Genetic Influence on the Sulcal Pits: On the Origin of the First Cortical Folds

The influence of genes on cortical structures has been assessed through various phenotypes. The sulcal pits, which are the putative first cortical folds, have for long been assumed to be under tight genetic control, but this was never quantified. We estimated the pit depth heritability in various brain regions using the high quality and large sample size of the Human Connectome Project pedigree cohort. Analysis of additive genetic variance indicated that their heritability ranges between 0.2 and 0.5 and displays a regional genetic control with an overall symmetric pattern between hemispheres. However, a noticeable asymmetry of heritability estimates is observed in the superior temporal sulcus and could thus be related to language lateralization. The heritability range estimated in this study reinforces the idea that cortical shape is determined primarily by nongenetic factors, which is consistent with the important increase of cortical folding from birth to adult life and thus predominantly constrained by environmental factors. Nevertheless, the genetic cues, implicated with various local levels of heritability in the formation of sulcal pits, play a fundamental role in the normal gyral pattern development. Quantifying their influence and identifying the underlying genetic variants would provide insight into neurodevelopmental disorders.

[1]  N. Gaab,et al.  Atypical Sulcal Pattern in Children with Developmental Dyslexia and At-Risk Kindergarteners. , 2016, Cerebral cortex.

[2]  J. Lefévre,et al.  On the growth and form of cortical convolutions , 2016, Nature Physics.

[3]  Angela D. Friederici,et al.  The ontogeny of the cortical language network , 2016, Nature Reviews Neuroscience.

[4]  C. Francks,et al.  Lateralization of gene expression in human language cortex , 2015, Cortex.

[5]  Guillaume Auzias,et al.  Deep sulcal landmarks: Algorithmic and conceptual improvements in the definition and extraction of sulcal pits , 2015, NeuroImage.

[6]  Steen Moeller,et al.  Heritability of fractional anisotropy in human white matter: A comparison of Human Connectome Project and ENIGMA-DTI data , 2015, NeuroImage.

[7]  Camino de Juan Romero,et al.  Discrete domains of gene expression in germinal layers distinguish the development of gyrencephaly , 2015, The EMBO journal.

[8]  M. Neale,et al.  Comparison of Twin and Extended Pedigree Designs for Obtaining Heritability Estimates , 2015, Behavior genetics.

[9]  Marc Brysbaert,et al.  New human-specific brain landmark: The depth asymmetry of superior temporal sulcus , 2015, Proceedings of the National Academy of Sciences.

[10]  J. Brodsky,et al.  Escaping the endoplasmic reticulum: why does a molecular chaperone leave home for greener pastures? , 2015, The EMBO journal.

[11]  Dinggang Shen,et al.  Spatial distribution and longitudinal development of deep cortical sulcal landmarks in infants , 2014, NeuroImage.

[12]  Paul M. Thompson,et al.  Impact of family structure and common environment on heritability estimation for neuroimaging genetics studies using Sequential Oligogenic Linkage Analysis Routines , 2014, Journal of medical imaging.

[13]  Rudolph Pienaar,et al.  Quantification and discrimination of abnormal sulcal patterns in polymicrogyria. , 2013, Cerebral cortex.

[14]  Peter Kochunov,et al.  Sulcal Depth-Position Profile Is a Genetically Mediated Neuroscientific Trait: Description and Characterization in the Central Sulcus , 2013, The Journal of Neuroscience.

[15]  Mert R. Sabuncu,et al.  A Surface-based Analysis of Language Lateralization and Cortical Asymmetry , 2013, Journal of Cognitive Neuroscience.

[16]  Jong-Min Lee,et al.  Automated Sulcal Depth Measurement on Cortical Surface Reflecting Geometrical Properties of Sulci , 2013, PloS one.

[17]  M. Nalls,et al.  Genome-Wide Association Study of Retinopathy in Individuals without Diabetes , 2013, PloS one.

[18]  P. Grant,et al.  Reliable Identification of Deep Sulcal Pits: The Effects of Scan Session, Scanner, and Surface Extraction Tool , 2013, PloS one.

[19]  Guilherme Carvalhal Ribas,et al.  Study of fetal and postnatal morphological development of the brain sulci. , 2013, Journal of neurosurgery. Pediatrics.

[20]  Bruce Fischl,et al.  FreeSurfer , 2012, NeuroImage.

[21]  Marisa O. Hollinshead,et al.  Identification of common variants associated with human hippocampal and intracranial volumes , 2012, Nature Genetics.

[22]  A. Dale,et al.  Hierarchical Genetic Organization of Human Cortical Surface Area , 2012, Science.

[23]  J. Rauschecker,et al.  Phoneme and word recognition in the auditory ventral stream , 2012, Proceedings of the National Academy of Sciences.

[24]  Steen Moeller,et al.  The Human Connectome Project: A data acquisition perspective , 2012, NeuroImage.

[25]  Jean-Francois Mangin,et al.  A robust cerebral asymmetry in the infant brain: The rightward superior temporal sulcus , 2011, NeuroImage.

[26]  P. Ellen Grant,et al.  Quantitative comparison and analysis of sulcal patterns using sulcal graph matching: A twin study , 2011, NeuroImage.

[27]  S. Dehaene,et al.  Cortical representation of the constituent structure of sentences , 2011, Proceedings of the National Academy of Sciences.

[28]  Paul M. Thompson,et al.  Genetics of Primary Cerebral Gyrification: Heritability of Length, Depth and Area of Primary Sulci in an Extended Pedigree of Papio Baboons , 2022 .

[29]  Jean-Francois Mangin,et al.  Structural asymmetries of perisylvian regions in the preterm newborn , 2010, NeuroImage.

[30]  Alan C. Evans,et al.  Spatial distribution of deep sulcal landmarks and hemispherical asymmetry on the cortical surface. , 2010, Cerebral cortex.

[31]  Guilherme Carvalhal Ribas,et al.  The cerebral sulci and gyri. , 2010, Neurosurgical focus.

[32]  Alan C. Evans,et al.  Depth potential function for folding pattern representation, registration and analysis , 2009, Medical Image Anal..

[33]  Katrin Amunts,et al.  The central sulcus: an observer-independent characterization of sulcal landmarks and depth asymmetry. , 2008, Cerebral cortex.

[34]  D. V. von Cramon,et al.  Deep sulcal landmarks provide an organizing framework for human cortical folding. , 2008, Cerebral cortex.

[35]  Katrin Amunts,et al.  Cortical Folding Patterns and Predicting Cytoarchitecture , 2007, Cerebral cortex.

[36]  M. Sigman,et al.  Functional organization of perisylvian activation during presentation of sentences in preverbal infants , 2006, Proceedings of the National Academy of Sciences.

[37]  Robert J Zatorre,et al.  Asymmetries of the planum temporale and Heschl's gyrus: relationship to language lateralization. , 2006, Brain : a journal of neurology.

[38]  Guillermo Sapiro,et al.  A geometric method for automatic extraction of sulcal fundi , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[39]  Y. Burnod,et al.  A morphogenetic model for the development of cortical convolutions. , 2005, Cerebral cortex.

[40]  Y. Samson,et al.  "Sulcal root" generic model: a hypothesis to overcome the variability of the human cortex folding patterns. , 2005, Neurologia medico-chirurgica.

[41]  Jean-Francois Mangin,et al.  Sulcal pattern and morphology of the superior temporal sulcus , 2004, NeuroImage.

[42]  A. Toga,et al.  Abnormal gyral complexity in first-episode schizophrenia , 2004, Biological Psychiatry.

[43]  S. Martinez,et al.  Neuroepithelial secondary organizers and cell fate specification in the developing brain , 2003, Brain Research Reviews.

[44]  D. Geschwind,et al.  Heritability of lobar brain volumes in twins supports genetic models of cerebral laterality and handedness , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[45]  Jerry L Prince,et al.  Automated Sulcal Segmentation Using Watersheds on the Cortical Surface , 2002, NeuroImage.

[46]  M. Hoch,et al.  [Evolution of the nervous system]. , 2002, Anasthesiologie, Intensivmedizin, Notfallmedizin, Schmerztherapie : AINS.

[47]  B L Miller,et al.  Molecular approaches to cerebral laterality: development and neurodegeneration. , 2001, American journal of medical genetics.

[48]  D. V. von Cramon,et al.  Sulcal variability of twins. , 1999, Cerebral cortex.

[49]  L. Almasy,et al.  Multipoint quantitative-trait linkage analysis in general pedigrees. , 1998, American journal of human genetics.

[50]  Michael C. Neale,et al.  The Use of Likelihood-Based Confidence Intervals in Genetic Models , 1997, Behavior genetics.

[51]  D. Weinberger,et al.  Genetic variability of human brain size and cortical gyral patterns. , 1997, Brain : a journal of neurology.

[52]  D. V. Essen,et al.  A tension-based theory of morphogenesis and compact wiring in the central nervous system , 1997, Nature.

[53]  D. V. van Essen,et al.  A tension-based theory of morphogenesis and compact wiring in the central nervous system. , 1997, Nature.

[54]  C. Amos Robust variance-components approach for assessing genetic linkage in pedigrees. , 1994, American journal of human genetics.

[55]  J. Cheverud,et al.  Heritability of brain size and surface features in rhesus macaques (Macaca mulatta). , 1990, Journal of Heredity.

[56]  P. Rakic Specification of cerebral cortical areas. , 1988, Science.