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Principal component analysis of FDG PET in amnestic MCI

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European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

The purpose of the study is to evaluate the combined accuracy of episodic memory performance and 18F-FDG PET in identifying patients with amnestic mild cognitive impairment (aMCI) converting to Alzheimer’s disease (AD), aMCI non-converters, and controls.

Methods

Thirty-three patients with aMCI and 15 controls (CTR) were followed up for a mean of 21 months. Eleven patients developed AD (MCI/AD) and 22 remained with aMCI (MCI/MCI). 18F-FDG PET volumetric regions of interest underwent principal component analysis (PCA) that identified 12 principal components (PC), expressed by coarse component scores (CCS). Discriminant analysis was performed using the significant PCs and episodic memory scores.

Results

PCA highlighted relative hypometabolism in PC5, including bilateral posterior cingulate and left temporal pole, and in PC7, including the bilateral orbitofrontal cortex, both in MCI/MCI and MCI/AD vs CTR. PC5 itself plus PC12, including the left lateral frontal cortex (LFC: BAs 44, 45, 46, 47), were significantly different between MCI/AD and MCI/MCI. By a three-group discriminant analysis, CTR were more accurately identified by PET-CCS + delayed recall score (100%), MCI/MCI by PET-CCS + either immediate or delayed recall scores (91%), while MCI/AD was identified by PET-CCS alone (82%). PET increased by 25% the correct allocations achieved by memory scores, while memory scores increased by 15% the correct allocations achieved by PET.

Conclusion

Combining memory performance and 18F-FDG PET yielded a higher accuracy than each single tool in identifying CTR and MCI/MCI. The PC containing bilateral posterior cingulate and left temporal pole was the hallmark of MCI/MCI patients, while the PC including the left LFC was the hallmark of conversion to AD.

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Acknowledgments

This study complies with the current Italian laws and received ethical approval.

We thank Dr. Giampiero Villavecchia for supervising PET acquisition and Dr. Alessandra Piccini for performing ApoE evaluation.

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Authors and Affiliations

  1. Clinical Neurophysiology, Department of Endocrinological and Medical Sciences, University of Genoa, Viale Benedetto XV, 6, 16132, Genoa, Italy

    Flavio Nobili, Nicola Girtler, Andrea Brugnolo, Barbara Dessi & Guido Rodriguez

  2. Alzheimer Evaluation Unit, S. Martino Hospital, Genoa, Italy

    Flavio Nobili, Nicola Girtler, Andrea Brugnolo, Barbara Dessi & Guido Rodriguez

  3. Head–Neck Department, S. Martino Hospital, Genoa, Italy

    Flavio Nobili, Nicola Girtler, Andrea Brugnolo, Barbara Dessi & Guido Rodriguez

  4. Institute of Cognitive Sciences and Technologies, CNR, Rome, Italy

    Dario Salmaso & Marco Pagani

  5. Institute of Cognitive Sciences and Technologies, CNR, Padua, Italy

    Dario Salmaso & Marco Pagani

  6. Nuclear Medicine Unit, Department of Internal Medicine, University of Genoa, Genoa, Italy

    Silvia Morbelli

  7. Nuclear Medicine Unit, Department of Imaging Diagnostics, Galliera Hospital, Genoa, Italy

    Arnoldo Piccardo

  8. Department of Nuclear Medicine, Karolinska Hospital, Stockholm, Sweden

    Stig A. Larsson & Marco Pagani

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  1. Flavio Nobili
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  2. Dario Salmaso
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  3. Silvia Morbelli
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  9. Guido Rodriguez
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  10. Marco Pagani
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Corresponding author

Correspondence to Flavio Nobili.

Appendix 1

Appendix 1

PCs may be treated as new variables, and their values can be computed for each subject. These values are known as factor scores, or component scores (CS), and are a linear combination of the variables included in the analysis. They should be used both to re-evaluate group differences and as predictor variables in diagnostic research. However, in the latter case, it is preferable to use an imperfect estimate (CCS) generated by the algebraic sum of all the VROIs with higher loading in a given factor. Therefore, as CCS take into account the sign of factor loadings, they can deeply differ from the individual VROI values belonging to each PC. Unlike CS, they are not a linear combination of each variable, but an estimate of PCs. Like CS, they are essentially uncorrelated to one another. An advantage of using CCS is that they can be more easily computed and interpreted than CS and they can also be compared among studies [38].

The number of factors was determined by the number of eigenvalues greater than one. Variables with an absolute factor loading greater than 0.5 were considered as representative of a given factor. This is an arbitrary value, but it is commonly used since it explains a moderate part of the variance of the factor. By increasing the value further, some variables may be eliminated from the calculation of CCS, thus reducing the variance explained by these scores. CCS were standardised to a 0–1 scale. The stability of the PCA was evaluated by means of the T2 Hotelling’s test. Hotelling’s T2 is a measure of the multivariate distance of each observation from the centre of the data set. When PCA is done, T2 and PROB can be saved. PROB is the upper-tail probability of T2. The robustness of the PCA can be assessed looking at the outliers.

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Nobili, F., Salmaso, D., Morbelli, S. et al. Principal component analysis of FDG PET in amnestic MCI. Eur J Nucl Med Mol Imaging 35, 2191–2202 (2008). https://doi.org/10.1007/s00259-008-0869-z

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