Callosal thickness profiles for prognosticating conversion from mild cognitive impairment to Alzheimer's disease: A classification approach. Issue 12 (22nd November 2018)

• Record Type: Journal Article
• Authors: Adamson, Chris; Beare, Richard; Ball, Gareth; Walterfang, Mark; Seal, Marc
• Is Part Of: Brain and behavior. Volume 8:Issue 12(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-11-22
• Subjects: Alzheimer's disease -- biomarker -- classification -- corpus callosum -- magnetic resonance imaging -- segmentation; Neurology -- Periodicals; Neurosciences -- Periodicals; Psychology -- Periodicals; Psychiatry -- Periodicals; 616.8005
• DOI: 10.1002/brb3.1142
• Languages: English
• ISSNs: 2162-3279

Abstract

Abstract: Introduction: Alzheimer's disease (AD) is the most common form of dementia. Finding biomarkers to prognosticate transition from mild cognitive impairment (MCI) to AD is important to clinical medicine. Promising imaging biomarkers of AD conversion identified so far include atrophy of the cerebral cortex and subcortical gray matter nuclei. Methods: This study introduces thickness and bending angle of the corpus callosum as a putative white matter marker of MCI to AD conversion. The corpus callosum is computationally less demanding to segment automatically compared to more complicated structures and a subject can be processed in a few minutes. We aimed to demonstrate that callosal shape and thickness measures provide a simple, effective, and accurate prognostication tool in ADNI dataset. Using longitudinal datasets, we classified MCI subjects based on conversion to AD assessed via cognitive testing. We evaluated the classification accuracy of callosal shape features in comparison with the existing "gold standard" cortical thickness and subcortical gray matter volume measures. Results: The callosal thickness measures were less accurate in classifying conversion status by cognitive scores compared to gray matter measures for AD. Conclusions: While this paper presented a negative result, this method may be more suitable for a disease of the white matter. Abstract : Alzheimer's disease is the most common form of dementia, and much research is devoted to finding neuroimaging markers for prognostication. This paper investigates the utility of the corpus callosum as a biomarker for conversion from mild cognitive impairment to Alzheimer's disease. The corpus callosum was not found to be as good as existing biomarkers; however, the method executes quickly and may be applied to diseases of white matter.