Fully automated analysis using BRAINS: AutoWorkup

The BRAINS (Brain Research: Analysis of Images, Networks, and Systems) image analysis software has been in use, and in constant development, for over 20 years. The original neuroimage analysis pipeline using BRAINS was designed as a semiautomated procedure to measure volumes of the cerebral lobes and subcortical structures, requiring manual intervention at several stages in the process. Through use of advanced image processing algorithms the need for manual intervention at stages of image realignment, tissue sampling, and mask editing have been eliminated. In addition, inhomogeneity correction, intensity normalization, and mask cleaning routines have been added to improve the accuracy and consistency of the results. The fully automated method, AutoWorkup, is shown in this study to be more reliable (ICC ≥ 0.96, Jaccard index ≥ 0.80, and Dice index ≥ 0.89 for all tissues in all regions) than the average of 18 manual raters. On a set of 1130 good quality scans, the failure rate for correct realignment was 1.1%, and manual editing of the brain mask was required on 4% of the scans. In other tests, AutoWorkup is shown to produce measures that are reliable for data acquired across scanners, scanner vendors, and across sequences. Application of AutoWorkup for the analysis of data from the 32-site, multivendor PREDICT-HD study yield estimates of reliability to be greater than or equal to 0.90 for all tissues and regions.

[1]  N C Andreasen,et al.  Automatic atlas-based volume estimation of human brain regions from MR images. , 1996, Journal of computer assisted tomography.

[2]  Gary E. Christensen,et al.  Synthesizing average 3D anatomical shapes , 2006, NeuroImage.

[3]  Martin Styner,et al.  Parametric estimate of intensity inhomogeneities applied to MRI , 2000, IEEE Transactions on Medical Imaging.

[4]  Jane S. Paulsen,et al.  Detection of Huntington’s disease decades before diagnosis: the Predict-HD study , 2007, Journal of Neurology, Neurosurgery, and Psychiatry.

[5]  Jane S. Paulsen,et al.  Striatal and white matter predictors of estimated diagnosis for Huntington disease , 2010, Brain Research Bulletin.

[6]  Bruce Hermann,et al.  Neuroanatomical correlates of cognitive phenotypes in temporal lobe epilepsy , 2009, Epilepsy & Behavior.

[7]  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.

[8]  Vincent Magnotta,et al.  Reliability and reproducibility of brain tissue volumetry from segmented MR scans , 2001, European Archives of Psychiatry and Clinical Neuroscience.

[9]  V. Magnotta,et al.  Hippocampal volume in chronic posttraumatic stress disorder (PTSD): MRI study using two different evaluation methods. , 2006, Journal of affective disorders.

[10]  J. Ehrhardt,et al.  Measurement of brain structures with artificial neural networks: two- and three-dimensional applications. , 1999, Radiology.

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

[12]  L. R. Dice.  Measures of the Amount of Ecologic Association Between Species , 1945 .

[13]  Matcheri S. Keshavan,et al.  Disruption of Orbitofrontal Cortex Laterality in Offspring from Multiplex Alcohol Dependence Families , 2009, Biological Psychiatry.

[14]  N C Andreasen,et al.  Improving tissue classification in MRI: a three-dimensional multispectral discriminant analysis method with automated training class selection. , 1999, Journal of computer assisted tomography.

[15]  Daniel S. O'Leary,et al.  Manual and Semiautomated Measurement of Cerebellar Subregions on MR Images , 2002, NeuroImage.

[16]  Klaus Sartor,et al.  MR imaging-based volumetry in patients with early-treated phenylketonuria. , 2005, AJNR. American journal of neuroradiology.

[17]  P. Buckley,et al.  Association Between Brain-Derived Neurotrophic Factor Val66met Gene Polymorphism and Progressive Brain Volume Changes in Schizophrenia , 2009 .

[18]  M. Weiner,et al.  Longitudinal Rates of Lobar Atrophy in Frontotemporal Dementia, Semantic Dementia, and Alzheimer's Disease , 2010, Alzheimer disease and associated disorders.

[19]  Benedicto Crespo-Facorro,et al.  Caudate nucleus volume and its clinical and cognitive correlations in first episode schizophrenia , 2007, Schizophrenia Research.

[20]  J. Fleiss,et al.  Intraclass correlations: uses in assessing rater reliability. , 1979, Psychological bulletin.

[21]  Stephanie Powell,et al.  Registration and machine learning-based automated segmentation of subcortical and cerebellar brain structures , 2008, NeuroImage.

[22]  Nancy C. Andreasen,et al.  Manual and Automated Measurement of the Whole Thalamus and Mediodorsal Nucleus Using Magnetic Resonance Imaging , 2002, NeuroImage.

[23]  N. Minshew,et al.  Brainstem volumetric alterations in children with autism , 2008, Psychological Medicine.

[24]  Henning Tiemeier,et al.  Cerebellum development during childhood and adolescence: A longitudinal morphometric MRI study , 2010, NeuroImage.

[25]  Jamie L. Hanson,et al.  Cerebellar Volume and Cognitive Functioning in Children Who Experienced Early Deprivation , 2009, Biological Psychiatry.

[26]  R W Cox,et al.  AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. , 1996, Computers and biomedical research, an international journal.

[27]  Vincent A Magnotta,et al.  Morphology of the cerebral cortex in preclinical Huntington's disease. , 2007, The American journal of psychiatry.

[28]  N C Andreasen,et al.  Magnetic resonance imaging of brain anomalies in fetal alcohol syndrome. , 1997, Pediatrics.

[29]  I. Agartz,et al.  Smaller cerebellar vermis but not hemisphere volumes in patients with chronic schizophrenia. , 2003, The American journal of psychiatry.

[30]  D S O'Leary,et al.  Effects of frequent marijuana use on brain tissue volume and composition , 2000, Neuroreport.

[31]  Vincent Magnotta,et al.  Hippocampal volume deficits and shape deformities in young biological relatives of schizophrenia probands , 2010, NeuroImage.

[32]  Ronald Pierson,et al.  Maximize uniformity summation heuristic (MUSH): a highly accurate simple method for intracranial delineation , 2009, Medical Imaging.

[33]  P. Jaccard.  THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.1 , 1912 .

[34]  Jane S. Paulsen,et al.  Preparing for preventive clinical trials: the Predict-HD study. , 2006, Archives of neurology.

[35]  Beng-Choon Ho,et al.  MRI brain volume abnormalities in young, nonpsychotic relatives of schizophrenia probands are associated with subsequent prodromal symptoms , 2007, Schizophrenia Research.

[36]  Nancy C. Andreasen,et al.  Inter- and intraoperator reliability of brain tissue measures using magnetic resonance imaging , 2003, European Archives of Psychiatry and Clinical Neuroscience.

[37]  R. McCarley,et al.  A review of MRI findings in schizophrenia , 2001, Schizophrenia Research.

[38]  Nancy Andreasen,et al.  Implications of starvation-induced change in right dorsal anterior cingulate volume in anorexia nervosa. , 2008, The International journal of eating disorders.

[39]  Philip Shaw,et al.  Cerebellar development and clinical outcome in attention deficit hyperactivity disorder. , 2007, The American journal of psychiatry.

[40]  Vincent A Magnotta,et al.  Effects of smoking marijuana on focal attention and brain blood flow , 2007, Human psychopharmacology.