An MRI-based strategy for differentiation of frontotemporal dementia and Alzheimer’s disease

Abstract

Background The differential diagnosis of frontotemporal dementia (FTD) and Alzheimer’s disease (AD) is difficult due to the overlaps of clinical symptoms. Structural magnetic resonance imaging (sMRI) presents distinct brain atrophy and potentially helps in their differentiation. In this study, we aim at deriving a novel integrated index by leveraging the volumetric measures in brain regions with significant difference between AD and FTD and developing an MRI-based strategy for the differentiation of FTD and AD. Methods In this study, the data were acquired from three different databases, including 47 subjects with FTD, 47 subjects with AD, and 47 normal controls in the NACC database; 50 subjects with AD in the ADNI database; and 50 subjects with FTD in the FTLDNI database. The MR images of all subjects were automatically segmented, and the brain atrophy, including the AD resemblance atrophy index (AD-RAI), was quantified using AccuBrain®. A novel MRI index, named the frontotemporal dementia index (FTDI), was derived as the ratio between the weighted sum of the volumetric indexes in “FTD dominant” structures over that obtained from “AD dominant” structures. The weights and the identification of “FTD/AD dominant” structures were acquired from the statistical analysis of NACC data. The differentiation performance of FTDI was validated using independent data from ADNI and FTLDNI databases. Results AD-RAI is a proven imaging biomarker to identify AD and FTD from NC with significantly higher values (p < 0.001 and AUC = 0.88) as we reported before, while no significant difference was found between AD and FTD (p = 0.647). FTDI showed excellent accuracy in identifying FTD from AD (AUC = 0.90; SEN = 89%, SPE = 75% with threshold value = 1.08). The validation using independent data from ADNI and FTLDNI datasets also confirmed the efficacy of FTDI (AUC = 0.93; SEN = 96%, SPE = 70% with threshold value = 1.08). Conclusions Brain atrophy in AD, FTD, and normal elderly shows distinct patterns. In addition to AD-RAI that is designed to detect abnormal brain atrophy in dementia, a novel index specific to FTD is proposed and validated. By combining AD-RAI and FTDI, an MRI-based decision strategy was further proposed as a promising solution for the differential diagnosis of AD and FTD in clinical practice.

Brain Volumetric Alterations in Preclinical HIV-Associated Neurocognitive Disorder Using Automatic Brain Quantification and Segmentation Tool

“…More technical characteristics and their comparison with other automatic brain image segmentation methods were described in detail in previous work ( Gao et al, 2020 ; Wang et al, 2020 ). This tool has been validated to be more accurate, objective, and easier to implement and has been widely used in various neurological and mental diseases ( Wang et al, 2019 ; Zhao et al, 2019 ; Dou et al, 2020 ; Mai et al, 2021 ; Yu et al, 2021 ).…”

“…To correct for the skull size difference, the relative volume was extracted as our volumetric brain measures for analysis. The relative volume of each brain region was defined as the absolute volume divided by the individual’s total intracranial volume ( Yu et al, 2021 ). The brain structures and lobe segmentations obtained from example data in each group for a visual inspection are shown in Figure 2 .…”

“…The ideal imaging-aided diagnosis method should be objective, easy to understand and operate, and economical and efficient. AccuBrain ® has been clinically applicable with Conformitè Européenne (CE) and Food and Drug Administration (FDA) approval and is considered to be a research-validated fully automatic neuroanatomical volumetric segmentation tool that can efficiently quantify various brain structure volumes in about 20 min, including brain parenchyma, ventricular regions, and atrophy of lobar regions ( Mai et al, 2021 ; Yu et al, 2021 ). AccuBrain ® has the ability to avoid variability induced by the difference in software parameter settings and could provide an incomparable user experience with respect to ease of use, which also enables a smooth translation of the research findings to clinical practice ( Liu et al, 2020 ).…”

Advances in computerized MRI‐based biomarkers in Alzheimer’s disease

“…Canu et al [ 70 ] conducted a multimodal MRI study by combining the cortical thickness and diffusion tensor measures and was able to distinguish patients with early‐onset Alzheimer’s disease (EOAD) and those with FTD with 82% accuracy from random forest analysis. Recently, by incorporating volumetric indexes in FTD‐dominant regions, Yu et al [ 71 ] developed the frontotemporal dementia index and achieved an AUC of 0.93, as validated in independent data from ADNI and the Frontotemporal Lobar Degeneration Neuroimaging Initiative database.…”

White matter microstructure alterations in frontotemporal dementia: Phenotype‐associated signatures and single‐subject interpretation

“…A common application of these approaches is the categorization of patients into FTD versus AD diagnostic groups. (Bouts et al., 2018 ; Bron et al., 2017 ; Canu et al., 2017 ; Frings et al., 2014 ; Hu et al., 2020 ; Kim et al., 2019 ; Ma et al., 2020 ; Möller et al., 2016 ; Wang et al., 2016 ; Yu et al., 2021 ). A key barrier to the development of successful machine learning algorithms in neurodegenerative conditions is the scarcity of uniformly acquired training data, especially in low‐incidence phenotypes such as ALS‐FTD, PLS‐FTD, and post‐polio syndrome (Aho‐Ozhan et al., 2016 ; Finegan et al., 2019 , 2021 ; Li Hi Shing, Lope, Chipika, et al., 2021 ; Li Hi Shing, Lope, McKenna, et al., 2021 ; Lule et al., 2010 ; McKenna, Corcia, et al., 2021 ; Pioro et al., 2020 ; Trojsi et al., 2015 , 2019 ; Trojsi, Di Nardo, Siciliano, et al., 2020 ).…”