Literature DB >> 15275929

Correction for intracranial volume in analysis of whole brain atrophy in multiple sclerosis: the proportion vs. residual method.

Michael P Sanfilipo1, Ralph H B Benedict, Robert Zivadinov, Rohit Bakshi.   

Abstract

Two techniques that correct (normalize) regional and whole brain volumes according to head size-the proportion method (tissue-to-intracranial volume ratio) and the residual method (regression-based predicted brain tissue volumes)-are used pervasively in neuroimaging research, but have received little critical evaluation or direct comparison. Using a quantitatively derived MRI data set of patients with multiple sclerosis (n = 18) and age-/sex-matched normal controls (n = 18), we introduced various types of error into estimates of intracranial volume (ICV) and absolute parenchymal volume (APV) to observe how this error affected the final outcome of normalized brain measures and their ability to detect group differences, as computed by a proportion (brain parenchymal fraction [BPF]) and residual method (predicted parenchymal volume [PPV]). The results indicated that systemic error in ICV and APV values considerably affected BPF means based on the proportion method, except with dependent-related systematic APV error, but essentially did not change statistical power associated with group differences in BPF. Random error altered BPF means to a much smaller extent, but was associated with moderate reductions in statistical power. On the other hand, PPV estimates based on the residual method were unaffected by these same ICV and APV errors, except with dependent-related systematic APV error, and were not associated with reductions in statistical power. Our findings suggest that head size correction of brain regions with the residual method generally may provide advantages over the proportion method.

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Year:  2004        PMID: 15275929     DOI: 10.1016/j.neuroimage.2004.03.037

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  66 in total

1.  Whole-brain atrophy in multiple sclerosis measured by automated versus semiautomated MR imaging segmentation.

Authors:  Jitendra Sharma; Michael P Sanfilipo; Ralph H B Benedict; Bianca Weinstock-Guttman; Frederick E Munschauer; Rohit Bakshi
Journal:  AJNR Am J Neuroradiol       Date:  2004 Jun-Jul       Impact factor: 3.825

2.  Volumetric and voxel-based morphometry findings in autism subjects with and without macrocephaly.

Authors:  Erin D Bigler; Tracy J Abildskov; Jo Ann Petrie; Michael Johnson; Nicholas Lange; Jonathan Chipman; Jeffrey Lu; William McMahon; Janet E Lainhart
Journal:  Dev Neuropsychol       Date:  2010       Impact factor: 2.253

3.  Relationship of medial temporal lobe atrophy, APOE genotype, and cognitive reserve in preclinical Alzheimer's disease.

Authors:  Anja Soldan; Corinne Pettigrew; Yi Lu; Mei-Cheng Wang; Ola Selnes; Marilyn Albert; Timothy Brown; J Tilak Ratnanather; Laurent Younes; Michael I Miller
Journal:  Hum Brain Mapp       Date:  2015-04-16       Impact factor: 5.038

4.  Early detection of Alzheimer's disease using MRI hippocampal texture.

Authors:  Lauge Sørensen; Christian Igel; Naja Liv Hansen; Merete Osler; Martin Lauritzen; Egill Rostrup; Mads Nielsen
Journal:  Hum Brain Mapp       Date:  2015-12-21       Impact factor: 5.038

5.  Engagement in Enriching Early-Life Activities Is Associated With Larger Hippocampal and Amygdala Volumes in Community-Dwelling Older Adults.

Authors:  Kyle D Moored; Thomas Chan; Vijay R Varma; Yi-Fang Chuang; Jeanine M Parisi; Michelle C Carlson
Journal:  J Gerontol B Psychol Sci Soc Sci       Date:  2020-09-14       Impact factor: 4.077

6.  Impact of the AGTR1 A1166C polymorphism on subcortical hyperintensities and cognition in healthy older adults.

Authors:  Lauren E Salminen; Peter R Schofield; Kerrie D Pierce; Thomas E Conturo; David F Tate; Elizabeth M Lane; Jodi M Heaps; Jacob D Bolzenius; Laurie M Baker; Erbil Akbudak; Robert H Paul
Journal:  Age (Dordr)       Date:  2014-07-01

7.  How Does the Accuracy of Intracranial Volume Measurements Affect Normalized Brain Volumes? Sample Size Estimates Based on 966 Subjects from the HUNT MRI Cohort.

Authors:  T I Hansen; V Brezova; L Eikenes; A Håberg; T R Vangberg
Journal:  AJNR Am J Neuroradiol       Date:  2015-04-09       Impact factor: 3.825

8.  Quantitative assessment of field strength, total intracranial volume, sex, and age effects on the goodness of harmonization for volumetric analysis on the ADNI database.

Authors:  Da Ma; Karteek Popuri; Mahadev Bhalla; Oshin Sangha; Donghuan Lu; Jiguo Cao; Claudia Jacova; Lei Wang; Mirza Faisal Beg
Journal:  Hum Brain Mapp       Date:  2018-11-15       Impact factor: 5.038

9.  Interhemispheric functional connectivity following prenatal or perinatal brain injury predicts receptive language outcome.

Authors:  Anthony Steven Dick; Anjali Raja Beharelle; Ana Solodkin; Steven L Small
Journal:  J Neurosci       Date:  2013-03-27       Impact factor: 6.167

10.  A robust method to estimate the intracranial volume across MRI field strengths (1.5T and 3T).

Authors:  Shiva Keihaninejad; Rolf A Heckemann; Gianlorenzo Fagiolo; Mark R Symms; Joseph V Hajnal; Alexander Hammers
Journal:  Neuroimage       Date:  2010-01-28       Impact factor: 6.556

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