinical · 2019Pooled it
- Morphometric MRI as a diagnostic biomarker of frontotemporal dementia: A systematic review to determine clinical applicability.NeuroImage. Clinical · 2018Pooled it
- Hybrid multimodal late fusion frameworks for bvFTD classification in imbalanced dementia datasets.Frontiers in aging neuroscience · 2026Article
- Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine.Psychoradiology · 2026Review
- Combining plasma neurofilament light chain and frontotemporal atrophy improves differentiation of bvFTD from primary psychiatric disorders.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Criminal Behavior in Frontotemporal Dementia: A Multimodal MRI Study.Human brain mapping · 2025Article
- Microstructural grey matter alterations in patients with behavioural variant frontotemporal dementia.Brain communications · 2025Article
- Article
- Behavioral variant frontotemporal dementia in patients with primary psychiatric disorder: A magnetic resonance imaging study.Brain and behavior · 2023Article
- Moral Emotions and Their Brain Structural Correlates Across Neurodegenerative Disorders.Journal of Alzheimer's disease : JAD · 2023Article
- Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference.Network neuroscience (Cambridge, Mass.) · 2023Article
- Prosopagnosia, Other Specific Cognitive Deficits, and Behavioral Symptoms: Comparison between Right Temporal and Behavioral Variant of Frontotemporal Dementia.Vision (Basel, Switzerland) · 2022Article
- Exploring Links Between Psychosis and Frontotemporal Dementia Using Multimodal Machine Learning: Dementia Praecox Revisited.JAMA psychiatry · 2022Article
- Comparative analysis of machine learning algorithms for multi-syndrome classification of neurodegenerative syndromes.Alzheimer's research & therapy · 2022Article
- Dementia-related psychosis and the potential role for pimavanserin.CNS spectrums · 2022Review
- Radiomics Model for Frontotemporal Dementia Diagnosis Using T1-Weighted MRI.Frontiers in neuroscience · 2022Article
- Assessing Versatile Machine Learning Models for Glioma Radiogenomic Studies across Hospitals.Cancers · 2021Article
- An MRI-based strategy for differentiation of frontotemporal dementia and Alzheimer's disease.Alzheimer's research & therapy · 2021Article
- Frontotemporal Pathology in Motor Neuron Disease Phenotypes: Insights From Neuroimaging.Frontiers in neurology · 2021Review
- Deep Neural Network to Differentiate Brain Activity Between Patients With First-Episode Schizophrenia and Healthy Individuals: A Multi-Channel Near Infrared Spectroscopy Study.Frontiers in psychiatry · 2021Article
Corrections and comments
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Authors and funding
16 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeFrontotemporal lobar degeneration (FTLD) is a common cause of early onset dementia. Behavioral variant frontotemporal dementia (bvFTD), its most common subtype, is characterized by deep alterations in behavior and personality. In 2011, new diagnostic criteria were suggested that incorporate imaging criteria into diagnostic algorithms. The study aimed at validating the potential of imaging criteria to individually predict diagnosis with machine learning algorithms. MATERIALS &
methodsBrain atrophy was measured with structural magnetic resonance imaging (MRI) at 3 Tesla in a multi-centric cohort of 52 bvFTD patients and 52 healthy control subjects from the German FTLD Consortium's Study. Beside group comparisons, diagnosis bvFTD vs. controls was individually predicted in each subject with support vector machine classification in MRI data across the whole brain or in frontotemporal, insular regions, and basal ganglia known to be mainly affected based on recent meta-analyses. Multi-center effects were controlled for with a new method, "leave one center out" conjunction analyses, i.e. repeatedly excluding subjects from each center from the analysis.
resultsGroup comparisons revealed atrophy in, most consistently, the frontal lobe in bvFTD beside alterations in the insula, basal ganglia and temporal lobe. Most remarkably, support vector machine classification enabled predicting diagnosis in single patients with a high accuracy of up to 84.6%, where accuracy was highest in a region-of-interest approach focusing on frontotemporal, insular regions, and basal ganglia in comparison with the whole brain approach.
conclusionOur study demonstrates that MRI, a widespread imaging technology, can individually identify bvFTD with high accuracy in multi-center imaging data, paving the road to personalized diagnostic approaches in the future.
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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.