MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: Reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths

Automated MRI-derived measurements of in-vivo human brain volumes provide novel insights into normal and abnormal neuroanatomy, but little is known about measurement reliability. Here we assess the impact of image acquisition variables (scan session, MRI sequence, scanner upgrade, vendor and field strengths), FreeSurfer segmentation pre-processing variables (image averaging, B1 field inhomogeneity correction) and segmentation analysis variables (probabilistic atlas) on resultant image segmentation volumes from older (n=15, mean age 69.5) and younger (both n=5, mean ages 34 and 36.5) healthy subjects. The variability between hippocampal, thalamic, caudate, putamen, lateral ventricular and total intracranial volume measures across sessions on the same scanner on different days is less than 4.3% for the older group and less than 2.3% for the younger group. Within-scanner measurements are remarkably reliable across scan sessions, being minimally affected by averaging of multiple acquisitions, B1 correction, acquisition sequence (MPRAGE vs. multi-echo-FLASH), major scanner upgrades (Sonata-Avanto, Trio-TrioTIM), and segmentation atlas (MPRAGE or multi-echo-FLASH). Volume measurements across platforms (Siemens Sonata vs. GE Signa) and field strengths (1.5 T vs. 3 T) result in a volume difference bias but with a comparable variance as that measured within-scanner, implying that multi-site studies may not necessarily require a much larger sample to detect a specific effect. These results suggest that volumes derived from automated segmentation of T1-weighted structural images are reliable measures within the same scanner platform, even after upgrades; however, combining data across platform and across field-strength introduces a bias that should be considered in the design of multi-site studies, such as clinical drug trials. The results derived from the young groups (scanner upgrade effects and B1 inhomogeneity correction effects) should be considered as preliminary and in need for further validation with a larger dataset.

[1]  Hans-Jochen Heinze,et al.  Quantitative MR analyses of the hippocampus: Unspecific metabolic changes in aging , 2004, Journal of Neurology.

[2]  Eui-Cheol Nam,et al.  Validation of hippocampal volumes measured using a manual method and two automated methods (FreeSurfer and IBASPM) in chronic major depressive disorder , 2008, Neuroradiology.

[3]  Nick C. Fox,et al.  Automated Measurement of Hippocampal Atrophy Using Fluid-Registered Serial MRI in AD and Controls , 2007 .

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

[5]  M W Vannier,et al.  Three-dimensional hippocampal MR morphometry with high-dimensional transformation of a neuroanatomic atlas. , 1997, Radiology.

[6]  Anders M. Dale,et al.  Reliability of MRI-derived measurements of human cerebral cortical thickness: The effects of field strength, scanner upgrade and manufacturer , 2006, NeuroImage.

[7]  P. Lachenbruch Statistical Power Analysis for the Behavioral Sciences (2nd ed.) , 1989 .

[8]  Norbert Schuff,et al.  Detecting Spatially Consistent Structural Differences in Alzheimer's and Fronto Temporal Dementia Using Deformation Morphometry , 2001, MICCAI.

[9]  Anders M. Dale,et al.  Reliability in multi-site structural MRI studies: Effects of gradient non-linearity correction on phantom and human data , 2006, NeuroImage.

[10]  Carl-Fredrik Westin,et al.  Fornix Integrity and Hippocampal Volume in Male Schizophrenic Patients , 2006, Biological Psychiatry.

[11]  J M Bland,et al.  Statistical methods for assessing agreement between two methods of clinical measurement , 1986 .

[12]  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.

[13]  Anne-Catherine Bachoud-Lévi,et al.  Distribution of grey matter atrophy in Huntington’s disease patients: A combined ROI-based and voxel-based morphometric study , 2006, NeuroImage.

[14]  A M Dale,et al.  Measuring the thickness of the human cerebral cortex from magnetic resonance images. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[15]  C D Good,et al.  The distribution of structural neuropathology in pre-clinical Huntington's disease. , 2002, Brain : a journal of neurology.

[16]  Jan Kassubek,et al.  Executive dysfunction in early stages of Huntington's disease is associated with striatal and insular atrophy: A neuropsychological and voxel-based morphometric study , 2005, Journal of the Neurological Sciences.

[17]  Eui-Cheol Nam,et al.  Validation of hippocampal volumes measured with one manual and two automated methods using FreeSurfer and IBASPM in chronic major depressive disorder , 2009, Neuroradiology.

[18]  F. Jessen,et al.  Multicenter assessment of reliability of cranial MRI , 2006, Neurobiology of Aging.

[19]  Anders M. Dale,et al.  Feasibility of Multi-site Clinical Structural Neuroimaging Studies of Aging Using Legacy Data , 2007, Neuroinformatics.

[20]  C. Jack,et al.  Brain atrophy rates predict subsequent clinical conversion in normal elderly and amnestic MCI , 2005, Neurology.

[21]  M. Miller Computational anatomy: shape, growth, and atrophy comparison via diffeomorphisms , 2004, NeuroImage.

[22]  Nick C Fox,et al.  The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods , 2008, Journal of magnetic resonance imaging : JMRI.

[23]  Paul M. Thompson,et al.  Brain structural mapping using a novel hybrid implicit/explicit framework based on the level-set method , 2005, NeuroImage.

[24]  Nick C Fox,et al.  Using serial registered brain magnetic resonance imaging to measure disease progression in Alzheimer disease: power calculations and estimates of sample size to detect treatment effects. , 2000, Archives of neurology.

[25]  N. Schuff,et al.  Measurement of hippocampal subfields and age-related changes with high resolution MRI at 4T , 2007, Neurobiology of Aging.

[26]  C. Jack,et al.  MRI as a biomarker of disease progression in a therapeutic trial of milameline for AD , 2003, Neurology.

[27]  J Kassubek,et al.  Topography of cerebral atrophy in early Huntington’s disease: a voxel based morphometric MRI study , 2004, Journal of Neurology, Neurosurgery & Psychiatry.

[28]  André J. W. van der Kouwe,et al.  Brain morphometry with multiecho MPRAGE , 2008, NeuroImage.

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

[30]  Wilhelm Gaus,et al.  Evidence for more widespread cerebral pathology in early HD: An MRI-based morphometric analysis , 2004, Neurology.

[31]  A. M. Dale,et al.  A hybrid approach to the skull stripping problem in MRI , 2004, NeuroImage.

[32]  P. Matthews,et al.  Measurement of Brain Change Over Time FMRIB Technical Report TR 00 SMS 1 ( A related paper has been submitted to JCAT ) , 2001 .

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

[34]  Nick C. Fox,et al.  Accuracy Assessment of Global and Local Atrophy Measurement Techniques with Realistic Simulated Longitudinal Data , 2007, MICCAI.

[35]  Bruce Fischl,et al.  Comparison of manual and automatic section positioning of brain MR images. , 2006, Radiology.

[36]  Clifford R. Jack,et al.  Comparison of different methodological implementations of voxel-based morphometry in neurodegenerative disease , 2005, NeuroImage.

[37]  Norbert Schuff,et al.  Longitudinal stability of MRI for mapping brain change using tensor-based morphometry , 2006, NeuroImage.

[38]  K J Anstey,et al.  The role of volumetric MRI in understanding mild cognitive impairment and similar classifications , 2003, Aging & mental health.

[39]  Nick C Fox,et al.  Haemodialysis and Cerebral Oedema , 2001, Nephron.

[40]  Michael I. Miller,et al.  Large Deformation Diffeomorphism and Momentum Based Hippocampal Shape Discrimination in Dementia of the Alzheimer type , 2007, IEEE Transactions on Medical Imaging.

[41]  Guy B. Williams,et al.  Comparative Reliability of Total Intracranial Volume Estimation Methods and the Influence of Atrophy in a Longitudinal Semantic Dementia Cohort , 2009, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[42]  Thomas E. Nichols,et al.  Twelfth Annual Meeting of the Organization for Human Brain Mapping , 2006, NeuroImage.

[43]  C. Jack,et al.  Alzheimer's Disease Neuroimaging Initiative , 2008 .

[44]  Karl J. Friston,et al.  Voxel-Based Morphometry—The Methods , 2000, NeuroImage.

[45]  Stephen M. Smith,et al.  Accurate, Robust, and Automated Longitudinal and Cross-Sectional Brain Change Analysis , 2002, NeuroImage.

[46]  P. Batchelor,et al.  International Society for Magnetic Resonance in Medicine , 1997 .

[47]  Nick C Fox,et al.  Brain atrophy progression measured from registered serial MRI: Validation and application to alzheimer's disease , 1997, Journal of magnetic resonance imaging : JMRI.

[48]  P. Matthews,et al.  Measurement of Brain Change Over Time FMRIB Technical Report TR 00 SMS 1 ( A related paper has been submitted to JCAT ) , 2001 .

[49]  C. Jack,et al.  Quantitative magnetic resonance techniques as surrogate markers of Alzheimer’s disease , 2004, NeuroRX.

[50]  Xiao Han,et al.  Atlas Renormalization for Improved Brain MR Image Segmentation Across Scanner Platforms , 2007, IEEE Transactions on Medical Imaging.

[51]  Anders M. Dale,et al.  On-line automatic slice positioning for brain MR imaging , 2005, NeuroImage.

[52]  Jacob Cohen Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[53]  Chris Frost,et al.  Cerebral Atrophy Measurement in Clinically Isolated Syndromes and Relapsing Remitting Multiple Sclerosis: A Comparison of Registration‐Based Methods , 2007, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[54]  D. Altman,et al.  STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT , 1986, The Lancet.

[55]  J. Ashburner,et al.  Progression of structural neuropathology in preclinical Huntington’s disease: a tensor based morphometry study , 2005, Journal of Neurology, Neurosurgery & Psychiatry.

[56]  Jan Kassubek,et al.  Thalamic atrophy in Huntington's disease co-varies with cognitive performance: a morphometric MRI analysis. , 2005, Cerebral cortex.

[57]  N. Makris,et al.  Decreased volume of left and total anterior insular lobule in schizophrenia , 2006, Schizophrenia Research.

[58]  Bradford C. Dickerson,et al.  Neuroimaging biomarkers for clinical trials of disease-modifying therapies in Alzheimer’s disease , 2005, NeuroRX.

[59]  James J. Levitt,et al.  Reduction of Caudate Nucleus Volumes in Neuroleptic-Naïve Female Subjects with Schizotypal Personality Disorder , 2006, Biological Psychiatry.

[60]  D. Louis Collins,et al.  ANIMAL+INSECT: Improved Cortical Structure Segmentation , 1999, IPMI.

[61]  Guido Gerig,et al.  Offering to Share: How to Put Heads Together in Autism Neuroimaging , 2008, Journal of autism and developmental disorders.

[62]  Randy L. Gollub,et al.  A Web Portal that Enables Collaborative Use of Advanced Medical Image Processing and Informatics Tools through the Biomedical Informatics Research Network (BIRN) , 2006, AMIA.

[63]  Michael I. Miller,et al.  Amygdala Volume Analysis in Female Twins with Major Depression , 2007, Biological Psychiatry.

[64]  S. DeKosky,et al.  Looking Backward to Move Forward: Early Detection of Neurodegenerative Disorders , 2003, Science.