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3. Neuropsychiatric Disease Classification Using Functional Connectomics -- Results of the Connectomics in NeuroImaging Transfer Learning Challenge

Author

Schirmer, Markus D., Venkataraman, Archana, Rekik, Islem, Kim, Minjeong, Mostofsky, Stewart H., Nebel, Mary Beth, Rosch, Keri, Seymour, Karen, Crocetti, Deana, Irzan, Hassna, Hütel, Michael, Ourselin, Sebastien, Marlow, Neil, Melbourne, Andrew, Levchenko, Egor, Zhou, Shuo, Kunda, Mwiza, Lu, Haiping, Dvornek, Nicha C., Zhuang, Juntang, Pinto, Gideon, Samal, Sandip, Zhang, Jennings, Bernal-Rusiel, Jorge L., Pienaar, Rudolph, and Chung, Ai Wern

Subjects
Quantitative Biology - Neurons and Cognition, Computer Science - Machine Learning
Abstract

Large, open-source consortium datasets have spurred the development of new and increasingly powerful machine learning approaches in brain connectomics. However, one key question remains: are we capturing biologically relevant and generalizable information about the brain, or are we simply overfitting to the data? To answer this, we organized a scientific challenge, the Connectomics in NeuroImaging Transfer Learning Challenge (CNI-TLC), held in conjunction with MICCAI 2019. CNI-TLC included two classification tasks: (1) diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) within a pre-adolescent cohort; and (2) transference of the ADHD model to a related cohort of Autism Spectrum Disorder (ASD) patients with an ADHD comorbidity. In total, 240 resting-state fMRI time series averaged according to three standard parcellation atlases, along with clinical diagnosis, were released for training and validation (120 neurotypical controls and 120 ADHD). We also provided demographic information of age, sex, IQ, and handedness. A second set of 100 subjects (50 neurotypical controls, 25 ADHD, and 25 ASD with ADHD comorbidity) was used for testing. Models were submitted in a standardized format as Docker images through ChRIS, an open-source image analysis platform. Utilizing an inclusive approach, we ranked the methods based on 16 different metrics. The final rank was calculated using the rank product for each participant across all measures. Furthermore, we assessed the calibration curves of each method. Five participants submitted their model for evaluation, with one outperforming all other methods in both ADHD and ASD classification. However, further improvements are needed to reach the clinical translation of functional connectomics. We are keeping the CNI-TLC open as a publicly available resource for developing and validating new classification methodologies in the field of connectomics., Comment: CNI-TLC was held in conjunction with MICCAI 2019

Published
2020

4. Brain Age Estimation Using LSTM on Children's Brain MRI

Author

He, Sheng, Gollub, Randy L., Murphy, Shawn N., Perez, Juan David, Prabhu, Sanjay, Pienaar, Rudolph, Robertson, Richard L., Grant, P. Ellen, and Ou, Yangming

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Brain age prediction based on children's brain MRI is an important biomarker for brain health and brain development analysis. In this paper, we consider the 3D brain MRI volume as a sequence of 2D images and propose a new framework using the recurrent neural network for brain age estimation. The proposed method is named as 2D-ResNet18+Long short-term memory (LSTM), which consists of four parts: 2D ResNet18 for feature extraction on 2D images, a pooling layer for feature reduction over the sequences, an LSTM layer, and a final regression layer. We apply the proposed method on a public multisite NIH-PD dataset and evaluate generalization on a second multisite dataset, which shows that the proposed 2D-ResNet18+LSTM method provides better results than traditional 3D based neural network for brain age estimation., Comment: ISBI 2020

Published
2020

5. CHIPS: A Service for Collecting, Organizing, Processing, and Sharing Medical Image Data in the Cloud

Author

Pienaar, Rudolph, Turk, Ata, Bernal-Rusiel, Jorge, Rannou, Nicolas, Haehn, Daniel, Grant, P. Ellen, and Krieger, Orran

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Computers and Society
Abstract

Web browsers are increasingly used as middleware platforms offering a central access point for service provision. Using backend containerization, RESTful APIs, and distributed computing allows for complex systems to be realized that address the needs of modern compute intense environments. In this paper, we present a web-based medical image data and information management software platform called CHIPS (Cloud Healthcare Image Processing Service). This cloud-based services allows for authenticated and secure retrieval of medical image data from resources typically found in hospitals, organizes and presents information in a modern feed-like interface, provides access to a growing library of plugins that process these data, allows for easy data sharing between users and provides powerful 3D visualization and real-time collaboration. Image processing is orchestrated across additional cloud-based resources using containerization technologies., Comment: 5 pages, VLDB-DMAH

Published
2017

12. Unravelling the Neural Basis of Spatial Delusions After Stroke

Author

Thiebaut De Schotten, Michel, Noble, Stephanie, Heuer, Katja, Bottenhorn, Katherine, Bilgin, Isil, Yang, Yu-Fang, Huntenburg, Julia, Bayer, Johanna M.M., Bethlehem, Richard A.I., Rhoads, Shawn, Vogelbacher, Christoph, Borghesani, Valentina, Levitis, Elizabeth, Wang, Hao-Ting, Van Den Bossche, Sofie, Kobeleva, Xenia, Legarreta, Jon Haitz, Guay, Samuel, Atay, Selim Melvin, Varoquaux, Gael, Huijser, Dorien, Sandström, Malin, Herholz, Peer, Nastase, Samuel, Badhwar, AmanPreet, Dumas, Guillaume, Schwab, Simon, Moia, Stefano, Dayan, Michael, Bassil, Yasmine, Brooks, Paula, Mancini, Matteo, Shine, James, O’Connor, David, Xie, Xihe, Poggiali, Davide, Friedrich, Patrick, Heinsfeld, Anibal, Riedl, Lydia, Toro, Roberto, Caballero-Gaudes, César, Eklund, Anders, Garner, Kelly, Nolan, Christopher, Demeter, Damion, Barrios, Fernando, Merchant, Junaid, McDevitt, Elizabeth, Oostenveld, Robert, Craddock, R. Cameron, Rokem, Ariel, Doyle, Andrew, Ghosh, Satrajit, Nikolaidis, Aki, Stanley, Olivia, Uruñuela, Eneko, Anousheh, Nasim, Arnatkeviciute, Aurina, Auzias, Guillaume, Bachar, Dipankar, Bannier, Elise, Basanisi, Ruggero, Basavaraj, Arshitha, Bedini, Marco, Bellec, Pierre, Benn, R. Austin, Berluti, Kathryn, Bollmann, Steffen, Bollmann, Saskia, Bradley, Claire, Brown, Jesse, Buchweitz, Augusto, Callahan, Patrick, Chan, Micaela, Chandio, Bramsh, Cheng, Theresa, Chopra, Sidhant, Chung, Ai Wern, Close, Thomas, Combrisson, Etienne, Cona, Giorgia, Constable, R. Todd, Cury, Claire, Dadi, Kamalaker, Damasceno, Pablo, Das, Samir, De Vico Fallani, Fabrizio, DeStasio, Krista, Dickie, Erin, Dorfschmidt, Lena, Duff, Eugene, Dupre, Elizabeth, Dziura, Sarah, Esper, Nathalia, Esteban, Oscar, Fadnavis, Shreyas, Flandin, Guillaume, Flannery, Jessica, Flournoy, John, Franco, Alexandre, Ganesan, Saampras, Gao, Siyuan, García Alanis, José, Garyfallidis, Eleftherios, Glatard, Tristan, Glerean, Enrico, Gonzalez-Castillo, Javier, Gould van Praag, Cassandra, Greene, Abigail, Gupta, Geetika, Hahn, Catherine Alice, Halchenko, Yaroslav, Handwerker, Daniel, Hartmann, Thomas, Hayot-Sasson, Valérie, Heunis, Stephan, Hoffstaedter, Felix, Hohmann, Daniela, Horien, Corey, Ioanas, Horea-Ioan, Iordan, Alexandru, Jiang, Chao, Joseph, Michael, Kai, Jason, Karakuzu, Agah, KENNEDY, David, Keshavan, Anisha, Khan, Ali, Kiar, Gregory, Klink, P. Christiaan, Koppelmans, Vincent, Koudoro, Serge, Laird, Angela, Langs, Georg, Laws, Marissa, Licandro, Roxane, Liew, Sook-Lei, Lipic, Tomislav, Litinas, Krisanne, Lurie, Daniel, Lussier, Désirée, Madan, Christopher, Mais, Lea-Theresa, Mansour L, Sina, Manzano-Patron, J.P., Maoutsa, Dimitra, Marcon, Matheus, Margulies, Daniel, Marinato, Giorgio, Marinazzo, Daniele, Markiewicz, Christopher, Meneguzzi, Felipe, Meunier, David, Milham, Michael, Mills, Kathryn, Momi, Davide, Moreau, Clara, Motala, Aysha, Moxon-Emre, Iska, Nichols, Thomas, Nielson, Dylan, Nilsonne, Gustav, Novello, Lisa, O’Brien, Caroline, Olafson, Emily, Oliver, Lindsay, Onofrey, John, Orchard, Edwina, Oudyk, Kendra, Park, Patrick, Parsapoor, Mahboobeh, Pasquini, Lorenzo, Peltier, Scott, Pernet, Cyril, Pienaar, Rudolph, Pinheiro-Chagas, Pedro, Poline, Jean-Baptiste, Qiu, Anqi, Quendera, Tiago, Rice, Laura, Rocha-Hidalgo, Joscelin, Rutherford, Saige, Scharinger, Mathias, Scheinost, Dustin, Shariq, Deena, Shaw, Thomas, Siless, Viviana, Simmonite, Molly, Sirmpilatze, Nikoloz, Spence, Hayli, Sprenger, Julia, Stajduhar, Andrija, Szinte, Martin, Takerkart, Sylvain, Tam, Angela, Tejavibulya, Link, Thome, Ina, Tomaz da Silva, Laura, Traut, Nicolas, Uddin, Lucina, Vallesi, Antonino, VanMeter, John, Vijayakumar, Nandita, di Oleggio Castello, Matteo Visconti, Vohryzek, Jakub, Vukojević, Jakša, Whitaker, Kirstie Jane, Whitmore, Lucy, Wideman, Steve, Witt, Suzanne, Xie, Hua, Xu, Ting, Yan, Chao-Gan, Yeh, Fang-Cheng, Yeo, B.T. Thomas, Zuo, Xi-Nian, Alves, Pedro, Fonseca, Ana, Silva, Daniela, Andrade, Matilde, Pinho‐e‐Melo, Teresa, Martins, Isabel, Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), and Repositório da Universidade de Lisboa

Subjects
0301 basic medicine, Male, medicine.medical_specialty, Support Vector Machine, Reduplicative paramnesia, Audiology, Logistic regression, Delusions, 03 medical and health sciences, [SCCO]Cognitive science, 0302 clinical medicine, Neuroimaging, Image Interpretation, Computer-Assisted, medicine, Humans, Functional disconnection, Prospective Studies, Stroke, ComputingMilieux_MISCELLANEOUS, Aged, Aged, 80 and over, Brain Mapping, business.industry, Anosognosia, Confounding, 16. Peace & justice, medicine.disease, Magnetic Resonance Imaging, 030104 developmental biology, Neurology, Case-Control Studies, Female, Neurology (clinical), Disconnection, business, Tomography, X-Ray Computed, 030217 neurology & neurosurgery
Abstract

© 2021 American Neurological Association, Objective: Knowing explicitly where we are is an interpretation of our spatial representations. Reduplicative paramnesia is a disrupting syndrome in which patients present a firm belief of spatial mislocation. Here, we studied the largest sample of patients with delusional misidentifications of space (i.e. reduplicative paramnesia) after stroke to shed light on their neurobiology. Methods: In a prospective, cumulative, case-control study, we screened 400 patients with acute right-hemispheric stroke. We included 64 cases and 233 controls. First, lesions were delimited and normalized. Then, we computed structural and functional disconnection maps using methods of lesion-track and network-mapping. The maps were compared, controlling for confounders. Second, we built a multivariate logistic model including clinical, behavioural and neuroimaging data. Finally, we performed a nested cross-validation of the model with a support-vector machine analysis. Results: The most frequent misidentification subtype was confabulatory mislocation (56%), followed by place reduplication (19%) and chimeric assimilation (13%). Our results indicate that structural disconnection is the strongest predictor of the syndrome and included two distinct streams, connecting right fronto-thalamic and right occipito-temporal structures. In the multivariate model, the independent predictors of reduplicative paramnesia were the structural disconnection map, lesion sparing of right dorsal fronto-parietal regions, age and anosognosia. Good discrimination accuracy was demonstrated (area under the curve = 0.80[0.75-0.85]). Interpretation: Our results localize the anatomical circuits that may have a role in the abnormal spatial-emotional binding and in the defective updating of spatial representations underlying reduplicative paramnesia. This novel data may contribute to better understand the pathophysiology of delusional syndromes after stroke., The authors acknowledge our colleagues from the Department of Neurology and from the Language Research Laboratory for their contribution to patient screening. This work received funding from “PRÉMIO JOÃO LOBO ANTUNES” – SCML (grant to P.N.A.), from “Bolsa de Investigação em Doenças Vasculares Cerebrais 2017”–SPAVC (grant to P.N.A.) and from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 818521 to M.T.S.).“Data were provided in part bythe Human Connectome Project, WU-Minn Consortium(Principal Investigators: David Van Essen and KamilUgurbil; 1U54 MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.

Published
2021

13. Brainhack: Developing a culture of open, inclusive, community-driven neuroscience

Author

Gau, Rémi, primary, Noble, Stephanie, additional, Heuer, Katja, additional, Bottenhorn, Katherine L., additional, Bilgin, Isil P., additional, Yang, Yu-Fang, additional, Huntenburg, Julia M., additional, Bayer, Johanna M.M., additional, Bethlehem, Richard A.I., additional, Rhoads, Shawn A., additional, Vogelbacher, Christoph, additional, Borghesani, Valentina, additional, Levitis, Elizabeth, additional, Wang, Hao-Ting, additional, Van Den Bossche, Sofie, additional, Kobeleva, Xenia, additional, Legarreta, Jon Haitz, additional, Guay, Samuel, additional, Atay, Selim Melvin, additional, Varoquaux, Gael P., additional, Huijser, Dorien C., additional, Sandström, Malin S., additional, Herholz, Peer, additional, Nastase, Samuel A., additional, Badhwar, AmanPreet, additional, Dumas, Guillaume, additional, Schwab, Simon, additional, Moia, Stefano, additional, Dayan, Michael, additional, Bassil, Yasmine, additional, Brooks, Paula P., additional, Mancini, Matteo, additional, Shine, James M., additional, O’Connor, David, additional, Xie, Xihe, additional, Poggiali, Davide, additional, Friedrich, Patrick, additional, Heinsfeld, Anibal S., additional, Riedl, Lydia, additional, Toro, Roberto, additional, Caballero-Gaudes, César, additional, Eklund, Anders, additional, Garner, Kelly G., additional, Nolan, Christopher R., additional, Demeter, Damion V., additional, Barrios, Fernando A., additional, Merchant, Junaid S., additional, McDevitt, Elizabeth A., additional, Oostenveld, Robert, additional, Craddock, R. Cameron, additional, Rokem, Ariel, additional, Doyle, Andrew, additional, Ghosh, Satrajit S., additional, Nikolaidis, Aki, additional, Stanley, Olivia W., additional, Uruñuela, Eneko, additional, Anousheh, Nasim, additional, Arnatkeviciute, Aurina, additional, Auzias, Guillaume, additional, Bachar, Dipankar, additional, Bannier, Elise, additional, Basanisi, Ruggero, additional, Basavaraj, Arshitha, additional, Bedini, Marco, additional, Bellec, Pierre, additional, Benn, R. Austin, additional, Berluti, Kathryn, additional, Bollmann, Steffen, additional, Bollmann, Saskia, additional, Bradley, Claire, additional, Brown, Jesse, additional, Buchweitz, Augusto, additional, Callahan, Patrick, additional, Chan, Micaela Y., additional, Chandio, Bramsh Q., additional, Cheng, Theresa, additional, Chopra, Sidhant, additional, Chung, Ai Wern, additional, Close, Thomas G., additional, Combrisson, Etienne, additional, Cona, Giorgia, additional, Constable, R. Todd, additional, Cury, Claire, additional, Dadi, Kamalaker, additional, Damasceno, Pablo F., additional, Das, Samir, additional, De Vico Fallani, Fabrizio, additional, DeStasio, Krista, additional, Dickie, Erin W., additional, Dorfschmidt, Lena, additional, Duff, Eugene P., additional, DuPre, Elizabeth, additional, Dziura, Sarah, additional, Esper, Nathalia B., additional, Esteban, Oscar, additional, Fadnavis, Shreyas, additional, Flandin, Guillaume, additional, Flannery, Jessica E., additional, Flournoy, John, additional, Forkel, Stephanie J., additional, Franco, Alexandre R., additional, Ganesan, Saampras, additional, Gao, Siyuan, additional, García Alanis, José C., additional, Garyfallidis, Eleftherios, additional, Glatard, Tristan, additional, Glerean, Enrico, additional, Gonzalez-Castillo, Javier, additional, Gould van Praag, Cassandra D., additional, Greene, Abigail S., additional, Gupta, Geetika, additional, Hahn, Catherine Alice, additional, Halchenko, Yaroslav O., additional, Handwerker, Daniel, additional, Hartmann, Thomas S., additional, Hayot-Sasson, Valérie, additional, Heunis, Stephan, additional, Hoffstaedter, Felix, additional, Hohmann, Daniela M., additional, Horien, Corey, additional, Ioanas, Horea-Ioan, additional, Iordan, Alexandru, additional, Jiang, Chao, additional, Joseph, Michael, additional, Kai, Jason, additional, Karakuzu, Agah, additional, Kennedy, David N., additional, Keshavan, Anisha, additional, Khan, Ali R., additional, Kiar, Gregory, additional, Klink, P. Christiaan, additional, Koppelmans, Vincent, additional, Koudoro, Serge, additional, Laird, Angela R., additional, Langs, Georg, additional, Laws, Marissa, additional, Licandro, Roxane, additional, Liew, Sook-Lei, additional, Lipic, Tomislav, additional, Litinas, Krisanne, additional, Lurie, Daniel J., additional, Lussier, Désirée, additional, Madan, Christopher R., additional, Mais, Lea-Theresa, additional, Mansour L, Sina, additional, Manzano-Patron, J.P., additional, Maoutsa, Dimitra, additional, Marcon, Matheus, additional, Margulies, Daniel S., additional, Marinato, Giorgio, additional, Marinazzo, Daniele, additional, Markiewicz, Christopher J., additional, Maumet, Camille, additional, Meneguzzi, Felipe, additional, Meunier, David, additional, Milham, Michael P., additional, Mills, Kathryn L., additional, Momi, Davide, additional, Moreau, Clara A., additional, Motala, Aysha, additional, Moxon-Emre, Iska, additional, Nichols, Thomas E., additional, Nielson, Dylan M., additional, Nilsonne, Gustav, additional, Novello, Lisa, additional, O’Brien, Caroline, additional, Olafson, Emily, additional, Oliver, Lindsay D., additional, Onofrey, John A., additional, Orchard, Edwina R., additional, Oudyk, Kendra, additional, Park, Patrick J., additional, Parsapoor, Mahboobeh, additional, Pasquini, Lorenzo, additional, Peltier, Scott, additional, Pernet, Cyril R., additional, Pienaar, Rudolph, additional, Pinheiro-Chagas, Pedro, additional, Poline, Jean-Baptiste, additional, Qiu, Anqi, additional, Quendera, Tiago, additional, Rice, Laura C., additional, Rocha-Hidalgo, Joscelin, additional, Rutherford, Saige, additional, Scharinger, Mathias, additional, Scheinost, Dustin, additional, Shariq, Deena, additional, Shaw, Thomas B., additional, Siless, Viviana, additional, Simmonite, Molly, additional, Sirmpilatze, Nikoloz, additional, Spence, Hayli, additional, Sprenger, Julia, additional, Stajduhar, Andrija, additional, Szinte, Martin, additional, Takerkart, Sylvain, additional, Tam, Angela, additional, Tejavibulya, Link, additional, Thiebaut de Schotten, Michel, additional, Thome, Ina, additional, Tomaz da Silva, Laura, additional, Traut, Nicolas, additional, Uddin, Lucina Q., additional, Vallesi, Antonino, additional, VanMeter, John W., additional, Vijayakumar, Nandita, additional, di Oleggio Castello, Matteo Visconti, additional, Vohryzek, Jakub, additional, Vukojević, Jakša, additional, Whitaker, Kirstie Jane, additional, Whitmore, Lucy, additional, Wideman, Steve, additional, Witt, Suzanne T., additional, Xie, Hua, additional, Xu, Ting, additional, Yan, Chao-Gan, additional, Yeh, Fang-Cheng, additional, Yeo, B.T. Thomas, additional, and Zuo, Xi-Nian, additional

Published
2021
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14. Neuropsychiatric disease classification using functional connectomics - results of the connectomics in neuroimaging transfer learning challenge

Author

Schirmer, Markus D., primary, Venkataraman, Archana, additional, Rekik, Islem, additional, Kim, Minjeong, additional, Mostofsky, Stewart H., additional, Nebel, Mary Beth, additional, Rosch, Keri, additional, Seymour, Karen, additional, Crocetti, Deana, additional, Irzan, Hassna, additional, Hütel, Michael, additional, Ourselin, Sebastien, additional, Marlow, Neil, additional, Melbourne, Andrew, additional, Levchenko, Egor, additional, Zhou, Shuo, additional, Kunda, Mwiza, additional, Lu, Haiping, additional, Dvornek, Nicha C., additional, Zhuang, Juntang, additional, Pinto, Gideon, additional, Samal, Sandip, additional, Zhang, Jennings, additional, Bernal-Rusiel, Jorge L., additional, Pienaar, Rudolph, additional, and Chung, Ai Wern, additional

Published
2021
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17. Cortical surface shape analysis based on spherical wavelets

Author

Yu, Peng, Grant, P. Ellen, Qi, Yuan, Han, Xiao, Segonne, Florent, Pienaar, Rudolph, Busa, Evelina, Pacheco, Jenni, Makris, Nikos, Buckner, Randy L., Golland, Polina, and Fischl, Bruce

Subjects
Magnetic resonance imaging -- Analysis, Business, Electronics, Electronics and electrical industries, Health care industry
Abstract

In vivo quantification of neuroanatomical shape variations is possible due to recent advances in medical imaging and has proven useful in the study of neuropathology and neurodevelopment. In this paper, we apply a spherical wavelet transformation to extract shape features of cortical surfaces reconstructed from magnetic resonance images (MRIs) of a set of subjects. The spherical wavelet transformation can characterize the underlying functions in a local fashion in both space and frequency, in contrast to spherical harmonics that have a global basis set. We perform principal component analysis (PCA) on these wavelet shape features to study patterns of shape variation within normal population from coarse to fine resolution. In addition, we study the development of cortical folding in newborns using the Gompertz model in the wavelet domain, which allows us to characterize the order of development of large-scale and finer folding patterns independently. Given a limited amount of training data, we use a regularization framework to estimate the parameters of the Gompertz model to improve the prediction performance on new data. We develop an efficient method to estimate this regularized Gompertz model based on the Broyden-Fletcher-Goldfarb-Shannon (BFGS) approximation. Promising results are presented using both PCA and the folding development model in the wavelet domain. The cortical folding development model provides quantitative anatomic information regarding macroscopic cortical folding development and may be of potential use as a biomarker for early diagnosis of neurologic deficits in newborns. Index Terms--Folding, MRI, multiscale, neurodevelopment.

Published
2007

19. Brain Age Estimation Using LSTM on Children's Brain MRI

Author

He, Sheng, primary, Gollub, Randy L., additional, Murphy, Shawn N., additional, Perez, Juan David, additional, Prabhu, Sanjay, additional, Pienaar, Rudolph, additional, Robertson, Richard L., additional, Grant, P. Ellen, additional, and Ou, Yangming, additional

Published
2020
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21. Disorganized Patterns of Sulcal Position in Fetal Brains with Agenesis of Corpus Callosum

Author

Tarui, Tomo, primary, Madan, Neel, additional, Farhat, Nabgha, additional, Kitano, Rie, additional, Ceren Tanritanir, Asye, additional, Graham, George, additional, Gagoski, Borjan, additional, Craig, Alexa, additional, Rollins, Caitlin K, additional, Ortinau, Cynthia, additional, Iyer, Vidya, additional, Pienaar, Rudolph, additional, Bianchi, Diana W, additional, Grant, P Ellen, additional, and Im, Kiho, additional

Published
2017
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23. Real-time volume rendering and tractography visualization on the web

Author

Universidad EAFIT. Departamento de Ingeniería Mecánica, Laboratorio CAD/CAM/CAE, Congote, John, Novo, Esther, Kabongo, Luis, Ginsburg, Dan, Gerhard, Stephan, Pienaar, Rudolph, Ruíz, Óscar E., Universidad EAFIT. Departamento de Ingeniería Mecánica, Laboratorio CAD/CAM/CAE, Congote, John, Novo, Esther, Kabongo, Luis, Ginsburg, Dan, Gerhard, Stephan, Pienaar, Rudolph, and Ruíz, Óscar E.

Abstract

In the field of computer graphics, Volume Rendering techniques allow the visualization of 3D datasets, and specifically, Volume Ray-Casting renders images from volumetric datasets, typically used in some scientific areas, such as medical imaging -- This article aims to describe the development of a combined visualization of tractography and volume rendering of brain T1 MRI images in an integrated way -- An innovative web viewer for interactive visualization of neuro-imaging data has been developed based on WebGL -- This recently developed standard enables the clients to use the web viewer on a wide range of devices, with the only requirement of a compliant web-browser -- As the majority of the rendering tasks take place in the client machine, the effect of bottlenecks and server overloading are minimized -- The web application presented is able to compete with desktop tools, even supporting high graphical demands and facing challenges regarding performance and scalability -- The developed software modules are available as open source code and include MRI volume data and tractography generated by the Diffusion Toolkit, and connectivity data from the Connectome Mapping Toolkit -- Our contribution for the Volume Web Viewer implements early ray termination step according to the tractography depthmap, combining volume images and estimated white matter fibers -- Furthermore, the depthmap system extension can be used for visualization of other types of data, where geometric and volume elements are displayed simultaneously

Published
2016

24. Neuroimaging in the Browser using the X Toolkit

Author

Grant Ellen, Rannou Nicolas, Pienaar Rudolph, Ahtam Banu, and Haehn Daniel

Subjects
Neuroimaging, Computer science, Human–computer interaction, Biomedical Engineering, Neuroscience (miscellaneous), Computer Science Applications
Published
2014

33. Reusable Client-Side JavaScript Modules for Immersive Web-Based Real-Time Collaborative Neuroimage Visualization

Author

Bernal-Rusiel, Jorge L., Rannou, Nicolas, Gollub, Randy L., Pieper, Steve, Murphy, Shawn, Robertson, Richard, Grant, Patricia E., and Pienaar, Rudolph

Subjects
Methods, collaborative visualization, interactive visualization, real-time collaboration, neuroimaging, HTML5, web services, telemedicine, Google Drive
Abstract

In this paper we present a web-based software solution to the problem of implementing real-time collaborative neuroimage visualization. In both clinical and research settings, simple and powerful access to imaging technologies across multiple devices is becoming increasingly useful. Prior technical solutions have used a server-side rendering and push-to-client model wherein only the server has the full image dataset. We propose a rich client solution in which each client has all the data and uses the Google Drive Realtime API for state synchronization. We have developed a small set of reusable client-side object-oriented JavaScript modules that make use of the XTK toolkit, a popular open-source JavaScript library also developed by our team, for the in-browser rendering and visualization of brain image volumes. Efficient realtime communication among the remote instances is achieved by using just a small JSON object, comprising a representation of the XTK image renderers' state, as the Google Drive Realtime collaborative data model. The developed open-source JavaScript modules have already been instantiated in a web-app called MedView, a distributed collaborative neuroimage visualization application that is delivered to the users over the web without requiring the installation of any extra software or browser plugin. This responsive application allows multiple physically distant physicians or researchers to cooperate in real time to reach a diagnosis or scientific conclusion. It also serves as a proof of concept for the capabilities of the presented technological solution.

Published
2017
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35. Cortical Surface Shape Analysis Based on Spherical Wavelets.

Author

Peng Yu, Grant, P. Ellen, Yuan Qi, Xiao Han, Ségonne, Florent, Pienaar, Rudolph, Busa, Evelina, Pacheco, Jenni, Makris, Nikos, Buckner, Randy L., Golland, Polina, and Fischl, Bruce

Subjects
DIAGNOSTIC imaging, MAGNETIC resonance imaging, MAGNETIC fields, WAVELETS (Mathematics), HIGHER nervous activity, PRINCIPAL components analysis, NEUROSCIENCES
Abstract

In vivo quantification of neuroanatomical shape variations is possible due to recent advances in medical imaging and has proven useful in the study of neuropathology and neurodevelopment. In this paper, we apply a spherical wavelet transformation to extract shape features of cortical surfaces reconstructed from magnetic resonance images (MRIs) of a set of subjects. The spherical wavelet transformation can characterize the underlying functions in a local fashion in both space and frequency, in contrast to spherical harmonics that have a global basis set. We perform principal component analysis (PCA) on these wavelet shape features to study patterns of shape variation within normal population from coarse to fine resolution. In addition, we study the development of cortical folding in newborns using the Gompertz model in the wavelet domain, which allows us to characterize the order of development of large-scale and finer folding patterns independently. Given a limited amount of training data, we use a regularization framework to estimate the parameters of the Gompertz model to improve the prediction performance on new data. We develop an efficient method to estimate this regularized Gompertz model based on the Broyden-Fletcher-Goldfarb-Shannon (BFGS) approximation. Promising results are presented using both PCA and the folding development model in the wavelet domain. The cortical folding development model provides quantitative anatomic information regarding macroscopic cortical folding development and may be of potential use as a biomarker for early diagnosis of neurologic deficits in newborns. [ABSTRACT FROM AUTHOR]

Published
2007
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36. Assessment of the Frequency-Domain Multi-Distance Method to Evaluate the Brain Optical Properties: Monte Carlo Simulations from Neonate to Adult

Author

Sliva, Danielle D., Dehaes, Mathieu, Grant, P. Ellen, Roche-Labarbe, Nadege, Pienaar, Rudolph, Boas, David A., Franceschini, Maria A., and Selb, Juliette J

Abstract

The near infrared spectroscopy (NIRS) frequency-domain multi-distance (FD-MD) method allows for the estimation of optical properties in biological tissue using the phase and intensity of radiofrequency modulated light at different source-detector separations. In this study, we evaluated the accuracy of this method to retrieve the absorption coefficient of the brain at different ages. Synthetic measurements were generated with Monte Carlo simulations in magnetic resonance imaging (MRI)-based heterogeneous head models for four ages: newborn, 6 and 12 month old infants, and adult. For each age, we determined the optimal set of source-detector separations and estimated the corresponding errors. Errors arise from different origins: methodological (FD-MD) and anatomical (curvature, head size and contamination by extra-cerebral tissues). We found that the brain optical absorption could be retrieved with an error between 8-24% in neonates and infants, while the error increased to 19-44% in adults over all source-detector distances. The dominant contribution to the error was found to be the head curvature in neonates and infants, and the extra-cerebral tissues in adults.

Published
2011
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37. BRAIN AGE ESTIMATION USING LSTM ON CHILDREN'S BRAIN MRI.

Author

He S, Gollub RL, Murphy SN, Perez JD, Prabhu S, Pienaar R, Robertson RL, Grant PE, and Ou Y

Abstract

Brain age prediction based on children's brain MRI is an important biomarker for brain health and brain development analysis. In this paper, we consider the 3D brain MRI volume as a sequence of 2D images and propose a new framework using the recurrent neural network for brain age estimation. The proposed method is named as 2D-ResNet18+Long short-term memory (LSTM), which consists of four parts: 2D ResNet18 for feature extraction on 2D images, a pooling layer for feature reduction over the sequences, an LSTM layer, and a final regression layer. We apply the proposed method on a public multisite NIH-PD dataset and evaluate generalization on a second multisite dataset, which shows that the proposed 2D-ResNet18+LSTM method provides better results than traditional 3D based neural network for brain age estimation.

Published
2020
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38. Disorganized Patterns of Sulcal Position in Fetal Brains with Agenesis of Corpus Callosum.

Author

Tarui T, Madan N, Farhat N, Kitano R, Ceren Tanritanir A, Graham G, Gagoski B, Craig A, Rollins CK, Ortinau C, Iyer V, Pienaar R, Bianchi DW, Grant PE, and Im K

Subjects
Female, Fetus, Humans, Image Interpretation, Computer-Assisted methods, Imaging, Three-Dimensional methods, Magnetic Resonance Imaging, Male, Neuroimaging methods, Agenesis of Corpus Callosum pathology, Cerebral Cortex pathology
Abstract

Fetuses with isolated agenesis of the corpus callosum (ACC) are associated with a broad spectrum of neurodevelopmental disability that cannot be specifically predicted in prenatal neuroimaging. We hypothesized that ACC may be associated with aberrant cortical folding. In this study, we determined altered patterning of early primary sulci development in fetuses with isolated ACC using novel quantitative sulcal pattern analysis which measures deviations of regional sulcal features (position, depth, and area) and their intersulcal relationships in 7 fetuses with isolated ACC (27.1 ± 3.8 weeks of gestation, mean ± SD) and 17 typically developing (TD) fetuses (25.7 ± 2.0 weeks) from normal templates. Fetuses with ACC showed significant alterations in absolute sulcal positions and relative intersulcal positional relationship compared to TD fetuses, which were not detected by traditional gyrification index. Our results reveal altered sulcal positional development even in isolated ACC that is present as early as the second trimester and continues throughout the fetal period. It might originate from altered white matter connections and portend functional variances in later life.

Published
2018
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39. ChRIS--A web-based neuroimaging and informatics system for collecting, organizing, processing, visualizing and sharing of medical data.

Author

Pienaar R, Rannou N, Bernal J, Hahn D, and Grant PE

Subjects
Diagnostic Imaging methods, Humans, Imaging, Three-Dimensional, Internet, Databases, Factual, Image Processing, Computer-Assisted methods, Information Dissemination methods, Neuroimaging methods, Software
Abstract

The utility of web browsers for general purpose computing, long anticipated, is only now coming into fruition. In this paper we present a web-based medical image data and information management software platform called ChRIS ([Boston] Children's Research Integration System). ChRIS' deep functionality allows for easy retrieval of medical image data from resources typically found in hospitals, organizes and presents information in a modern feed-like interface, provides access to a growing library of plugins that process these data - typically on a connected High Performance Compute Cluster, allows for easy data sharing between users and instances of ChRIS and provides powerful 3D visualization and real time collaboration.

Published
2015
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40. Cortical Surface Shape Analysis Based on Spherical Wavelet Transformation.

Author

Yu P, Han X, Ségonne F, Pienaar R, Buckner RL, Golland P, Grant PE, and Fischl B

Abstract

Shape analysis of neuroanatomical structures has proven useful in the study of neuropathology and neurodevelopment. Advances in medical imaging have made it possible to study this shape variation in vivo. In this paper, we propose the use of a spherical wavelet transformation to extract cortical surface shape features, as wavelets can characterize the underlying functions in a local fashion in both space and frequency. Our results demonstrate the utility of the wavelet approach in both detecting the spatial scale and pattern of shape variation in synthetic data, and for quantifying and visualizing shape variations of cortical surface models in subject populations.

Published
2006
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