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Estimation of phase signal change in neuronal current MRI for evoke response of tactile detection with realistic somatosensory laminar network model

BagheriMofidi, S. M ; Sharif University of Technology | 2016

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  1. Type of Document: Article
  2. DOI: 10.1007/s13246-016-0467-5
  3. Publisher: Springer Netherlands , 2016
  4. Abstract:
  5. Magnetic field generated by neuronal activity could alter magnetic resonance imaging (MRI) signals but detection of such signal is under debate. Previous researches proposed that magnitude signal change is below current detectable level, but phase signal change (PSC) may be measurable with current MRI systems. Optimal imaging parameters like echo time, voxel size and external field direction, could increase the probability of detection of this small signal change. We simulate a voxel of cortical column to determine effect of such parameters on PSC signal. We extended a laminar network model for somatosensory cortex to find neuronal current in each segment of pyramidal neurons (PN). 60,000 PNs of simulated network were positioned randomly in a voxel. Biot–savart law applied to calculate neuronal magnetic field and additional phase. The procedure repeated for eleven neuronal arrangements in the voxel. PSC signal variation with the echo time and voxel size was assessed. The simulated results show that PSC signal increases with echo time, especially 100/80 ms after stimulus for gradient echo/spin echo sequence. It can be up to 0.1 mrad for echo time = 175 ms and voxel size = 1.48 × 1.48 × 2.18 mm3. With echo time less than 25 ms after stimulus, it was just acquired effects of physiological noise on PSC signal. The absolute value of the signal increased with decrease of voxel size, but its components had complex variation. External field orthogonal to local surface of cortex maximizes the signal. Expected PSC signal for tactile detection in the somatosensory cortex increase with echo time and have no oscillation
  6. Keywords:
  7. Neuronal current MRI ; Neuronal magnetic field ; Phase signal change ; Complex networks ; Magnetic fields ; Magnetic resonance imaging ; Magnetism ; Neuronal activities ; Phase signals ; Physiological noise ; Probability of detection ; Signal variations ; Simulated networks ; Somatosensory ; Signal detection ; Biological model ; Nerve cell ; Physiology ; Procedures ; Signal processing ; Somatosensory cortex ; Somatosensory evoked potential ; Evoked potentials, somatosensory ; Magnetic resonance imaging ; Models, neurological ; Neurons ; Signal processing, computer-assisted ; Somatosensory cortex ; Touch
  8. Source: Australasian Physical and Engineering Sciences in Medicine ; Volume 39, Issue 3 , 2016 , Pages 717-726 ; 01589938 (ISSN)
  9. URL: https://link.springer.com/article/10.1007%2Fs13246-016-0467-5