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


G. Gerig, I. Oguz, S. Gouttard, J. Lee, H. An, W. Lin, M. McMurray, K. Grewen, J. Johns, M.A. Styner. “Synergy of image analysis for animal and human neuroimaging supports translational research on drug abuse,” In Frontiers in Child and Neurodevelopmental Psychiatry, Vol. 2, Edited by Linda Mayes, pp. 9 pages. 2011.
ISSN: 1664-0640
DOI: 10.3389/fpsyt.2011.00053

G. Gerig, J.H. Gilmore, W. Lin. “Brain Maturation of Newborns and Infants,” Encyclopedia on Early Childhood Development (online): Brain Development in Children - According to Experts, Montreal, Quebec, Centre of Excellence for Early Childhood Development and Strategic Knowledge Cluster on Early Child Development, pp. 1--6. 2011.


Recently, imaging studies of early human development have received more attention, as improved modeling methods might lead to a clearer understanding of the origin, timing, and nature of differences in neurodevelopmental disorders. Non-invasive magnetic resonance imaging (MRI) can provide three-dimensional images of the infant brain in less than 20 minutes, with unprecedented anatomical details and contrast of brain anatomy, cortical and subcortical structures and brain connectivity. Repeating MRI at different stages of development, e.g., in yearly intervals starting after birth, gives scientists the opportunity to study the trajectory of brain growth and compare individual growth trajectories to normative models. These comparisons become highly relevant in personalized medicine, where early diagnosis is a critical juncture for timing and therapy types.

L.K. Ha, M.W. Prastawa, G. Gerig, J.H. Gilmore, C.T. Silva. “Efficient Probabilistic and Geometric Anatomical Mapping Using Particle Mesh Approximation on GPUs,” In International Journal of Biomedical Imaging, Special Issue in Parallel Computation in Medical Imaging Applications, Vol. 2011, Note: Article ID 572187, pp. 16 pages. 2011.
DOI: 10.1155/2011/572187

H.C. Hazlett, M. Poe, G. Gerig, M. Styner, C. Chappell, R.G. Smith, C. Vachet, J. Piven. “Early Brain Overgrowth in Autism Associated with an Increase in Cortical Surface Area Before Age 2,” In Arch of Gen Psych, Vol. 68, No. 5, pp. 467--476. 2011.
DOI: 10.1001/archgenpsychiatry.2011.39

A. Irimia, M.C. Chambers, J.R. Alger, M. Filippou, M.W. Prastawa, Bo Wang, D. Hovda, G. Gerig, A.W. Toga, R. Kikinis, P.M. Vespa, J.D. Van Horn. “Comparison of acute and chronic traumatic brain injury using semi-automatic multimodal segmentation of MR volumes,” In Journal of Neurotrauma, Vol. 28, No. 11, pp. 2287--2306. November, 2011.
DOI: 10.1089/neu.2011.1920
PubMed ID: 21787171


Although neuroimaging is essential for prompt and proper management of traumatic brain injury (TBI), there is a regrettable and acute lack of robust methods for the visualization and assessment of TBI pathophysiology, especially for of the purpose of improving clinical outcome metrics. Until now, the application of automatic segmentation algorithms to TBI in a clinical setting has remained an elusive goal because existing methods have, for the most part, been insufficiently robust to faithfully capture TBI-related changes in brain anatomy. This article introduces and illustrates the combined use of multimodal TBI segmentation and time point comparison using 3D Slicer, a widely-used software environment whose TBI data processing solutions are openly available. For three representative TBI cases, semi-automatic tissue classification and 3D model generation are performed to perform intra-patient time point comparison of TBI using multimodal volumetrics and clinical atrophy measures. Identification and quantitative assessment of extra- and intra-cortical bleeding, lesions, edema, and diffuse axonal injury are demonstrated. The proposed tools allow cross-correlation of multimodal metrics from structural imaging (e.g., structural volume, atrophy measurements) with clinical outcome variables and other potential factors predictive of recovery. In addition, the workflows described are suitable for TBI clinical practice and patient monitoring, particularly for assessing damage extent and for the measurement of neuroanatomical change over time. With knowledge of general location, extent, and degree of change, such metrics can be associated with clinical measures and subsequently used to suggest viable treatment options.

Keywords: namic

S.H. Kim, V. Fonov, J. Piven, J. Gilmore, C. Vachet, G. Gerig, D.L. Collins, M. Styner. “Spatial Intensity Prior Correction for Tissue Segmentation in the Developing human Brain,” In Proceedings of IEEE ISBI 2011, pp. 2049--2052. 2011.
DOI: 10.1109/ISBI.2011.5872815

R.C. Knickmeyer, C. Kang, S. Woolson, K.J. Smith, R.M. Hamer, W. Lin, G. Gerig, M. Styner, J.H. Gilmore. “Twin-Singleton Differences in Neonatal Brain Structure,” In Twin Research and Human Genetics, Vol. 14, No. 3, pp. 268--276. 2011.
ISSN: 1832-4274
DOI: 10.1375/twin.14.3.268

N. Sadeghi, M.W. Prastawa, P.T. Fletcher, J.H. Gilmore, W. Lin, G. Gerig. “Statistical Growth Modeling of Longitudinal DT-MRI for Regional Characterization of Early Brain Development,” In Proceedings of the Medical Image Computing and Computer Assisted Intervention (MICCAI) 2011 Workshop on Image Analysis of Human Brain Development, pp. 1507--1510. 2011.
DOI: 10.1109/ISBI.2012.6235858


A population growth model that represents the growth trajectories of individual subjects is critical to study and understand neurodevelopment. This paper presents a framework for jointly estimating and modeling individual and population growth trajectories, and determining significant regional differences in growth pattern characteristics applied to longitudinal neuroimaging data. We use non-linear mixed effect modeling where temporal change is modeled by the Gompertz function. The Gompertz function uses intuitive parameters related to delay, rate of change, and expected asymptotic value; all descriptive measures which can answer clinical questions related to growth. Our proposed framework combines nonlinear modeling of individual trajectories, population analysis, and testing for regional differences. We apply this framework to the study of early maturation in white matter regions as measured with diffusion tensor imaging (DTI). Regional differences between anatomical regions of interest that are known to mature differently are analyzed and quantified. Experiments with image data from a large ongoing clinical study show that our framework provides descriptive, quantitative information on growth trajectories that can be directly interpreted by clinicians. To our knowledge, this is the first longitudinal analysis of growth functions to explain the trajectory of early brain maturation as it is represented in DTI.

Keywords: namic

F. Shi, D. Shen, P.-T. Yap, Y. Fan, J.-Z. Cheng, H. An, L.L. Wald, G. Gerig, J.H. Gilmore, W. Lin. “CENTS: Cortical Enhanced Neonatal Tissue Segmentation,” In Human Brain Mapping HBM, Vol. 32, No. 3, Note: ePub 5 Aug 2010, pp. 382--396. March, 2011.
DOI: 10.1002/hbm.21023
PubMed ID: 20690143

Y. Wang, A. Gupta, Z. Liu, H. Zhang, M.L. Escolar, J.H. Gilmore, S. Gouttard, P. Fillard, E. Maltbie, G. Gerig, M. Styner. “DTI registration in atlas based fiber analysis of infantile Krabbe disease,” In Neuroimage, pp. (in print). 2011.
PubMed ID: 21256236

H. Zhu, L. Kong, R. Li, M.S. Styner, G. Gerig, W. Lin, J.H. Gilmore. “FADTTS: Functional Analysis of Diffusion Tensor Tract Statistics,” In NeuroImage, Vol. 56, No. 3, pp. 1412--1425. 2011.
DOI: 10.1016/j.neuroimage.2011.01.075
PubMed ID: 21335092


M. El-Sayed, R.G. Steen, M.D. Poe, T.C. Bethea, G. Gerig, J. Lieberman, L. Sikich. “Deficits in gray matter volume in psychotic youth with schizophrenia-spectrum disorders are not evident in psychotic youth with mood disorders,” In J Psychiatry Neurosci, July, 2010.

M. El-Sayed, R.G. Steen, M.D. Poe, T.C. Bethea, G. Gerig, J. Lieberman, L. Sikich. “Brain volumes in psychotic youth with schizophrenia and mood disorders,” In Journal of Psychiatry and Neuroscience, Vol. 35, No. 4, pp. 229--236. July, 2010.
PubMed ID: 20569649

J.H. Gilmore, C. Kang, D.D. Evans, H.M. Wolfe, M.D. Smith, J.A. Lieberman, W. Lin, R.M. Hamer, M. Styner, G. Gerig. “Prenatal and Neonatal Brain Structure and White Matter Maturation in Children at High Risk for Schizophrenia,” In American Journal of Psychiatry, Vol. 167, No. 9, Note: Epub 2010 Jun 1, pp. 1083--1091. September, 2010.
PubMed ID: 20516153

J.H. Gilmore, J.E. Schmitt, R.C. Knickmeyer, J.K. Smith, W. Lin, M. Styner, G. Gerig, M.C. Neale. “Genetic and environmental contributions to neonatal brain structure: A twin study,” In Human Brain Mapping, Vol. 31, No. 8, Note: ePub 8 Jan 2010, pp. 1174--1182. 2010.
PubMed ID: 20063301

K. Gorczowski, M. Styner, J.Y. Jeong, J.S. Marron, J. Piven, H.C. Hazlett, S.M. Pizer, G. Gerig. “Multi-object analysis of volume, pose, and shape using statistical discrimination,” In IEEE Trans Pattern Anal Mach Intell., Vol. 32, No. 4, pp. 652--661. April, 2010.
DOI: 10.1109/TPAMI.2009.92
PubMed ID: 20224121

L. Ha, M.W. Prastawa, G. Gerig, J.H. Gilmore, C.T. Silva, S. Joshi. “Image Registration Driven by Combined Probabilistic and Geometric Descriptors,” In Med Image Comput Comput Assist Interv., Vol. 13, No. 2, pp. 602--609. 2010.
PubMed ID: 20879365

L.K. Ha, M.W. Prastawa, G. Gerig, J.H. Gilmore, C.T. Silva, S. Joshi. “Image Registration Driven by Combined Probabilistic and Geometric Descriptors,” In Proceedings of Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010, Lecture Notes in Computer Science (LNCS), Vol. 6362/2010, pp. 602--609. 2010.
DOI: 10.1007/978-3-642-15745-5_74


Deformable image registration in the presence of considerable contrast differences and large-scale size and shape changes represents a significant challenge for image registration. A representative driving application is the study of early brain development in neuroimaging, which requires co-registration of images of the same subject across time or building 4-D population atlases. Growth during the first few years of development involves significant changes in size and shape of anatomical structures but also rapid changes in tissue properties due to myelination and structuring that are reflected in the multi-modal Magnetic Resonance (MR) contrastmeasurements. We propose a new registration method that generates a mapping between brain anatomies represented as a multi-compartment model of tissue class posterior images and geometries.We transform intensity patterns into combined probabilistic and geometric descriptors that drive thematching in a diffeomorphic framework, where distances between geometries are represented using currents which does not require geometric correspondence. We show preliminary results on the registrations of neonatal brainMRIs to two-year old infantMRIs using class posteriors and surface boundaries of structures undergoing major changes. Quantitative validation demonstrates that our proposedmethod generates registrations that better preserve the consistency of anatomical structures over time.

Keywords: netl

Z. Liu, C. Goodlett, G. Gerig, M. Styner. “Evaluation of DTI Property Maps as Basis of DTI Atlas Building,” In SPIE Medical Imaging, Vol. 7623, 762325, February, 2010.
DOI: 10.1117/12.844911

Z. Liu, Y. Wang, G. Gerig, S. Gouttard, R. Tao, T. Fletcher, M.A. Styner. “Quality control of diffusion weighted images,” In SPIE Medical Imaging, Vol. 7628, 76280J, February, 2010.
DOI: 10.1117/12.844748