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Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach.

Salvatore C, Cerasa A, Battista P, Gilardi MC, Quattrone A, Castiglioni I, Alzheimer's Disease Neuroimaging Initiati - Front Neurosci (2015)

Bottom Line: CN, 66% MCIc vs.MCInc (nested 20-fold cross validation).Our data encourage the application of computer-based diagnosis in clinical practice of AD opening new prospective in the early management of AD patients.

View Article: PubMed Central - PubMed

Affiliation: Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR) Milan, Italy.

ABSTRACT
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 difference between AD and patients with mild cognitive impairment (MCI), not considering the impact of MCI patients who will (MCIc) or not convert (MCInc) to AD. Morphological T1-weighted MRIs of 137 AD, 76 MCIc, 134 MCInc, and 162 healthy controls (CN) selected from the Alzheimer's disease neuroimaging initiative (ADNI) cohort, were used by an optimized machine learning algorithm. Voxels influencing the classification between these AD-related pre-clinical phases involved hippocampus, entorhinal cortex, basal ganglia, gyrus rectus, precuneus, and cerebellum, all critical regions known to be strongly involved in the pathophysiological mechanisms of AD. Classification accuracy was 76% AD vs. CN, 72% MCIc vs. CN, 66% MCIc vs. MCInc (nested 20-fold cross validation). Our data encourage the application of computer-based diagnosis in clinical practice of AD opening new prospective in the early management of AD patients.

No MeSH data available.


Related in: MedlinePlus

Explained Variance as a function of the number of considered Principal Components, when using GM tissue probability map and no smoothing, for the following comparisons: AD vs. CN, MCIc vs. CN, MCIc vs. MCInc.
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Figure 3: Explained Variance as a function of the number of considered Principal Components, when using GM tissue probability map and no smoothing, for the following comparisons: AD vs. CN, MCIc vs. CN, MCIc vs. MCInc.

Mentions: Figures 3, 4 show, as representative examples, the explained variance as a function of the number of considered PCs, before (Figure 3) and after (Figure 4) sorting them in accordance to their FDR. Plots are shown for the comparisons between AD and CN, MCIc, and CN, MCIc, and MCInc when using GM tissue probability maps and no smoothing. The trend of explained variance as a function of the number of considered PCs was modified by the application of FDR-analysis. In particular, FDR-analysis allowed the most discriminative information for class separation to be contained in the first few principal components. This is shown by the step in the explained variance in correspondence with a low number of components for the comparisons between AD vs. CN and MCIc vs. CN (Figure 4).


Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach.

Salvatore C, Cerasa A, Battista P, Gilardi MC, Quattrone A, Castiglioni I, Alzheimer's Disease Neuroimaging Initiati - Front Neurosci (2015)

Explained Variance as a function of the number of considered Principal Components, when using GM tissue probability map and no smoothing, for the following comparisons: AD vs. CN, MCIc vs. CN, MCIc vs. MCInc.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4555016&req=5

Figure 3: Explained Variance as a function of the number of considered Principal Components, when using GM tissue probability map and no smoothing, for the following comparisons: AD vs. CN, MCIc vs. CN, MCIc vs. MCInc.
Mentions: Figures 3, 4 show, as representative examples, the explained variance as a function of the number of considered PCs, before (Figure 3) and after (Figure 4) sorting them in accordance to their FDR. Plots are shown for the comparisons between AD and CN, MCIc, and CN, MCIc, and MCInc when using GM tissue probability maps and no smoothing. The trend of explained variance as a function of the number of considered PCs was modified by the application of FDR-analysis. In particular, FDR-analysis allowed the most discriminative information for class separation to be contained in the first few principal components. This is shown by the step in the explained variance in correspondence with a low number of components for the comparisons between AD vs. CN and MCIc vs. CN (Figure 4).

Bottom Line: CN, 66% MCIc vs.MCInc (nested 20-fold cross validation).Our data encourage the application of computer-based diagnosis in clinical practice of AD opening new prospective in the early management of AD patients.

View Article: PubMed Central - PubMed

Affiliation: Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR) Milan, Italy.

ABSTRACT
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 difference between AD and patients with mild cognitive impairment (MCI), not considering the impact of MCI patients who will (MCIc) or not convert (MCInc) to AD. Morphological T1-weighted MRIs of 137 AD, 76 MCIc, 134 MCInc, and 162 healthy controls (CN) selected from the Alzheimer's disease neuroimaging initiative (ADNI) cohort, were used by an optimized machine learning algorithm. Voxels influencing the classification between these AD-related pre-clinical phases involved hippocampus, entorhinal cortex, basal ganglia, gyrus rectus, precuneus, and cerebellum, all critical regions known to be strongly involved in the pathophysiological mechanisms of AD. Classification accuracy was 76% AD vs. CN, 72% MCIc vs. CN, 66% MCIc vs. MCInc (nested 20-fold cross validation). Our data encourage the application of computer-based diagnosis in clinical practice of AD opening new prospective in the early management of AD patients.

No MeSH data available.


Related in: MedlinePlus