TY - JOUR
T1 - A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages
AU - Rathore, Saima
AU - Habes, Mohamad
AU - Iftikhar, Muhammad Aksam
AU - Shacklett, Amanda
AU - Davatzikos, Christos
N1 - Funding Information:
This work was partially supported by NIH Grant R01AG14971. We would also like to thank the anonymous reviewers, whose detailed and thoughtful comments helped us significantly improve the paper.
Publisher Copyright:
© 2017 Elsevier Inc.
PY - 2017/7/15
Y1 - 2017/7/15
N2 - Neuroimaging has made it possible to measure pathological brain changes associated with Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been increasingly integrated into imaging signatures of AD by means of classification frameworks, offering promising tools for individualized diagnosis and prognosis. We reviewed neuroimaging-based studies for AD and mild cognitive impairment classification, selected after online database searches in Google Scholar and PubMed (January, 1985–June, 2016). We categorized these studies based on the following neuroimaging modalities (and sub-categorized based on features extracted as a post-processing step from these modalities): i) structural magnetic resonance imaging [MRI] (tissue density, cortical surface, and hippocampal measurements), ii) functional MRI (functional coherence of different brain regions, and the strength of the functional connectivity), iii) diffusion tensor imaging (patterns along the white matter fibers), iv) fluorodeoxyglucose positron emission tomography (FDG-PET) (metabolic rate of cerebral glucose), and v) amyloid-PET (amyloid burden). The studies reviewed indicate that the classification frameworks formulated on the basis of these features show promise for individualized diagnosis and prediction of clinical progression. Finally, we provided a detailed account of AD classification challenges and addressed some future research directions.
AB - Neuroimaging has made it possible to measure pathological brain changes associated with Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been increasingly integrated into imaging signatures of AD by means of classification frameworks, offering promising tools for individualized diagnosis and prognosis. We reviewed neuroimaging-based studies for AD and mild cognitive impairment classification, selected after online database searches in Google Scholar and PubMed (January, 1985–June, 2016). We categorized these studies based on the following neuroimaging modalities (and sub-categorized based on features extracted as a post-processing step from these modalities): i) structural magnetic resonance imaging [MRI] (tissue density, cortical surface, and hippocampal measurements), ii) functional MRI (functional coherence of different brain regions, and the strength of the functional connectivity), iii) diffusion tensor imaging (patterns along the white matter fibers), iv) fluorodeoxyglucose positron emission tomography (FDG-PET) (metabolic rate of cerebral glucose), and v) amyloid-PET (amyloid burden). The studies reviewed indicate that the classification frameworks formulated on the basis of these features show promise for individualized diagnosis and prediction of clinical progression. Finally, we provided a detailed account of AD classification challenges and addressed some future research directions.
KW - Alzheimer's disease
KW - Classification
KW - Feature extraction
KW - Machine learning
KW - Mild cognitive impairment
KW - Neuroimaging
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U2 - 10.1016/j.neuroimage.2017.03.057
DO - 10.1016/j.neuroimage.2017.03.057
M3 - Review article
C2 - 28414186
AN - SCOPUS:85018778702
SN - 1053-8119
VL - 155
SP - 530
EP - 548
JO - NeuroImage
JF - NeuroImage
ER -