Behavioral variant frontotemporal dementia in patients with primary psychiatric disorder: A magnetic resonance imaging study. Issue 4 (2nd March 2023)

• Record Type: Journal Article
• Authors: Tafuri, Benedetta; Filardi, Marco; Frisullo, Maria Elisa; De Blasi, Roberto; Rizzo, Giovanni; Nigro, Salvatore; Logroscino, Giancarlo
• Is Part Of: Brain and behavior. Volume 13:Issue 4(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-03-02
• Subjects: behavioral variant frontotemporal dementia -- machine learning -- MRI -- primary psychiatric disorder -- support vector machine; Neurology -- Periodicals; Neurosciences -- Periodicals; Psychology -- Periodicals; Psychiatry -- Periodicals; 616.8005
• DOI: 10.1002/brb3.2896
• Languages: English
• ISSNs: 2162-3279

Abstract

Abstract: Background: The clinical diagnosis of behavioral variant frontotemporal dementia (bvFTD) in patients with a history of primary psychiatric disorder (PPD) is challenging. PPD shows the typical cognitive impairments observed in patients with bvFTD. Therefore, the correct identification of bvFTD onset in patients with a lifetime history of PPD is pivotal for an optimal management. Methods: Twenty‐nine patients with PPD were included in this study. After clinical and neuropsychological evaluations, 16 patients with PPD were clinically classified as bvFTD (PPD‐bvFTD+), while in 13 cases clinical symptoms were associated with the typical course of the psychiatric disorder itself (PPD‐bvFTD–). Voxel‐ and surface‐based investigations were used to characterize gray matter changes. Volumetric and cortical thickness measures were used to predict the clinical diagnosis at a single‐subject level using a support vector machine (SVM) classification framework. Finally, we compared classification performances of magnetic resonance imaging (MRI) data with automatic visual rating scale of frontal and temporal atrophy. Results: PPD‐bvFTD+ showed a gray matter decrease in thalamus, hippocampus, temporal pole, lingual, occipital, and superior frontal gyri compared to PPD‐bvFTD– ( p < .05, family‐wise error‐corrected). SVM classifier showed a discrimination accuracy of 86.2% in differentiating PPD patients with bvFTD from those without bvFTD. Conclusions: Our study highlights the utility of machine learning applied to structural MRI data to support the clinician in the diagnosis of bvFTD in patients with a history of PPD. Gray matter atrophy in temporal, frontal, and occipital brain regions may represent a useful hallmark for a correct identification of dementia in PPD at a single‐subject level. Abstract :