Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Deep brain stimulation
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.

SCI Publications


C. Goodlett, P.T. Fletcher, J. Gilmore, G. Gerig. “Group Statistics of DTI Fiber Bundles Using Spatial Functions of Tensor Measures,” In Medical Image Computing and Computer-Assisted Intervention (MICCAI 2008), Springer Verlag, pp. 1068--1075. 2008.
PubMed ID: 18979851

S. Gouttard, M. Styner, M.W. Prastawa, J. Piven, G. Gerig. “Assessment of Reliability of Multi-site Neuroimaging via Traveling Phantom Study,” In Proceedings of Medical Image Computing and Computer Assisted Intervention 2008, Lecture Notes in Computer Science LNCS, Vol. 5242, pp. 263--270. September, 2008.

R.C. Knickmeyer, S. Gouttard, C. Kang, D. Evans, K. Wilber, K.J. Smith, R.M. Hamer, W. Lin, G. Gerig, J.H. Gilmore. “A Structural MRI Study of Human Brain Development from Birth to Two Years,” In The Journal of Neuroscience, Vol. 28, No. 47, pp. 12176--12182. Nov, 2008.
PubMed ID: 19020011

M. Kubicki, M. Styner, S. Bouix, G. Gerig, D. Markant, K. Smith, R. Kikinis, R.W. McCarley, M.E. Shenton. “Reduced Interhemispheric Connectivity in Schizophrenia- Tractography Based Segmentation of the Corpus Callosum,” In Schizophrenia Research, Vol. 106, No. 2-3, pp. 125--131. December, 2008.

W. Lin, Q. Zhu, W. Gao, Y. Chen, C.-H. Toh, M. Styner, G. Gerig, J.K. Smith, B. Biswal, J. Gilmore. “Functional Connectivity Magnetic Resonance Imaging Reveals Cortical Functional Connectivity in the Developing Brain,” In American Journal of Neuroradiology, Vol. 29, pp. 1883--1889. Fall, 2008.

N. Mukherjee, C. Kang, H.M. Wolfe, B.S. Hertzberg, J.K. Smith, W. Lin, G. Gerig, R.M. Hamer, J.H. Gilmore. “Discordance of Prenatal and Neonatal Brain Development in Twins,” In Early Human Development, pp. (in press). August, 2008.

M.W. Prastawa, G. Gerig. “Brain Lesion Segmentation Through Physical Model Estimation,” In Lecture Notes in Computer Science, Vol. 5358, pp. 562--571. 2008.
DOI: 10.1007/978-3-540-89639-5_54

M. Styner, I. Oguz, T. Heimann, G. Gerig. “Minimum Description Length with Local Geometry,” In Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2008), pp. 1283--1286. 2008.
DOI: 10.1109/ISBI.2008.4541238

S. Xu, M. Styner, J.H. Gilmore, G. Gerig. “Multivariate Longitudinal Statistics for Neonatal-Pediatric Brain Tissue Development,” In Proceedings of SPIE Medical Imaging 2008, Vol. 6914, February, 2008.
DOI: 10.1117/12.773966


The topic of studying the growth of human brain development has become of increasing interests in the neuroimaging community. Cross-sectional studies may allow comparisons between means of different age groups, but they do not provide any growth model that integrates the continuum of time, nor do they present any information about how individuals/population change over time. Longitudinal data analysis method arises as a strong tool to address these questions. In this paper, we use longitudinal analysis methods to study tissue development in early brain growth; a novel approach of multivariate longitudinal analysis is applied to study the associations between the growth of different brain tissues. We present in this paper the methodologies to statistically study scalar (univariate) and vector (multivariate) longitudinal data, and our exploratory results in the study of neonatal-pediatric brain tissue development. We obtained growth curves as a quadratic function of time for all three tissues. The quadratic terms were then tested to be statistically signicant, showing that there was indeed a quadratic growth of tissues in early brain development. Moreover, our result shows that there is a positive correlation between repeated measurements of any single tissue, and among those of different tissues. Our approach is generic in natural and thus can be applied to any longitudinal data with multiple outcomes, even brain structures. Also, our joint mixed model is flexible enough to allow incomplete and unbalanced data, i.e. subjects do not need to have the same number of measurements, or be measured at the exact time points.

Keywords: ucnia

S. Xu, M. Styner, J. Gilmore, J. Piven, G. Gerig. “Multivariate Nonlinear Mixed Model to Analyze Longitudinal Image Data: MRI Study of Early Brain Development,” In Proceedings of IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA) 2008, IEEE Computer Society, pp. 1--8. June, 2008.
ISBN: 978-1-4244-2339-2
DOI: 10.1109/CVPRW.2008.4563011

F. Zhang, E.R. Hancock, C. Goodlett, G. Gerig. “Probabilistic White Matter Fiber Tracking using, Particle Filtering and von Mises-Fisher Sampling,” In Medical Image Analysis (MedIA), pp. (in print). 2008.


M.K. Belmonte, J.C. Mazziotta, N.J. Minshew, A.C. Evans, E. Courchesne, S.R. Dager, S.Y. Bookheimer, E.H. Aylward, D.G. Amaral, R.M. Cantor, D.C. Chugani, A.M. Dale, C. Davatzikos, G. Gerig, M.R. Herbert, J.E. Lainhart, D.G. Murphy, J. Piven, A.L. Reiss, R.T. Schultz, T.A. Zeffiro, S. Levi-Pearl, C. Lajonchere, S.A. Colamarino. “Offering to Share: How to Put Heads Together in Autism Neuroimaging,” In Journal of Autism and Developmental Disorders JADD, Vol. 38, pp. 2--13. 2007.

C.J. Cascio, G. Gerig, J. Piven. “Diffusion Tensor Imaging: Application to the Study of the Developing Brain,” In J Am Acad Child Adolesc Psychiatry, Vol. 46, No. 2, pp. 213--223. February, 2007.

J.H. Gilmore, W. Lin, I. Corouge, Y.S. Vetsa, J.K. Smith, C. Kang, H. Gu, R.M. Hamer, J.A. Lieberman, G. Gerig. “Early Postnatal Development of Corpus Callosum and Corticospinal White Matter Assessed with Quantitative Tractography,” In American Journal of Neuroradiology, Vol. 28, No. 9, pp. 1789--1795. October, 2007.

J.H. Gilmore, W. Lin, M.W. Prastawa, C.B. Looney, Y.S.K. Vetsa, R.C. Knickmeyer, D.D. Evans, J.K. Smith, R.M. Hamer, J.A. Lieberman, G. Gerig. “Regional Gray Matter Growth, Sexual Dimorphism, and Cerebral Asymmetry in the Neonatal Brain,” In Journal of Neuroscience, Vol. 27, No. 6, pp. 1255--1260. 2007.

J.H. Gilmore, L. Smith, C. Kang, R. Hamer, H. Wolfe, B. Hertzberg, J.K. Smith, N. Chescheir, W. Lin, G. Gerig. “Neonatal Brain Structure in Children with Prenatal Isolated Mild Ventriculomegaly,” In Proceedings American Conference of Neuropharmacology (ACNP) 2007, Boca Raton, FL, Note: abstract., 2007.

J.H. Gilmore, W. Lin, M.W. Prastawa, C.B. Looney, Y.S.K. Vetsa, R.C. Knickmeyer, D.D. Evans, J.K. Smith, R.M. Hamer, J.A. Lieberman, G. Gerig. “Regional Gray Matter Growth, Sexual Dimorphism, and Cerebral Asymmetry in the Neonatal Brain,” In Journal of Neuroscience, Vol. 27, No. 6, pp. 1255--1260. 2007.

C. Goodlett, P.T. Fletcher, W. Lin, G. Gerig. “Quantification of Measurement Error in DTI: Theoretical Predictions and Validation,” In Proceedings of The 10th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2007), Lecture Notes in Computer Science, Vol. 4792, pp. 10--17. November, 2007.
PubMed ID: 18051038

C.B. Goodlett, P.T. Fletcher, W. Lin, G. Gerig. “Noise-Induced Bias in Low-Direction Diffusion Tensor MRI: Replication of Monte-Carlo Simulation with In-Vivo Scans,” In Proceedings of ISMRM 2007, 2007.

K. Gorczowski, M. Styner, J.Y. Jeong, J.S. Marron, J. Piven, H.C. Hazlett, S.M. Pizer, G. Gerig. “Statistical Shape Analysis of Multi-Object Complexes,” In Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1--8. 2007.