Investigating developmental changes in scalp-to-cortex correspondence using diffuse optical tomography sensitivity in infancy

Abstract. Significance: Diffuse optical tomography (DOT) uses near-infrared light spectroscopy (NIRS) to measure changes in cerebral hemoglobin concentration. Anatomical interpretations of NIRS data require accurate descriptions of the cranio-cerebral relations and DOT sensitivity to the underlying cortical structures. Such information is limited for pediatric populations because they undergo rapid head and brain development. Aim: We aim to investigate age-related differences in scalp-to-cortex distance and mapping between scalp locations and cortical regions of interest (ROIs) among infants (2 weeks to 24 months with narrow age bins), children (4 and 12 years), and adults (20 to 24 years). Approach: We used spatial scalp projection and photon propagation simulation methods with age-matched realistic head models based on MRIs. Results: There were age-group differences in the scalp-to-cortex distances in infancy. The developmental increase was magnified in children and adults. There were systematic age-related differences in the probabilistic mappings between scalp locations and cortical ROIs. Conclusions: Our findings have important implications in the design of sensor placement and making anatomical interpretations in NIRS and fNIRS research. Age-appropriate, realistic head models should be used to provide anatomical guidance for standalone DOT data in infants.

[1]  J. Richards,et al.  The developmental trajectory of fronto‐temporoparietal connectivity as a proxy of the default mode network: a longitudinal fNIRS investigation , 2020, Human brain mapping.

[2]  A. Ehlis,et al.  Simulation of Near-Infrared Light Absorption Considering Individual Head and Prefrontal Cortex Anatomy: Implications for Optical Neuroimaging , 2011, PloS one.

[3]  D. Boas,et al.  Volumetric diffuse optical tomography of brain activity. , 2003, Optics letters.

[4]  John E. Richards,et al.  Age-specific MRI brain and head templates for healthy adults from 20 through 89 years of age , 2015, Front. Aging Neurosci..

[5]  Daniel Rueckert,et al.  Regional growth and atlasing of the developing human brain , 2016, NeuroImage.

[6]  David A. Boas,et al.  Noninvasive Imaging of Cerebral Activation with Diffuse Optical Tomography , 2009 .

[7]  Wanze Xie,et al.  Brains for all the ages: structural neurodevelopment in infants and children from a life-span perspective. , 2015, Advances in child development and behavior.

[8]  M. Styner,et al.  Longitudinal development of cortical and subcortical gray matter from birth to 2 years. , 2012, Cerebral cortex.

[9]  D. Boas,et al.  Three dimensional Monte Carlo code for photon migration through complex heterogeneous media including the adult human head. , 2002, Optics express.

[10]  Tal Kenet,et al.  The Pediatric Imaging, Neurocognition, and Genetics (PING) Data Repository , 2016, NeuroImage.

[11]  Richard N. Aslin,et al.  Top-down modulation in the infant brain: Learning-induced expectations rapidly affect the sensory cortex at 6 months , 2015, Proceedings of the National Academy of Sciences.

[12]  Jacques Felblinger,et al.  Automated cortical projection of EEG sensors: Anatomical correlation via the international 10–10 system , 2009, NeuroImage.

[13]  C. Rorden,et al.  Stereotaxic display of brain lesions. , 2000, Behavioural neurology.

[14]  John E. Richards,et al.  Evaluating Methods for Constructing Average High-Density Electrode Positions , 2014, Brain Topography.

[15]  Ippeita Dan,et al.  Spatial registration for functional near-infrared spectroscopy: From channel position on the scalp to cortical location in individual and group analyses , 2014, NeuroImage.

[16]  R. Aslin,et al.  Developmental Cognitive Neuroscience Near-infrared Spectroscopy: a Report from the Mcdonnell Infant Methodology Consortium , 2022 .

[17]  Sarah Lloyd-Fox,et al.  Using functional near‐infrared spectroscopy to assess social information processing in poor urban Bangladeshi infants and toddlers , 2019, Developmental science.

[18]  Sean C. L. Deoni,et al.  Quantifying cortical development in typically developing toddlers and young children, 1–6 years of age , 2017, NeuroImage.

[19]  Arthur W. Toga,et al.  Construction of a 3D probabilistic atlas of human cortical structures , 2008, NeuroImage.

[20]  A. Blasi,et al.  Illuminating the developing brain: The past, present and future of functional near infrared spectroscopy , 2010, Neuroscience & Biobehavioral Reviews.

[21]  J. Richards,et al.  Fronto-temporoparietal connectivity and self-awareness in 18-month-olds: A resting state fNIRS study , 2019, Developmental Cognitive Neuroscience.

[22]  Ippeita Dan,et al.  Referential framework for transcranial anatomical correspondence for fNIRS based on manually traced sulci and gyri of an infant brain , 2014, Neuroscience Research.

[23]  Carmen E Sanchez,et al.  Age-Specific MRI Templates for Pediatric Neuroimaging , 2012, Developmental neuropsychology.

[24]  Dinggang Shen,et al.  The UNC/UMN Baby Connectome Project (BCP): An overview of the study design and protocol development , 2019, NeuroImage.

[25]  Masako Okamoto,et al.  Three-dimensional probabilistic anatomical cranio-cerebral correlation via the international 10–20 system oriented for transcranial functional brain mapping , 2004, NeuroImage.

[26]  A. Villringer,et al.  Optical Imaging of Brain Function and Metabolism , 1993, Advances in Experimental Medicine and Biology.

[27]  G. Strangman,et al.  Depth Sensitivity and Source-Detector Separations for Near Infrared Spectroscopy Based on the Colin27 Brain Template , 2013, PLoS ONE.

[28]  João Ricardo Sato,et al.  fNIRS Optodes’ Location Decider (fOLD): a toolbox for probe arrangement guided by brain regions-of-interest , 2018, Scientific Reports.

[29]  David A. Boas,et al.  Tetrahedral mesh generation from volumetric binary and grayscale images , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[30]  Audrey R. Nath,et al.  The Developmental Trajectory of Brain-Scalp Distance from Birth through Childhood: Implications for Functional Neuroimaging , 2011, PloS one.

[31]  Timothy Edward John Behrens,et al.  Anatomically related grey and white matter abnormalities in adolescent-onset schizophrenia. , 2007, Brain : a journal of neurology.

[32]  Beatriz Luna,et al.  fNIRS evidence of prefrontal regulation of frustration in early childhood , 2014, NeuroImage.

[33]  David A Boas,et al.  Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units. , 2009, Optics express.

[34]  Qianqian Fang,et al.  Graphics processing unit-accelerated mesh-based Monte Carlo photon transport simulations , 2019, Journal of biomedical optics.

[35]  Qianqian Fang,et al.  Improving model-based functional near-infrared spectroscopy analysis using mesh-based anatomical and light-transport models , 2020, Neurophotonics.

[36]  H. Ayaz,et al.  Investigation of the source‐detector separation in near infrared spectroscopy for healthy and clinical applications , 2019, Journal of biophotonics.

[37]  Ippeita Dan,et al.  Macroanatomical Landmarks Featuring Junctions of Major Sulci and Fissures and Scalp Landmarks Based on the International 10–10 System for Analyzing Lateral Cortical Development of Infants , 2017, Front. Neurosci..

[38]  Masako Okamoto,et al.  Automated cortical projection of head-surface locations for transcranial functional brain mapping , 2005, NeuroImage.

[39]  A. Villringer,et al.  Non-invasive optical spectroscopy and imaging of human brain function , 1997, Trends in Neurosciences.

[40]  Alan C. Evans,et al.  The NIH MRI study of normal brain development , 2006, NeuroImage.

[41]  Declan G. M. Murphy,et al.  Coregistering functional near-infrared spectroscopy with underlying cortical areas in infants , 2014, Neurophotonics.

[42]  Carmen E Sanchez,et al.  Neurodevelopmental MRI brain templates for children from 2 weeks to 4 years of age. , 2012, Developmental psychobiology.

[43]  Lindsey J. Powell,et al.  Using individual functional channels of interest to study cortical development with fNIRS. , 2018, Developmental science.

[44]  Ippeita Dan,et al.  Stable and convenient spatial registration of stand-alone NIRS data through anchor-based probabilistic registration , 2012, Neuroscience Research.

[45]  Sabrina Brigadoi,et al.  How short is short? Optimum source–detector distance for short-separation channels in functional near-infrared spectroscopy , 2015, Neurophotonics.

[46]  A. Humeau,et al.  Depth sensitivity analysis of functional near-infrared spectroscopy measurement using three-dimensional Monte Carlo modelling-based magnetic resonance imaging , 2010, Lasers in Medical Science.

[47]  E. Okada,et al.  Monte Carlo prediction of near-infrared light propagation in realistic adult and neonatal head models. , 2003, Applied optics.

[48]  Brian B. Avants,et al.  N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.

[49]  John E. Richards,et al.  Cortical sources of ERP in prosaccade and antisaccade eye movements using realistic source models , 2013, Front. Syst. Neurosci..

[50]  Matteo Fischetti,et al.  Array Designer: automated optimized array design for functional near-infrared spectroscopy , 2018, Neurophotonics.

[51]  Daniel P. Kennedy,et al.  The Autism Brain Imaging Data Exchange: Towards Large-Scale Evaluation of the Intrinsic Brain Architecture in Autism , 2013, Molecular Psychiatry.

[52]  Stephen M. Smith,et al.  A global optimisation method for robust affine registration of brain images , 2001, Medical Image Anal..

[53]  J. Richards,et al.  Stereotaxic Magnetic Resonance Imaging Brain Atlases for Infants from 3 to 12 Months , 2015, Developmental Neuroscience.

[54]  Kang Lee,et al.  Neural correlates of own- and other-race face recognition in children: A functional near-infrared spectroscopy study , 2014, NeuroImage.

[55]  Rebecca C. Knickmeyer,et al.  Regional Gray Matter Growth, Sexual Dimorphism, and Cerebral Asymmetry in the Neonatal Brain , 2007, The Journal of Neuroscience.

[56]  Hendrik Santosa,et al.  Investigation of the sensitivity of functional near-infrared spectroscopy brain imaging to anatomical variations in 5- to 11-year-old children , 2017, Neurophotonics.

[57]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[58]  Armin Raznahan,et al.  How Does Your Cortex Grow? , 2011, The Journal of Neuroscience.

[59]  Lauren L Emberson,et al.  Isolating the effects of surface vasculature in infant neuroimaging using short-distance optical channels: a combination of local and global effects , 2016, Neurophotonics.

[60]  J. Gilmore,et al.  Mapping Longitudinal Development of Local Cortical Gyrification in Infants from Birth to 2 Years of Age , 2014, The Journal of Neuroscience.

[61]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[62]  Richard N. Aslin,et al.  The Lateral Occipital Cortex Is Selective for Object Shape, Not Texture/Color, at Six Months , 2017, The Journal of Neuroscience.

[63]  Martin Styner,et al.  r Human Brain Mapping 000:000–000 (2010) r Genetic and Environmental Contributions to Neonatal Brain Structure: A Twin Study* , 2022 .

[64]  J. Gilmore,et al.  Spatial Patterns, Longitudinal Development, and Hemispheric Asymmetries of Cortical Thickness in Infants from Birth to 2 Years of Age , 2015, The Journal of Neuroscience.

[65]  J. Richards,et al.  Brain Development in Infants , 2020 .

[66]  John E. Richards,et al.  A database of age-appropriate average MRI templates , 2016, NeuroImage.

[67]  Tomer Fekete,et al.  A stand-alone method for anatomical localization of NIRS measurements , 2011, NeuroImage.

[68]  Alan C. Evans,et al.  Total and regional brain volumes in a population-based normative sample from 4 to 18 years: the NIH MRI Study of Normal Brain Development. , 2012, Cerebral cortex.

[69]  Matthieu Perrot,et al.  Anatomical correlations of the international 10–20 sensor placement system in infants , 2014, NeuroImage.

[70]  David A. Boas,et al.  Validating atlas-guided DOT: A comparison of diffuse optical tomography informed by atlas and subject-specific anatomies , 2012, NeuroImage.

[71]  Daniel Rueckert,et al.  Automatic anatomical brain MRI segmentation combining label propagation and decision fusion , 2006, NeuroImage.

[72]  Clare E. Elwell,et al.  Cortical Activation to Action Perception is Associated with Action Production Abilities in Young Infants , 2013, Cerebral cortex.

[73]  Quan Zhang,et al.  Scalp and skull influence on near infrared photon propagation in the Colin27 brain template , 2014, NeuroImage.

[74]  Valer Jurcak,et al.  10/20, 10/10, and 10/5 systems revisited: Their validity as relative head-surface-based positioning systems , 2007, NeuroImage.

[75]  Daniel Rueckert,et al.  Automatic segmentation of brain MRIs of 2-year-olds into 83 regions of interest , 2008, NeuroImage.

[76]  J. Hirsch,et al.  The present and future use of functional near‐infrared spectroscopy (fNIRS) for cognitive neuroscience , 2018, Annals of the New York Academy of Sciences.

[77]  Qianqian Fang,et al.  Dual-grid mesh-based Monte Carlo algorithm for efficient photon transport simulations in complex three-dimensional media , 2019, Journal of biomedical optics.

[78]  C. Hansman,et al.  Growth of interorbital distance and skull thickness as observed in roentgenographic measurements. , 1966, Radiology.

[79]  Richard N. Aslin,et al.  Using fNIRS to examine occipital and temporal responses to stimulus repetition in young infants: Evidence of selective frontal cortex involvement , 2016, Developmental Cognitive Neuroscience.

[80]  Simon R. Arridge,et al.  A 4D neonatal head model for diffuse optical imaging of pre-term to term infants , 2014, NeuroImage.

[81]  Arno Klein,et al.  A reproducible evaluation of ANTs similarity metric performance in brain image registration , 2011, NeuroImage.