At University of Arkansas for Medical Sciences we have installed the world's first magnetoencephalography (MEG) system specifically designed and modified for fetal and newborn assessment. The non-invasive system called SARA (Squid Array for Reproductive Assessment) consists of 151 primary superconducting sensors which detect biomagnetic fields from the body. Since the installation of SARA, significant progress has been made towards the ultimate goal of developing a clinical neurological assessment tool for the developing fetus. Since the last funding cycle we have shown that the fetal spontaneous brain signals can be extracted from biomagnetic recordings with sufficient signal to noise ratio. Further we have shown its applicability in tracking the neurological activity of growth restricted fetuses. The success of these studies was largely dependent on the development of appropriate analysis tools. Based on our experience we believe the analysis strategy needs to be improved to deal with non-stationarity aspects of the spontaneous fetal MEG data that were encountered in long duration data sets. The non-stationarity was mainly caused by maternal and fetal movement. The spatial filter based beamformer approach was tested and implemented in the previous funding cycle and produced good results in short segments that are relatively stationary. The beamformer approach requires an accurate head model and its performance in the non-stationary segments was suboptimal. In order to advance this technology, we propose two novel approaches that are not dependent on a reliable head model and can deal with non-stationarity issues in long duration data sets. We also want to develop clinically relevant indices that can track the fetal neurological maturation. Further we plan to extend our studies to the other high-risk subgroups such as chronic and pregnancy-induced hypertensive mothers. The inclusion of this group in our studies could further emphasize the potential clinical value of spontaneous fetal MEG brain recordings, especially prior to 34 weeks of gestation, when the decision to deliver needs to be optimized based on the long-term effects of premature delivery.
The specific aims of the project are:
Aim 1 : Develop analysis tools for improved detection of spontaneous fetal MEG activity in recordings with large non-stationarities. - Separating fetal MEG from other interfering sources.
Aim2 : Develop clinically relevant quantification indices for spontaneous fetal MEG that can be applied across the gestational age to monitor the neurological development of the fetus. (a) Detect the distinct discontinuous brain patterns observed in fetal MEG. (2) Calculate burst duration (BD) and interburst interval (IBI) observed in the detected discontinuous brain patterns. (3) Correlate fetal MEG with fetal states - quiet and active sleep;quiet and active awake.
Aim 3 : Assess the ontogeny of the maturation indices such as percent discontinuity, burst duration and interburst interval in fetal spontaneous brain activit signals of low-risk pregnancies and compare it to pregnancies classified as high-risk due to maternal hypertension both chronic and pregnancy-induced.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Research Project (R01)
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Special Emphasis Panel (ZRG1-NT-L (09))
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Peng, Grace
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University of Arkansas for Medical Sciences
Obstetrics & Gynecology
Schools of Medicine
Little Rock
United States
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Vairavan, S; Ulusar, U D; Eswaran, H et al. (2016) A computer-aided approach to detect the fetal behavioral states using multi-sensor Magnetocardiographic recordings. Comput Biol Med 69:44-51
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Haddad, Naim; Govindan, Rathinaswamy B; Vairavan, Srinivasan et al. (2011) Correlation between fetal brain activity patterns and behavioral states: an exploratory fetal magnetoencephalography study. Exp Neurol 228:200-5
Govindan, R B; Vairavan, S; Ulusar, U D et al. (2011) A novel approach to track fetal movement using multi-sensor magnetocardiographic recordings. Ann Biomed Eng 39:964-72
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