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Studying Metabolic Brain Connectivity Using 2-Deoxy-2-[18F]Fluoro-D-Glucose Dynamic Positron Emission Tomography at the Single-subject Level
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Metabolic connectivity-based single subject classification by multi-regional linear approximation in the rat.

Maximilian Grosch1, Leonie Beyer2, Magdalena Lindner1

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A new method using [18F]-FDG PET scans classifies brain network changes after injury. This technique accurately identifies different stages of neurological disorders, outperforming machine learning in rat models.

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Systems Biology

Background:

  • Positron emission tomography (PET) with [18F]-FDG measures brain metabolism.
  • Metabolic connectivity patterns reveal cerebral network alterations in neurological disorders.
  • These patterns may aid in diagnostic decisions for neurological conditions.

Purpose of the Study:

  • Establish a novel statistical classification method for brain metabolic connectivity.
  • Assess the classification accuracy of this method in different time-dependent states after unilateral labyrinthectomy (UL) in rats.
  • Compare the method's performance against random and machine learning classifications.

Main Methods:

  • Utilized [18F]-FDG PET measurements at baseline and 1, 3, 7, 15 days post-UL in rats.
  • Determined whole-brain metabolic connectivity patterns using Pearson's correlation of regional uptake values.
  • Fitted connectivity patterns with linear functions and investigated thresholds for classification.
  • Classified rats based on congruence between their PET patterns and fitted class patterns.

Main Results:

  • Achieved classification accuracies of 84.3% (3 classes), 75.0% (4 classes), and 54.1% (5 classes).
  • The novel method outperformed random and machine learning classifications on the same dataset.
  • Optimal classification thresholds were identified as |r| > 0.65 and d = 4 using Siegel's slope estimator.

Conclusions:

  • The developed connectivity-based classification method is effective for staging neurological network disorders.
  • This PET-based approach shows potential for supporting diagnostic decisions in conditions like neurodegenerative syndromes.
  • The method demonstrates competitive performance and potential methodological advantages over machine learning in specific applications.