Automated Brain MRI Volumetry Differentiates Early Stages of Alzheimer’s Disease From Normal Aging

Zhao, Weina; Luo, Yishan; Zhao, Lei; Mok, Vincent Chung Tong; Su, Li; Yin, Changhao; Sun, Yu; Lu, Jie; Shi, Lin; Han, Ying

doi:10.1177/0891988719862637

Cited by 27 publications

(19 citation statements)

References 40 publications

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“… Rooden et al, (2018) [ 74 ] Memory clinic consultation T1 MRI Cross-sectional NC: n = 42 (68 ± 9.2) SCD: n = 25 (68 ± 9.1) SCD showed hippocampal atrophy. Zhao et al, (2019) [ 89 , 126 ] SCD-I Working Group T1 MRI Cross-sectional NC: n = 42 (64.24 ± 6.16) SCD: n = 35 (64.53 ± 7.29) aMCI: n = 43 (67.47 ± 10.03) AD: n = 41 (68.88 ± 7.86) No difference in hippocampal volume between NC and SCD. Ryu et al (2017) [ 78 ] Memory clinic consultation T1 MRI and DTI Cross-sectional NC: n = 27 (70.6 ± 6.1) SCD: n = 18 (69.9 ± 6.3) SCD showed lower entorhinal cortical volumes and lower FA and higher MD in the hippocampal body and entorhinal WM compared with NC.…”

Section: Structural Mri and Diffusion Mrimentioning

confidence: 99%

“…Over the last decade, classification methods based on imaging have been increasingly integrated to identify the imaging signature of AD [ 201 – 203 ], offering promising tools for individualized diagnoses and prognostic predictions. However, until recently, neuroimaging-based studies for classifying SCD have been scarce [ 111 , 126 , 135 , 204 – 206 ]. The early identification of SCD and the prediction of disease progression at the individual level is important for timely interventions.…”

Section: Shortcomings and Emerging Trendsmentioning

confidence: 99%

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Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer’s disease

Wang

Huang

et al. 2020

Mol Neurodegeneration

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146

135

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Subjective cognitive decline (SCD) is regarded as the first clinical manifestation in the Alzheimer’s disease (AD) continuum. Investigating populations with SCD is important for understanding the early pathological mechanisms of AD and identifying SCD-related biomarkers, which are critical for the early detection of AD. With the advent of advanced neuroimaging techniques, such as positron emission tomography (PET) and magnetic resonance imaging (MRI), accumulating evidence has revealed structural and functional brain alterations related to the symptoms of SCD. In this review, we summarize the main imaging features and key findings regarding SCD related to AD, from local and regional data to connectivity-based imaging measures, with the aim of delineating a multimodal imaging signature of SCD due to AD. Additionally, the interaction of SCD with other risk factors for dementia due to AD, such as age and the Apolipoprotein E (ApoE) ɛ4 status, has also been described. Finally, the possible explanations for the inconsistent and heterogeneous neuroimaging findings observed in individuals with SCD are discussed, along with future directions. Overall, the literature reveals a preferential vulnerability of AD signature regions in SCD in the context of AD, supporting the notion that individuals with SCD share a similar pattern of brain alterations with patients with mild cognitive impairment (MCI) and dementia due to AD. We conclude that these neuroimaging techniques, particularly multimodal neuroimaging techniques, have great potential for identifying the underlying pathological alterations associated with SCD. More longitudinal studies with larger sample sizes combined with more advanced imaging modeling approaches such as artificial intelligence are still warranted to establish their clinical utility.

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Section: Structural Mri and Diffusion Mrimentioning

confidence: 99%

Section: Shortcomings and Emerging Trendsmentioning

confidence: 99%

Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer’s disease

Wang

Huang

et al. 2020

Mol Neurodegeneration

Self Cite

146

135

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“…This rate can be helpful to have a more informed understanding whether an individual with MCI will later progress to AD or not 105,143,144 . The output of automated segmentation methods can also be used in training of intelligent classification methods such as those using artificial neural networks and support vector machines, which has shown promising results [145][146][147][148][149][150][151][152][153][154] .…”

Section: Availability Of the Reliable Automated Segmentation Methods mentioning

confidence: 99%

A Large-scale Comparison of Cortical and Subcortical Structural Segmentation Methods in Alzheimer’s Disease: a Statistical Approach

Zamani

Sadr

Javadi

2020

Preprint

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Alzheimer's disease (AD) is a neurodegenerative disease that leads to anatomical atrophy, as evidenced by magnetic resonance imaging (MRI). Automated segmentation methods are developed to help with the segmentation of different brain areas. However, their reliability has yet to be fully investigated. To have a more comprehensive understanding of the distribution of changes in AD, as well as investigating the reliability of different segmentation methods, in this study we compared volumes of cortical and subcortical brain segments, using automated segmentation methods in more than 60 areas between AD and healthy controls (HC). A total of 44 MRI images (22 AD and 22 HC, 50% females) were taken from the minimal interval resonance imaging in Alzheimer's disease (MIRIAD) dataset. HIPS, volBrain, CAT and BrainSuite segmentation methods were used for the subfields of hippocampus, and the rest of the brain. While HIPS, volBrain and CAT showed strong conformity with the past literature, BrainSuite misclassified several brain areas. Additionally, the volume of the brain areas that successfully discriminated between AD and HC showed a correlation with mini mental state examination (MMSE) scores. The two methods of volBrain and CAT showed a very strong correlation. These two methods, however, did not correlate with BrainSuite. Our results showed that automated segmentation methods HIPS, volBrain and CAT can be used in the classification of AD and HC. This is an indication that such methods can be used to inform researchers and clinicians of underlying mechanisms and progression of AD.

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“…Accu-Brain® performs brain structure segmentation based on a multi-atlas image registration scheme. The accuracy of hippocampus segmentation of AccuBrain® was validated using data from the ADNI database [36], and more technical description of AccuBrain® and its comparison with other automatic brain image segmentation schemes were described in our previous work [37][38][39]. Since the main objective of this study is to identify FTD from NC and AD, we focus on the brain regions known to be associated with cognition and behavior, including the brain parenchyma, typical subcortical structures (bilateral hippocampus, amygdala and caudate, etc.…”

Section: Fig 1 Subject Screening From the Nacc Databasementioning

confidence: 99%

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

Mai

Ruan

et al. 2021

Alz Res Therapy

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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.

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Automated Brain MRI Volumetry Differentiates Early Stages of Alzheimer’s Disease From Normal Aging

Cited by 27 publications

References 40 publications

Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer’s disease

Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer’s disease

A Large-scale Comparison of Cortical and Subcortical Structural Segmentation Methods in Alzheimer’s Disease: a Statistical Approach

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

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