Prediction of Incipient Alzheimer’s Disease Dementia in Patients with Mild Cognitive Impairment

Ardekani, Babak A.; Bermudez, Elaine; Mubeen, Asim M.; Bachman, Alvin H.

doi:10.3233/jad-160594

Cited by 62 publications

(51 citation statements)

References 61 publications

(91 reference statements)

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“…In particular, we reported those characteristics that are related to the highest performance reached by RF in each study. Regarding the cohort diagnosis, two works (Tripoliti et al, 2007 ; Lebedev et al, 2014 ) investigated Alzheimer's patients (AD) and healthy controls (HC), four works (Cabral et al, 2013 ; Sivapriya et al, 2015 ; Maggipinto et al, 2017 ; Son et al, 2017 ) had AD, HC, and MCI, two studies (Gray et al, 2013 ; Moradi et al, 2015 ) considered AD, HC, stable MCI (sMCI), and progressive MCI (pMCI, converted to AD), two had sMCI and pMCI (Wang et al, 2016 ; Ardekani et al, 2017 ), one had HC and MCI (Lebedeva et al, 2017 ) and one (Oppedal et al, 2015 ) had AD, HC, and Lewy-body dementia (LBD) patients.…”

Section: Resultsmentioning

confidence: 99%

“…All studies, except two (Cabral et al, 2013 ; Maggipinto et al, 2017 ), which used FDG-PET and DTI acquisition respectively, investigated structural MRI data alone (Lebedev et al, 2014 ; Moradi et al, 2015 ; Ardekani et al, 2017 ; Lebedeva et al, 2017 ) or in combination with features extracted from other modalities, that is FDG-PET (Gray et al, 2013 ; Sivapriya et al, 2015 ), florbetapir-PET (Wang et al, 2016 ), FLAIR (Oppedal et al, 2015 ) and fMRI (Tripoliti et al, 2007 ; Son et al, 2017 ).…”

Section: Resultsmentioning

confidence: 99%

“…Eight works (Tripoliti et al, 2007 ; Cabral et al, 2013 ; Lebedev et al, 2014 ; Moradi et al, 2015 ; Sivapriya et al, 2015 ; Ardekani et al, 2017 ; Lebedeva et al, 2017 ; Maggipinto et al, 2017 ) applied feature selection/elimination for reducing the dimension of the variables space. The number of trees used in the RF was not specified in two cases (Moradi et al, 2015 ; Son et al, 2017 ).…”

Section: Resultsmentioning

confidence: 99%

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Random Forest Algorithm for the Classification of Neuroimaging Data in Alzheimer's Disease: A Systematic Review

Sarica

Cerasa

Quattrone

2017

Front. Aging Neurosci.

474

256

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Objective: Machine learning classification has been the most important computational development in the last years to satisfy the primary need of clinicians for automatic early diagnosis and prognosis. Nowadays, Random Forest (RF) algorithm has been successfully applied for reducing high dimensional and multi-source data in many scientific realms. Our aim was to explore the state of the art of the application of RF on single and multi-modal neuroimaging data for the prediction of Alzheimer's disease.Methods: A systematic review following PRISMA guidelines was conducted on this field of study. In particular, we constructed an advanced query using boolean operators as follows: (“random forest” OR “random forests”) AND neuroimaging AND (“alzheimer's disease” OR alzheimer's OR alzheimer) AND (prediction OR classification). The query was then searched in four well-known scientific databases: Pubmed, Scopus, Google Scholar and Web of Science.Results: Twelve articles—published between the 2007 and 2017—have been included in this systematic review after a quantitative and qualitative selection. The lesson learnt from these works suggest that when RF was applied on multi-modal data for prediction of Alzheimer's disease (AD) conversion from the Mild Cognitive Impairment (MCI), it produces one of the best accuracies to date. Moreover, the RF has important advantages in terms of robustness to overfitting, ability to handle highly non-linear data, stability in the presence of outliers and opportunity for efficient parallel processing mainly when applied on multi-modality neuroimaging data, such as, MRI morphometric, diffusion tensor imaging, and PET images.Conclusions: We discussed the strengths of RF, considering also possible limitations and by encouraging further studies on the comparisons of this algorithm with other commonly used classification approaches, particularly in the early prediction of the progression from MCI to AD.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Random Forest Algorithm for the Classification of Neuroimaging Data in Alzheimer's Disease: A Systematic Review

Sarica

Cerasa

Quattrone

2017

Front. Aging Neurosci.

474

256

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“…Previously, we used the magnitude of HPF and its rate of change with respect to time as features in machine learning algorithms used for differentiating healthy subjects from those with AD, 22 as well as for differentiating stable patients with MCI from those with an incipient AD diagnosis. 33 Future studies will need to be conducted to assess the influence of including HPF asymmetry and its rate of change as additional classification features on the performance of machine learning algorithms.…”

Section: Discussionmentioning

confidence: 99%

Sexual Dimorphism and Hemispheric Asymmetry of Hippocampal Volumetric Integrity in Normal Aging and Alzheimer Disease

Ardekani

Hadid

Blessing

et al. 2019

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE:Asymmetric atrophy of the hippocampus is an important clinical finding in normal aging and Alzheimer disease. In this study, we investigate the associations between the magnitude and asymmetry of hippocampal volumetric integrity and age, sex, and dementia severity. MATERIALS AND METHODS:We have recently developed a rapid fully automatic algorithm to measure the hippocampal parenchymal fraction, an index of hippocampal volumetric integrity on structural MR imaging of the brain. We applied this algorithm to measure the hippocampal parenchymal fraction bilaterally on 775 MR imaging volumes scanned from 198 volunteers in a publicly available data base. All subjects were right-handed and older than 60 years of age. Subjects were categorized as cognitively healthy (n ϭ 98), with mild cognitive impairment (n ϭ 70), or with mild/moderate Alzheimer disease (n ϭ 30). We used linear mixed-effects models to analyze the hippocampal parenchymal fraction and its asymmetry with respect to age, sex, dementia severity, and intracranial volume. RESULTS:After controlling for age, sex, and intracranial volume, we found that the magnitude of the hippocampal parenchymal fraction decreased and its asymmetry increased significantly with dementia severity. Also, hippocampal parenchymal fraction asymmetry was significantly higher in men after controlling for all other variables, but there was no sex effect on hippocampal parenchymal fraction magnitude. The magnitude of the hippocampal parenchymal fraction decreased and its asymmetry increased significantly with age in subjects who were cognitively healthy, but associations with age were different in nature in the mild cognitive impairment and Alzheimer disease groups. CONCLUSIONS:Hippocampal atrophy progresses asymmetrically with age in cognitively healthy subjects. Hippocampal parenchymal fraction asymmetry is significantly higher in men than women and in mild cognitive impairment/Alzheimer disease relative to cognitively healthy individuals. ABBREVIATIONS: AD ϭ Alzheimer disease; AI ϭ asymmetry index; ART ϭ Automatic Registration Toolbox; CN ϭ cognitively normal; DX ϭ diagnostic group factor; ICV ϭ intracranial volume; HPF ϭ hippocampal parenchymal fraction; LHPF ϭ left HPF; MCI ϭ mild cognitive impairment; RHPF ϭ right HPF A lzheimer disease (AD) is the most common form of demen-

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“…[1] Regional based biological change in different regions of functionally connected anatomical regions may differ the AD, MCI, and CN. [2] An automatic algorithm Hippocampal volume Integrity is developed to estimate the hippocampal volume to differentiate AD, MIC and CN. [3] Change in White matter is estimated to analyse the effected area of brain due to Alzheimer's.…”

Section: Introductionmentioning

confidence: 99%

Alzheimer’s Disease Classification using Leung-Malik Filtered Bank Features and Weak Classifier

Basheera¹,

Ram²

2019

IJRTE

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We propose a frame work to classify Brain MRI images in to Alzheimer’s Disease (AD), Cognitive Normal (CN) and Mild Cognitive impairments (MCI). We use 114No’s of T2 weighted MRI Volumes. We extracted relative texture features from Leung-Malik Filter bank, k means is used to generate Bag of Dictionary (BoD) from LM Filtered images. We performed binary classification and Multi class Classification using different Classifiers, Adaboost Classifier gives better performance both in binary and multi class classifications in comparison with other classifiers. Performance of proposed system is enhanced than compared to the existing techniques. It has Sensitivity for AD-CN 89.8, AD-MCI 78.82, AD-CN-MCI 77.77, Specificity for AD-CN79.22, AD-MCI 80.00, AD-CN-MCI 58.88, and positive prediction value for AD-CN 79.48, AD-MCI 83.75, AD-CN-MCI 68.47 and Accuracy AD-CN 84.24, AD-MCI 79.33, AD-CN-MCI 72.88.

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Prediction of Incipient Alzheimer’s Disease Dementia in Patients with Mild Cognitive Impairment

Abstract: The method presented in this paper can be used to separate stable MCI patients from those who are at early stages of AD dementia with high accuracy. There may be stronger indicators of imminent AD dementia in women with MCI as compared to men.

Cited by 62 publications

References 61 publications

Random Forest Algorithm for the Classification of Neuroimaging Data in Alzheimer's Disease: A Systematic Review

Random Forest Algorithm for the Classification of Neuroimaging Data in Alzheimer's Disease: A Systematic Review

Sexual Dimorphism and Hemispheric Asymmetry of Hippocampal Volumetric Integrity in Normal Aging and Alzheimer Disease

Alzheimer’s Disease Classification using Leung-Malik Filtered Bank Features and Weak Classifier

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