Dr Sergio Luiz Novi Junior earned his PhD in Applied Physics from the University of Campinas (UNICAMP) in 2022, focusing on Biomedical Optics and Neuroscience. His doctoral work was honored with the prestigious CAPES Grand Prize for Best PhD Thesis across Exact, Technological, and Multidisciplinary Sciences in Brazil, as well as the CAPES Best PhD Thesis in Physics & Astrophysics. Following his doctorate, Dr Novi completed a postdoctoral fellowship at Western University in Canada, where he led a research team in Dr. Adrian Owen’s lab, and was awarded a competitive 3-year CIHR Postdoctoral Fellowship in 2024. He also served as Tech Leader & AI Research Specialist at Samsung R&D Institute in Brazil, where he led AI deployment for Samsung Smartwatches and authored a patent.
Dr Novi’s research interests focus on advancing functional near-infrared spectroscopy (fNIRS) as a robust clinical brain monitoring technology, with particular emphasis on motion artifact correction, systemic physiological noise reduction, and anatomical precision. His clinical work spans pediatric sleep-disordered breathing, consciousness detection in ICU settings, and neurodevelopmental assessment from neonates to older adults. With over 30 peer-reviewed publications in leading journals including PNAS, JAMA Otolaryngology, Annals of Neurology, Biomedical Optics Express, and Neurophotonics, as well as 1 patent request and numerous national and international presentations, Dr Novi leads innovative research bridging physics-based methods with clinically meaningful outcomes. As a Research Scientist at the University of Maryland School of Medicine, he is currently developing AI algorithms for risk stratification in pediatric sleep-disordered breathing and contributing to NIH R01-funded research. His work has received international recognition and continues to establish fNIRS as a reliable tool for routine clinical use.
Selected Publications
Methodological Foundations: Solving the “Noise” Problem in fNIRS: Early in my career, I recognized that the clinical utility of fNIRS was fundamentally limited by three critical challenges: motion artifacts, anatomical uncertainty, and systemic physiological noise. To address motion contamination, I developed and validated a hybrid motion correction algorithm that significantly outperforms standard techniques, particularly during complex tasks such as speech production. To enhance anatomical precision, I engineered a custom neuronavigator system that integrates spatial information, yielding substantial improvements in data reproducibility. I further established robust protocols for systemic physiological noise removal, demonstrating that proper correction for global physiology is essential for accurate resting-state network mapping. Finally, I introduced graph-theoretic frameworks to quantify complex brain network topologies, establishing rigorous analytical methods for characterizing resting-state connectivity patterns in both health and disease. These methodological advances have enabled fNIRS to transition from a research tool to a clinically viable neuroimaging modality.
• Novi, S.L., Roberts, E., … & Tellis, C.M. (2020). Functional near-infrared spectroscopy for speech protocols: characterization of motion artifacts and guidelines for improving data analysis. Neurophotonics, 7(1), 015001.
• Novi, S.L., Forero, E.J., Silva, J.A.I.R., et al. (2020). Integration of spatial information increases reproducibility in functional near-infrared spectroscopy. Frontiers in Neuroscience, 14, 746.
• Abdalmalak, A.*, Novi, S.L.*, Kazazian, K., et al. (2022). Effects of systemic physiology on mapping resting-state networks using functional near-infrared spectroscopy. Frontiers in Neuroscience, 16, 803297. (*Co-First Authors)
• Novi, S.L., Rodrigues, R.B.M.L., & Mesquita, R.C. (2016). Resting state connectivity patterns with near-infrared spectroscopy data of the whole head. Biomedical Optics Express, 7(7), 2524-2537.
Clinical Translation I: Pediatric Sleep & Early Development: I have leveraged my methodological expertise to investigate brain development and dysfunction in vulnerable pediatric populations. At the University of Maryland School of Medicine, I am spearheading the fNIRS analysis for studies examining pediatric sleep-disordered breathing (SDB), where I have identified specific prefrontal deficits associated with hypoxic burden that may underlie cognitive and behavioral impairments. Previously, I characterized cortical responses in preterm infants, providing evidence that tactile and auditory processing patterns can serve as early biomarkers for neurodevelopmental delays. This work bridges advanced signal processing with urgent clinical needs in pediatric neurology and sleep medicine.
• Navarathna, N.*, Novi, S.L.*, et al. (2025). Assessing Executive Function in Pediatric Sleep‐Disordered Breathing Using Functional Neuroimaging. Otolaryngology–Head and Neck Surgery. (*Co-First Authors)
• Machado, A.C.C.P., … Novi, S.L., … & Bouzada, M.C.F. (2023). Can tactile reactivity in preterm born infants be explained by an immature cortical response to tactile stimulation in the first year? Journal of Perinatology, 43 (6), 728-734.
• Bertachini, A.L.L., … Novi, S.L., et al. (2021). Hearing brain evaluated using near-infrared spectroscopy in congenital toxoplasmosis. Scientific Reports, 11(1), 10135.
• De Oliveira, S.R., … Novi, S.L., et al. (2018). Association between hemodynamic activity and motor performance in six-month-old infants: an fNIRS study. Neurophotonics, 5(1), 011016.
Clinical Translation II: Acute Brain Injury & Consciousness: In collaboration with the Adrian Owen Lab, I deployed my noise-removal pipelines to detect covert consciousness in ICU patients who appear behaviorally non-responsive. Our groundbreaking work, published in PNAS and Annals of Neurology, demonstrated that fNIRS can reliably detect command-following brain activity at the bedside, offering a practical and portable alternative to fMRI for critically ill patients who cannot be transported to neuroimaging suites. This research establishes fNIRS as a transformative tool for consciousness assessment in intensive care settings, with direct implications for clinical decision-making and prognostication.
• Kazazian, K., … Novi, S.L., … & Debicki, D.B. (2024). Functional near-infrared spectroscopy: A novel tool for detecting consciousness after acute severe brain injury. Proceedings of the National Academy of Sciences (PNAS), 121(36).
• Kazazian, K., … Novi, S.L., et al. (2025). Detecting awareness in the intensive care unit using functional near‐infrared spectroscopy. Annals of Neurology, 98(6), 1201-1209.
• Laforge, G., … Novi, S.L., et al. (2025). Parallel EEG-fNIRS assessments of covert cognition in behaviorally non-responsive ICU patients. Journal of Neurology, 272(2), 148.
• Forero, E.J., Novi, S.L., Avelar, W.M., et al. (2017). Use of near-infrared spectroscopy to probe occlusion severity in patients diagnosed with carotid atherosclerotic disease. Medical Research Archives. (*Co-First Authors)
Setting Standards for AI in Clinical Neuroimaging: As Artificial Intelligence transforms medicine, I am working to establish rigorous standards for its application in otolaryngology and neuroimaging. I recently led a comprehensive narrative review in JAMA Otolaryngology, articulating a strategic roadmap for integrating Deep Learning into clinical practice while addressing critical challenges of interpretability, generalizability, and regulatory compliance. Additionally, my work on “brain fingerprinting” using fNIRS-based subject identification provided one of the first proof-of-concept that optical signals contain unique, stable biological signatures independent of superficial physiological noise. Extending these identification frameworks, I have demonstrated the prognostic utility of computational models by identifying specific resting-state network topologies that predict neurologic recovery in patients with severe acute brain injury, paving the way for personalized prognostication in neurocritical care.
• Novi, S.L., Navarathna, N., … & Isaiah, A. (2026). Deep learning in otolaryngology: a narrative review. JAMA Otolaryngology–Head & Neck Surgery, 152 (1), 71-80.
• Novi, S.L., Carvalho, A.C., … & Mesquita, R.C. (2023). Revealing the spatiotemporal requirements for accurate subject identification with resting-state functional connectivity: a simultaneous fNIRS-fMRI study. Neurophotonics, 10(1), 013510.
• Kolisnyk, M., Kazazian, K., Rego, K., Novi, S.L., et al. (2023). Predicting neurologic recovery after severe acute brain injury using resting-state networks. Journal of Neurology, 270(12), 6071-6080.