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13 results on '"López-López, Narciso"'

1. GeoSP: A parallel method for a cortical surface parcellation based on geodesic distance

Author

López-López, Narciso, Vázquez, Andrea, Poupon, Cyril, Mangin, Jean-François, Ladra, Susana, and Guevara, Pamela

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We present GeoSP, a parallel method that creates a parcellation of the cortical mesh based on a geodesic distance, in order to consider gyri and sulci topology. The method represents the mesh with a graph and performs a K-means clustering in parallel. It has two modes of use, by default, it performs the geodesic cortical parcellation based on the boundaries of the anatomical parcels provided by the Desikan-Killiany atlas. The other mode performs the complete parcellation of the cortex. Results for both modes and with different values for the total number of sub-parcels show homogeneous sub-parcels. Furthermore, the execution time is 82 s for the whole cortex mode and 18 s for the Desikan-Killiany atlas subdivision, for a parcellation into 350 sub-parcels. The proposed method will be available to the community to perform the evaluation of data-driven cortical parcellations. As an example, we compared GeoSP parcellation with Desikan-Killiany and Destrieux atlases in 50 subjects, obtaining more homogeneous parcels for GeoSP and minor differences in structural connectivity reproducibility across subjects., Comment: This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Actions H2020-MSCA-RISE-2015 BIRDS GA No. 690941, ANID PFCHA/DOCTORADO NACIONAL/2016-21160342, ANID FONDECYT 1190701, ANID PIA/Anillo de Investigaci\'on en Ciencia y Tecnolog\'ia ACT172121, and ANID Basal Project FB0008

Published
2021
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2. Cortical surface parcellation based on intra-subject white matter fiber clustering

Author

López-López, Narciso, Vázquez, Andrea, Poupon, Cyril, Mangin, Jean-François, and Guevara, Pamela

Subjects
Quantitative Biology - Neurons and Cognition, Computer Science - Computer Vision and Pattern Recognition
Abstract

We present a hybrid method that performs the complete parcellation of the cerebral cortex of an individual, based on the connectivity information of the white matter fibers from a whole-brain tractography dataset. The method consists of five steps, first intra-subject clustering is performed on the brain tractography. The fibers that make up each cluster are then intersected with the cortical mesh and then filtered to discard outliers. In addition, the method resolves the overlapping between the different intersection regions (sub-parcels) throughout the cortex efficiently. Finally, a post-processing is done to achieve more uniform sub-parcels. The output is the complete labeling of cortical mesh vertices, representing the different cortex sub-parcels, with strong connections to other sub-parcels. We evaluated our method with measures of brain connectivity such as functional segregation (clustering coefficient), functional integration (characteristic path length) and small-world. Results in five subjects from ARCHI database show a good individual cortical parcellation for each one, composed of about 200 subparcels per hemisphere and complying with these connectivity measures., Comment: This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Actions H2020-MSCA-RISE-2015 BIRDS GA No. 690941, CONICYT PFCHA/ DOCTORADO NACIONAL/2016-21160342, CONICYT FONDECYT 1190701, CONICYT PIA/Anillo de Investigaci\'on en Ciencia y Tecnolog\'ia ACT172121 and CONICYT Basal Center FB0008

Published
2020
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3. Parallel optimization of fiber bundle segmentation for massive tractography datasets

Author

Vázquez, Andrea, López-López, Narciso, Labra, Nicole, Figueroa, Miguel, Poupon, Cyril, Mangin, Jean-François, Hernández, Cecilia, and Guevara, Pamela

Subjects
Computer Science - Data Structures and Algorithms, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Neurons and Cognition
Abstract

We present an optimized algorithm that performs automatic classification of white matter fibers based on a multi-subject bundle atlas. We implemented a parallel algorithm that improves upon its previous version in both execution time and memory usage. Our new version uses the local memory of each processor, which leads to a reduction in execution time. Hence, it allows the analysis of bigger subject and/or atlas datasets. As a result, the segmentation of a subject of 4,145,000 fibers is reduced from about 14 minutes in the previous version to about 6 minutes, yielding an acceleration of 2.34. In addition, the new algorithm reduces the memory consumption of the previous version by a factor of 0.79., Comment: This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk{\l}odowska-Curie Actions H2020-MSCA-RISE-2015 BIRDS GA No. 690941, CONICYT PFCHA/ DOCTORADO NACIONAL/2016-21160342, CONICYT FONDECYT 1161427, CONICYT PIA/Anillo de Investigaci\'on en Ciencia y Tecnolog\'ia ACT172121, CONICYT BASAL FB0008 and from CONICYT Basal FB0001

Published
2019
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4. Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

Author

Schilling, Kurt G., Rheault, François, Petit, Laurent, Hansen, Colin B., Nath, Vishwesh, Yeh, Fang-Cheng, Girard, Gabriel, Barakovic, Muhamed, Rafael-Patino, Jonathan, Yu, Thomas, Fischi-Gomez, Elda, Pizzolato, Marco, Ocampo-Pineda, Mario, Schiavi, Simona, Canales-Rodríguez, Erick J., Daducci, Alessandro, Granziera, Cristina, Innocenti, Giorgio, Thiran, Jean-Philippe, Mancini, Laura, Wastling, Stephen, Cocozza, Sirio, Petracca, Maria, Pontillo, Giuseppe, Mancini, Matteo, Vos, Sjoerd B., Vakharia, Vejay N., Duncan, John S., Melero, Helena, Manzanedo, Lidia, Sanz-Morales, Emilio, Peña-Melián, Ángel, Calamante, Fernando, Attyé, Arnaud, Cabeen, Ryan P., Korobova, Laura, Toga, Arthur W., Vijayakumari, Anupa Ambili, Parker, Drew, Verma, Ragini, Radwan, Ahmed, Sunaert, Stefan, Emsell, Louise, De Luca, Alberto, Leemans, Alexander, Bajada, Claude J., Haroon, Hamied, Azadbakht, Hojjatollah, Chamberland, Maxime, Genc, Sila, Tax, Chantal M.W., Yeh, Ping-Hong, Srikanchana, Rujirutana, Mcknight, Colin D., Yang, Joseph Yuan-Mou, Chen, Jian, Kelly, Claire E., Yeh, Chun-Hung, Cochereau, Jerome, Maller, Jerome J., Welton, Thomas, Almairac, Fabien, Seunarine, Kiran K, Clark, Chris A., Zhang, Fan, Makris, Nikos, Golby, Alexandra, Rathi, Yogesh, O'Donnell, Lauren J., Xia, Yihao, Aydogan, Dogu Baran, Shi, Yonggang, Fernandes, Francisco Guerreiro, Raemaekers, Mathijs, Warrington, Shaun, Michielse, Stijn, Ramírez-Manzanares, Alonso, Concha, Luis, Aranda, Ramón, Meraz, Mariano Rivera, Lerma-Usabiaga, Garikoitz, Roitman, Lucas, Fekonja, Lucius S., Calarco, Navona, Joseph, Michael, Nakua, Hajer, Voineskos, Aristotle N., Karan, Philippe, Grenier, Gabrielle, Legarreta, Jon Haitz, Adluru, Nagesh, Nair, Veena A., Prabhakaran, Vivek, Alexander, Andrew L., Kamagata, Koji, Saito, Yuya, Uchida, Wataru, Andica, Christina, Abe, Masahiro, Bayrak, Roza G., Wheeler-Kingshott, Claudia A.M. Gandini, D'Angelo, Egidio, Palesi, Fulvia, Savini, Giovanni, Rolandi, Nicolò, Guevara, Pamela, Houenou, Josselin, López-López, Narciso, Mangin, Jean-François, Poupon, Cyril, Román, Claudio, Vázquez, Andrea, Maffei, Chiara, Arantes, Mavilde, Andrade, José Paulo, Silva, Susana Maria, Calhoun, Vince D., Caverzasi, Eduardo, Sacco, Simone, Lauricella, Michael, Pestilli, Franco, Bullock, Daniel, Zhan, Yang, Brignoni-Perez, Edith, Lebel, Catherine, Reynolds, Jess E, Nestrasil, Igor, Labounek, René, Lenglet, Christophe, Paulson, Amy, Aulicka, Stefania, Heilbronner, Sarah R., Heuer, Katja, Chandio, Bramsh Qamar, Guaje, Javier, Tang, Wei, Garyfallidis, Eleftherios, Raja, Rajikha, Anderson, Adam W., Landman, Bennett A., and Descoteaux, Maxime

Published
2021
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5. Efficient Representation of Multidimensional Data over Hierarchical Domains

Author

Brisaboa, Nieves R., Cerdeira-Pena, Ana, López-López, Narciso, Navarro, Gonzalo, Penabad, Miguel R., and Silva-Coira, Fernando

Subjects
Computer Science - Data Structures and Algorithms, Computer Science - Databases
Abstract

We consider the problem of representing multidimensional data where the domain of each dimension is organized hierarchically, and the queries require summary information at a different node in the hierarchy of each dimension. This is the typical case of OLAP databases. A basic approach is to represent each hierarchy as a one-dimensional line and recast the queries as multidimensional range queries. This approach can be implemented compactly by generalizing to more dimensions the $k^2$-treap, a compact representation of two-dimensional points that allows for efficient summarization queries along generic ranges. Instead, we propose a more flexible generalization, which instead of a generic quadtree-like partition of the space, follows the domain hierarchies across each dimension to organize the partitioning. The resulting structure is much more efficient than a generic multidimensional structure, since queries are resolved by aggregating much fewer nodes of the tree., Comment: This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk{\l}odowska-Curie Actions H2020-MSCA-RISE-2015 BIRDS GA No. 690941

Published
2016
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8. Tractography dissection variability

Author

Schilling, Kurt G., Rheault, François, Petit, Laurent, Hansen, Colin B., Nath, Vishwesh, Yeh, Fang Cheng, Girard, Gabriel, Barakovic, Muhamed, Rafael-Patino, Jonathan, Yu, Thomas, Fischi-Gomez, Elda, Pizzolato, Marco, Ocampo-Pineda, Mario, Schiavi, Simona, Canales-Rodríguez, Erick J., Daducci, Alessandro, Granziera, Cristina, Innocenti, Giorgio, Thiran, Jean Philippe, Mancini, Laura, Wastling, Stephen, Cocozza, Sirio, Petracca, Maria, Pontillo, Giuseppe, Mancini, Matteo, Vos, Sjoerd B., Vakharia, Vejay N., Duncan, John S., Melero, Helena, Manzanedo, Lidia, Sanz-Morales, Emilio, Peña-Melián, Ángel, Calamante, Fernando, Attyé, Arnaud, Cabeen, Ryan P., Korobova, Laura, Toga, Arthur W., Vijayakumari, Anupa Ambili, Parker, Drew, Verma, Ragini, Radwan, Ahmed, Sunaert, Stefan, Emsell, Louise, De Luca, Alberto, Leemans, Alexander, Bajada, Claude J., Haroon, Hamied, Azadbakht, Hojjatollah, Chamberland, Maxime, Genc, Sila, Tax, Chantal M.W., Yeh, Ping Hong, Srikanchana, Rujirutana, Mcknight, Colin D., Yang, Joseph Yuan Mou, Chen, Jian, Kelly, Claire E., Yeh, Chun Hung, Cochereau, Jerome, Maller, Jerome J., Welton, Thomas, Almairac, Fabien, Seunarine, Kiran K., Clark, Chris A., Zhang, Fan, Makris, Nikos, Golby, Alexandra, Rathi, Yogesh, O'Donnell, Lauren J., Xia, Yihao, Aydogan, Dogu Baran, Shi, Yonggang, Fernandes, Francisco Guerreiro, Raemaekers, Mathijs, Warrington, Shaun, Michielse, Stijn, Ramírez-Manzanares, Alonso, Concha, Luis, Aranda, Ramón, Meraz, Mariano Rivera, Lerma-Usabiaga, Garikoitz, Roitman, Lucas, Fekonja, Lucius S., Calarco, Navona, Joseph, Michael, Nakua, Hajer, Voineskos, Aristotle N., Karan, Philippe, Grenier, Gabrielle, Legarreta, Jon Haitz, Adluru, Nagesh, Nair, Veena A., Prabhakaran, Vivek, Alexander, Andrew L., Kamagata, Koji, Saito, Yuya, Uchida, Wataru, Andica, Christina, Abe, Masahiro, Bayrak, Roza G., Wheeler-Kingshott, Claudia A.M.Gandini, D'Angelo, Egidio, Palesi, Fulvia, Savini, Giovanni, Rolandi, Nicolò, Guevara, Pamela, Houenou, Josselin, López-López, Narciso, Mangin, Jean François, Poupon, Cyril, Román, Claudio, Vázquez, Andrea, Maffei, Chiara, Arantes, Mavilde, Andrade, José Paulo, Silva, Susana Maria, Calhoun, Vince D., Caverzasi, Eduardo, Sacco, Simone, Lauricella, Michael, Pestilli, Franco, Bullock, Daniel, Zhan, Yang, Brignoni-Perez, Edith, Lebel, Catherine, Reynolds, Jess E., Nestrasil, Igor, Labounek, René, Lenglet, Christophe, Paulson, Amy, Aulicka, Stefania, Heilbronner, Sarah R., Heuer, Katja, Chandio, Bramsh Qamar, Guaje, Javier, Tang, Wei, Garyfallidis, Eleftherios, Raja, Rajikha, Anderson, Adam W., Landman, Bennett A., Descoteaux, Maxime, Vanderbilt University, Université de Sherbrooke, Université de Bordeaux, University of Pittsburgh, CIBM Center for BioMedical Imaging, University of Basel, Swiss Federal Institute of Technology Lausanne, Technical University of Denmark, University of Verona, Karolinska Institutet, UCL Hospitals NHS Foundation Trust, University of Naples Federico II, University of Sussex, University College London, Epilepsy Society, Universidad Rey Juan Carlos, Complutense University, University of Sydney, University of Southern California, University of Pennsylvania, KU Leuven, University Medical Center Utrecht, University of Malta, AINOSTICS Limited, Cardiff University, Walter Reed Army Institute of Research, Royal Children's Hospital, Murdoch Children's Research Institute, Chang Gung University, CHU de Poitiers, General Electric Healthcare, National Neuroscience Institute of Singapore, CHU de Nice, Harvard University, Department of Neuroscience and Biomedical Engineering, University of Nottingham, Maastricht University, Consejo Nacional de Ciencia y Tecnologia Mexico, Universidad Nacional Autónoma de México, Centro de Investigacion Cientifica y de Educacion Superior de Ensenada, Stanford University, Charité - Universitätsmedizin Berlin, University of Toronto, University of Wisconsin-Madison, Juntendo University, University of Pavia, IRCCS Fondazione Istituto Neurologico Casimiro Mondino - Pavia, Universidad de Concepción, Université Paris-Saclay, Massachusetts General Hospital, University of Porto, Georgia State University, University of California San Francisco, University of Texas at Austin, Shenzhen Institute of Advanced Technology, University of Calgary, University of Minnesota Twin Cities, Masaryk University, Université de Paris, Indiana University, University of Arkansas for Medical Sciences, Aalto-yliopisto, and Aalto University

Subjects
Fiber pathways, Dissection, White matter, Bundle segmentation, Tractography
Abstract

Funding Information: This work was conducted in part using the resources of the Advanced Computing Center for Research and Education at Vanderbilt University, Nashville, TN. KS, BL, CH were supported by the National Institutes of Health under award numbers R01EB017230, and T32EB001628, and in part by ViSE/VICTR VR3029 and the National Center for Research Resources, Grant UL1 RR024975-01. This work was also possible thanks to the support of the Institutional Research Chair in NeuroInformatics of Université de Sherbrooke, NSERC and Compute Canada (MD, FR). MP received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 754462. The Wisconsin group acknowledges the support from a core grant to the Waisman Center from the National Institute of Child Health and Human Development (IDDRC U54 HD090256). NSF OAC-1916518, NSF IIS-1912270, NSF IIS-1636893, NSF BCS-1734853, NIH NIBIB 1R01EB029272-01, and a Microsoft Faculty Fellowship to F.P. LF acknowledges the support of the Cluster of Excellence Matters of Activity. Image Space Material funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany´s Excellence Strategy – EXC 2025. SW is supported by a Medical Research Council PhD Studentship UK [MR/N013913/1]. The Nottingham group's processing was performed using the University of Nottingham's Augusta HPC service and the Precision Imaging Beacon Cluster. JPA, MA and SMS acknowledges the support of FCT - Fundação para a Ciência e a Tecnologia within CINTESIS, R&D Unit (reference UID/IC/4255/2013). MM was funded by the Wellcome Trust through a Sir Henry Wellcome Postdoctoral Fellowship [213722/Z/18/Z]. EJC-R is supported by the Swiss National Science Foundation (SNSF, Ambizione grant PZ00P2 185814/1). CMWT is supported by a Sir Henry Wellcome Fellowship (215944/Z/19/Z) and a Veni grant from the Dutch Research Council (NWO) (17331). FC acknowledges the support of the National Health and Medical Research Council ofAustralia (APP1091593 and APP1117724) and the Australian Research Council (DP170101815). NSF OAC-1916518, NSF IIS-1912270, NSF IIS-1636893, NSF BCS-1734853, Microsoft Faculty Fellowship to F.P. D.B. was partially supported by NIH NIMH T32-MH103213 to William Hetrick (Indiana University). CL is partly supported by NIH grants P41 EB027061 and P30 NS076408 “Institutional Center Cores for Advanced Neuroimaging. JYMY received positional funding from the Royal Children's Hospital Foundation (RCH 1000). JYMY, JC, and CEK acknowledge the support of the Royal Children's Hospital Foundation, Murdoch Children's Research Institute, The University of Melbourne Department of Paediatrics, and the Victorian Government's Operational Infrastructure Support Program. C-HY is grateful to the Ministry of Science and Technology of Taiwan (MOST 109-2222-E-182-001-MY3) for the support. LC acknowledges support from CONACYT and UNAM. ARM acknowledges support from CONACYT. LJO, YR, and FZ were supported by NIH P41EB015902 and R01MH119222. AJG was supported by P41EB015898. NM was supported by R01MH119222, K24MH116366, and R01MH111917. This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 785907 & 945539 (HBP SGA2 & SGA3), and from the ANR IFOPASUBA- 19-CE45-0022-01. PG, CR, NL and AV were partially supported by ANID-Basal FB0008 and ANID-FONDECYT 1190701 grants. We would like to acknowledge John C Gore, Hiromasa Takemura, Anastasia Yendiki, and Riccardo Galbusera for their helplful suggestions regarding the analysis, figures, and discussions. Funding Information: This work was conducted in part using the resources of the Advanced Computing Center for Research and Education at Vanderbilt University, Nashville, TN. KS, BL, CH were supported by the National Institutes of Health under award numbers R01EB017230, and T32EB001628, and in part by ViSE/VICTR VR3029 and the National Center for Research Resources, Grant UL1 RR024975-01. This work was also possible thanks to the support of the Institutional Research Chair in NeuroInformatics of Universit? de Sherbrooke, NSERC and Compute Canada (MD, FR). MP received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk?odowska-Curie grant agreement No 754462. The Wisconsin group acknowledges the support from a core grant to the Waisman Center from the National Institute of Child Health and Human Development (IDDRC U54 HD090256). NSF OAC-1916518, NSF IIS-1912270, NSF IIS-1636893, NSF BCS-1734853, NIH NIBIB 1R01EB029272-01, and a Microsoft Faculty Fellowship to F.P. LF acknowledges the support of the Cluster of Excellence Matters of Activity. Image Space Material funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany?s Excellence Strategy ? EXC 2025. SW is supported by a Medical Research Council PhD Studentship UK [MR/N013913/1]. The Nottingham group's processing was performed using the University of Nottingham's Augusta HPC service and the Precision Imaging Beacon Cluster. JPA, MA and SMS acknowledges the support of FCT - Funda??o para a Ci?ncia e a Tecnologia within CINTESIS, R&D Unit (reference UID/IC/4255/2013). MM was funded by the Wellcome Trust through a Sir Henry Wellcome Postdoctoral Fellowship [213722/Z/18/Z]. EJC-R is supported by the Swiss National Science Foundation (SNSF, Ambizione grant PZ00P2 185814/1). CMWT is supported by a Sir Henry Wellcome Fellowship (215944/Z/19/Z) and a Veni grant from the Dutch Research Council (NWO) (17331). FC acknowledges the support of the National Health and Medical Research Council of Australia (APP1091593 and APP1117724) and the Australian Research Council (DP170101815). NSF OAC-1916518, NSF IIS-1912270, NSF IIS-1636893, NSF BCS-1734853, Microsoft Faculty Fellowship to F.P. D.B. was partially supported by NIH NIMH T32-MH103213 to William Hetrick (Indiana University). CL is partly supported by NIH grants P41 EB027061 and P30 NS076408 ?Institutional Center Cores for Advanced Neuroimaging. JYMY received positional funding from the Royal Children's Hospital Foundation (RCH 1000). JYMY, JC, and CEK acknowledge the support of the Royal Children's Hospital Foundation, Murdoch Children's Research Institute, The University of Melbourne Department of Paediatrics, and the Victorian Government's Operational Infrastructure Support Program. C-HY is grateful to the Ministry of Science and Technology of Taiwan (MOST 109-2222-E-182-001-MY3) for the support. LC acknowledges support from CONACYT and UNAM. ARM acknowledges support from CONACYT. LJO, YR, and FZ were supported by NIH P41EB015902 and R01MH119222. AJG was supported by P41EB015898. NM was supported by R01MH119222, K24MH116366, and R01MH111917. This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 785907 & 945539 (HBP SGA2 & SGA3), and from the ANR IFOPASUBA- 19-CE45-0022-01. PG, CR, NL and AV were partially supported by ANID-Basal FB0008 and ANID-FONDECYT 1190701 grants. We would like to acknowledge John C Gore, Hiromasa Takemura, Anastasia Yendiki, and Riccardo Galbusera for their helplful suggestions regarding the analysis, figures, and discussions. Publisher Copyright: © 2021 White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols foreach fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.

Published
2021

10. Tractography dissection variability: what happens when 42 groups dissect 14 white matter bundles on the same dataset?

Author

Schilling, Kurt G., primary, Rheault, François, additional, Petit, Laurent, additional, Hansen, Colin B., additional, Nath, Vishwesh, additional, Yeh, Fang-Cheng, additional, Girard, Gabriel, additional, Barakovic, Muhamed, additional, Rafael-Patino, Jonathan, additional, Yu, Thomas, additional, Fischi-Gomez, Elda, additional, Pizzolato, Marco, additional, Ocampo-Pineda, Mario, additional, Schiavi, Simona, additional, Canales-Rodríguez, Erick J., additional, Daducci, Alessandro, additional, Granziera, Cristina, additional, Innocenti, Giorgio, additional, Thiran, Jean-Philippe, additional, Mancini, Laura, additional, Wastling, Stephen, additional, Cocozza, Sirio, additional, Petracca, Maria, additional, Pontillo, Giuseppe, additional, Mancini, Matteo, additional, Vos, Sjoerd B., additional, Vakharia, Vejay N., additional, Duncan, John S., additional, Melero, Helena, additional, Manzanedo, Lidia, additional, Sanz-Morales, Emilio, additional, Peña-Melián, Ángel, additional, Calamante, Fernando, additional, Attyé, Arnaud, additional, Cabeen, Ryan P., additional, Korobova, Laura, additional, Toga, Arthur W., additional, Vijayakumari, Anupa Ambili, additional, Parker, Drew, additional, Verma, Ragini, additional, Radwan, Ahmed, additional, Sunaert, Stefan, additional, Emsell, Louise, additional, De Luca, Alberto, additional, Leemans, Alexander, additional, Bajada, Claude J., additional, Haroon, Hamied, additional, Azadbakht, Hojjatollah, additional, Chamberland, Maxime, additional, Genc, Sila, additional, Tax, Chantal M. W., additional, Yeh, Ping-Hong, additional, Srikanchana, Rujirutana, additional, Mcknight, Colin D., additional, Yang, Joseph Yuan-Mou, additional, Chen, Jian, additional, Kelly, Claire E., additional, Yeh, Chun-Hung, additional, Cochereau, Jerome, additional, Maller, Jerome J., additional, Welton, Thomas, additional, Almairac, Fabien, additional, Seunarine, Kiran K, additional, Clark, Chris A., additional, Zhang, Fan, additional, Makris, Nikos, additional, Golby, Alexandra, additional, Rathi, Yogesh, additional, O’Donnell, Lauren J., additional, Xia, Yihao, additional, Aydogan, Dogu Baran, additional, Shi, Yonggang, additional, Fernandes, Francisco Guerreiro, additional, Raemaekers, Mathijs, additional, Warrington, Shaun, additional, Michielse, Stijn, additional, Ramírez-Manzanares, Alonso, additional, Concha, Luis, additional, Aranda, Ramón, additional, Meraz, Mariano Rivera, additional, Lerma-Usabiaga, Garikoitz, additional, Roitman, Lucas, additional, Fekonja, Lucius S., additional, Calarco, Navona, additional, Joseph, Michael, additional, Nakua, Hajer, additional, Voineskos, Aristotle N., additional, Karan, Philippe, additional, Grenier, Gabrielle, additional, Legarreta, Jon Haitz, additional, Adluru, Nagesh, additional, Nair, Veena A., additional, Prabhakaran, Vivek, additional, Alexander, Andrew L., additional, Kamagata, Koji, additional, Saito, Yuya, additional, Uchida, Wataru, additional, Andica, Christina, additional, Abe, Masahiro, additional, Bayrak, Roza G., additional, Gandini Wheeler-Kingshott, Claudia A.M., additional, D’Angelo, Egidio, additional, Palesi, Fulvia, additional, Savini, Giovanni, additional, Rolandi, Nicolò, additional, Guevara, Pamela, additional, Houenou, Josselin, additional, López-López, Narciso, additional, Mangin, Jean-François, additional, Poupon, Cyril, additional, Román, Claudio, additional, Vázquez, Andrea, additional, Maffei, Chiara, additional, Arantes, Mavilde, additional, Andrade, José Paulo, additional, Silva, Susana Maria, additional, Calhoun, Vince D., additional, Caverzasi, Eduardo, additional, Sacco, Simone, additional, Lauricella, Michael, additional, Pestilli, Franco, additional, Bullock, Daniel, additional, Zhan, Yang, additional, Brignoni-Perez, Edith, additional, Lebel, Catherine, additional, Reynolds, Jess E, additional, Nestrasil, Igor, additional, Labounek, René, additional, Lenglet, Christophe, additional, Paulson, Amy, additional, Aulicka, Stefania, additional, Heilbronner, Sarah R., additional, Heuer, Katja, additional, Chandio, Bramsh Qamar, additional, Guaje, Javier, additional, Tang, Wei, additional, Garyfallidis, Eleftherios, additional, Raja, Rajikha, additional, Anderson, Adam W., additional, Landman, Bennett A., additional, and Descoteaux, Maxime, additional

Published
2020
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12. From Coarse to Fine-Grained Parcellation of the Cortical Surface Using a Fiber-Bundle Atlas

Author

López-López, Narciso, Vázquez, Andrea, Houenou, Josselin, Poupon, Cyril, Mangin, Jean-François, Ladra, Susana, Guevara, Pamela, López-López, Narciso, Vázquez, Andrea, Houenou, Josselin, Poupon, Cyril, Mangin, Jean-François, Ladra, Susana, and Guevara, Pamela

Abstract

[Abstract] In this article, we present a hybrid method to create fine-grained parcellations of the cortical surface, from a coarse-grained parcellation according to an anatomical atlas, based on cortico-cortical connectivity. The connectivity information is obtained from segmented superficial and deep white matter bundles, according to bundle atlases, instead of the whole tractography. Thus, a direct matching between the fiber bundles and the cortical regions is obtained, avoiding the problem of finding the correspondence of the cortical parcels among subjects. Generating parcels from segmented fiber bundles can provide a good representation of the human brain connectome since they are based on bundle atlases that contain the most reproducible short and long connections found on a population of subjects. The method first processes the tractography of each subject and extracts the bundles of the atlas, based on a segmentation algorithm. Next, the intersection between the fiber bundles and the cortical mesh is calculated, to define the initial and final intersection points of each fiber. A fiber filtering is then applied to eliminate misclassified fibers, based on the anatomical definition of each bundle and the labels of Desikan-Killiany anatomical parcellation. A parcellation algorithm is then performed to create a subdivision of the anatomical regions of the cortex, which is reproducible across subjects. This step resolves the overlapping of the fiber bundle extremities over the cortical mesh within each anatomical region. For the analysis, the density of the connections and the degree of overlapping, is considered and represented with a graph. One of our parcellations, an atlas composed of 160 parcels, achieves a reproducibility across subjects of ≈0.74, based on the average Dice's coefficient between subject's connectivity matrices, rather than ≈0.73 obtained for a macro anatomical parcellation of 150 parcels. Moreover, we compared two of our parcellations with s

Published
2020

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