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Source-Based Morphometry Multivariate Approach to Analyze [123I]FP-CIT SPECT Imaging.

Enrico Premi1,2, V D Calhoun3,4, V Garibotto5

  • 1Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy. zedtower@gmail.com.

Molecular Imaging and Biology
|February 15, 2017
PubMed
Summary
This summary is machine-generated.

Source-based morphometry (SBM) using [123I]FP-CIT SPECT imaging offers a novel way to analyze dopamine transporter (DAT) and serotonin transporter (SERT) systems. This multivariate approach can identify distinct patterns in neurodegenerative parkinsonism beyond traditional methods.

Keywords:
Parkinson’s diseaseSource-based morphometryStatistical parametric mapping[123I]FP-CIT imaging

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Area of Science:

  • Neuroimaging
  • Nuclear Medicine
  • Radiochemistry

Background:

  • [123I]FP-CIT (DaTSCAN®) SPECT imaging is standard for assessing presynaptic dopamine transporter (DAT) in neurodegenerative parkinsonism.
  • Beyond DAT, [123I]FP-CIT may offer insights into other monoaminergic systems like serotonin transporter (SERT).
  • Independent Component Analysis (ICA) within Source-Based Morphometry (SBM) presents a novel multivariate method for exploring neurotransmission networks.

Purpose of the Study:

  • To evaluate [123I]FP-CIT binding using SBM, a multivariate approach, and compare it with univariate Statistical Parametric Mapping (SPM).
  • To explore the potential of SBM in identifying distinct patterns of monoaminergic transporter binding in neurodegenerative parkinsonism.

Main Methods:

  • One hundred forty-three subjects (84 with Parkinson's disease, 59 controls) underwent [123I]FP-CIT DaTSCAN® imaging.
  • Multivariate SBM and univariate SPM approaches were used to analyze [123I]FP-CIT binding.
  • ICA was implemented within SBM to identify "neurotransmission" networks based on voxel relationships.

Main Results:

  • SPM only showed reduced striatal [123I]FP-CIT binding in Parkinson's disease (PD) patients.
  • SBM identified six non-artefactual sources, including basal ganglia, cortical regions, and brainstem.
  • Three SBM sources (basal ganglia, cortical regions) showed significantly different [123I]FP-CIT binding between PD and controls.

Conclusions:

  • ICA-based SBM is a feasible multivariate approach for [123I]FP-CIT SPECT imaging.
  • This method can identify specific variance patterns in striatal and extrastriatal regions.
  • SBM may enhance the differentiation of neurodegenerative parkinsonisms by analyzing broader monoaminergic network patterns.