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Reliability and Utility of Manual and Automated Estimates of Total Intracranial Volume

Published online by Cambridge University Press:  05 October 2017

Samuel J. Crowley
Affiliation:
Clinical and Health Psychology, University of Florida, Gainesville, Florida, Gainesville, Florida
Jared J. Tanner
Affiliation:
Clinical and Health Psychology, University of Florida, Gainesville, Florida, Gainesville, Florida
Daniel Ramon
Affiliation:
Clinical and Health Psychology, University of Florida, Gainesville, Florida, Gainesville, Florida
Nadine A. Schwab
Affiliation:
Clinical and Health Psychology, University of Florida, Gainesville, Florida, Gainesville, Florida
Loren P. Hizel
Affiliation:
Clinical and Health Psychology, University of Florida, Gainesville, Florida, Gainesville, Florida
Catherine C. Price*
Affiliation:
Clinical and Health Psychology, University of Florida, Gainesville, Florida, Gainesville, Florida
*
Correspondence and reprint requests to: Catherine Price, Clinical and Health Psychology, University of Florida, Gainesville, FL 32610. E-mail: cep23@phhp.ufl.edu

Abstract

Objectives: Total intracranial volume (TICV) is an important control variable in brain–behavior research, yet its calculation has challenges. Manual TICV (Manual) is labor intensive, and automatic methods vary in reliability. To identify an accurate automatic approach we assessed the reliability of two FreeSurfer TICV metrics (eTIV and Brainmask) relative to manual TICV. We then assessed how these metrics alter associations between left entorhinal cortex (ERC) volume and story retention. Methods: Forty individuals with Parkinson’s disease (PD) and 40 non-PD peers completed a brain MRI and memory testing. Manual metrics were compared to FreeSurfer’s Brainmask (a skull strip mask with total volume of gray, white, and most cerebrospinal fluid) and eTIV (calculated using the transformation matrix into Talairach space). Volumes were compared with two-way interclass correlations and dice similarity indices. Associations between ERC volume and Wechsler Memory Scale-Third Edition Logical Memory retention were examined with and without correction using each TICV method. Results: Brainmask volumes were larger and eTIV volumes smaller than Manual. Both automated metrics correlated highly with Manual. All TICV metrics explained additional variance in the ERC-Memory relationship, although none were significant. Brainmask explained slightly more variance than other methods. Conclusions: Our findings suggest Brainmask is more reliable than eTIV for TICV correction in brain-behavioral research. (JINS, 2018, 24, 206–211)

Type
Brief Communication
Copyright
Copyright © The International Neuropsychological Society 2017 

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References

REFERENCES

Altman, D.G., & Bland, J.M. (1983). Measurement in medicine: The analysis of method comparison studies. The Statistician, 307317.CrossRefGoogle Scholar
Bigler, E.D., & Tate, D.F. (2001). Brain volume, intracranial volume, and dementia. Investigations in Radiology, 36, 539546.Google Scholar
Bigler, E.D., Neeley, E.S., Miller, M.J., Tate, D.F., Rice, S.A., Cleavinger, H., &Welsh-Bohmer, K. (2004). Cerebral volume loss, cognitive deficit and neuropsychological performance: Comparative measures of brain atrophy: I. Dementia. Journal of the International Neuropsychological Society, 10(03), 442452.Google Scholar
Buckner, R.L., Head, D., Parker, J., Fotenos, A.F., Marcus, D., Morris, J.C., &&Snyder, A.Z. (2004). 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. NeuroImage, 23, 724738.Google Scholar
Fischl, B., Salat, D.H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., &Montillo, A. (2002). Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron, 33(3), 341355.Google Scholar
Folstein, M.F., Folstein, S.E., & McHugh, P.R. (1975). “Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189198.Google Scholar
Insausti, R., Juottonen, K., Soininen, H., Insausti, A.M., Partanen, K., Vainio, P., &Pitkänen, A. (1998). MR volumetric analysis of the human entorhinal, perirhinal, and temporopolar cortices. AJNR American Journal of Neuroradiology, 19(4), 659671.Google Scholar
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage, 17(2), 825841.CrossRefGoogle ScholarPubMed
Keihaninejad, S., Heckemann, R.A., Fagiolo, G., Symms, M.R., Hajnal, J.V., & Hammers, A. (2010). A robust method to estimate the intracranial volume across MRI field strengths (1.5T and 3T). NeuroImage, 50, 14271437.Google Scholar
Lawrence, I., & Lin, K. (1989). A concordance correlation coefficient to evaluate reproducibility. Biometrics, 255268.Google Scholar
Malmberg, F., Larsson, E.M., Simmons, A., Ahlström, H., Johansson, L., & Kullberg, J. (2015). Intracranial volume normalization methods: considerations when investigating gender differences in regional brain volume. Psychiatry Research: Neuroimaging, 231(3), 227235.Google Scholar
McCarthy, C.S., Ramprashad, A., Thompson, C., Botti, J.A., Coman, I.L., & Kates, W.R. (2015). A comparison of FreeSurfer-generated data with and without manual intervention. Frontiers in Neuroscience, 9, 379.Google Scholar
Nordenskjöld, R., Malmberg, F., Larsson, E.M., Simmons, A., Ahlström, H., Johansson, L., && Kullberg, J. (2015). Intracranial volume normalization methods: considerations when investigating gender differences in regional brain volume. Psychiatry Research: Neuroimaging, 231(3), 227235.CrossRefGoogle ScholarPubMed
Price, C.C., Wood, M.F., Leonard, C.M., Towler, S., Ward, J., Montijo, H., &Schmalfuss, I. (2010). Entorhinal cortex volume in older adults: Reliability and validity considerations for three published measurement protocols. Journal of the International Neuropsychological Society, 16(05), 846855.Google Scholar
Ridgway, G., Barnes, J., Pepple, T., & Fox, N. (2011). Estimation of total intracranial volume; a comparison of methods. Alzheimers Dementia, 7(4), S62S63.Google Scholar
Sargolzaei, S., Goryawala, M., Cabrerizo, M., Chen, G., Jayakar, P., Duara, R., &Adjouadi, M. (2014). Comparative reliability analysis of publicly available software packages for automatic intracranial volume estimation. Engineering in Medicine and Biology Society (EMBC), 36th Annual International Conference of the IEEE: 2014 IEEE. 2342–2345.Google Scholar
Segonne, F., Dale, A.M., Busa, E., Glessner, M., Salat, D., Hahn, H.K., && Fischl, B. (2004). A hybrid approach to the skull-strip ping problem in MRI. NeuroImage, 22(3), 10601075.Google Scholar
Tanner, J.J., Mareci, T.H., Okun, M.S., Bowers, D., Libon, D.J., & Price, C.C. (2015). Temporal lobe and frontal-subcortical dissociations in non-demented Parkinson’s disease with verbal memory impairment. PLoS One, 10, e0133792.Google Scholar
Wechsler, D. (1997). Wechsler memory scale: Third edition. San Antonio, TX: The Psychological Corporation.Google Scholar
Whitwell, J.L, Crum, W.R., Watt, H.C., & Fox, N.C. (2001). Normalization of cerebral volumes by use of intracranial volume: –cations for longitudinal quantitative MR imaging. AJNR American Journal of Neuroradiology, 22, 14831489.Google Scholar
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