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Noise Reduction in Arterial Spin Labeling Based Functional Connectivity Using Nuisance Variables.

Kay Jann1, Robert X Smith1, Edgar A Rios Piedra1

  • 1Laboratory of FMRI Technology, Department of Neurology, University of California Los Angeles Los Angeles, CA, USA.

Frontiers in Neuroscience
|September 8, 2016
PubMed
Summary
This summary is machine-generated.

Noise reduction strategies are essential for accurate functional connectivity (FC) analysis using Arterial Spin Labeling (ASL) perfusion imaging. Removing physiological and motion noise improves the detection of altered brain connectivity in autism spectrum disorder (ASD).

Keywords:
arterial spin labeling (ASL)blood oxygenation level dependent (BOLD)cerebral blood flow (CBF)default mode network (DMN)functional connectivity (FC)noise reduction

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Area of Science:

  • Neuroimaging
  • Functional Connectivity Analysis
  • Autism Spectrum Disorder Research

Background:

  • Arterial Spin Labeling (ASL) perfusion imaging is increasingly used for functional connectivity (FC) analysis.
  • Noise reduction is critical to mitigate confounding effects from head motion and physiological signals in FC analysis, similar to BOLD imaging.
  • The effectiveness of various noise reduction strategies (NRS) on ASL-based FC analysis requires systematic evaluation.

Purpose of the Study:

  • To systematically evaluate different noise reduction strategies (NRS) for improving perfusion-based FC analysis using ASL.
  • To compare the performance of NRS on two ASL sequences: 3D background-suppressed (BS) GRASE pCASL and dual-echo 2D-EPI pCASL.
  • To assess the utility of NRS in identifying altered FC in children with autism spectrum disorder (ASD) compared to typically developing (TD) children.

Main Methods:

  • Five different NRS were applied to ASL data from healthy adults using 3D BS GRASE pCASL and dual-echo 2D-EPI pCASL sequences.
  • Seed-based FC analysis was performed using data from a posterior cingulate cortex (PCC) seed.
  • FC analysis was conducted on ASL data from a cohort of children with ASD and TD children, incorporating NRS.

Main Results:

  • NRS involving nuisance variables significantly affected FC analysis, particularly for connections within the default mode network (DMN).
  • Physiological noise reduction improved spatial specificity, while head motion regressors enhanced long-range connectivity, consistent with BOLD-FC findings.
  • 3D BS GRASE pCASL demonstrated superior temporal SNR compared to dual-echo 2D-EPI pCASL, with comparable NRS effectiveness.
  • Noise reduction, including physiological and motion parameters, was crucial for detecting altered long-range connectivity from PCC to frontal areas in ASD.

Conclusions:

  • This study is the first to systematically evaluate NRS for ASL-based FC analysis.
  • 3D BS GRASE pCASL is recommended for FC analysis due to its high temporal SNR.
  • Removing physiological noise and motion parameters is critical for identifying altered FC in neurodevelopmental disorders like ASD.