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Estimating Intracranial Volume in Brain Research: An Evaluation of Methods

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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.

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References

  • Ambarki, K., Lindqvist, T., Wåhlin, A., Petterson, E., Warntjes, M. J. B., Birgander, R., & Eklund, A. (2012). Evaluation of automatic measurement of the intracranial volume based on quantitative MR imaging. American Journal of Neuroradiology, 33(10), 1951–1956.

    Article  CAS  PubMed  Google Scholar 

  • Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry—the methods. NeuroImage, 11(6), 805–821.

    Article  CAS  PubMed  Google Scholar 

  • Ashburner, J., & Friston, K. J. (2005). Unified segmentation. NeuroImage, 26(3), 839–851.

    Article  PubMed  Google Scholar 

  • Bendel, P., Koivisto, T., Äikiä, M., Niskanen, E., Könönen, M., Hänninen, T., & Vanninen, R. (2010). Atrophic enlargement of CSF volume after subarachnoid hemorrhage: correlation with neuropsychological outcome. American Journal of Neuroradiology, 31(2), 370–376.

    Article  CAS  PubMed  Google Scholar 

  • Buckner, R. L. (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(2), 724–738.

    Article  PubMed  Google Scholar 

  • Cardenas, V. A., Chao, L. L., Blumenfeld, R., Song, E., Meyerhoff, D. J., Weiner, M. W., & Studholme, C. (2005). Using automated morphometry to detect associations between ERP latency and structural brain MRI in normal adults. Human Brain Mapping, 25(3), 317–327.

    Article  PubMed Central  PubMed  Google Scholar 

  • Chee, M. W. L., Zheng, H., Goh, J. O. S., Park, D., & Sutton, B. P. (2011). Brain structure in young and old East Asians and Westerners: comparisons of structural volume and cortical thickness. Journal of Cognitive Neuroscience, 23(5), 1065–1079.

    Article  PubMed Central  PubMed  Google Scholar 

  • Cox, R. W. (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29(3), 162–173.

    Article  CAS  PubMed  Google Scholar 

  • Cullen, K. R., Wallace, S., Magnotta, V. A., Bockholt, J., Ehrlich, S., Gollub, R. L., & Lauriello, J. (2012). Cigarette smoking and white matter microstructure in schizophrenia. Psychiatry Research: Neuroimaging, 201(2), 152–158.

    Article  PubMed Central  PubMed  Google Scholar 

  • Dale, A. M., Fischl, B., & Sereno, M. I. (1999). Cortical surface-based analysis: I. Segmentation and surface reconstruction. NeuroImage, 9(2), 179–194.

    Article  CAS  PubMed  Google Scholar 

  • Duara, R., Loewenstein, D. A., Greig, M., Acevedo, A., Potter, E., Appel, J., & Barker, W. (2010). Reliability and validity of an algorithm for the diagnosis of normal cognition, mild cognitive impairment, and dementia: implications for multicenter research studies. The American Journal of Geriatric Psychiatry, 18(4), 363–370.

    Article  PubMed Central  PubMed  Google Scholar 

  • Dukart, J., Mueller, K., Villringer, A., Kherif, F., Draganski, B., Frackowiak, R., & Schroeter, M. L. (2013). Relationship between imaging biomarkers, Age, progression and symptom severity in Alzheimer’s disease. NeuroImage: Clinical, 3, 84–94.

    Article  Google Scholar 

  • Eddin, A. S., Wang, J., Wu, W., Sargolzaei, S., Bjornson, B., Jones, R. A., & Adjouadi, M. (2014). The effects of pediatric epilepsy on a language connectome. Human Brain Mapping.

  • Ehrlich, S., Morrow, E. M., Roffman, J. L., Wallace, S. R., Naylor, M., Bockholt, H. J., & White, T. (2010). The COMT Val108/158Met polymorphism and medial temporal lobe volume try in patients with schizophrenia and healthy adults. NeuroImage, 53(3), 992–1000.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Eritaia, J., Wood, S. J., Stuart, G. W., Bridle, N., Dudgeon, P., Maruff, P., & Pantelis, C. (2000). An optimized method for estimating intracranial volume from magnetic resonance images. Magnetic Resonance in Medicine, 44(6), 973–977.

    Article  CAS  PubMed  Google Scholar 

  • Fennema-Notestine, C., Hagler, D. J., McEvoy, L. K., Fleisher, A. S., Wu, E. H., Karow, D. S., & Dale, A. M. (2009). Structural MRI biomarkers for preclinical and mild Alzheimer’s disease. Human Brain Mapping, 30(10), 3238–3253.

    Article  PubMed Central  PubMed  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), 189–198.

    Article  CAS  PubMed  Google Scholar 

  • Garibotto, V., Borroni, B., Agosti, C., Premi, E., Alberici, A., Eickhoff, S. B., & Perani, D. (2011). Subcortical and deep cortical atrophy in frontotemporal lobar degeneration. Neurobiology of Aging, 32(5), 875–884.

    Article  PubMed  Google Scholar 

  • Germeyan, S. C., Kalikhman, D., Jones, L., & Theodore, W. H. (2014). Automated versus manual hippocampal segmentation in preoperative and postoperative patients with epilepsy. Epilepsia, 55(9), 1374–9.

    Article  PubMed Central  PubMed  Google Scholar 

  • Gong, G., Alexander, R. P., Shi, F., Beaulieu, C., & Gross, D. W. (2012). Isolated febrile seizures are not associated with structural abnormalities of the limbic system. Epilepsy Research, 102(3), 216–20.

    Article  PubMed  Google Scholar 

  • Graca, A. M., Cardoso, K. R. V., da Costa, J. M. F. P., & Cowan, F. M. (2013). Cerebral volume at term age: comparison between preterm and term-born infants using cranial ultrasound. Early Human Development, 89(9), 643–8.

    Article  PubMed  Google Scholar 

  • Groves, A. R., Smith, S. M., Fjell, A. M., Tamnes, C. K., Walhovd, K. B., Douaud, G., & Westlye, L. T. (2012). Benefits of multi-modal fusion analysis on a large-scale dataset: life-span patterns of inter-subject variability in cortical morphometry and white matter microstructure. Neuroimage.

  • Guillen, M. R., Adjouadi, M., Bernal, B., Ayala, M., Barreto, A., Rishe, N., & Gaillard, W. (2009). A knowledge-based database system for visual rating of fMRI activation patterns for brain language networks. Paper presented at the The Fifth Richard Tapia Celebration of Diversity in Computing Conference: Intellect, Initiatives, Insight, and Innovations.

  • Heckemann, R. A., Keihaninejad, S., Aljabar, P., Gray, K. R., Nielsen, C., Rueckert, D., & Hammers, A. (2011). Automatic morphometry in Alzheimer’s disease and mild cognitive impairment. NeuroImage, 56(4), 2024–2037.

    Article  PubMed Central  PubMed  Google Scholar 

  • Ikram, M. A., Fornage, M., Smith, A. V., Seshadri, S., Schmidt, R., Debette, S., & Taal, H. R. (2012). Common variants at 6q22 and 17q21 are associated with intracranial volume. Nature Genetics, 44(5), 539.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Jack, C. R., Knopman, D. S., Weigand, S. D., Wiste, H. J., Vemuri, P., Lowe, V., & Ivnik, R. J. (2012). An operational approach to National Institute on Aging–Alzheimer’s Association criteria for preclinical Alzheimer disease. Annals of Neurology, 71(6), 765–775.

    Article  PubMed Central  PubMed  Google Scholar 

  • Jack, C. R., Jr., Knopman, D. S., Jagust, W. J., Petersen, R. C., Weiner, M. W., Aisen, P. S., & Weigand, S. D. (2013). Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. The Lancet Neurology, 12(2), 207–216.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., & Smith, S. M. (2012). Fsl. Neuroimage, 62(2), 782–790.

    Article  PubMed  Google Scholar 

  • Jenkinson, M., Pechaud, M., & Smith, S. (2005). BET2: MR-based estimation of brain, skull and scalp surfaces. Paper presented at the Eleventh annual meeting of the organization for human brain mapping.

  • 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 (1.5 T and 3T). NeuroImage, 50(4), 1427–1437.

    Article  PubMed Central  PubMed  Google Scholar 

  • Kochan, N. A., Breakspear, M., Valenzuela, M., Slavin, M. J., Brodaty, H., Wen, W., & Sachdev, P. S. (2011). Cortical responses to a graded working memory challenge predict functional decline in mild cognitive impairment. Biological Psychiatry, 70(2), 123–130.

    Article  PubMed  Google Scholar 

  • Lahlou, M., Guillen, M., Adjouadi, M., & Gaillard, W. (2006). An online web-based repository site of fMRI medical images and clinical data for childhood epilepsy. Paper presented at the 11th world congress on internet in medicine.

  • Lampert, E. J., Choudhury, K. R., Hostage, C. A., Petrella, J. R., & Doraiswamy, P. M. (2013). Prevalence of Alzheimer’s pathologic endophenotypes in asymptomatic and mildly impaired first-degree relatives. PloS One, 8(4), e60747.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Malone, I. B., Leung, K. K., Clegg, S., Barnes, J., Whitwell, J. L., Ashburner, J., & Ridgway, G. R. (2015). Accurate automatic estimation of total intracranial volume: a nuisance variable with less nuisance. NeuroImage, 104, 366–372.

    Article  PubMed Central  PubMed  Google Scholar 

  • Martin Bland, J., & Altman, D. (1986). Statistical methods for assessing agreement between two methods of clinical measurement. The Lancet, 327(8476), 307–310.

    Article  Google Scholar 

  • Morey, R. A., Selgrade, E. S., Wagner, H. R., Huettel, S. A., Wang, L., & McCarthy, G. (2010). Scan–rescan reliability of subcortical brain volumes derived from automated segmentation. Human Brain Mapping, 31(11), 1751–1762.

    PubMed Central  PubMed  Google Scholar 

  • Nordenskjöld, R. (2014). Analysis of Human Brain MRI: Contributions to Regional Volume Studies.

  • Nordenskjöld, R., Malmberg, F., Larsson, E.-M., Simmons, A., Brooks, S. J., Lind, L., & Kullberg, J. (2013). Intracranial volume estimated with commonly used methods could introduce bias in studies including brain volume measurements. Neuroimage.

  • Pa, J., Boxer, A., Chao, L. L., Gazzaley, A., Freeman, K., Kramer, J., & Johnson, J. K. (2009). Clinical‐neuroimaging characteristics of dysexecutive mild cognitive impairment. Annals of Neurology, 65(4), 414–423.

    Article  PubMed Central  PubMed  Google Scholar 

  • Pengas, G., Pereira, J., Williams, G. B., & Nestor, P. J. (2009). Comparative reliability of total intracranial volume estimation methods and the influence of atrophy in a longitudinal semantic dementia cohort. Journal of Neuroimaging, 19(1), 37–46.

    Article  PubMed  Google Scholar 

  • Peper, J. S., Schnack, H. G., Brouwer, R. M., Van Baal, G. C. M., Pjetri, E., Szekely, E., & Evans, A. C. (2009). Heritability of regional and global brain structure at the onset of puberty: a magnetic resonance imaging study in 9‐year‐old twin pairs. Human Brain Mapping, 30(7), 2184–2196.

    Article  PubMed  Google Scholar 

  • Piguet, O., Petersén, Å., Yin Ka Lam, B., Gabery, S., Murphy, K., Hodges, J. R., & Halliday, G. M. (2011). Eating and hypothalamus changes in behavioral‐variant frontotemporal dementia. Annals of Neurology, 69(2), 312–319.

    Article  PubMed Central  PubMed  Google Scholar 

  • Piper, R. J., Yoong, M. M., Pujar, S., & Chin, R. F. (2014). Estimating intracranial volume using intracranial area in healthy children and those with childhood status epilepticus. Brain and Behavior, 4(6), 936–942.

    Article  PubMed Central  PubMed  Google Scholar 

  • Ridgway, G., Barnes, J., Pepple, T., & Fox, N. (2011). Estimation of total intracranial volume; a comparison of methods. Alzheimer’s and Dementia, 7(4), S62–S63.

    Article  Google Scholar 

  • Ritvanen, A. G., de Oliveira, M. E., Koivikko, M. P., Hallila, H. O., Haaja, J. K., Koljonen, V. S., & Paulasto-Kröckel, M. M. (2013). Mesh-based method for measuring intracranial volume in patients with craniosynostosis. International Journal of Computer Assisted Radiology and Surgery, 1–7.

  • Roussotte, F. F., Sulik, K. K., Mattson, S. N., Riley, E. P., Jones, K. L., Adnams, C. M., & Sowell, E. R. (2012). Regional brain volume reductions relate to facial dysmorphology and neurocognitive function in fetal alcohol spectrum disorders. Human Brain Mapping, 33(4), 920–937.

    Article  PubMed  Google Scholar 

  • Sonmez, O. F., Temel, Y., Visser-Vandewalle, V., Sahin, B., & Odacı, E. (2013). A new evaluation method for the intracranial volume changes and subdural effusion of patients following endoscopic third ventriculostomy. Clinical Neurology and Neurosurgery, 115(2), 160–164.

    Article  PubMed  Google Scholar 

  • Szentkuti, A., Guderian, S., Schiltz, K., Münte, T. F., Heinze, H.-J., & Düzel, E. (2004). Quantitative MR analyses of the hippocampus: unspecific metabolic changes in aging. Journal of Neurology, 251(11), 1345–1353.

    Article  PubMed  Google Scholar 

  • Taki, Y., Thyreau, B., Kinomura, S., Sato, K., Goto, R., Wu, K., & Fukuda, H. (2013). A longitudinal study of the relationship between personality traits and the annual rate of volume changes in regional gray matter in healthy adults. Human Brain Mapping, 34(12), 3347–3353.

    Article  PubMed  Google Scholar 

  • Thambisetty, M., An, Y., Kinsey, A., Koka, D., Saleem, M., Gϋntert, A., & Lovestone, S. (2012). Plasma clusterin concentration is associated with longitudinal brain atrophy in mild cognitive impairment. NeuroImage, 59(1), 212–217.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Trivedi, M. A., Stoub, T. R., Murphy, C. M., George, S., Shah, R. C., Whitfield-Gabrieli, S., & Stebbins, G. T. (2011). Entorhinal cortex volume is associated with episodic memory related brain activation in normal aging and amnesic mild cognitive impairment. Brain Imaging and Behavior, 5(2), 126–136.

    Article  PubMed Central  PubMed  Google Scholar 

  • Wang, Z., Zhang, Z., Jiao, Q., Liao, W., Chen, G., Sun, K., & Liu, Y. (2012). Impairments of thalamic nuclei in idiopathic generalized epilepsy revealed by a study combining morphological and functional connectivity MRI. PloS One, 7(7), e39701.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Westman, E., Aguilar, C., Muehlboeck, J.-S., & Simmons, A. (2013). Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer’s disease and mild cognitive impairment. Brain Topography, 26(1), 9–23.

    Article  PubMed Central  PubMed  Google Scholar 

  • Whitwell, J. L., Crum, W. R., Watt, H. C., & Fox, N. C. (2001). Normalization of cerebral volumes by use of intracranial volume: implications for longitudinal quantitative MR imaging. American Journal of Neuroradiology, 22(8), 1483–1489.

    CAS  PubMed  Google Scholar 

  • Wilke, M., & Holland, S. K. (2003). Variability of gray and white matter during normal development: a voxel-based MRI analysis. Neuroreport, 14(15), 1887.

    Article  PubMed Central  PubMed  Google Scholar 

  • Wilke, M., Schmithorst, V., & Holland, S. (2003). Normative pediatric brain data for spatial normalization and segmentation differs from standard adult data. Magnetic Resonance in Medicine, 50(4), 749–757.

    Article  CAS  PubMed  Google Scholar 

  • Wilke, M., Schmithorst, V. J., & Holland, S. K. (2002). Assessment of spatial normalization of whole‐brain magnetic resonance images in children. Human Brain Mapping, 17(1), 48–60.

    Article  PubMed  Google Scholar 

  • Winston, G. P., Cardoso, M. J., Williams, E. J., Burdett, J. L., Bartlett, P. A., Espak, M., & Ourselin, S. (2013). Automated hippocampal segmentation in patients with epilepsy: available free online. Epilepsia, 54(12), 2166–2173.

    Article  PubMed Central  PubMed  Google Scholar 

  • Zhou, Q., Goryawala, M., Cabrerizo, M., Barker, W., Duara, R., & Adjouadi, M. (2014). Significance of normalization on anatomical MRI measures in predicting Alzheimer’s disease. The Scientific World Journal, 2014, 541802.

    PubMed Central  PubMed  Google Scholar 

  • Ziaei, A., Yeganeh, H., Faez, K., & Sargolzaei, S. (2008). A novel approach for contrast enhancement in biomedical images based on histogram equalization. Paper presented at the BioMedical Engineering and Informatics, 2008. BMEI 2008. International Conference on.

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Acknowledgments

This work is supported by the National Science Foundation under grants CNS-0959985, CNS-1042341, HRD-0833093, and IIP-1230661. The support of the Ware Foundation is greatly appreciated. The authors would like to thank the anonymous reviewers for their comments and suggestions which significantly improved the quality of this work.

Conflict of Interests

The authors have no commercial, financial, or other relationship related to the subject of this paper that could constitute or suggest a conflict of interest.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA

    Saman Sargolzaei, Arman Sargolzaei, Mercedes Cabrerizo, Mohammed Goryawala & Malek Adjouadi

  2. Scientific and Statistical Computing Core, NIMH/NIH/HHS, Bethesda, MD, USA

    Gang Chen

  3. Baptist Health Neuroscience Center, Baptist Hospital, Miami, FL, USA

    Alberto Pinzon-Ardila & Sergio M. Gonzalez-Arias

  4. Department of Neuroscience, Herbert Wertheim College of Medicine, Florida International University, Miami, FL, USA

    Sergio M. Gonzalez-Arias

  5. Department of Biomedical Engineering, Florida International University, Miami, FL, USA

    Malek Adjouadi

  6. 10555W. Flagler St, ECE 2220, Miami, FL, 33174, USA

    Malek Adjouadi

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  1. Saman Sargolzaei
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  2. Arman Sargolzaei
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  3. Mercedes Cabrerizo
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Sargolzaei, S., Sargolzaei, A., Cabrerizo, M. et al. Estimating Intracranial Volume in Brain Research: An Evaluation of Methods. Neuroinform 13, 427–441 (2015). https://doi.org/10.1007/s12021-015-9266-5

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