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.

Public Health Relevance

Evaluation of fetal cerebral functions presents a very significant and essential goal since we currently lack the means to directly monitor such functions.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
2R01EB007826-04A1
Application #
8297261
Study Section
Special Emphasis Panel (ZRG1-NT-L (09))
Program Officer
Peng, Grace
Project Start
2007-08-06
Project End
2014-08-31
Budget Start
2012-09-15
Budget End
2013-08-31
Support Year
4
Fiscal Year
2012
Total Cost
$259,949
Indirect Cost
$83,508
Name
University of Arkansas for Medical Sciences
Department
Obstetrics & Gynecology
Type
Schools of Medicine
DUNS #
122452563
City
Little Rock
State
AR
Country
United States
Zip Code
72205
Avci, R; Escalona-Vargas, D; Siegel, E R et al. (2018) Coupling Analysis of Fetal and Maternal Heart Rates via Transfer Entropy Using Magnetocardiography. Conf Proc IEEE Eng Med Biol Soc 2018:1-4
Avci, Recep; Wilson, James D; Escalona-Vargas, Diana et al. (2018) Tracking Fetal Movement Through Source Localization From Multisensor Magnetocardiographic Recordings. IEEE J Biomed Health Inform 22:758-765
Wilson, James D; Haueisen, Jens (2017) Separation of Physiological Signals Using Minimum Norm Projection Operators. IEEE Trans Biomed Eng 64:904-916
Escalona-Vargas, D; Siegel, E R; Murphy, P et al. (2017) Selection of Reference Channels Based on Mutual Information for Frequency-Dependent Subtraction Method Applied to Fetal Biomagnetic Signals. IEEE Trans Biomed Eng 64:1115-1122
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
Vairavan, Srinivasan; Govindan, Rathinaswamy B; Haddad, Naim et al. (2014) Quantification of fetal magnetoencephalographic activity in low-risk fetuses using burst duration and interburst interval. Clin Neurophysiol 125:1353-9
Sriram, Bhargavi; Wilson, James D; Govindan, Rathinaswamy B et al. (2013) The effect of applying orthogonal projection technique in short window segments to obtain fetal magnetocardiogram. Conf Proc IEEE Eng Med Biol Soc 2013:421-4
Sriram, Bhargavi; Mencer, Margret A; McKelvey, Samantha et al. (2013) Differences in the sleep states of IUGR and low-risk fetuses: An MCG study. Early Hum Dev 89:815-9
Eswaran, Hari; Govindan, Rathinaswamy B; Haddad, Naim I et al. (2012) Spectral power differences in the brain activity of growth-restricted and normal fetuses. Early Hum Dev 88:451-4
Vrba, J; McCubbin, J; Govindan, R B et al. (2012) Removal of interference from fetal MEG by frequency dependent subtraction. Neuroimage 59:2475-84

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