Metabolic connectivity-based single subject classification by multi-regional linear approximation in the rat
Maximilian Grosch,
Leonie Beyer,
Magdalena Lindner,
Lena Kaiser,
Seyed-Ahmad Ahmadi,
Anna Stockbauer,
Peter Bartenstein,
Marianne Dieterich,
Matthias Brendel,
Andreas Zwergal,
Sibylle Ziegler
Affiliations
Maximilian Grosch
German Center for Vertigo and Balance Disorders, DSGZ, University Hospital, Ludwig-Maximilians-University Munich, Marchioninistrasse 15, D-81377 Munich, Germany; Department of Nuclear Medicine, University Hospital, LMU Munich, Munich Germany; Corresponding author at: German Center for Vertigo and Balance Disorders, University Hospital, Ludwig-Maximilians-University Munich, Marchioninistrasse 15, D-81377 Munich, Germany.
Leonie Beyer
Department of Nuclear Medicine, University Hospital, LMU Munich, Munich Germany
Magdalena Lindner
German Center for Vertigo and Balance Disorders, DSGZ, University Hospital, Ludwig-Maximilians-University Munich, Marchioninistrasse 15, D-81377 Munich, Germany; Department of Nuclear Medicine, University Hospital, LMU Munich, Munich Germany
Lena Kaiser
Department of Nuclear Medicine, University Hospital, LMU Munich, Munich Germany
Seyed-Ahmad Ahmadi
German Center for Vertigo and Balance Disorders, DSGZ, University Hospital, Ludwig-Maximilians-University Munich, Marchioninistrasse 15, D-81377 Munich, Germany
Anna Stockbauer
Department of Nuclear Medicine, University Hospital, LMU Munich, Munich Germany
Peter Bartenstein
Department of Nuclear Medicine, University Hospital, LMU Munich, Munich Germany; Munich Cluster of Systems Neurology, SyNergy, Munich, Germany
Marianne Dieterich
German Center for Vertigo and Balance Disorders, DSGZ, University Hospital, Ludwig-Maximilians-University Munich, Marchioninistrasse 15, D-81377 Munich, Germany; Department of Neurology, University Hospital, LMU Munich, Munich, Germany; Munich Cluster of Systems Neurology, SyNergy, Munich, Germany
Matthias Brendel
Department of Nuclear Medicine, University Hospital, LMU Munich, Munich Germany; Munich Cluster of Systems Neurology, SyNergy, Munich, Germany
Andreas Zwergal
German Center for Vertigo and Balance Disorders, DSGZ, University Hospital, Ludwig-Maximilians-University Munich, Marchioninistrasse 15, D-81377 Munich, Germany; Department of Neurology, University Hospital, LMU Munich, Munich, Germany
Sibylle Ziegler
Department of Nuclear Medicine, University Hospital, LMU Munich, Munich Germany
Metabolic connectivity patterns on the basis of [18F]-FDG positron emission tomography (PET) are used to depict complex cerebral network alterations in different neurological disorders and therefore may have the potential to support diagnostic decisions. In this study, we established a novel statistical classification method taking advantage of differential time-dependent states of whole-brain metabolic connectivity following unilateral labyrinthectomy (UL) in the rat and explored its classification accuracy.The dataset consisted of repeated [18F]-FDG PET measurements at baseline and 1, 3, 7, and 15 days (= maximum of 5 classes) after UL with 17 rats per measurement day. Classification in different stages after UL was performed by determining connectivity patterns for the different classes by Pearson's correlation between uptake values in atlas-based segmented brain regions. Connections were fitted with a linear function, with which different thresholds on the correlation coefficient (r = [0.5, 0.85]) were investigated. Rats were classified by determining the congruence of their PET uptake pattern with the fitted connectivity patterns in the classes.Overall, the classification accuracy with this method was 84.3% for 3 classes, 75.0% for 4 classes, and 54.1% for 5 classes and outperformed random classification as well as machine learning classification on the same dataset. The optimal classification thresholds of the correlation coefficient and distance-to-fit were found to be |r| > 0.65 and d = 4 when using Siegel's slope estimator for fitting.This connectivity-based classification method can compete with machine learning classification and may have methodological advantages when applied to support PET-based diagnostic decisions in neurological network disorders (such as neurodegenerative syndromes).
