Individual Prediction of Behavioral Variant Frontotemporal Dementia Development Using Multivariate Pattern Analysis of Magnetic Resonance Imaging Data
| Autor: | Paul Zhutovsky, Mike P. Wattjes, Guido van Wingen, Rajat M. Thomas, Willem B Bruin, Yolande A.L. Pijnenburg, Annemiek Dols, Everard G. B. Vijverberg |
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| Přispěvatelé: | Graduate School, ANS - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Global Health, APH - Mental Health, Adult Psychiatry, Neurology, Other Research, Divisions, Psychiatry, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Male Multivariate statistics Pediatrics medicine.medical_specialty Support Vector Machine Pattern analysis Neuroimaging 03 medical and health sciences 0302 clinical medicine medicine Humans Gray Matter Predictive biomarker Cognitive Symptoms medicine.diagnostic_test business.industry General Neuroscience Brain Magnetic resonance imaging General Medicine Middle Aged medicine.disease Prognosis Magnetic Resonance Imaging Psychiatry and Mental health Clinical Psychology 030104 developmental biology Binary classification Frontotemporal Dementia Multivariate Analysis Female Geriatrics and Gerontology business 030217 neurology & neurosurgery Frontotemporal dementia |
| Zdroj: | Journal of Alzheimer s disease, 68(3), 1229-1241. IOS Press Zhutovsky, P, Vijverberg, E G B, Bruin, W B, Thomas, R M, Wattjes, M P, Pijnenburg, Y A L, van Wingen, G A & Dols, A 2019, ' Individual Prediction of Behavioral Variant Frontotemporal Dementia Development Using Multivariate Pattern Analysis of Magnetic Resonance Imaging Data ', Journal of Alzheimer's Disease, vol. 68, no. 3, pp. 1229-1241 . https://doi.org/10.3233/JAD-181004 Journal of Alzheimer's Disease, 68(3), 1229-1241. IOS Press |
| ISSN: | 1387-2877 |
| DOI: | 10.3233/JAD-181004 |
| Popis: | Patients with behavioral variant of frontotemporal dementia (bvFTD) initially may only show behavioral and/or cognitive symptoms that overlap with other neurological and psychiatric disorders. The diagnostic accuracy is dependent on progressive symptoms worsening and frontotemporal abnormalities on neuroimaging findings. Predictive biomarkers could facilitate the early detection of bvFTD. Objective: To determine the prognostic accuracy of clinical and structural MRI data using a support vector machine (SVM) classification to predict the 2-year clinical follow-up diagnosis in a group of patients presenting late-onset behavioral changes. Methods: Data from 73 patients were included and divided into probable/definite bvFTD (n=18), neurological (n=28), and psychiatric (n=27) groups based on 2-year follow-up diagnosis. Grey-matter volumes were extracted from baseline structural MRI scans. SVM classifiers were used to perform three binary classifications: bvFTD versus neurological and psychiatric, bvFTD versus neurological, and bvFTD versus psychiatric group(s), and one multi-class classification. Classification performance was determined for clinical and neuroimaging data separately and their combination using 5-fold cross-validation. Results: Accuracy of the binary classification tasks ranged from 72-82% (p |
| Databáze: | OpenAIRE |
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