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Reconstruction of the Brain from Skull Fossils Using Computational Anatomy

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Dynamics of Learning in Neanderthals and Modern Humans Volume 2

Abstract

We investigated the presumed differences in learning abilities between Neanderthals and modern humans by combining evidence from the morphological analysis of fossil brains and functional mapping of modern human brain functions. To achieve this, we established a method for extrapolating to human brain functions from skull anatomy data, which are the only data available to compare modern humans and Neanderthals. Over the last three years, we have developed a skull-based image analysis method based on the computational anatomy technique, which is a standard method used in neuroimaging research, such as functional magnetic resonance imaging (fMRI) and voxel-based morphometry (VBM). In this report, we introduce the concept and practice of our method, and also present some prototype studies to investigate the performance of the proposed approach.

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References

  • Ashburner J (2007) A fast diffeomorphic image registration algorithm. Neuroimage 38:95–113

    Article  Google Scholar 

  • Ashburner J, Friston KJ (1999) Nonlinear spatial normalization using basis functions. Hum Brain Mapp 7:254–266

    Article  Google Scholar 

  • Ashburner J, Friston KJ (2000) Voxel-based morphometry—the methods. Neuroimage 11:805–821

    Article  Google Scholar 

  • Ashburner J, Friston KJ (2005) Unified segmentation. Neuroimage 26:839–851

    Article  Google Scholar 

  • Ashburner J, Friston KJ (2007) Computational anatomy (part2). In: Friston KJ, Ashburner J, Kiebel SJ, Nichols TE, Penny WD (eds) Statistical parametric mapping: the analysis of functional brain images. Academic, New York

    Google Scholar 

  • Ashburner J, Friston KJ (2011) Diffeomorphic registration using geodesic shooting and Gauss-Newton optimisation. Neuroimage 55:954–967

    Article  Google Scholar 

  • Ashburner J, Hutton C, Frackowiak R, Johnsrude I, Price C, Friston K (1998) Identifying global anatomical differences: deformation-based morphometry. Hum Brain Mapp 6:348–357

    Article  Google Scholar 

  • Avants B, Gee JC (2004) Geodesic estimation for large deformation anatomical shape averaging and interpolation. Neuroimage 23(Suppl 1):S139–150

    Article  Google Scholar 

  • Bass WM (1995) Human osteology: a laboratory and field manual, 4th edn. Missouri Archaeological Society, Columbia

    Google Scholar 

  • Bookstein FL (1997) Landmark methods for forms without landmarks: morphometrics of group differences in outline shape. Med Image Anal 1:225–243

    Article  Google Scholar 

  • Cao J, Worsley KJ (1999) The detection of local shape changes via the geometry of Hotelling's T2 field. Ann Statist 27:925–942

    Article  Google Scholar 

  • Chung MK, Worsley KJ, Paus T, Cherif C, Collins DL, Giedd JN, Rapoport JL, Evans AC (2001) A unified statistical approach to deformation-based morphometry. Neuroimage 14:595–606

    Article  Google Scholar 

  • Chung MK, Worsley KJ, Robbins S, Paus T, Taylor J, Giedd JN, Rapoport JL, Evans AC (2003) Deformation-based surface morphometry applied to gray matter deformation. Neuroimage 18:198–213

    Article  Google Scholar 

  • Chung MK, Worsley KJ, Nacewicz BM, Dalton KM, Davidson RJ (2010) General multivariate linear modeling of surface shapes using SurfStat. Neuroimage 53:491–505

    Article  Google Scholar 

  • Collignon A, Maes F, Delaere D, Vandermeulen D, Suetens P, Suetens P, Marchal G (1995) Automated multi-modality image registration based on information theory. In: Bizais Y, Barillot C, Paola PD (eds) Proceedings of information processing in medical imaging, Lecture notes in computer science. Kluwer Academic, Dordrecht

    Google Scholar 

  • Friston KJ, Ashburner J, Frith CD, Poline JB, Heather JD, Frackowiak RSJ (1995) Spatial registration and normalization of images. Hum Brain Mapp 2:165–189

    Article  Google Scholar 

  • Gaser C, Volz HP, Kiebel S, Riehemann S, Sauer H (1999) Detecting structural changes in whole brain based on nonlinear deformations—application to schizophrenia research. Neuroimage 10:107–113

    Article  Google Scholar 

  • Good CD, Johnsrude IS, Ashburner J, Henson RN, Friston KJ, Frackowiak RSJ (2001a) A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage 14:21–36

    Article  Google Scholar 

  • Good CD, Johnsrude I, Ashburner J, Henson RN, Friston KJ, Frackowiak RSJ (2001b) Cerebral asymmetry and the effects of sex and handedness on brain structure: a voxel-based morphometric analysis of 465 normal adult human brains. Neuroimage 14:685–700

    Article  Google Scholar 

  • Hill DL, Batchelor PG, Holden M, Hawkes DJ (2001) Medical image registration. Phys Med Biol 46:R1–45

    Article  Google Scholar 

  • Hua X, Leow AD, Lee S, Klunder AD, Toga AW, Lepore N, Chou YY, Brun C, Chiang MC, Barysheva M, Jack CR Jr, Bernstein MA, Britson PJ, Ward CP, Whitwell JL, Borowski B, Fleisher AS, Fox NC, Boyes RG, Barnes J, Harvey D, Kornak J, Schuff N, Boreta L, Alexander GE, Weiner MW, Thompson PM, Alzheimer's Disease Neuroimaging I (2008a) 3D characterization of brain atrophy in Alzheimer's disease and mild cognitive impairment using tensor-based morphometry. Neuroimage 41:19–34

    Article  Google Scholar 

  • Hua X, Leow AD, Parikshak N, Lee S, Chiang MC, Toga AW, Jack CR Jr, Weiner MW, Thompson PM, Alzheimer's Disease Neuroimaging I (2008b) Tensor-based morphometry as a neuroimaging biomarker for Alzheimer's disease: an MRI study of 676 AD, MCI, and normal subjects. Neuroimage 43:458–469

    Article  Google Scholar 

  • Im K, Lee JM, Lee J, Shin YW, Kim IY, Kwon US, Kima SI (2006) Gender difference analysis of cortical thickness in healthy young adults with surface-based methods. Neuroimage 31:31–38

    Article  Google Scholar 

  • Lee AD, Leow AD, Lu A, Reiss AL, Hall S, Chiang MC, Toga AW, Thompson PM (2007) 3D pattern of brain abnormalities in Fragile X syndrome visualized using tensor-based morphometry. Neuroimage 34:924–938

    Article  Google Scholar 

  • Miller MI, Beg MF, Ceritoglu C, Stark C (2005) Increasing the power of functional maps of the medial temporal lobe by using large deformation diffeomorphic metric mapping. Proc Natl Acad Sci U S A 102:9685–9690

    Article  Google Scholar 

  • Miller MI, Trouve A, Younes L (2006) Geodesic shooting for computational anatomy. J Math Imaging Vis 24:209–228

    Article  Google Scholar 

  • Wang L, Beg F, Ratnanather T, Ceritoglu C, Younes L, Morris JC, Csernansky JG, Miller MI (2007) Large deformation diffeomorphism and momentum based hippocampal shape discrimination in dementia of the Alzheimer type. IEEE Trans Med Imaging 26:462–470

    Article  Google Scholar 

  • Worsley KJ, Taylor JE, Tomaiuolo F, Lerch J (2004) Unified univariate and multivariate random field theory. Neuroimage 23(Suppl 1):S189–195

    Article  Google Scholar 

  • Zuk TD, Atkins MS (1996) A comparison of manual and automatic methods for registering scans of the head. IEEE Trans Med Imaging 15:732–744

    Article  Google Scholar 

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Correspondence to Takanori Kochiyama .

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Kochiyama, T., Tanabe, H.C., Ogihara, N. (2014). Reconstruction of the Brain from Skull Fossils Using Computational Anatomy. In: Akazawa, T., Ogihara, N., C Tanabe, H., Terashima, H. (eds) Dynamics of Learning in Neanderthals and Modern Humans Volume 2. Replacement of Neanderthals by Modern Humans Series. Springer, Tokyo. https://doi.org/10.1007/978-4-431-54553-8_22

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