scholarly journals Using machine learning to quantify structural MRI neurodegeneration patterns of Alzheimer's disease into dementia score: Independent validation on 8,834 images from ADNI, AIBL, OASIS, and MIRIAD databases

2020 ◽  
Vol 41 (14) ◽  
pp. 4127-4147
Author(s):  
Karteek Popuri ◽  
Da Ma ◽  
Lei Wang ◽  
Mirza Faisal Beg
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Mri ◽  
Independent Validation

Neuroimage Classification for Early Diagnosis of Alzheimer’s Disease

2012 ◽  
pp. 336-350
Author(s):  
Yong Fan ◽  
Christos Davatzikos
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Psychiatric Disorders ◽  
General Framework ◽  
Disease Onset ◽  
Structural Mri ◽  
Early Prediction ◽  
Psychometric Assessment ◽  
Combining Information

Diagnostic criteria for neurological and psychiatric disorders are typically based on clinical and psychometric assessment, which might not be effective for early detection of the disease onset. For brain disorders such as Alzheimer’s Disease (AD), neuroimaging can potentially play an important role in the development of imaging-based biomarkers. Following voxel-wise univariate neuroimage analysis methods, machine learning and pattern recognition based neuroimage analysis techniques have been increasingly adopted in neuroimaging studies of neurological and psychiatric disorders, aiming to provide tools that classify individuals, based on their neuroimaging scans, rather than detect statistical group difference. The machine learning based methods, optimally combining information of multiple measures derived from images, have demonstrated promising performance in diagnosis of AD and early prediction of conversion of Mild Cognitive Impairment (MCI) individuals. This chapter introduces the general framework of such techniques with a focus on structural MRI analyses and their applications to studies of AD.

scholarly journals A machine learning-based holistic approach for diagnoses within the Alzheimer's disease spectrum

2020 ◽  
Author(s):  
Noemi Massetti ◽  
Alberto Granzotto ◽  
Manuela Bomba ◽  
Stefano Delli Pizzi ◽  
Alessandra Mosca ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Healthy Aging ◽  
Holistic Approach ◽  
Structural Mri ◽  
Relative Contribution ◽  
Disease Spectrum ◽  
Organ Specific ◽  
The Central Nervous System

Alzheimer's disease (AD) is a neurodegenerative condition driven by a multifactorial etiology. We employed a machine learning (ML) based algorithm and the wealth of information offered by the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to investigate the relative contribution of clinically relevant factors for identifying subjects affected by Mild Cognitive Impairment (MCI), a transitional status between healthy aging and dementia. Our ML-based Random Forest (RF) algorithm did not help predict clinical outcomes and the AD conversion of MCI subjects. On the other hand, non-converting (ncMCI) subjects were correctly classified and predicted. Two neuropsychological tests, the FAQ and ADAS13, were the most relevant features used for the classification and prediction of younger, under 70, ncMCI subjects. Structural MRI data combined with systemic parameters and the cardiovascular status were instead the most critical factors for the classification of over 70 ncMCI subjects. Our results support the notion that AD is not an organ-specific condition and results from pathological processes inside and outside the Central Nervous System.

scholarly journals Diagnosis and Prognosis of Alzheimer’s Disease Using Brain Morphometry and White Matter Connectomes

2018 ◽  
Author(s):  
Yun Wang ◽  
Chenxiao Xu ◽  
Ji-Hwan Park ◽  
Seonjoo Lee ◽  
Yaakov Stern ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Diffusion Mri ◽  
Structural Mri ◽  
List Type ◽  
Csf Biomarker ◽  
Multimodal Mri ◽  
And Diffusion

ABSTRACTAccurate, reliable prediction of risk for Alzheimer’s disease (AD) is essential for early, disease-modifying therapeutics. Multimodal MRI, such as structural and diffusion MRI, is likely to contain complementary information of neurodegenerative processes in AD. Here we tested the utility of the multimodal MRI (T1-weighted structure and diffusion MRI), combined with high-throughput brain phenotyping—morphometry and structural connectomics—and machine learning, as a diagnostic tool for AD. We used, firstly, a clinical cohort at a dementia clinic (National Health Insurance Service-Ilsan Hospital [NHIS-IH]; N=211; 110 AD, 64 mild cognitive impairment [MCI], and 37 cognitively normal with subjective memory complaints [SMC]) to test the diagnostic models; and, secondly, Alzheimer’s Disease Neuroimaging Initiative (ADNI)-2 to test the generalizability. Our machine learning models trained on the morphometric and connectome estimates (number of features=34,646) showed optimal classification accuracy (AD/SMC: 97% accuracy, MCI/SMC: 83% accuracy; AD/MCI: 97% accuracy) in NHIS-IH cohort, outperforming a benchmark model (FLAIR-based white matter hyperintensity volumes). In ADNI-2 data, the combined connectome and morphometry model showed similar or superior accuracies (AD/HC: 96%; MCI/HC: 70%; AD/MCI: 75% accuracy) compared with the CSF biomarker model (t-tau, p-tau, and Amyloid β, and ratios). In predicting MCI to AD progression in a smaller cohort of ADNI-2 (n=60), the morphometry model showed similar performance with 69% accuracy compared with CSF biomarker model with 70% accuracy. Our comparison of classifiers trained on structural MRI, diffusion MRI, FLAIR, and CSF biomarkers show the promising utility of the white matter structural connectomes in classifying AD and MCI in addition to the widely used structural MRI-based morphometry, when combined with machine learning.HighlightsWe showed the utility of multimodal MRI, combining morphometry and white matter connectomes, to classify the diagnosis of AD and MCI using machine learning.In predicting the progression from MCI to AD, the morphometry model showed the best performance.Two independent clinical datasets were used in this study: one for model building, the other for generalizability testing.

Alzheimer's Disease Brain Areas: The Machine Learning Support for Blind Localization

2016 ◽  
Vol 13 (5) ◽  
pp. 498-508 ◽  
Author(s):  
V. Vigneron ◽  
A. Kodewitz ◽  
A. M. Tome ◽  
S. Lelandais ◽  
E. Lang
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Learning Support ◽  
Brain Areas

scholarly journals Machine Learning for the Classification of Alzheimer’s Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1071
Author(s):  
Lucia Billeci ◽  
Asia Badolato ◽  
Lorenzo Bachi ◽  
Alessandro Tonacci
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Classification Problem ◽  
Computer Algorithms ◽  
Imaging Data

Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.

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