Landmarking the Brain for Geometric Morphometric Analysis: An Error Study

Neuroanatomic phenotypes are often assessed using volumetric analysis. Although powerful and versatile, this approach is limited in that it is unable to quantify changes in shape, to describe how regions are interrelated, or to determine whether changes in size are global or local. Statistical shape analysis using coordinate data from biologically relevant landmarks is the preferred method for testing these aspects of phenotype. To date, approximately fifty landmarks have been used to study brain shape. Of the studies that have used landmark-based statistical shape analysis of the brain, most have not published protocols for landmark identification or the results of reliability studies on these landmarks. The primary aims of this study were two-fold: (1) to collaboratively develop detailed data collection protocols for a set of brain landmarks, and (2) to complete an intra- and inter-observer validation study of the set of landmarks. Detailed protocols were developed for 29 cortical and subcortical landmarks using a sample of 10 boys aged 12 years old. Average intra-observer error for the final set of landmarks was 1.9 mm with a range of 0.72 mm–5.6 mm. Average inter-observer error was 1.1 mm with a range of 0.40 mm–3.4 mm. This study successfully establishes landmark protocols with a minimal level of error that can be used by other researchers in the assessment of neuroanatomic phenotypes.

[1]  M. Yücel,et al.  Structural brain abnormalities in major depressive disorder: a selective review of recent MRI studies. , 2009, Journal of affective disorders.

[2]  Gabriele Lohmann,et al.  Automatic labelling of the human cortical surface using sulcal basins , 2000, Medical Image Anal..

[3]  Anqi Qiu,et al.  Quantitative evaluation of LDDMM, FreeSurfer, and CARET for cortical surface mapping , 2010, NeuroImage.

[4]  Richard M. Leahy,et al.  Comparison of landmark-based and automatic methods for cortical surface registration , 2010, NeuroImage.

[5]  A. Convit,et al.  Hippocampal damage and memory impairments as possible early brain complications of type 2 diabetes , 2007, Diabetologia.

[6]  N. Makris,et al.  MRI-based brain volumetrics: emergence of a developmental brain science , 1999, Brain and Development.

[7]  H. J. Jerison Chapter 9 – Evolution of the Brain in Birds , 1973 .

[8]  F. Aboitiz,et al.  Does bigger mean better? Evolutionary determinants of brain size and structure. , 1996, Brain, behavior and evolution.

[9]  P H Harvey,et al.  Comparing brains. , 1990, Science.

[10]  Daniel Govier,et al.  Central nervous system phenotypes in craniosynostosis , 2002, Journal of anatomy.

[11]  Kristina Aldridge Patterns of differences in brain morphology in humans as compared to extant apes. , 2011, Journal of human evolution.

[12]  T. Deacon Rethinking mammalian brain evolution , 1990 .

[13]  C J Valeri,et al.  Capturing data from three-dimensional surfaces using fuzzy landmarks. , 1998, American journal of physical anthropology.

[14]  E. Bullmore,et al.  The University of Birmingham ( Live System ) Are There Progressive Brain Changes in Schizophrenia ? A Meta-Analysis of Structural Magnetic Resonance Imaging Studies , 2016 .

[15]  Lei Wang,et al.  Metric distances between hippocampal shapes indicate different rates of change over time in nondemented and demented subjects. , 2012, Current Alzheimer research.

[16]  Greg Harris,et al.  Structural MR image processing using the BRAINS2 toolbox. , 2002, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[17]  J. Gerhart,et al.  Cells, Embryos and Evolution , 1997 .

[18]  F. James Rohlf,et al.  A geometric morphometric assessment of change in midline brain structural shape following a first episode of schizophrenia , 2000, Biological Psychiatry.

[19]  J. Richtsmeier,et al.  Relationship of brain and skull in pre‐ and postoperative sagittal synostosis , 2005, Journal of anatomy.

[20]  F. Bookstein,et al.  Morphometric Tools for Landmark Data: Geometry and Biology , 1999 .

[21]  Kiralee M. Hayashi,et al.  Mapping cortical change in Alzheimer's disease, brain development, and schizophrenia , 2004, NeuroImage.

[22]  Nancy C Andreasen,et al.  Three‐dimensional morphometric analysis of brain shape in nonsyndromic orofacial clefting , 2009, Journal of anatomy.

[23]  F. Rohlf,et al.  Geometric morphometrics: Ten years of progress following the ‘revolution’ , 2004 .

[24]  Karl J. Friston,et al.  Identifying Global Anatomical Differences: Deformation-Based Morphometry , 1998, NeuroImage.

[25]  J. Rilling,et al.  Evolution of the Brain: In Humans – Specializations in a Comparative Perspective , 2009 .

[26]  Fred L Bookstein,et al.  Landmark-based morphometric analysis of first-episode schizophrenia , 1999, Biological Psychiatry.

[27]  Ronald Pierson,et al.  Fully automated analysis using BRAINS: AutoWorkup , 2011, NeuroImage.

[28]  B. J. Casey,et al.  Imaging the developing brain: what have we learned about cognitive development? , 2005, Trends in Cognitive Sciences.

[29]  Patrick Marais,et al.  Three‐dimensional surface deformation‐based shape analysis of hippocampus and caudate nucleus in children with fetal alcohol spectrum disorders , 2014, Human brain mapping.

[30]  L Lemieux,et al.  Identifying homologous anatomical landmarks on reconstructed magnetic resonance images of the human cerebral cortical surface , 1998, Journal of anatomy.

[31]  E. Duchesnay,et al.  A framework to study the cortical folding patterns , 2004, NeuroImage.

[32]  Isabelle Bloch,et al.  A primal sketch of the cortex mean curvature: a morphogenesis based approach to study the variability of the folding patterns , 2003, IEEE Transactions on Medical Imaging.

[33]  S. L. Free,et al.  Landmark-Based Morphometrics of the Normal Adult Brain Using MRI , 2001, NeuroImage.

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

[35]  T. Kemper,et al.  Neuroanatomic observations of the brain in autism: a review and future directions , 2005, International Journal of Developmental Neuroscience.

[36]  Fred L. Bookstein,et al.  Landmark methods for forms without landmarks: morphometrics of group differences in outline shape , 1997, Medical Image Anal..

[37]  E. Courchesne,et al.  Brain growth across the life span in autism: Age-specific changes in anatomical pathology , 2011, Brain Research.

[38]  D. V. van Essen,et al.  Cortical Folding Abnormalities in Autism Revealed by Surface-Based Morphometry , 2007, The Journal of Neuroscience.

[39]  P. Gunz,et al.  Advances in Geometric Morphometrics , 2009, Evolutionary Biology.

[40]  G. Roth,et al.  Evolution of the brain and intelligence , 2005, Trends in Cognitive Sciences.

[41]  D. V. van Essen,et al.  A Population-Average, Landmark- and Surface-based (PALS) atlas of human cerebral cortex. , 2005, NeuroImage.

[42]  Richard M. Leahy,et al.  Article in Press G Model Journal of Neuroscience Methods Semi-automated Method for Delineation of Landmarks on Models of the Cerebral Cortex , 2022 .

[43]  J. Richtsmeier,et al.  Brain morphology in nonsyndromic unicoronal craniosynostosis. , 2005, The anatomical record. Part A, Discoveries in molecular, cellular, and evolutionary biology.

[44]  Thomas E. Wehrly,et al.  An Invariant Approach to Statistical Analysis of Shapes , 2004, Technometrics.

[45]  N C Andreasen,et al.  Image processing for the study of brain structure and function: problems and programs. , 1992, The Journal of neuropsychiatry and clinical neurosciences.

[46]  Arthur W. Toga,et al.  Statistical shape analysis of the corpus callosum in Schizophrenia , 2013, NeuroImage.

[47]  J. Rapoport,et al.  Structural MRI of Pediatric Brain Development: What Have We Learned and Where Are We Going? , 2010, Neuron.

[48]  Fred L. Bookstein,et al.  Spatial relationships of neuroanatomic landmarks in schizophrenia , 1996, Psychiatry Research: Neuroimaging.

[49]  N. Raz,et al.  Differential Aging of the Brain: Patterns, Cognitive Correlates and Modifiers , 2022 .

[50]  Anuj Srivastava,et al.  Statistical Shape Analysis , 2014, Computer Vision, A Reference Guide.

[51]  N C Andreasen,et al.  Voxel processing techniques for the antemortem study of neuroanatomy and neuropathology using magnetic resonance imaging. , 1993, The Journal of neuropsychiatry and clinical neurosciences.

[52]  J L Ringo,et al.  Neuronal interconnection as a function of brain size. , 1991, Brain, behavior and evolution.

[53]  N C Andreasen,et al.  Techniques for measuring sulcal/gyral patterns in the brain as visualized through magnetic resonance scanning: BRAINPLOT and BRAINMAP. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[54]  E. Keverne,et al.  Genomic imprinting and the differential roles of parental genomes in brain development. , 1996, Brain research. Developmental brain research.