Automated landmark identification for human cortical surface-based registration

Volume-based registration (VBR) is the predominant method used in human neuroimaging to compensate for individual variability. However, surface-based registration (SBR) techniques have an inherent advantage over VBR because they respect the topology of the convoluted cortical sheet. There is evidence that existing SBR methods indeed confer a registration advantage over affine VBR. Landmark-SBR constrains registration using explicit landmarks to represent corresponding geographical locations on individual and atlas surfaces. The need for manual landmark identification has been an impediment to the widespread adoption of Landmark-SBR. To circumvent this obstacle, we have implemented and evaluated an automated landmark identification (ALI) algorithm for registration to the human PALS-B12 atlas. We compared ALI performance with that from two trained human raters and one expert anatomical rater (ENR). We employed both quantitative and qualitative quality assurance metrics, including a biologically meaningful analysis of hemispheric asymmetry. ALI performed well across all quality assurance tests, indicating that it yields robust and largely accurate results that require only modest manual correction (<10 min per subject). ALI largely circumvents human error and bias and enables high throughput analysis of large neuroimaging datasets for inter-subject registration to an atlas.

[1]  A W Toga,et al.  Working memory and DLPFC inefficiency in schizophrenia: the FBIRN study. , 2009, Schizophrenia bulletin.

[2]  Deanna M. Barch,et al.  When less is more: TPJ and default network deactivation during encoding predicts working memory performance , 2010, NeuroImage.

[3]  A. Dale,et al.  High‐resolution intersubject averaging and a coordinate system for the cortical surface , 1999, Human brain mapping.

[4]  M. Raichle,et al.  Anatomic Localization and Quantitative Analysis of Gradient Refocused Echo-Planar fMRI Susceptibility Artifacts , 1997, NeuroImage.

[5]  Russell A. Poldrack,et al.  Large-scale automated synthesis of human functional neuroimaging data , 2011, Nature Methods.

[6]  Brenna Argall,et al.  SUMA: an interface for surface-based intra- and inter-subject analysis with AFNI , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[7]  J C Mazziotta,et al.  Automated image registration: II. Intersubject validation of linear and nonlinear models. , 1998, Journal of computer assisted tomography.

[8]  D. V. van Essen,et al.  Mapping Human Cortical Areas In Vivo Based on Myelin Content as Revealed by T1- and T2-Weighted MRI , 2011, The Journal of Neuroscience.

[9]  M. Jenkinson,et al.  Non-linear optimisation FMRIB Technial Report TR 07 JA 1 , 2007 .

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

[11]  John G. Csernansky,et al.  Comparing surface-based and volume-based analyses of functional neuroimaging data in patients with schizophrenia , 2008, NeuroImage.

[12]  Bryan R. Conroy,et al.  Function-based Intersubject Alignment of Human Cortical Anatomy , 2009, Cerebral cortex.

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

[14]  Anders M. Dale,et al.  Sequence-independent segmentation of magnetic resonance images , 2004, NeuroImage.

[15]  David C. Van Essen,et al.  Application of Information Technology: An Integrated Software Suite for Surface-based Analyses of Cerebral Cortex , 2001, J. Am. Medical Informatics Assoc..

[16]  M. Jenkinson.  Non-linear registration aka Spatial normalisation , 2007 .

[17]  Timothy S. Coalson,et al.  Parcellations and hemispheric asymmetries of human cerebral cortex analyzed on surface-based atlases. , 2012, Cerebral cortex.

[18]  J. Mazziotta,et al.  Automated image registration , 1993 .

[19]  Scott T. Grafton,et al.  Automated image registration: I. General methods and intrasubject, intramodality validation. , 1998, Journal of computer assisted tomography.

[20]  A. Toga,et al.  Cortical variability and asymmetry in normal aging and Alzheimer's disease. , 1998, Cerebral cortex.

[21]  Deanna M. Barch,et al.  Resisting emotional interference: Brain regions facilitating working memory performance during negative distraction , 2010, Cognitive, affective & behavioral neuroscience.

[22]  B. Argall,et al.  Simplified intersubject averaging on the cortical surface using SUMA , 2006, Human brain mapping.

[23]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[24]  A. Dale,et al.  Whole Brain Segmentation Automated Labeling of Neuroanatomical Structures in the Human Brain , 2002, Neuron.

[25]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[26]  Satrajit S. Ghosh,et al.  Evaluation of volume-based and surface-based brain image registration methods , 2010, NeuroImage.

[27]  A. Dale,et al.  Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System , 1999, NeuroImage.

[28]  Ron Kikinis,et al.  Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002 , 2002, Lecture Notes in Computer Science.

[29]  Abraham Z. Snyder,et al.  A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume , 2004, NeuroImage.

[30]  Christian Windischberger,et al.  Toward discovery science of human brain function , 2010, Proceedings of the National Academy of Sciences.

[31]  Jeffrey R. Binder,et al.  Volumetric vs. surface-based alignment for localization of auditory cortex activation , 2005, NeuroImage.

[32]  A. Galaburda,et al.  Individual variability in cortical organization: Its relationship to brain laterality and implications to function , 1990, Neuropsychologia.

[33]  Timothy S. Coalson,et al.  A Surface-Based Analysis of Hemispheric Asymmetries and Folding of Cerebral Cortex in Term-Born Human Infants , 2010, The Journal of Neuroscience.

[34]  Isabelle Corouge,et al.  Inter Subject Registration of Functional and Anatomical Data Using SPM , 2002, MICCAI.

[35]  Lei Wang,et al.  Symmetric abnormalities in sulcal patterning in schizophrenia , 2008, NeuroImage.

[36]  D. V. van Essen,et al.  Symmetry of Cortical Folding Abnormalities in Williams Syndrome Revealed by Surface-Based Analyses , 2006, The Journal of Neuroscience.

[37]  A. Toga,et al.  Detection and mapping of abnormal brain structure with a probabilistic atlas of cortical surfaces. , 1997, Journal of computer assisted tomography.

[38]  Rainer Goebel,et al.  Analysis of functional image analysis contest (FIAC) data with brainvoyager QX: From single‐subject to cortically aligned group general linear model analysis and self‐organizing group independent component analysis , 2006, Human brain mapping.