Literature DB >> 25822811

Estimating Intracranial Volume in Brain Research: An Evaluation of Methods.

Saman Sargolzaei1, Arman Sargolzaei1, Mercedes Cabrerizo1, Gang Chen2, Mohammed Goryawala1, Alberto Pinzon-Ardila3, Sergio M Gonzalez-Arias3,4, Malek Adjouadi5,6,7.   

Abstract

Intracranial volume (ICV) is a standard measure often used in morphometric analyses to correct for head size in brain studies. Inaccurate ICV estimation could introduce bias in the outcome. The current study provides a decision aid in defining protocols for ICV estimation across different subject groups in terms of sampling frequencies that can be optimally used on the volumetric MRI data, and type of software most suitable for use in estimating the ICV measure. Four groups of 53 subjects are considered, including adult controls (AC, adults with Alzheimer's disease (AD), pediatric controls (PC) and group of pediatric epilepsy subjects (PE). Reference measurements were calculated for each subject by manually tracing intracranial cavity without sub-sampling. The reliability of reference measurements were assured through intra- and inter- variation analyses. Three publicly well-known software packages (FreeSurfer Ver. 5.3.0, FSL Ver. 5.0, SPM8 and SPM12) were examined in their ability to automatically estimate ICV across the groups. Results on sub-sampling studies with a 95 % confidence showed that in order to keep the accuracy of the inter-leaved slice sampling protocol above 99 %, sampling period cannot exceed 20 mm for AC, 25 mm for PC, 15 mm for AD and 17 mm for the PE groups. The study assumes a priori knowledge about the population under study into the automated ICV estimation. Tuning of the parameters in FSL and the use of proper atlas in SPM showed significant reduction in the systematic bias and the error in ICV estimation via these automated tools. SPM12 with the use of pediatric template is found to be a more suitable candidate for PE group. SPM12 and FSL subjected to tuning are the more appropriate tools for the PC group. The random error is minimized for FS in AD group and SPM8 showed less systematic bias. Across the AC group, both SPM12 and FS performed well but SPM12 reported lesser amount of systematic bias.

Entities:  

Keywords:  FSL (RRID:nif-0000-00305); FreeSurfer (RRID:nif-0000-00304); Intracranial volume estimation; MRI; SPM (RRID:nif-0000-00343)

Mesh:

Year:  2015        PMID: 25822811     DOI: 10.1007/s12021-015-9266-5

Source DB:  PubMed          Journal:  Neuroinformatics        ISSN: 1539-2791


  54 in total

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2.  Evaluation of automatic measurement of the intracranial volume based on quantitative MR imaging.

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4.  Normalization of cerebral volumes by use of intracranial volume: implications for longitudinal quantitative MR imaging.

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Journal:  AJNR Am J Neuroradiol       Date:  2001-09       Impact factor: 3.825

5.  Cortical responses to a graded working memory challenge predict functional decline in mild cognitive impairment.

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6.  Structural MRI biomarkers for preclinical and mild Alzheimer's disease.

Authors:  Christine Fennema-Notestine; Donald J Hagler; Linda K McEvoy; Adam S Fleisher; Elaine H Wu; David S Karow; Anders M Dale
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7.  Comparative reliability of total intracranial volume estimation methods and the influence of atrophy in a longitudinal semantic dementia cohort.

Authors:  George Pengas; João M S Pereira; Guy B Williams; Peter J Nestor
Journal:  J Neuroimaging       Date:  2008-05-19       Impact factor: 2.486

Review 8.  FSL.

Authors:  Mark Jenkinson; Christian F Beckmann; Timothy E J Behrens; Mark W Woolrich; Stephen M Smith
Journal:  Neuroimage       Date:  2011-09-16       Impact factor: 6.556

9.  Estimating intracranial volume using intracranial area in healthy children and those with childhood status epilepticus.

Authors:  Rory J Piper; Michael M Yoong; Suresh Pujar; Richard F Chin
Journal:  Brain Behav       Date:  2014-08-28       Impact factor: 2.708

10.  Prevalence of Alzheimer's pathologic endophenotypes in asymptomatic and mildly impaired first-degree relatives.

Authors:  Erika J Lampert; Kingshuk Roy Choudhury; Christopher A Hostage; Jeffrey R Petrella; P Murali Doraiswamy
Journal:  PLoS One       Date:  2013-04-17       Impact factor: 3.240

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  16 in total

1.  Lifetime major depression and grey-matter volume

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Journal:  J Psychiatry Neurosci       Date:  2019-01-01       Impact factor: 6.186

2.  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

3.  Cortical complexity as a measure of age-related brain atrophy.

Authors:  Christopher R Madan; Elizabeth A Kensinger
Journal:  Neuroimage       Date:  2016-04-19       Impact factor: 6.556

4.  Age-related differences in the structural complexity of subcortical and ventricular structures.

Authors:  Christopher R Madan; Elizabeth A Kensinger
Journal:  Neurobiol Aging       Date:  2016-10-27       Impact factor: 4.673

5.  Relationship Between Interpersonal Depressive Symptoms and Reduced Amygdala Volume in People with Multiple Sclerosis: Considerations for Clinical Practice.

Authors:  Sarah Haines; Ernest Butler; Stephen Stuckey; Robert Hester; Lisa B Grech
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6.  Altered Associations between Pain Symptoms and Brain Morphometry in the Pain Matrix of HIV-Seropositive Individuals.

Authors:  Deborrah Castillo; Thomas Ernst; Eric Cunningham; Linda Chang
Journal:  J Neuroimmune Pharmacol       Date:  2017-09-02       Impact factor: 4.147

7.  Hippocampal Structure Predicts Statistical Learning and Associative Inference Abilities during Development.

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Journal:  J Cogn Neurosci       Date:  2016-08-30       Impact factor: 3.225

8.  A Comparative Study of Automatic Approaches for Preclinical MRI-based Brain Segmentation in the Developing Rat.

Authors:  Saman Sargolzaei; Yan Cai; Stephanie M Wolahan; Bilwaj Gaonkar; Arman Sargolzaei; Christopher C Giza; Neil G Harris
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9.  Inclusion of Neuropsychological Scores in Atrophy Models Improves Diagnostic Classification of Alzheimer's Disease and Mild Cognitive Impairment.

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10.  A large-scale comparison of cortical thickness and volume methods for measuring Alzheimer's disease severity.

Authors:  Christopher G Schwarz; Jeffrey L Gunter; Heather J Wiste; Scott A Przybelski; Stephen D Weigand; Chadwick P Ward; Matthew L Senjem; Prashanthi Vemuri; Melissa E Murray; Dennis W Dickson; Joseph E Parisi; Kejal Kantarci; Michael W Weiner; Ronald C Petersen; Clifford R Jack
Journal:  Neuroimage Clin       Date:  2016-05-30       Impact factor: 4.881

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