Predicting dementia development in Parkinson's disease using Bayesian network classifiers

• Published in: Psychiatry research. Neuroimaging Vol. 213; no. 2; pp. 92 - 98
• Main Authors: Morales, Dinora A., Vives-Gilabert, Yolanda, Gómez-Ansón, Beatriz, Bengoetxea, Endika, Larrañaga, Pedro, Bielza, Concha, Pagonabarraga, Javier, Kulisevsky, Jaime, Corcuera-Solano, Idoia, Delfino, Manuel
• Format: Journal Article
• Language: English
• Published: Shannon Elsevier Ireland Ltd 30.08.2013; Elsevier
• Subjects: Adult and adolescent clinical studies; Aged; Bayes Theorem; Biological and medical sciences; Brain - pathology; Cognitive Dysfunction - complications; Cognitive Dysfunction - diagnosis; Cognitive Dysfunction - pathology; Degenerative and inherited degenerative diseases of the nervous system. Leukodystrophies. Prion diseases; Dementia - complications; Dementia - diagnosis; Dementia - pathology; Feature selection; Female; Freesurfer segmentation; Humans; Image Interpretation, Computer-Assisted; Machine learning methods; Magnetic Resonance Imaging; Male; MCI; Medical sciences; MRI; Nervous system (semeiology, syndromes); Nervous system as a whole; Neuroimaging; Neurology; Organic mental disorders. Neuropsychology; Parkinson Disease - complications; Parkinson Disease - pathology; Predictive Value of Tests; Psychiatry; Psychology. Psychoanalysis. Psychiatry; Psychopathology. Psychiatry; Radiology; Sensitivity and Specificity; Support Vector Machine; Neuroimaging; MCI; Feature selection; Freesurfer segmentation; MRI; Machine learning methods; Human; Nervous system diseases; Segmentation; Image processing; Parkinson disease; Nuclear magnetic resonance imaging; Cerebral disorder; Learning; Acquisition process; Central nervous system disease; Development; Bayes network; Medical imagery; Degenerative disease; Extrapyramidal syndrome; Dementia
• ISSN: 0925-4927; 1872-7506
• DOI: 10.1016/j.pscychresns.2012.06.001

Parkinson's disease (PD) has broadly been associated with mild cognitive impairment (PDMCI) and dementia (PDD). Researchers have studied surrogate, neuroanatomic biomarkers provided by magnetic resonance imaging (MRI) that may help in the early diagnosis of this condition. In this article, four classification models (naïve Bayes, multivariate filter-based naïve Bayes, filter selective naïve Bayes and support vector machines, SVM) have been applied to evaluate their capacity to discriminate between cognitively intact patients with Parkinson's disease (PDCI), PDMCI and PDD. For this purpose, the MRI studies of 45 subjects (16 PDCI, 15 PDMCI and 14 PDD) were acquired and post-processed with Freesurfer, obtaining 112 variables (volumes of subcortical structures and thickness of cortical parcels) per subject. A multivariate filter-based naïve Bayes model was found to be the best classifier, having the highest cross-validated sensitivity, specificity and accuracy. Additionally, the most relevant variables related to dementia in PD, as predicted by our classifiers, were cerebral white matter, and volumes of the lateral ventricles and hippocampi.