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Related Research Units

Computational Health Informatics Program Research

Research Overview

Dr. Bosl’s primary research focus is in clinical neurophysiology and neurodiagnostics. Projects to discover patterns in infant EEG signals that can serve as biomarkers for autism, neurocognitive effects of complex trauma, and other neurodevelopmental pathologies continue with collaborations at BCH in Developmental Medicine and Psychiatry. A proposal to move this work to the bedside was awarded support by the BCH Innovation & Digital Health Accelerator award. Bill is working with Tobi Loddenkemper in the BCH Epilepsy clinic to find measures of ‘epileptigenicity’ or tendency to have epileptic seizures, and on a crowd-sourcing approach to annotation of clinical EEGs for research. Other funded projects involve neurodevelopment in premature infants (Rutgers), development of a very early intervention for neural impairments associated with autism (University of Texas San Antonio Medical Center), and development of a prototype infant EEG device for primary care use (with Quantum Applied Science and Research). He also works with colleagues at Oxford on the emergence of neurological impairment in Kenyan children following cerebral malaria (with the Kenya Medical Research Institute). Clinical applications for all of this work will require integration of EEG-derived information with other patient data in the EHR, and presentation to clinicians, topics that are being developed with Ken Mandl.

 

Research Background

Dr. Bosl is currently Visiting Associate Professor of Pediatrics at HMS and Visiting faculty with CHIP. He is also an Associate Professor of Health Informatics, Clinical Psychology, and Data Science at the University of San Francisco, where he was the founding director of the Master’s degree program in Health Informatics.

Before beginning research in neuroscience and biomedical informatics at BCH, Dr. Bosl was a computational physicist at Lawrence Livermore National Laboratory and trained in computational geophysics (Stanford PhD). As a researcher with Stanford University he invented a method for computing properties of porous materials from CT scans, which led to the commercialization of new technology that is currently replacing laboratory core analysis globally.

He joined the Boston Children’s Hospital Informatics program in 2005 to begin research on neural development. Although initially looking at molecular models of neurodevelopment, his focus shifted to the pragmatic matter of finding digital biomarkers for atypical neurodevelopment using approaches that could potentially be implemented in primary care settings. His approach to biomarker discovery integrated his previous background in nonlinear physics and signal processing.

While at Boston Children’s Hospital, he also undertook additional graduate training in developmental neurobiology and neurotechnology (MIT) and completed a second PhD in Behavioral Neuroscience at Boston University School of Medicine (2016). The focus of his dissertation was on information technology for monitoring neurodevelopment and digital biomarkers for detection of atypical neurodevelopment. In 2013 he took on the role of Director of a new graduate program in health informatics at the University of San Francisco (USF). Although this position required considerable administrative effort initially, in 2017 Dr. Bosl renewed his research affiliation with Boston Children’s Hospital (visiting faculty) and Harvard Medical School (Visiting Associate Professor), specifically to facilitate collaborative research with faculty in behavioral medicine, neurology, and computational health informatics.

Publications

  1. A QR Code for the Brain: A dynamical systems framework for computing neurophysiological biomarkers. Res Sq. 2024 Sep 18. View Abstract
  2. Development of a Multivariable Seizure Likelihood Assessment Based on Clinical Information and Short Autonomic Activity Recordings for Children With Epilepsy. Pediatr Neurol. 2023 Nov; 148:118-127. View Abstract
  3. A biomarker discovery framework for childhood anxiety. Front Psychiatry. 2023; 14:1158569. View Abstract
  4. Ellen R. Grass Lecture: The Future of Neurodiagnostics and Emergence of a New Science. Neurodiagn J. 2023 Mar; 63(1):1-13. View Abstract
  5. Seizure-related differences in biosignal 24-h modulation patterns. Sci Rep. 2022 09 05; 12(1):15070. View Abstract
  6. Measuring Real-Time Medication Effects From Electroencephalography. J Clin Neurophysiol. 2024 Jan 01; 41(1):72-82. View Abstract
  7. Coarse-graining and the Haar wavelet transform for multiscale analysis. Bioelectron Med. 2022 Feb 02; 8(1):3. View Abstract
  8. Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months. J Neurodev Disord. 2021 11 30; 13(1):57. View Abstract
  9. Measuring the effects of sleep on epileptogenicity with multifrequency entropy. Clin Neurophysiol. 2021 09; 132(9):2012-2018. View Abstract
  10. Prediction of Seizure Recurrence. A Note of Caution. Front Neurol. 2021; 12:675728. View Abstract
  11. Nonlinear Analysis of Visually Normal EEGs to Differentiate Benign Childhood Epilepsy with Centrotemporal Spikes (BECTS). Sci Rep. 2020 05 21; 10(1):8419. View Abstract
  12. Prevalence of dyslipidemia associated with complications in diabetic patients: a nationwide study in Thailand. Lipids Health Dis. 2019 Apr 06; 18(1):90. View Abstract
  13. EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach. Sci Rep. 2018 05 01; 8(1):6828. View Abstract
  14. The Emerging Role of Neurodiagnostic Informatics in Integrated Neurological and Mental Health Care. Neurodiagn J. 2018; 58(3):143-153. View Abstract
  15. How Useful Is Electroencephalography in the Diagnosis of Autism Spectrum Disorders and the Delineation of Subtypes: A Systematic Review. Front Psychiatry. 2017; 8:121. View Abstract
  16. Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy. Epilepsy Behav. 2014 Aug; 37:291-307. View Abstract
  17. Scalable decision support at the point of care: a substitutable electronic health record app for monitoring medication adherence. Interact J Med Res. 2013 Jul 22; 2(2):e13. View Abstract
  18. Automated quantification of spikes. Epilepsy Behav. 2013 Feb; 26(2):143-52. View Abstract
  19. Response: Infant EEG activity as a biomarker for autism: a promising approach or a false promise? BMC Med. 2011 May 20; 9:60. View Abstract
  20. EEG complexity as a biomarker for autism spectrum disorder risk. BMC Med. 2011 Feb 22; 9:18. View Abstract
  21. The role of noise and positive feedback in the onset of autosomal dominant diseases. BMC Syst Biol. 2010 Jun 29; 4:93. View Abstract
  22. Rule-based cell systems model of aging using feedback loop motifs mediated by stress responses. PLoS Comput Biol. 2010 Jun 17; 6(6):e1000820. View Abstract
  23. Multiscale data reduction with flexible saliency criterion for biological image analysis. Annu Int Conf IEEE Eng Med Biol Soc. 2009; 2009:3703-6. View Abstract
  24. Systems biology by the rules: hybrid intelligent systems for pathway modeling and discovery. BMC Syst Biol. 2007 Feb 15; 1:13. View Abstract
  25. Mitotic-exit control as an evolved complex system. Cell. 2005 May 06; 121(3):325-33. View Abstract

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