Identification of Alzheimer’s Disease on the Basis of a Voxel-Wise Approach

Beheshti, Iman; Hossein-Abad, Hadi Mahdipour

doi:10.3390/app9153063

Cited by 5 publications

(3 citation statements)

References 54 publications

(58 reference statements)

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“…However, these types of models have been found to be unsuitable for the evaluation of narrow age ranges, such as the younger or elderly groups. Several studies have reported the bias estimation of different age range groups, underestimation of younger ages, and overestimation of older age due to the systematic bias of inherent regression methods [11,42]. In fact, our model also demonstrated an inherent bias in brain age prediction in elderly brains (Fig.…”

Section: Discussionmentioning

confidence: 57%

“…Brain age represents not only the current brain aging status but also the acceleration of brain aging [7,8] by using the difference between chronological age and predicted age [9]. These differences have been associated with a number of neurodevelopmental [10] and neurodegenerative diseases, such as Alzheimer's disease (AD) and Parkinson's disease [11].…”

Section: Introductionmentioning

confidence: 99%

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Brain age prediction improves the early detection of Alzheimer’s disease in East Asian elderly

Choi

Park

Lee

et al. 2023

Preprint

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IntroductionBrain age prediction is used to quantify the pathological and cognitive changes associated with brain aging. However, the predicted age derived from certain models can result in biased estimation and the concealment of inherent aged brain function.MethodsWe constructed a brain age prediction model for the East Asian elderly brain using the brain volume and cortical thickness features from cognitively normal (CN) brains. Furthermore, our model was used to estimate different diagnoses and to construct a classification model of mild cognitive impairment (MCI) conversion and Alzheimer’s disease (AD) conversion.ResultsOur model showed a strong association of the brain age difference (BAD) with three diagnosis groups. In addition, the classification models of MCI conversion and AD conversion showed acceptable and robust performances, respectively (area under the curve [AUC] = 0.66, AUC = 0.76).DiscussionWe believe that our model can be used to estimate the predicted status of an East Asian elderly brain. Moreover, the MCI conversion model has the potential to prevent severe cognitive impairment and can be used for the early detection of AD.

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Section: Discussionmentioning

confidence: 57%

Section: Introductionmentioning

confidence: 99%

Brain age prediction improves the early detection of Alzheimer’s disease in East Asian elderly

Choi

Park

Lee

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…Early diagnosis of AD produced promising outcomes. Furthermore, to find out the stable MCI (sMCI) and progressive MCI (pMCI) is a challenging task for researchers (Beheshti et al, 2019). The ADNI healthcare database consists of data on every individual's enlistment, demography, physical exams, and critical concentration.…”

Section: Introductionmentioning

confidence: 99%

Health care intelligent system: A neural network based method for early diagnosis of Alzheimer's disease using MRI images

2022

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Alzheimer's disease (AD) is a neurodegenerative disease that causes memory loss and is considered the most common type of dementia. In many countries, AD is commonly affecting senior citizens having an aged more than 65 years. Machine learningbased approaches have some limitations due to data pre-processing issues. We propose a health care intelligent system based on a deep convolutional neural network (DCNN) in this research work. It classifies normal control (NC), mild cognitive impairment (MCI), and AD. The proposed model is employed on white matter (WM), and grey matter (GM) tissues with more cognitive decline features. In the experimental process, we used 375 Magnetic Resonance Image (MRI) subjects collected from Alzheimer's disease neuroimaging initiative (ADNI), including 130 NC people, 120 MCI patients, and 125 AD patients. We extract three major regions during preprocessing, that is, WM, GM and cerebrospinal fluid (CSF). This study shows promising classification results for NC versus AD 97.94%, MCI versus AD 92.84%, and NC versus MCI 88.15% on GM images. Furthermore, our proposed model attained 95.97%, 90.82%, and 86.87% on the same three binary classes on WM tissue, respectively. When comparing existing studies in terms of accuracy and other evaluation parameters, we found that our proposed approach shows better results than those approaches based on the CNN method.

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Predicting mild cognitive impairments from cognitively normal brains using a novel brain age estimation model based on structural magnetic resonance imaging

Choi,

Park,

Lee

et al. 2023

Cerebral Cortex

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Brain age prediction is a practical method used to quantify brain aging and detect neurodegenerative diseases such as Alzheimer’s disease (AD). However, very few studies have considered brain age prediction as a biomarker for the conversion of cognitively normal (CN) to mild cognitive impairment (MCI). In this study, we developed a novel brain age prediction model using brain volume and cortical thickness features. We calculated an acceleration of brain age (ABA) derived from the suggested model to estimate different diagnostic groups (CN, MCI, and AD) and to classify CN to MCI and MCI to AD conversion groups. We observed a strong association between ABA and the 3 diagnostic groups. Additionally, the classification models for CN to MCI conversion and MCI to AD conversion exhibited acceptable and robust performances, with area under the curve values of 0.66 and 0.76, respectively. We believe that our proposed model provides a reliable estimate of brain age for elderly individuals and can identify those at risk of progressing from CN to MCI. This model has great potential to reveal a diagnosis associated with a change in cognitive decline.

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Identification of Alzheimer’s Disease on the Basis of a Voxel-Wise Approach

Cited by 5 publications

References 54 publications

Brain age prediction improves the early detection of Alzheimer’s disease in East Asian elderly

Brain age prediction improves the early detection of Alzheimer’s disease in East Asian elderly

Health care intelligent system: A neural network based method for early diagnosis of Alzheimer's disease using MRI images

Predicting mild cognitive impairments from cognitively normal brains using a novel brain age estimation model based on structural magnetic resonance imaging

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