Topographic regularity for tract filtering in brain connectivity

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Standard

Topographic regularity for tract filtering in brain connectivity. / Wang, Junyan; Aydogan, Dogu Baran; Varma, Rohit; Toga, Arthur W.; Shi, Yonggang.

Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings. Springer Verlag, 2017. s. 263-274 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vuosikerta 10265 LNCS).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Harvard

Wang, J, Aydogan, DB, Varma, R, Toga, AW & Shi, Y 2017, Topographic regularity for tract filtering in brain connectivity. julkaisussa Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vuosikerta. 10265 LNCS, Springer Verlag, Sivut 263-274, Boone, Yhdysvallat, 25/06/2017. https://doi.org/10.1007/978-3-319-59050-9_21

APA

Wang, J., Aydogan, D. B., Varma, R., Toga, A. W., & Shi, Y. (2017). Topographic regularity for tract filtering in brain connectivity. teoksessa Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings (Sivut 263-274). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vuosikerta 10265 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-59050-9_21

Vancouver

Wang J, Aydogan DB, Varma R, Toga AW, Shi Y. Topographic regularity for tract filtering in brain connectivity. julkaisussa Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings. Springer Verlag. 2017. s. 263-274. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-59050-9_21

Author

Wang, Junyan ; Aydogan, Dogu Baran ; Varma, Rohit ; Toga, Arthur W. ; Shi, Yonggang. / Topographic regularity for tract filtering in brain connectivity. Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings. Springer Verlag, 2017. Sivut 263-274 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Bibtex - Lataa

@inproceedings{3c4da134e1764cf1af1da0bc31e6361c,
title = "Topographic regularity for tract filtering in brain connectivity",
abstract = "The preservation of the spatial relationships among axonal pathways has long been studied and known to be critical for many functions of the brain. Being a fundamental property of the brain connections, there is an intuitive understanding of topographic regularity in neuroscience but yet to be systematically explored in connectome imaging research. In this work, we propose a general mathematical model for topographic regularity of fiber bundles that is consistent with its neuroanatomical understanding. Our model is based on a novel group spectral graph analysis (GSGA) framework motivated by spectral graph theory and tensor decomposition. GSGA provides a common set of eigenvectors for the graphs formed by topographic proximity measures whose preservation along individual tracts in return is modeled as topographic regularity. To demonstrate the application of this novel measure of topographic regularity, we apply it to filter fiber tracts from connectome imaging. Using large-scale data from the Human Connectome Project (HCP), we show that our novel algorithm can achieve better performance than existing methods on the filtering of both individual bundles and whole brain tractograms.",
author = "Junyan Wang and Aydogan, {Dogu Baran} and Rohit Varma and Toga, {Arthur W.} and Yonggang Shi",
year = "2017",
month = "1",
day = "1",
doi = "10.1007/978-3-319-59050-9_21",
language = "English",
isbn = "9783319590493",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "263--274",
booktitle = "Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings",
address = "Germany",

}

RIS - Lataa

TY - GEN

T1 - Topographic regularity for tract filtering in brain connectivity

AU - Wang, Junyan

AU - Aydogan, Dogu Baran

AU - Varma, Rohit

AU - Toga, Arthur W.

AU - Shi, Yonggang

PY - 2017/1/1

Y1 - 2017/1/1

N2 - The preservation of the spatial relationships among axonal pathways has long been studied and known to be critical for many functions of the brain. Being a fundamental property of the brain connections, there is an intuitive understanding of topographic regularity in neuroscience but yet to be systematically explored in connectome imaging research. In this work, we propose a general mathematical model for topographic regularity of fiber bundles that is consistent with its neuroanatomical understanding. Our model is based on a novel group spectral graph analysis (GSGA) framework motivated by spectral graph theory and tensor decomposition. GSGA provides a common set of eigenvectors for the graphs formed by topographic proximity measures whose preservation along individual tracts in return is modeled as topographic regularity. To demonstrate the application of this novel measure of topographic regularity, we apply it to filter fiber tracts from connectome imaging. Using large-scale data from the Human Connectome Project (HCP), we show that our novel algorithm can achieve better performance than existing methods on the filtering of both individual bundles and whole brain tractograms.

AB - The preservation of the spatial relationships among axonal pathways has long been studied and known to be critical for many functions of the brain. Being a fundamental property of the brain connections, there is an intuitive understanding of topographic regularity in neuroscience but yet to be systematically explored in connectome imaging research. In this work, we propose a general mathematical model for topographic regularity of fiber bundles that is consistent with its neuroanatomical understanding. Our model is based on a novel group spectral graph analysis (GSGA) framework motivated by spectral graph theory and tensor decomposition. GSGA provides a common set of eigenvectors for the graphs formed by topographic proximity measures whose preservation along individual tracts in return is modeled as topographic regularity. To demonstrate the application of this novel measure of topographic regularity, we apply it to filter fiber tracts from connectome imaging. Using large-scale data from the Human Connectome Project (HCP), we show that our novel algorithm can achieve better performance than existing methods on the filtering of both individual bundles and whole brain tractograms.

UR - http://www.scopus.com/inward/record.url?scp=85020504430&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-59050-9_21

DO - 10.1007/978-3-319-59050-9_21

M3 - Conference contribution

SN - 9783319590493

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 263

EP - 274

BT - Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings

PB - Springer Verlag

ER -

ID: 29135032