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Can apparent resting state connectivity arise from systemic fluctuations?

Yunjie Tong1, Lia M Hocke2, Xiaoying Fan1

  • 1McLean Imaging Center, McLean Hospital Belmont, MA, USA ; Department of Psychiatry, Harvard University Medical School Boston, MA, USA.

Frontiers in Human Neuroscience
|June 2, 2015
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Summary

Resting state networks (RSNs) observed in functional MRI may stem from physiological fluctuations, not just neuronal activity. Systemic low-frequency oscillations can create patterns mimicking RSNs, suggesting a vascular basis for some connectivity findings.

Keywords:
BOLD fMRIcerebral blood flowresting state networksslow oscillationssystemic oscillations

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Area of Science:

  • Neuroimaging
  • Systems Neuroscience
  • Physiology

Background:

  • Resting state functional MRI (fMRI) blood oxygenation level dependent (BOLD) signal fluctuations are traditionally interpreted as reflecting neuronal activity and functional connectivity.
  • Consistent resting state networks (RSNs) are observed across diverse populations and conditions, suggesting fundamental brain organization.

Purpose of the Study:

  • To investigate whether systemic, non-neuronal physiological low-frequency oscillations (sLFOs) in the BOLD signal can independently generate spatial patterns resembling RSNs.
  • To determine if RSNs detected in resting-state analyses might partially originate from vascular anatomy and systemic physiological processes rather than solely neuronal connectivity.

Main Methods:

  • Generated synthetic resting state fMRI data for 11 subjects using subject-specific sLFOs derived from peripheral optical imaging or a recursive method.
  • Applied group independent component analysis (ICA) to both the synthetic and real resting state fMRI data.
  • Compared the spatial patterns identified by ICA in the synthetic data (simulation results) with those from the real data.

Main Results:

  • Group ICA successfully detected several well-established RSNs, including visual, motor, and default mode networks (DMNs), in the synthetic data generated solely from sLFOs.
  • Similar RSNs were identified in both the synthetic (simulated) and real resting state fMRI datasets.
  • The findings indicate that sLFOs can produce stable spatial patterns that are detected as RSNs.

Conclusions:

  • RSNs identified during resting state may, to a significant extent, represent vascular anatomy influenced by systemic physiological fluctuations.
  • The study challenges the exclusive interpretation of RSNs as purely neuronal connectivity, highlighting the contribution of non-neuronal physiological processes.
  • Further research is needed to disentangle the contributions of neuronal activity versus physiological oscillations to observed resting state networks.