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Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample.
The paper lists 337 species from Magurski National Park (MNP): 314 lichens, 18 lichenicolous fungi, four saprotrophic fungi and one lichenicolous myxomycete; 112 of them are new for MNP, 75 are reported for the first time for the Beskid Niski Mts, and two are new for Poland. Selected species are accompanied by taxonomic notes and remarks on their distribution in Poland and other Carpathian ranges. First records of Intralichen lichenicola, Burgoa angulosa and Verrucaria policensis and a second record of Epigloea urosperma are given for the whole Carpathian range, and Fuscidea arboricola was recorded for the first time in the Western Carpathians. Halecania viridescens and Mycomicrothelia confusa are new for the Polish Carpathians. The records of Absconditella pauxilla, Collema crispum, Licea parasitica and Rinodina griseosoralifera in MNP are their second known localities for the range. 93 species, mainly rare or threatened in Poland, were reported from MNP in the 20th century but were not refound.