Neonatal brain dynamic functional connectivity: impact of preterm birth and association with early childhood neurodevelopment

Brain functional dynamics have been linked to emotion and cognition in mature individuals, where alterations are associated with mental ill-health and neurodevelopmental conditions (such as autism spectrum disorder). Although reliable resting-state networks have been consistently identified in neonates, little is known about the early development of dynamic brain functional connectivity and whether it is linked to later neurodevelopmental outcomes in childhood. In this study we characterised dynamic functional connectivity in the first few weeks of postnatal life and evaluated whether early dynamic functional connectivity: i) changes with age in the neonatal period ii) is altered by preterm birth and iii) is associated with neurodevelopmental and behavioural outcomes at 18 months. Global brain dynamics in preterm-born infants were atypical when compared with term-born controls, and associated with atypical social, sensory, and repetitive behaviours measured by the Quantitative Checklist for Autism in Toddlers (Q-CHAT) scores at 18 months of age. On a modular scale, we identified six transient states of neonatal dynamic functional connectivity: three whole-brain synchronisation states and three regional synchrony states occupying occipital, sensory-motor, and frontal regions. Modular characteristics of these brain states were correlated with postmenstrual age and postnatal days at scan. Preterm-born infants had increased occurrence of frontal and occipital states. Higher neonatal sensory-motor synchronisation was associated with lower motor and language outcome scores at 18 months. Lower frequency of occurrence of whole-brain synchronisation states and higher frequency of occurrence of the sensory-motor state were associated with higher Q-CHAT scores at 18 months. Overall, we show that a dynamic landscape of brain connectivity is already established by the time of birth in the human brain. This landscape is altered by preterm birth and its profile is linked to neurodevelopmental outcomes in toddlerhood.

[1]  D. Murphy,et al.  Clinical, socio-demographic, and parental correlates of early autism traits in a community cohort , 2023, bioRxiv.

[2]  Shaihan J. Malik,et al.  The Developing Human Connectome Project Neonatal Data Release , 2022, Frontiers in Neuroscience.

[3]  E. Burdet,et al.  Development of functional organization within the sensorimotor network across the perinatal period , 2022, Human brain mapping.

[4]  Richard F. Betzel,et al.  Edge-centric analysis of time-varying functional brain networks with applications in autism spectrum disorder , 2021, NeuroImage.

[5]  S. Baron-Cohen,et al.  Quantitative Checklist for Autism in Toddlers (Q-CHAT). A population screening study with follow-up: the case for multiple time-point screening for autism , 2021, BMJ Paediatrics Open.

[6]  Ursula A. Tooley,et al.  Environmental influences on the pace of brain development , 2021, Nature Reviews Neuroscience.

[7]  S. Brem,et al.  Brain dynamics of (a)typical reading development—a review of longitudinal studies , 2021, NPJ science of learning.

[8]  Д. М. Максимов,et al.  Апробация методики «Bayley Scales of Infant and Toddler Development – Third Edition» , 2020 .

[9]  Yuan Shi,et al.  Changes of Dynamic Functional Connectivity Associated With Maturity in Late Preterm Infants , 2020, Frontiers in Pediatrics.

[10]  Jaime Fern'andez del R'io,et al.  Array programming with NumPy , 2020, Nature.

[11]  D. Rueckert,et al.  Parental age effects on neonatal white matter development , 2020, NeuroImage: Clinical.

[12]  J. Hajnal,et al.  Emerging functional connectivity differences in newborn infants vulnerable to autism spectrum disorders , 2020, Translational Psychiatry.

[13]  Morten L. Kringelbach,et al.  Ghost Attractors in Spontaneous Brain Activity: Recurrent Excursions Into Functionally-Relevant BOLD Phase-Locking States , 2020, Frontiers in Systems Neuroscience.

[14]  D. Rueckert,et al.  The Developing Human Connectome Project: typical and disrupted perinatal functional connectivity , 2020, bioRxiv.

[15]  Vince D. Calhoun,et al.  Questions and controversies in the study of time-varying functional connectivity in resting fMRI , 2020, Network Neuroscience.

[16]  Daniel Rueckert,et al.  The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants , 2019, NeuroImage.

[17]  Joel Nothman,et al.  SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python , 2019, ArXiv.

[18]  S. Counsell,et al.  Factors associated with atypical brain development in preterm infants: insights from magnetic resonance imaging. , 2019, Neuropathology and applied neurobiology.

[19]  Dinggang Shen,et al.  Development of Dynamic Functional Architecture during Early Infancy , 2019, bioRxiv.

[20]  Morten L. Kringelbach,et al.  Dynamical exploration of the repertoire of brain networks at rest is modulated by psilocybin , 2019, NeuroImage.

[21]  Victor M. Saenger,et al.  Breakdown of Whole-brain Dynamics in Preterm-born Children , 2019, Cerebral cortex.

[22]  James A. Roberts,et al.  Large-scale brain modes reorganize between infant sleep states and carry prognostic information for preterms , 2019, Nature Communications.

[23]  Antonis D. Savva,et al.  Assessment of dynamic functional connectivity in resting‐state fMRI using the sliding window technique , 2019, Brain and behavior.

[24]  Gustavo Deco,et al.  Altered ability to access a clinically relevant control network in patients remitted from major depressive disorder , 2019, Human brain mapping.

[25]  Mark H. Johnson,et al.  Increased cortical reactivity to repeated tones at 8 months in infants with later ASD , 2019, Translational Psychiatry.

[26]  Stephen M Smith,et al.  Spatial parcellations, spectral filtering, and connectivity measures in fMRI: Optimizing for discrimination , 2018, Human brain mapping.

[27]  S. Counsell,et al.  Factors associated with atypical brain development in preterm infants: insights from magnetic resonance imaging , 2019 .

[28]  Yihui Xie,et al.  knitr: A Comprehensive Tool for Reproducible Research in R , 2018, Implementing Reproducible Research.

[29]  C. Smyser,et al.  Aberrant structural and functional connectivity and neurodevelopmental impairment in preterm children , 2018, Journal of Neurodevelopmental Disorders.

[30]  Vince D Calhoun,et al.  Dynamic connectivity and the effects of maturation in youth with attention deficit hyperactivity disorder , 2018, Network Neuroscience.

[31]  Linda Douw,et al.  Dynamic Functional Connectivity and Symptoms of Parkinson’s Disease: A Resting-State fMRI Study , 2018, Front. Aging Neurosci..

[32]  R. Joseph,et al.  The risk of neurodevelopmental disorders at age 10 years associated with blood concentrations of interleukins 4 and 10 during the first postnatal month of children born extremely preterm , 2018, Cytokine.

[33]  M. Bulsara,et al.  Prevalence of Autism Spectrum Disorder in Preterm Infants: A Meta-analysis , 2018, Pediatrics.

[34]  Mike Anderson,et al.  Cognitive outcomes in children and adolescents born very preterm: a meta‐analysis , 2018, Developmental medicine and child neurology.

[35]  E Burdet,et al.  Somatotopic Mapping of the Developing Sensorimotor Cortex in the Preterm Human Brain , 2018, Cerebral cortex.

[36]  Daniel Rueckert,et al.  Unbiased construction of a temporally consistent morphological atlas of neonatal brain development , 2018, bioRxiv.

[37]  A. Strafella,et al.  Dynamic functional connectivity in Parkinson's disease patients with mild cognitive impairment and normal cognition , 2017, NeuroImage: Clinical.

[38]  Peter A. Bandettini,et al.  Task-based dynamic functional connectivity: Recent findings and open questions , 2017, NeuroImage.

[39]  Per B. Brockhoff,et al.  lmerTest Package: Tests in Linear Mixed Effects Models , 2017 .

[40]  Dimitri Van De Ville,et al.  The dynamic functional connectome: State-of-the-art and perspectives , 2017, NeuroImage.

[41]  Gustavo Deco,et al.  Cognitive performance in healthy older adults relates to spontaneous switching between states of functional connectivity during rest , 2017, Scientific Reports.

[42]  G. Rees,et al.  Brain network dynamics in high-functioning individuals with autism , 2017, Nature Communications.

[43]  V. Calhoun,et al.  Disruption to control network function correlates with altered dynamic connectivity in the wider autism spectrum , 2017, NeuroImage: Clinical.

[44]  Hui Zhang,et al.  Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movement , 2017, NeuroImage.

[45]  Philip J. Brittain,et al.  Real-Life Impact of Executive Function Impairments in Adults Who Were Born Very Preterm , 2017, Journal of the International Neuropsychological Society.

[46]  Mark Tommerdahl,et al.  Reduced GABA and altered somatosensory function in children with autism spectrum disorder , 2017, Autism research : official journal of the International Society for Autism Research.

[47]  Chiara Nosarti,et al.  Early development of structural networks and the impact of prematurity on brain connectivity , 2017, NeuroImage.

[48]  Tomoki Arichi,et al.  A dedicated neonatal brain imaging system , 2016, Magnetic resonance in medicine.

[49]  Yong He,et al.  Early Development of Functional Network Segregation Revealed by Connectomic Analysis of the Preterm Human Brain , 2016, Cerebral cortex.

[50]  Gustavo Deco,et al.  The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core , 2016, bioRxiv.

[51]  R. Payne,et al.  Adjusted indices of multiple deprivation to enable comparisons within and between constituent countries of the UK including an illustration using mortality rates , 2016, BMJ Open.

[52]  Jesper Andersson,et al.  A multi-modal parcellation of human cerebral cortex , 2016, Nature.

[53]  L. Schieve,et al.  Population impact of preterm birth and low birth weight on developmental disabilities in US children. , 2016, Annals of epidemiology.

[54]  Jean-Philippe Thiran,et al.  Brain network characterization of high-risk preterm-born school-age children , 2016, NeuroImage: Clinical.

[55]  Yihui Xie,et al.  A General-Purpose Package for Dynamic Report Generation in R , 2016 .

[56]  Stamatios N. Sotiropoulos,et al.  An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging , 2016, NeuroImage.

[57]  Chiara Nosarti,et al.  Reinforcement of the Brain's Rich-Club Architecture Following Early Neurodevelopmental Disruption Caused by Very Preterm Birth , 2016, Cerebral cortex.

[58]  Joseph V. Hajnal,et al.  Machine-learning to characterise neonatal functional connectivity in the preterm brain , 2016, NeuroImage.

[59]  Etienne Burdet,et al.  Maturation of Sensori-Motor Functional Responses in the Preterm Brain , 2015, Cerebral cortex.

[60]  Anish Mitra,et al.  Resting-State Network Complexity and Magnitude Are Reduced in Prematurely Born Infants. , 2016, Cerebral cortex.

[61]  Wei Gao,et al.  Functional Network Development During the First Year: Relative Sequence and Socioeconomic Correlations. , 2015, Cerebral cortex.

[62]  Gustavo Deco,et al.  Task-Driven Activity Reduces the Cortical Activity Space of the Brain: Experiment and Whole-Brain Modeling , 2015, PLoS Comput. Biol..

[63]  P. Brockhoff,et al.  Tests in Linear Mixed Effects Models , 2015 .

[64]  Peter J Hellyer,et al.  Cognitive Flexibility through Metastable Neural Dynamics Is Disrupted by Damage to the Structural Connectome , 2015, The Journal of Neuroscience.

[65]  Yihui Xie,et al.  Dynamic Documents with R and knitr , 2015 .

[66]  H. Wickham Simple, Consistent Wrappers for Common String Operations , 2015 .

[67]  Dimitri Van De Ville,et al.  On spurious and real fluctuations of dynamic functional connectivity during rest , 2015, NeuroImage.

[68]  V. Calhoun,et al.  The Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery , 2014, Neuron.

[69]  Chiara Nosarti,et al.  Preterm birth and structural brain alterations in early adulthood , 2014, NeuroImage: Clinical.

[70]  Steen Moeller,et al.  ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging , 2014, NeuroImage.

[71]  D. Bates,et al.  Fitting Linear Mixed-Effects Models Using lme4 , 2014, 1406.5823.

[72]  H. Laufs,et al.  Decoding Wakefulness Levels from Typical fMRI Resting-State Data Reveals Reliable Drifts between Wakefulness and Sleep , 2014, Neuron.

[73]  Daniel Rueckert,et al.  Automatic Whole Brain MRI Segmentation of the Developing Neonatal Brain , 2014, IEEE Transactions on Medical Imaging.

[74]  Ludovica Griffanti,et al.  Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers , 2014, NeuroImage.

[75]  Eswar Damaraju,et al.  Tracking whole-brain connectivity dynamics in the resting state. , 2014, Cerebral cortex.

[76]  Krzysztof J. Gorgolewski,et al.  Dynamic network participation of functional connectivity hubs assessed by resting-state fMRI , 2014, Front. Hum. Neurosci..

[77]  Peter J Hellyer,et al.  The Control of Global Brain Dynamics: Opposing Actions of Frontoparietal Control and Default Mode Networks on Attention , 2014, The Journal of Neuroscience.

[78]  Vince D. Calhoun,et al.  Functional connectivity in the developing brain: A longitudinal study from 4 to 9months of age , 2014, NeuroImage.

[79]  Timothy O. Laumann,et al.  Methods to detect, characterize, and remove motion artifact in resting state fMRI , 2014, NeuroImage.

[80]  J. K. Smith,et al.  Development of human brain cortical network architecture during infancy , 2014, Brain Structure and Function.

[81]  Thomas E. Nichols,et al.  Functional connectomics from resting-state fMRI , 2013, Trends in Cognitive Sciences.

[82]  David A. Leopold,et al.  Dynamic functional connectivity: Promise, issues, and interpretations , 2013, NeuroImage.

[83]  Charles Raybaud,et al.  The premature brain: developmental and lesional anatomy , 2013, Neuroradiology.

[84]  Yong He,et al.  BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics , 2013, PloS one.

[85]  Jonas Obleser,et al.  The Brain Dynamics of Rapid Perceptual Adaptation to Adverse Listening Conditions , 2013, The Journal of Neuroscience.

[86]  N. Robertson,et al.  Autism and intellectual disability , 2013, Journal of Neurology.

[87]  Atta Abbas,et al.  DIAGNOSTIC AND STATISTICAL MANUAL OF MENTAL DISORDERS, FIFTH EDITION , 2013 .

[88]  Murray Shanahan,et al.  Metastability and chimera states in modular delay and pulse-coupled oscillator networks. , 2012, Chaos.

[89]  Mikko Sams,et al.  Functional Magnetic Resonance Imaging Phase Synchronization as a Measure of Dynamic Functional Connectivity , 2012, Brain Connect..

[90]  Abraham Z. Snyder,et al.  Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion , 2012, NeuroImage.

[91]  E. Walker,et al.  Diagnostic and Statistical Manual of Mental Disorders , 2013 .

[92]  J. Gilmore,et al.  Infant Brain Atlases from Neonates to 1- and 2-Year-Olds , 2011, PloS one.

[93]  F. Stanley,et al.  Autism and Intellectual Disability Are Differentially Related to Sociodemographic Background at Birth , 2011, PloS one.

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

[95]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[96]  A. Snyder,et al.  Longitudinal analysis of neural network development in preterm infants. , 2010, Cerebral cortex.

[97]  F. Turkheimer,et al.  Emergence of resting state networks in the preterm human brain , 2010, Proceedings of the National Academy of Sciences.

[98]  Liang Wang,et al.  Dynamic functional reorganization of the motor execution network after stroke. , 2010, Brain : a journal of neurology.

[99]  C. Barthélémy,et al.  Atypical activation of the mirror neuron system during perception of hand motion in autism , 2010, Brain Research.

[100]  Kent A. Kiehl,et al.  A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia , 2010, Magnetic Resonance Materials in Physics, Biology and Medicine.

[101]  B. Peterson,et al.  Normal Development of Brain Circuits , 2010, Neuropsychopharmacology.

[102]  Wes McKinney,et al.  Data Structures for Statistical Computing in Python , 2010, SciPy.

[103]  L. Ment,et al.  Imaging biomarkers of outcome in the developing preterm brain , 2009, The Lancet Neurology.

[104]  Hadley Wickham,et al.  ggplot2 - Elegant Graphics for Data Analysis (2nd Edition) , 2017 .

[105]  Peter Fransson,et al.  Spontaneous Brain Activity in the Newborn Brain During Natural Sleep—An fMRI Study in Infants Born at Full Term , 2009, Pediatric Research.

[106]  D. Hay,et al.  The links between prenatal stress and offspring development and psychopathology: disentangling environmental and inherited influences , 2009, Psychological Medicine.

[107]  J. Andrews-Hanna,et al.  The brain's default network: Anatomy, function, and consequence of disruption , 2009 .

[108]  J. Heron,et al.  The impact of maternal depression in pregnancy on early child development , 2008, BJOG : an international journal of obstetrics and gynaecology.

[109]  J. Soul,et al.  Positive Screening for Autism in Ex-preterm Infants: Prevalence and Risk Factors , 2008, Pediatrics.

[110]  S. Baron-Cohen,et al.  The Q-CHAT (Quantitative CHecklist for Autism in Toddlers): A Normally Distributed Quantitative Measure of Autistic Traits at 18–24 Months of Age: Preliminary Report , 2008, Journal of autism and developmental disorders.

[111]  F. Schmidt Meta-Analysis , 2008 .

[112]  Peter Fransson,et al.  Resting-state networks in the infant brain , 2007, Proceedings of the National Academy of Sciences.

[113]  W. Dunn,et al.  Sensory processing in children with and without autism: a comparative study using the short sensory profile. , 2007, The American journal of occupational therapy : official publication of the American Occupational Therapy Association.

[114]  J. Culham,et al.  The role of parietal cortex in visuomotor control: What have we learned from neuroimaging? , 2006, Neuropsychologia.

[115]  M. Newman,et al.  Finding community structure in networks using the eigenvectors of matrices. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[116]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[117]  S. Rogers,et al.  Annotation: what do we know about sensory dysfunction in autism? A critical review of the empirical evidence. , 2005, Journal of child psychology and psychiatry, and allied disciplines.

[118]  T. To,et al.  Risk markers for poor developmental attainment in young children: results from a longitudinal national survey. , 2004, Archives of pediatrics & adolescent medicine.

[119]  Stefan Skare,et al.  How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging , 2003, NeuroImage.

[120]  T. O'Connor,et al.  Effects of antenatal stress and anxiety: Implications for development and psychiatry. , 2002, The British journal of psychiatry : the journal of mental science.

[121]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[122]  J E Janosky,et al.  Maternal education and measures of early speech and language. , 1999, Journal of speech, language, and hearing research : JSLHR.

[123]  P. Cornelius,et al.  Approximate F-tests of multiple degree of freedom hypotheses in generalized least squares analyses of unbalanced split-plot experiments , 1996 .

[124]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[125]  Boualem Boashash,et al.  Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals , 1992, Proc. IEEE.

[126]  Boualem Boashash,et al.  Estimating and interpreting the instantaneous frequency of a signal. II. A/lgorithms and applications , 1992, Proc. IEEE.

[127]  F. G. Giesbrecht,et al.  Two-stage analysis based on a mixed model: large-sample asymptotic theory and small-sample simulation results , 1985 .

[128]  Yoshiki Kuramoto,et al.  Chemical Oscillations, Waves, and Turbulence , 1984, Springer Series in Synergetics.

[129]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[130]  T. Caliński,et al.  A dendrite method for cluster analysis , 1974 .