Multiparametric MRI to distinguish early onset Alzheimer's disease and behavioural variant of frontotemporal dementia

Abstract

This prospective study explored whether an approach combining structural [cortical thickness and white matter (WM) microstructure] and resting state functional MRI can aid differentiation between 62 early onset Alzheimer's disease (EOAD) and 27 behavioural variant of frontotemporal dementia (bvFTD) patients. Random forest and receiver operator characteristic curve analyses assessed the ability of MRI in classifying the two clinical syndromes. All patients showed a distributed pattern of brain alterations relative to controls. Compared to bvFTD, EOAD patients showed bilateral inferior parietal cortical thinning and decreased default mode network functional connectivity. Compared to EOAD, bvFTD patients showed bilateral orbitofrontal and temporal cortical thinning, and WM damage of the corpus callosum, bilateral uncinate fasciculus, and left superior longitudinal fasciculus. Random forest analysis revealed that left inferior parietal cortical thickness (accuracy 0.78, specificity 0.76, sensitivity 0.83) and WM integrity of the right uncinate fasciculus (accuracy 0.81, specificity 0.96, sensitivity 0.43) were the best predictors of clinical diagnosis. The combination of cortical thickness and DT MRI measures was able to distinguish patients with EOAD and bvFTD with accuracy 0.82, specificity 0.76, and sensitivity 0.96. The diagnostic ability of MRI models was confirmed in a subsample of patients with biomarker-based clinical diagnosis. Multiparametric MRI is useful to identify brain alterations which are specific to EOAD and bvFTD. A severe cortical involvement is suggestive of EOAD, while a prominent WM damage is indicative of bvFTD.

Single Subject Classification of Alzheimer’s Disease and Behavioral Variant Frontotemporal Dementia Using Anatomical, Diffusion Tensor, and Resting-State Functional Magnetic Resonance Imaging

“…Furthermore, in line with previous work, we observed that concatenating all measures did not result in the most accurate classification model and as more measures were added the level of improvement became less apparent [29,31,61]. The marginal improvements observed for some measures or combinations may indicate a negligible contribution to the model.…”

“…Classification methods that combined these measures invariably showed improvement of classification accuracy over models that used these measures separately [15,18,31].…”

“…GM-and DTI-derived measures have been heralded as important corroborating measures for differentiating AD and bvFTD patients [15,18,19,31]. Classification methods that combined these measures invariably showed improvement of classification accuracy over models that used these measures separately [15,18,31].…”

“…Multiparametric MRI-based statistical algorithms integrate various MRI measures to compute a single, quantitative probabilistic index and have shown to be more accurate in differentiating cognitively healthy from demented patients than single MRI measure-based algorithms alone [18,19,29,31,32]. We speculate that MRI-based classification algorithms that differentiate dementia-types benefit from integrating structural and functional connectivity measures.…”

Early Onset Alzheimer's Disease

“…Emerging imaging techniques, like Multiparametric MRI, are attempting to outline structural differences and resting state differences between the two diagnoses. 6 Some risk factors for dementia, including lower cardiovascular fitness in early life, lower cognitive reserve in early adulthood, and traumatic brain injury (TBI), seem to have a greater impact on these patients. Counterintuitively, these patients have fewer medical comorbidities such as diabetes and other conditions which impact vasculature.…”