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Steyde G, Spairani E, Magenes G, Signorini MG. Fetal heart rate spectral analysis in raw signals and PRSA-derived curve: normal and pathological fetuses discrimination. Med Biol Eng Comput 2024; 62:437-447. [PMID: 37889432 PMCID: PMC10794317 DOI: 10.1007/s11517-023-02953-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 10/16/2023] [Indexed: 10/28/2023]
Abstract
Cardiotocography (CTG) is the most common technique for electronic fetal monitoring and consists of the simultaneous recording of fetal heart rate (FHR) and uterine contractions. In analogy with the adult case, spectral analysis of the FHR signal can be used to assess the functionality of the autonomic nervous system. To do so, several methods can be employed, each of which has its strengths and limitations. This paper aims at performing a methodological investigation on FHR spectral analysis adopting 4 different spectrum estimators and a novel PRSA-based spectral method. The performances have been evaluated in terms of the ability of the various methods to detect changes in the FHR in two common pregnancy complications: intrauterine growth restriction (IUGR) and gestational diabetes. A balanced dataset containing 2178 recordings distributed between the 32nd and 38th week of gestation was used. The results show that the spectral method derived from the PRSA better differentiates high-risk pregnancies vs. controls compared to the others. Specifically, it more robustly detects an increase in power percentage within the movement frequency band and a decrease in high frequency between pregnancies at high risk in comparison to those at low risk.
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Affiliation(s)
- Giulio Steyde
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milano, Italy.
| | - Edoardo Spairani
- Electrical, Computer and Biomedical Engineering Department, Università di Pavia, 27100, Pavia, Italy
| | - Giovanni Magenes
- Electrical, Computer and Biomedical Engineering Department, Università di Pavia, 27100, Pavia, Italy
| | - Maria G Signorini
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milano, Italy
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2
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Zhao Z, Zhu J, Jiao P, Wang J, Zhang X, Lu X, Zhang Y. Hybrid-FHR: a multi-modal AI approach for automated fetal acidosis diagnosis. BMC Med Inform Decis Mak 2024; 24:19. [PMID: 38247009 PMCID: PMC10801938 DOI: 10.1186/s12911-024-02423-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 01/10/2024] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND In clinical medicine, fetal heart rate (FHR) monitoring using cardiotocography (CTG) is one of the most commonly used methods for assessing fetal acidosis. However, as the visual interpretation of CTG depends on the subjective judgment of the clinician, this has led to high inter-observer and intra-observer variability, making it necessary to introduce automated diagnostic techniques. METHODS In this study, we propose a computer-aided diagnostic algorithm (Hybrid-FHR) for fetal acidosis to assist physicians in making objective decisions and taking timely interventions. Hybrid-FHR uses multi-modal features, including one-dimensional FHR signals and three types of expert features designed based on prior knowledge (morphological time domain, frequency domain, and nonlinear). To extract the spatiotemporal feature representation of one-dimensional FHR signals, we designed a multi-scale squeeze and excitation temporal convolutional network (SE-TCN) backbone model based on dilated causal convolution, which can effectively capture the long-term dependence of FHR signals by expanding the receptive field of each layer's convolution kernel while maintaining a relatively small parameter size. In addition, we proposed a cross-modal feature fusion (CMFF) method that uses multi-head attention mechanisms to explore the relationships between different modalities, obtaining more informative feature representations and improving diagnostic accuracy. RESULTS Our ablation experiments show that the Hybrid-FHR outperforms traditional previous methods, with average accuracy, specificity, sensitivity, precision, and F1 score of 96.8, 97.5, 96, 97.5, and 96.7%, respectively. CONCLUSIONS Our algorithm enables automated CTG analysis, assisting healthcare professionals in the early identification of fetal acidosis and the prompt implementation of interventions.
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Affiliation(s)
- Zhidong Zhao
- School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China.
| | - Jiawei Zhu
- College of Electronics and Information Engineering, Hangzhou Dianzi University, Hangzhou, China
| | - Pengfei Jiao
- School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China
| | - Jinpeng Wang
- School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China
| | - Xiaohong Zhang
- College of Electronics and Information Engineering, Hangzhou Dianzi University, Hangzhou, China
| | - Xinmiao Lu
- College of Electronics and Information Engineering, Hangzhou Dianzi University, Hangzhou, China
| | - Yefei Zhang
- School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China
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Cao Z, Wang G, Xu L, Li C, Hao Y, Chen Q, Li X, Liu G, Wei H. Intelligent antepartum fetal monitoring via deep learning and fusion of cardiotocographic signals and clinical data. Health Inf Sci Syst 2023; 11:16. [PMID: 36950107 PMCID: PMC10025176 DOI: 10.1007/s13755-023-00219-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Accepted: 02/27/2023] [Indexed: 03/21/2023] Open
Abstract
Purpose Cardiotocography (CTG), which measures uterine contraction (UC) and fetal heart rate (FHR), is a crucial tool for assessing fetal health during pregnancy. However, traditional computerized cardiotocography (cCTG) approaches have non-negligible calibration errors in feature extraction and heavily rely on the expertise and prior experience to define diagnostic features from CTG or FHR signals. Although previous works have studied deep learning methods for extracting CTG or FHR features, these methods still neglect the clinical information of pregnant women. Methods In this paper, we proposed a multimodal deep learning architecture (MMDLA) for intelligent antepartum fetal monitoring that is capable of performing automatic CTG feature extraction, fusion with clinical data and classification. The multimodal feature fusion was achieved by concatenating high-level CTG features, which were extracted from preprocessed CTG signals via a convolution neural network (CNN) with six convolution layers and five fully connected layers, and the clinical data of pregnant women. Eventually, light gradient boosting machine (LGBM) was implemented as fetal status assessment classifier. The effectiveness of MMDLA was evaluated using a dataset of 16,355 cases, each of which includes FHR signal, UC signal and pertinent clinical data like maternal age and gestational age. Results With an accuracy of 90.77% and an area under the curve (AUC) value of 0.9201, the multimodal features performed admirably. The data imbalance issue was also effectively resolved by the LGBM classifier, with a normal-F1 value of 0.9376 and an abnormal-F1 value of 0.8223. Conclusion In summary, the proposed MMDLA is conducive to the realization of intelligent antepartum fetal monitoring.
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Affiliation(s)
- Zhen Cao
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, 510006 China
| | - Guoqiang Wang
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, 510006 China
| | - Ling Xu
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, 510006 China
| | - Chaowei Li
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, 510006 China
- Nvogene Co., Ltd., Tianjing, China
| | - Yuexing Hao
- Department of Human Centered Design, Cornell University, Ithaca, NY USA
| | - Qinqun Chen
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, 510006 China
| | - Xia Li
- Guangzhou Medical University Second Affiliated Hospital, Guangzhou, China
| | - Guiqing Liu
- The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Hang Wei
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, 510006 China
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Rescinito R, Ratti M, Payedimarri AB, Panella M. Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis. Healthcare (Basel) 2023; 11:healthcare11111617. [PMID: 37297757 DOI: 10.3390/healthcare11111617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 05/29/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023] Open
Abstract
BACKGROUND IntraUterine Growth Restriction (IUGR) is a global public health concern and has major implications for neonatal health. The early diagnosis of this condition is crucial for obtaining positive outcomes for the newborn. In recent years Artificial intelligence (AI) and machine learning (ML) techniques are being used to identify risk factors and provide early prediction of IUGR. We performed a systematic review (SR) and meta-analysis (MA) aimed to evaluate the use and performance of AI/ML models in detecting fetuses at risk of IUGR. METHODS We conducted a systematic review according to the PRISMA checklist. We searched for studies in all the principal medical databases (MEDLINE, EMBASE, CINAHL, Scopus, Web of Science, and Cochrane). To assess the quality of the studies we used the JBI and CASP tools. We performed a meta-analysis of the diagnostic test accuracy, along with the calculation of the pooled principal measures. RESULTS We included 20 studies reporting the use of AI/ML models for the prediction of IUGR. Out of these, 10 studies were used for the quantitative meta-analysis. The most common input variable to predict IUGR was the fetal heart rate variability (n = 8, 40%), followed by the biochemical or biological markers (n = 5, 25%), DNA profiling data (n = 2, 10%), Doppler indices (n = 3, 15%), MRI data (n = 1, 5%), and physiological, clinical, or socioeconomic data (n = 1, 5%). Overall, we found that AI/ML techniques could be effective in predicting and identifying fetuses at risk for IUGR during pregnancy with the following pooled overall diagnostic performance: sensitivity = 0.84 (95% CI 0.80-0.88), specificity = 0.87 (95% CI 0.83-0.90), positive predictive value = 0.78 (95% CI 0.68-0.86), negative predictive value = 0.91 (95% CI 0.86-0.94) and diagnostic odds ratio = 30.97 (95% CI 19.34-49.59). In detail, the RF-SVM (Random Forest-Support Vector Machine) model (with 97% accuracy) showed the best results in predicting IUGR from FHR parameters derived from CTG. CONCLUSIONS our findings showed that AI/ML could be part of a more accurate and cost-effective screening method for IUGR and be of help in optimizing pregnancy outcomes. However, before the introduction into clinical daily practice, an appropriate algorithmic improvement and refinement is needed, and the importance of quality assessment and uniform diagnostic criteria should be further emphasized.
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Affiliation(s)
- Riccardo Rescinito
- Department of Translational Medicine (DiMeT), University of Eastern Piedmont/Piemonte Orientale (UPO), 28100 Novara, Italy
| | - Matteo Ratti
- Department of Translational Medicine (DiMeT), University of Eastern Piedmont/Piemonte Orientale (UPO), 28100 Novara, Italy
| | - Anil Babu Payedimarri
- Department of Translational Medicine (DiMeT), University of Eastern Piedmont/Piemonte Orientale (UPO), 28100 Novara, Italy
| | - Massimiliano Panella
- Department of Translational Medicine (DiMeT), University of Eastern Piedmont/Piemonte Orientale (UPO), 28100 Novara, Italy
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Joarder R, Kasap B, Ghiasi S. RT-TRAQ: An algorithm for real-time tracking of faint quasi-periodic signals in noisy time series. SMART HEALTH (AMSTERDAM, NETHERLANDS) 2023; 28:100392. [PMID: 37974565 PMCID: PMC10653118 DOI: 10.1016/j.smhl.2023.100392] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
We present an algorithm for live tracking of quasi-periodic faint signals in non-stationary, noisy, and phase-desynchronized time series measurements that commonly arise in embedded applications, such as wearable health monitoring. The first step of Rt-Traq is to continuously select fixed-length windows based on the rise or fall of data values in the stream. Subsequently, Rt-Traq calculates an averaged representative window, and its spectrum, whose frequency peaks reveal the underlying quasi-periodic signals. As each new data sample comes in, Rt-Traq incrementally updates the spectrum, to continuously track the signals through time. We develop several alternate implementations of the proposed algorithm. We evaluate their performance in tracking maternal and fetal heart rate using non-invasive photoplethysmography (PPG) data collected by a wearable device from animal experiments as well as a number of pregnant women who participated in our study. Our empirical results demonstrate improvements compared to competing approaches. We also analyze the memory requirement and complexity trade-offs between the implementations, which impact their demand on platform resources for real-time operation.
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Affiliation(s)
- Rishad Joarder
- Dept. of Electrical and Computer Engineering, University of California Davis, Davis, CA 95616, USA
| | - Begum Kasap
- Dept. of Electrical and Computer Engineering, University of California Davis, Davis, CA 95616, USA
| | - Soheil Ghiasi
- Dept. of Electrical and Computer Engineering, University of California Davis, Davis, CA 95616, USA
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King VJ, Bennet L, Stone PR, Clark A, Gunn AJ, Dhillon SK. Fetal growth restriction and stillbirth: Biomarkers for identifying at risk fetuses. Front Physiol 2022; 13:959750. [PMID: 36060697 PMCID: PMC9437293 DOI: 10.3389/fphys.2022.959750] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 07/29/2022] [Indexed: 11/13/2022] Open
Abstract
Fetal growth restriction (FGR) is a major cause of stillbirth, prematurity and impaired neurodevelopment. Its etiology is multifactorial, but many cases are related to impaired placental development and dysfunction, with reduced nutrient and oxygen supply. The fetus has a remarkable ability to respond to hypoxic challenges and mounts protective adaptations to match growth to reduced nutrient availability. However, with progressive placental dysfunction, chronic hypoxia may progress to a level where fetus can no longer adapt, or there may be superimposed acute hypoxic events. Improving detection and effective monitoring of progression is critical for the management of complicated pregnancies to balance the risk of worsening fetal oxygen deprivation in utero, against the consequences of iatrogenic preterm birth. Current surveillance modalities include frequent fetal Doppler ultrasound, and fetal heart rate monitoring. However, nearly half of FGR cases are not detected in utero, and conventional surveillance does not prevent a high proportion of stillbirths. We review diagnostic challenges and limitations in current screening and monitoring practices and discuss potential ways to better identify FGR, and, critically, to identify the “tipping point” when a chronically hypoxic fetus is at risk of progressive acidosis and stillbirth.
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Affiliation(s)
- Victoria J. King
- Fetal Physiology and Neuroscience Group, Department of Physiology, The University of Auckland, Auckland, New Zealand
| | - Laura Bennet
- Fetal Physiology and Neuroscience Group, Department of Physiology, The University of Auckland, Auckland, New Zealand
| | - Peter R. Stone
- Department of Obstetrics and Gynaecology, The University of Auckland, Auckland, New Zealand
| | - Alys Clark
- Department of Obstetrics and Gynaecology, The University of Auckland, Auckland, New Zealand
- Auckland Biomedical Engineering Institute, The University of Auckland, Auckland, New Zealand
| | - Alistair J. Gunn
- Fetal Physiology and Neuroscience Group, Department of Physiology, The University of Auckland, Auckland, New Zealand
| | - Simerdeep K. Dhillon
- Fetal Physiology and Neuroscience Group, Department of Physiology, The University of Auckland, Auckland, New Zealand
- *Correspondence: Simerdeep K. Dhillon,
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FHRGAN: Generative adversarial networks for synthetic fetal heart rate signal generation in low-resource settings. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.01.070] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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8
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Explainable Computational Intelligence Model for Antepartum Fetal Monitoring to Predict the Risk of IUGR. ELECTRONICS 2022. [DOI: 10.3390/electronics11040593] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Intrauterine Growth Restriction (IUGR) is a restriction of the fetus that involves the abnormal growth rate of the fetus, and it has a huge impact on the new-born’s health. Machine learning (ML) algorithms can help in early prediction and discrimination of the abnormality of the fetus’ health to assist in reducing the risk during the antepartum period. Therefore, in this study, Random Forest (RF), Support Vector Machine (SVM), K Nearest Neighbor (KNN) and Gradient Boosting (GB) was utilized to discriminate whether a fetus was healthy or suffering from IUGR based on the fetal heart rate (FHR). The Recursive Feature Elimination (RFE) method was used to select the significant feature for the classification of fetus. Furthermore, the study Explainable Artificial Intelligence (EAI) was implemented using LIME and SHAP to generate the explanation and to add comprehensibility in the proposed models. The experimental results indicate that RF achieved the highest accuracy (0.97) and F1-score (0.98) with the reduced set of features. However, the SVM outperformed it in terms of Positive Predictive Value (PPV) and specificity (SP). The performance of the model was further validated using another dataset and found that it outperformed the baseline studies for both the datasets. The proposed model can aid doctors in monitoring fetal health and enhancing the prediction process.
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Artificial intelligence in obstetrics. Obstet Gynecol Sci 2021; 65:113-124. [PMID: 34905872 PMCID: PMC8942755 DOI: 10.5468/ogs.21234] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 11/02/2021] [Indexed: 11/10/2022] Open
Abstract
This study reviews recent advances on the application of artificial intelligence for the early diagnosis of various maternal-fetal conditions such as preterm birth and abnormal fetal growth. It is found in this study that various machine learning methods have been successfully employed for different kinds of data capture with regard to early diagnosis of maternal-fetal conditions. With the more popular use of artificial intelligence, ethical issues should also be considered accordingly.
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Lucchini M, Shuffrey LC, Nugent JD, Pini N, Sania A, Shair M, Brink L, du Plessis C, Odendaal HJ, Nelson ME, Friedrich C, Angal J, Elliott AJ, Groenewald CA, Burd LT, Myers MM, Fifer WP. Effects of Prenatal Exposure to Alcohol and Smoking on Fetal Heart Rate and Movement Regulation. Front Physiol 2021; 12:594605. [PMID: 34400909 PMCID: PMC8363599 DOI: 10.3389/fphys.2021.594605] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 06/24/2021] [Indexed: 11/13/2022] Open
Abstract
Negative associations of prenatal tobacco and alcohol exposure (PTE and PAE) on birth outcomes and childhood development have been well documented, but less is known about underlying mechanisms. A possible pathway for the adverse fetal outcomes associated with PTE and PAE is the alteration of fetal autonomic nervous system development. This study assessed PTE and PAE effects on measures of fetal autonomic regulation, as quantified by heart rate (HR), heart rate variability (SD-HR), movement, and HR-movement coupling in a population of fetuses at ≥ 34 weeks gestational age. Participants are a subset of the Safe Passage Study, a prospective cohort study that enrolled pregnant women from clinical sites in Cape Town, South Africa, and the Northern Plains region, United States. PAE was defined by six levels: no alcohol, low quit early, high quit early, low continuous, moderate continuous, and high continuous; while PTE by 4 levels: no smoking, quit early, low continuous, and moderate/high continuous. Linear regression analyses of autonomic measures were employed controlling for fetal sex, gestational age at assessment, site, maternal education, household crowding, and depression. Analyses were also stratified by sleep state (1F and 2F) and site (South Africa, N = 4025, Northern Plains, N = 2466). The final sample included 6491 maternal-fetal-dyad assessed in the third trimester [35.21 ± 1.26 (mean ± SD) weeks gestation]. PTE was associated with a decrease in mean HR in state 2F, in a dose dependent fashion, only for fetuses of mothers who continued smoking after the first trimester. In state 1F, there was a significant increase in mean HR in fetuses whose mother quit during the first trimester. This effect was driven by the Norther Plains cohort. PTE was also associated with a significant reduction in fetal movement in the most highly exposed group. In South Africa a significant increase in mean HR both for the high quit early and the high continuous group was observed. In conclusion, this investigation addresses a critical knowledge gap regarding the relationship between PTE and PAE and fetal autonomic regulation. We believe these results can contribute to elucidating mechanisms underlying risk for adverse outcomes.
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Affiliation(s)
- Maristella Lucchini
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, United States
- Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, NY, United States
| | - Lauren C. Shuffrey
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, United States
- Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, NY, United States
| | - J. David Nugent
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, United States
- Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, NY, United States
| | - Nicoló Pini
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, United States
- Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, NY, United States
| | - Ayesha Sania
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, United States
| | - Margaret Shair
- Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, NY, United States
| | - Lucy Brink
- Department of Obstetrics and Gynecology, Faculty of Medicine and Health Science, Stellenbosch University, Cape Town, South Africa
| | - Carlie du Plessis
- Department of Obstetrics and Gynecology, Faculty of Medicine and Health Science, Stellenbosch University, Cape Town, South Africa
| | - Hein J. Odendaal
- Department of Obstetrics and Gynecology, Faculty of Medicine and Health Science, Stellenbosch University, Cape Town, South Africa
| | - Morgan E. Nelson
- Center for Pediatric and Community Research, Avera Research Institute, Sioux Falls, SD, United States
- Department of Pediatrics, University of South Dakota School of Medicine, Sioux Falls, SD, United States
| | - Christa Friedrich
- Center for Pediatric and Community Research, Avera Research Institute, Sioux Falls, SD, United States
- Department of Pediatrics, University of South Dakota School of Medicine, Sioux Falls, SD, United States
| | - Jyoti Angal
- Center for Pediatric and Community Research, Avera Research Institute, Sioux Falls, SD, United States
- Department of Pediatrics, University of South Dakota School of Medicine, Sioux Falls, SD, United States
| | - Amy J. Elliott
- Center for Pediatric and Community Research, Avera Research Institute, Sioux Falls, SD, United States
- Department of Pediatrics, University of South Dakota School of Medicine, Sioux Falls, SD, United States
| | - Coen A. Groenewald
- Department of Pediatrics, University of South Dakota School of Medicine, Sioux Falls, SD, United States
| | - Larry T. Burd
- Department of Pediatrics, University of North Dakota Medical School, Grand Forks, ND, United States
| | - Michael M. Myers
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, United States
- Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, NY, United States
- Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, United States
| | - William P. Fifer
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, United States
- Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, NY, United States
- Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, United States
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