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Quantitative MRI Brain Studies in Mild Cognitive Impairment and Alzheimer's Disease: A Methodological Review

Published on Jan 1, 2018in IEEE Reviews in Biomedical Engineering
· DOI :10.1109/RBME.2018.2796598
Stephanos Leandrou1
Estimated H-index: 1
(European University Cyprus),
Styliani Petroudi9
Estimated H-index: 9
+ 2 AuthorsConstantinos S. Pattichis34
Estimated H-index: 34
(University of Cyprus)
Classifying and predicting Alzheimer's disease (AD) in individuals with memory disorders through clinical and psychometric assessment is challenging, especially in mild cognitive impairment (MCI) subjects. Quantitative structural magnetic resonance imaging acquisition methods in combination with computer-aided diagnosis are currently being used for the assessment of AD. These acquisitions methods include voxel-based morphometry, volumetric measurements in specific regions of interest (ROIs), cortical thickness measurements, shape analysis, and texture analysis. This review evaluates the aforementioned methods in the classification of cases into one of the following three groups: normal controls, MCI, and AD subjects. Furthermore, the performance of the methods is assessed on the prediction of conversion from MCI to AD. In parallel, it is also assessed which ROIs are preferred in both classification and prognosis through the different states of the disease. Structural changes in the early stages of the disease are more pronounced in the medial temporal lobe, especially in the entorhinal cortex, whereas with disease progression, both entorhinal cortex and hippocampus offer similar discriminative power. However, for the conversion from MCI subjects to AD, entorhinal cortex provides better predictive accuracies rather than other structures, such as the hippocampus.
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Published on Nov 18, 2016
Ian J. Goodfellow38
Estimated H-index: 38
Yoshua Bengio111
Estimated H-index: 111
(Université de Montréal),
Aaron C. Courville47
Estimated H-index: 47
(Université de Montréal)
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book ...
6,464 Citations
Published on Mar 1, 2016in Human Brain Mapping 4.93
Lauge Sørensen10
Estimated H-index: 10
(University of Copenhagen),
Christian Igel25
Estimated H-index: 25
(University of Copenhagen)
+ 4 AuthorsMads Nielsen28
Estimated H-index: 28
(University of Copenhagen)
Cognitive impairment in patients with Alzheimer's disease (AD) is associated with reduction in hippocampal volume in magnetic resonance imaging (MRI). However, it is unknown whether hippocampal texture changes in persons with mild cognitive impairment (MCI) that does not have a change in hippocampal volume. We tested the hypothesis that hippocampal texture has association to early cognitive loss beyond that of volumetric changes. The texture marker was trained and evaluated using T1-weighted MRI...
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Published on Feb 1, 2016in Neurobiology of Aging 4.45
Christiane Möller14
Estimated H-index: 14
(VU University Medical Center),
Anne Hafkemeijer12
Estimated H-index: 12
(Leiden University)
+ 10 AuthorsFrederik Barkhof14
Estimated H-index: 14
(VU University Medical Center)
We examined patterns of cortical thickness loss and cognitive decline over time in 19 patients with Alzheimer's disease (AD), 10 with behavioral variant frontotemporal dementia (bvFTD), and 34 controls with a mean interval of 2.1 ± 0.4 years. We measured vertexwise and regional cortical thickness changes of 6 lobar regions of interest between groups with the longitudinal FreeSurfer pipeline. Compared with controls, AD and bvFTD had a steeper rate of cognitive decline and showed faster cortical t...
15 Citations Source Cite
Published on Sep 1, 2015in Frontiers in Neuroscience 3.88
Christian Salvatore8
Estimated H-index: 8
(National Research Council),
Antonio Cerasa33
Estimated H-index: 33
(National Research Council)
+ 3 AuthorsIsabella Castiglioni21
Estimated H-index: 21
(National Research Council)
Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as to lessen the time and cost of clinical trials. Magnetic Resonance (MR)-related biomarkers have been recently identified by the use of machine learning methods for the in vivo differential diagnosis of AD. However, The vast majority of neuroimaging papers investigating this topic are focused on the differe...
40 Citations Source Cite
Published on Sep 1, 2015in Journal of Neuroimaging 1.95
Seok Woo Moon11
Estimated H-index: 11
(Konkuk University),
Ivo D. Dinov42
Estimated H-index: 42
(University of Southern California)
+ 5 AuthorsArthur W. Toga131
Estimated H-index: 131
(University of Southern California)
BACKGROUND AND PURPOSE This study investigates 36 subjects aged 55-65 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to expand our knowledge of early-onset (EO) Alzheimer's Disease (EO-AD) using neuroimaging biomarkers. METHODS Nine of the subjects had EO-AD, and 27 had EO mild cognitive impairment (EO-MCI). The structural ADNI data were parcellated using BrainParser, and the 15 most discriminating neuroimaging markers between the two cohorts were extracted using the Global...
7 Citations Source Cite
Published on Jul 1, 2015in Alzheimers & Dementia 12.76
Luc Bracoud7
Estimated H-index: 7
Eva Bouguen1
Estimated H-index: 1
+ 9 AuthorsMaria Pueyo1
Estimated H-index: 1
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Published on Feb 1, 2015in Nature Protocols 12.42
Florian Kurth20
Estimated H-index: 20
(University of California, Los Angeles),
Christian Gaser53
Estimated H-index: 53
Eileen Luders43
Estimated H-index: 43
This article provides guidelines on how to perform a voxel-based gray matter asymmetry analysis, taking structural brain images from the initial data pre-processing through statistical analyses and the final interpretation of the significance maps.
42 Citations Source Cite
Published on Oct 1, 2014in Clinical Nuclear Medicine 6.31
Charles Marcus1
Estimated H-index: 1
(University of Baltimore),
Esther Mena1
Estimated H-index: 1
(University of Baltimore),
Rathan M. Subramaniam27
Estimated H-index: 27
Objectives The aim of this article was to review the current role of brain PET in the diagnosis of Alzheimer dementia. The characteristic patterns of glucose metabolism on brain FDG-PET can help in differentiating Alzheimer’s disease from other causes of dementia such as frontotemporal dementia and dementia of Lewy body. Amyloid brain PET may exclude significant amyloid deposition and thus Alzheimer’s disease in appropriate clinical setting.
37 Citations Source Cite
Published on Sep 15, 2014in Journal of medical imaging
Antonio Martínez-Torteya5
Estimated H-index: 5
(Monterrey Institute of Technology and Higher Education),
Juan Rodriguez-Rojas3
Estimated H-index: 3
(Monterrey Institute of Technology and Higher Education)
+ 3 AuthorsJosé G. Tamez-Peña16
Estimated H-index: 16
(Monterrey Institute of Technology and Higher Education)
Early diagnoses of Alzheimer’s disease (AD) would confer many benefits. Several biomarkers have been proposed to achieve such a task, where features extracted from magnetic resonance imaging (MRI) have played an important role. However, studies have focused exclusively on morphological characteristics. This study aims to determine whether features relating to the signal and texture of the image could predict mild cognitive impairment (MCI) to AD progression. Clinical, biological, and positron em...
5 Citations Source Cite
Matthew Nitzken9
Estimated H-index: 9
(University of Louisville),
Manuel F. Casanova50
Estimated H-index: 50
(University of Louisville)
+ 3 AuthorsAyman El-Baz28
Estimated H-index: 28
(University of Louisville)
The survey outlines and compares popular com- putational techniques for quantitative description of shapes of major structural parts of the human brain, including me- dial axis and skeletal analysis, geodesic distances, Procrustes analysis, deformable models, spherical harmonics, deformation morphometry, as well as other less widely used techniques. Their advantages, drawbacks, and emerging trends, as well as results of application, in particular, for early computer-aided diagnostics, are discus...
6 Citations Source Cite
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Published on Dec 1, 2018 in Digital Image Computing: Techniques and Applications
Medhani Menikdiwela1
Estimated H-index: 1
(Australian National University),
Chuong V. Nguyen10
Estimated H-index: 10
(Australian National University),
Marnie E. Shaw15
Estimated H-index: 15
(Australian National University)
Deep learning has been applied to learn and classify brain disease using volumetric MRI scans with an accuracy approaching or even exceeding that of a human expert. This is typically done by applying convolutional neural networks to slices of a 3D brain image volume. Each slice of the brain volume, however, represents only a small cross-sectional area of the cortical layer. On the other hand, convolutional neural networks are less well developed for 3D volumes. Therefore we sought to apply deep ...
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