Accurate automatic estimation of total intracranial volume: A nuisance variable with less nuisance

Total intracranial volume (TIV/ICV) is an important covariate for volumetric analyses of the brain and brain regions, especially in the study of neurodegenerative diseases, where it can provide a proxy of maximum pre-morbid brain volume. The gold-standard method is manual delineation of brain scans, but this requires careful work by trained operators. We evaluated Statistical Parametric Mapping 12 (SPM12) automated segmentation for TIV measurement in place of manual segmentation and also compared it with SPM8 and FreeSurfer 5.3.0. For T1-weighted MRI acquired from 288 participants in a multi-centre clinical trial in Alzheimer's disease we find a high correlation between SPM12 TIV and manual TIV (R2 = 0.940, 95% Confidence Interval (0.924, 0.953)), with a small mean difference (SPM12 40.4 ± 35.4 ml lower than manual, amounting to 2.8% of the overall mean TIV in the study). The correlation with manual measurements (the key aspect when using TIV as a covariate) for SPM12 was significantly higher (p < 0.001) than for either SPM8 (R2 = 0.577 CI (0.500, 0.644)) or FreeSurfer (R2 = 0.801 CI (0.744, 0.843)). These results suggest that SPM12 TIV estimates are an acceptable substitute for labour-intensive manual estimates even in the challenging context of multiple centres and the presence of neurodegenerative pathology. We also briefly discuss some aspects of the statistical modelling approaches to adjust for TIV.

[1]  Karl J. Friston,et al.  Unified segmentation , 2005, NeuroImage.

[2]  Christos Davatzikos,et al.  Voxel-Based Morphometry Using the RAVENS Maps: Methods and Validation Using Simulated Longitudinal Atrophy , 2001, NeuroImage.

[3]  Nick C Fox,et al.  Effects of Aβ immunization (AN1792) on MRI measures of cerebral volume in Alzheimer disease , 2005, Neurology.

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

[5]  上田 敬太 Investigating association of brain volumes with intracranial capacity in schizophrenia , 2011 .

[6]  M. Folstein,et al.  Clinical diagnosis of Alzheimer's disease , 1984, Neurology.

[7]  Evan Fletcher,et al.  Effects of T2-weighted MRI based cranial volume measurements on studies of the aging brain , 2013, Medical Imaging.

[8]  Nick C Fox,et al.  Normalization of cerebral volumes by use of intracranial volume: implications for longitudinal quantitative MR imaging. , 2001, AJNR. American journal of neuroradiology.

[9]  Paul M. Thompson,et al.  Development of brain structural connectivity between ages 12 and 30: A 4-Tesla diffusion imaging study in 439 adolescents and adults , 2013, NeuroImage.

[10]  Daniel Rueckert,et al.  Information Extraction from Medical Images (IXI): Developing an e-Science Application Based on the Globus Toolkit , 2003 .

[11]  Sébastien Ourselin,et al.  MIRIAD—Public release of a multiple time point Alzheimer's MR imaging dataset , 2013, NeuroImage.

[12]  G Dunn,et al.  Modelling method comparison data , 1999, Statistical methods in medical research.

[13]  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.

[14]  Nikolaus Weiskopf,et al.  Quantitative multi-parameter mapping of R1, PD*, MT, and R2* at 3T: a multi-center validation , 2013, Front. Neurosci..

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

[16]  Joseph V. Hajnal,et al.  A robust method to estimate the intracranial volume across MRI field strengths (1.5T and 3T) , 2010, NeuroImage.

[17]  D. Louis Collins,et al.  Brain templates and atlases , 2012, NeuroImage.

[18]  Jozsef Janszky,et al.  Are there any gender differences in the hippocampus volume after head-size correction? A volumetric and voxel-based morphometric study , 2014, Neuroscience Letters.

[19]  Nick C Fox,et al.  Clinical effects of Aβ immunization (AN1792) in patients with AD in an interrupted trial , 2005, Neurology.

[20]  Frederik Maes,et al.  Assessing age-related gray matter decline with voxel-based morphometry depends significantly on segmentation and normalization procedures , 2014, Front. Aging Neurosci..

[21]  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.

[22]  Rohit Bakshi,et al.  Correction for intracranial volume in analysis of whole brain atrophy in multiple sclerosis: the proportion vs. residual method , 2004, NeuroImage.

[23]  Josephine Barnes,et al.  Estimation of total intracranial volume; a comparison of methods , 2011, Alzheimer's & Dementia.

[24]  Sébastien Ourselin,et al.  Head size, age and gender adjustment in MRI studies: a necessary nuisance? , 2010, NeuroImage.

[25]  M. Folstein,et al.  Clinical diagnosis of Alzheimer's disease: Report of the NINCDS—ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease , 2011, Neurology.

[26]  Terry M. Peters,et al.  3D statistical neuroanatomical models from 305 MRI volumes , 1993, 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference.

[27]  L A Farrer,et al.  Head circumference, atrophy, and cognition , 2010, Neurology.

[28]  Nikos Makris,et al.  Adjustment for whole brain and cranial size in volumetric brain studies: a review of common adjustment factors and statistical methods. , 2006, Harvard review of psychiatry.

[29]  Nick C Fox,et al.  Interactive algorithms for the segmentation and quantitation of 3-D MRI brain scans. , 1997, Computer methods and programs in biomedicine.

[30]  Nancy C. Andreasen,et al.  Problems with ratio and proportion measures of imaged cerebral structures , 1991, Psychiatry Research: Neuroimaging.

[31]  Daniel Rueckert,et al.  Cerebral atrophy measurements using Jacobian integration: Comparison with the boundary shift integral , 2006, NeuroImage.

[32]  S. Folstein,et al.  "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. , 1975, Journal of psychiatric research.

[33]  Nikolaus Weiskopf,et al.  Unified segmentation based correction of R1 brain maps for RF transmit field inhomogeneities (UNICORT) , 2011, NeuroImage.

[34]  A. Simmons,et al.  Regional Magnetic Resonance Imaging Measures for Multivariate Analysis in Alzheimer’s Disease and Mild Cognitive Impairment , 2012, Brain Topography.

[35]  Lars Johansson,et al.  Intracranial volume estimated with commonly used methods could introduce bias in studies including brain volume measurements , 2013, NeuroImage.

[36]  J. Bartlett,et al.  Reliability, repeatability and reproducibility: analysis of measurement errors in continuous variables , 2008, Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology.

[37]  Jonathan E. Peelle,et al.  Adjusting for global effects in voxel-based morphometry: Gray matter decline in normal aging , 2012, NeuroImage.

[38]  Jean A. Frazier,et al.  Statistical adjustments for brain size in volumetric neuroimaging studies: Some practical implications in methods , 2011, Psychiatry Research: Neuroimaging.

[39]  Daniel H. Mathalon,et al.  Correction for head size in brain-imaging measurements , 1993, Psychiatry Research: Neuroimaging.