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. 2021 Apr 21;11(1):40.
doi: 10.1186/s13550-021-00774-x.

Correlation of [18F]florbetaben textural features and age of onset of Alzheimer's disease: a principal components analysis approach

Jing Li  1 , Emanuele Antonecchia  1   2 , Marco Camerlenghi  3 , Agostino Chiaravalloti  4   5 , Qian Chu  6   7 , Alfonso Di Costanzo  8 , Zhen Li  6   9 , Lin Wan  10 , Xiangsong Zhang  11 , Nicola D'Ascenzo  12   13 , Orazio Schillaci  2   14 , Qingguo Xie  15   16
Affiliations

Correlation of [18F]florbetaben textural features and age of onset of Alzheimer's disease: a principal components analysis approach

Jing Li et al. EJNMMI Res. .

Abstract

Background: When Alzheimer's disease (AD) is occurring at an early onset before 65 years old, its clinical course is generally more aggressive than in the case of a late onset. We aim at identifying [[Formula: see text]F]florbetaben PET biomarkers sensitive to differences between early-onset Alzheimer's disease (EOAD) and late-onset Alzheimer's disease (LOAD). We conducted [[Formula: see text]F]florbetaben PET/CT scans of 43 newly diagnosed AD subjects. We calculated 93 textural parameters for each of the 83 Hammers areas. We identified 41 independent principal components for each brain region, and we studied their Spearman correlation with the age of AD onset, by taking into account multiple comparison corrections. Finally, we calculated the probability that EOAD and LOAD patients have different amyloid-[Formula: see text] ([Formula: see text]) deposition by comparing the mean and the variance of the significant principal components obtained in the two groups with a 2-tailed Student's t-test.

Results: We found that four principal components exhibit a significant correlation at a 95% confidence level with the age of onset in the left lateral part of the anterior temporal lobe, the right anterior orbital gyrus of the frontal lobe, the right lateral orbital gyrus of the frontal lobe and the left anterior part of the superior temporal gyrus. The data are consistent with the hypothesis that EOAD patients have a significantly different [[Formula: see text]F]florbetaben uptake than LOAD patients in those four brain regions.

Conclusions: Early-onset AD implies a very irregular pattern of [Formula: see text] deposition. The authors suggest that the identified textural features can be used as quantitative biomarkers for the diagnosis and characterization of EOAD patients.

Keywords: Early-onset Alzheimer’s disease; Positron emission tomography; Textural analysis.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Registration of PET image and Hammers N30R83 Atlas. First, a the NIFTI PET image is normalized to the standard template (standard space composed of 91×109×91 voxels with a resolution of 2×2×2 mm3). Second, b the Hammers N30R83 Atlas is normalized to the standard template space. Third, the VOIs are further extracted. As an example, the superior temporal gyrus c and the parietal gyrus d are shown
Fig. 2
Fig. 2
Aβ plaques in the right lateral orbital gyrus of the frontal lobe, observed with [18F]florbetaben PET. The right lateral orbital gyrus of the frontal lobe: maximal projection [18F]florbetaben PET images of EOAD (upper row) and LOAD (lower row) patients. The SUV does not exhibit any significant difference between the two classes of patients
Fig. 3
Fig. 3
Average SUV in the temporal lobe and the frontal lobe observed with [18F]florbetaben PET. The average SUV of [18F]florbetaben uptake does not exhibit any correlation with the age of onset of AD. A linear regression with 95% confidence bands is shown on the plots for better visualization of the correlations
Fig. 4
Fig. 4
Correlation of the significant principal components and age of onset of AD in the temporal lobe. Principal components exhibiting significant correlation with the age of onset of AD |r|>0.5;P<0.05/Nindr. A linear regression with 95% confidence bands is shown on the plots for better visualization of the correlations. The features extracted from AD patients in the left lateral part of the anterior temporal lobe (a) and in the left anterior part of the superior temporal gyrus (b) exhibit a significant difference with respect to the background estimated from control subjects, as confirmed also by the Student’s t-test on the distributions of the two patient categories with respect to a threshold age A (c, d)
Fig. 5
Fig. 5
Correlation of the significant principal components and age of onset of AD in the frontal lobe. Principal components exhibiting significant correlation with the age of onset of AD |r|>0.5;P<0.05/Nindr. A linear regression with 95% confidence bands is shown on the plots for better visualization of the correlations. The features extracted from AD patients in the right lateral orbital gyrus of the frontal lobe (a) and in the right anterior orbital gyrus of the frontal lobe (b) exhibit a significant difference with respect to the background estimated from control subjects, as confirmed also by the Student’s t-test on the distributions of the two patient categories with respect to a threshold age A (c, d).
Fig. 6
Fig. 6
Candle plot in the temporal lobe and the frontal lobe for LOAD, EOAD, and background estimated from control subjects. Candle plots of the distribution of the significant principal components for LOAD, EOAD, and background from control subjects. The age of onset of AD is classified according to the threshold age A = 65. The average value of the parameters for EOAD patients exhibits difference with respect to LOAD and background estimated from control subjects, in support to the assumption that the identified principal components describe intrinsic early-onset features of the spatial distribution of the Aβ deposition in [18F]florbetaben PET
Fig. 7
Fig. 7
Example of analysis in the lateral orbital gyrus, frontal lobe. Example of EOAD verification: the three-dimensional ROI corresponding to the right lateral orbital gyrus in the frontal lobe is extracted from the [18F]florbetaben PET images, the textural parameters are calculated and compared with the database of patients collected in this paper, identifying EOAD candidates
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