Literatura académica sobre el tema "Bundle based tractography"
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Artículos de revistas sobre el tema "Bundle based tractography"
Sweet, Jennifer A., Sinem Balta Beylergil, Suraj Thyagaraj, Eric Z. Herring, Jesse E. Drapekin, Keming Gao, Joseph R. Calabrese, Jonathan P. Miller y Cameron C. McIntyre. "Clinical Evaluation of Cingulum Bundle Connectivity for Neurosurgical Hypothesis Development". Neurosurgery 86, n.º 5 (2 de julio de 2019): 724–35. http://dx.doi.org/10.1093/neuros/nyz225.
Texto completoAmeis, Stephanie H., Jin Fan, Conrad Rockel, Latha Soorya, A. Ting Wang y Evdokia Anagnostou. "Altered cingulum bundle microstructure in autism spectrum disorder". Acta Neuropsychiatrica 25, n.º 5 (27 de febrero de 2013): 275–82. http://dx.doi.org/10.1017/neu.2013.2.
Texto completoGuo, Zhe, Yi Wang, Tao Lei, Yangyu Fan y Xiuwei Zhang. "DTI Image Registration under Probabilistic Fiber Bundles Tractography Learning". BioMed Research International 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/4674658.
Texto completoBehrman-Lay, Ashley M., Christina Usher, Thomas E. Conturo, Stephen Correia, David H. Laidlaw, Elizabeth M. Lane, Jacob Bolzenius et al. "Fiber bundle length and cognition: a length-based tractography MRI study". Brain Imaging and Behavior 9, n.º 4 (7 de noviembre de 2014): 765–75. http://dx.doi.org/10.1007/s11682-014-9334-8.
Texto completoBaur, Alexander DJ, Tareef Daqqaq, Federico Collettini, Timm Denecke, Bernd Hamm, Tahir Durmus y Michael Scheel. "Influence of fractional anisotropy thresholds on diffusion tensor imaging tractography of the periprostatic neurovascular bundle and selected pelvic tissues: do visualized tracts really represent nerves?" Acta Radiologica 58, n.º 4 (20 de julio de 2016): 472–80. http://dx.doi.org/10.1177/0284185116651004.
Texto completoRomán, Claudio, Cecilia Hernández, Miguel Figueroa, Josselin Houenou, Cyril Poupon, Jean-François Mangin y Pamela Guevara. "Superficial white matter bundle atlas based on hierarchical fiber clustering over probabilistic tractography data". NeuroImage 262 (noviembre de 2022): 119550. http://dx.doi.org/10.1016/j.neuroimage.2022.119550.
Texto completoBriggs, Robert G., Onur Tanglay, Nicholas B. Dadario, Isabella M. Young, R. Dineth Fonseka, Jorge Hormovas, Vukshitha Dhanaraj et al. "The Unique Fiber Anatomy of Middle Temporal Gyrus Default Mode Connectivity". Operative Neurosurgery 21, n.º 1 (30 de abril de 2021): E8—E14. http://dx.doi.org/10.1093/ons/opab109.
Texto completoBurks, Joshua D., Andrew K. Conner, Phillip A. Bonney, Chad A. Glenn, Cordell M. Baker, Lillian B. Boettcher, Robert G. Briggs, Daniel L. O’Donoghue, Dee H. Wu y Michael E. Sughrue. "Anatomy and white matter connections of the orbitofrontal gyrus". Journal of Neurosurgery 128, n.º 6 (junio de 2018): 1865–72. http://dx.doi.org/10.3171/2017.3.jns162070.
Texto completoBracht, T., A. N. Doidge, P. A. Keedwell y D. K. Jones. "Hedonic tone is associated with left supero-lateral medial forebrain bundle microstructure". Psychological Medicine 45, n.º 4 (15 de agosto de 2014): 865–74. http://dx.doi.org/10.1017/s0033291714001949.
Texto completoLin, Yueh-Hsin, Nicholas B. Dadario, Jorge Hormovas, Isabella M. Young, Robert G. Briggs, Alana E. MacKenzie, Ali H. Palejwala et al. "Anatomy and White Matter Connections of the Superior Parietal Lobule". Operative Neurosurgery 21, n.º 3 (10 de julio de 2021): E199—E214. http://dx.doi.org/10.1093/ons/opab174.
Texto completoTesis sobre el tema "Bundle based tractography"
Guevara, Olivares Miguel. "Disentangling the short white matter connections using a fiber's geometry based dimensional reduction approach". Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPAST053.
Texto completoThe study of superficial white matter (SWM) has often been left aside, mainly because of its high variability. Higher quality acquisition methods and the development of new analysis tools have facilitated the study of SWM from diffusion MRI and tractography. Brain connectivity and cortical folding pattern must be strongly related, especially for short U-fibers, which circumvent the folds. As the folding patterns morphology is specific to each human being, so should be the underlying fibers configuration. In this work we created a pipeline to disentangle the short white matter connections into their different configurations and to characterize their relation with other structures.First a method to delineate short bundles from a tractography set was built using a hybrid approach, by extracting fibers connecting two cortical regions of interest (ROIs) (incorporating anatomical information) and then clustering them into bundles (considering their shape), reproducible across subjects. Subjects were aligned by a T1-based affine transformation and a deterministic tractography database (79 subjects) was used. This generated a whole brain streamline bundle atlas, which allows distance-based segmentation of the bundles in new subjects, in order to perform clinical studies over specific connections. The bundles obtained were compared against other two publicly available atlases (using alternative non-linear alignment across subjects), to evaluate their reproducibility given different methods and databases. A non-negligible number of bundles were found similar among the three atlases. As SWM bundle definition is still a subjective matter, over-segmentation can nevertheless occur. However, even greater granularity is required when aiming to classify the different bundle configurations. This level of disentanglement was achieved by an ISOMAP dimensionality reduction algorithm. It aimed to stratify the population based on their fibers using geometrical changes across subjects. For each region under study, the fibers surrounding a specific sulcus were targeted and therefore the ROIs were selected accordingly. These regions are: central sulcus, superior temporal sulcus, cingulate sulcus and precentral gyrus. The method was applied over 816/897 subjects of the S900 release of the HCP database and a preprocessed probabilistic tractography database. For each region the fibers were extracted, sampled and then used in the ISOMAP computation, which in turn was employed to split the population in ten groups. In each group a refined version of a short bundle identification method was applied, in order to obtain reproducible bundles. These were then automatically matched with their corresponding ones in the other groups, based on a centroid fiber distance. A Hysteresis principle was used to recover missing bundles (previously discarded) in each group. In order to identify the bundles driving the differences reflected on each ISOMAP dimension, the correlation of the fibers geometry with the subjects ISOMAP values was performed, by using a “bundle to tractogram” distance for each pair of subjects. The fiber-based ISOMAP values were also compared to a sulcus-based ones, obtaining a high correlation for the first dimension. The bundles showing correlation with the ISOMAP values show coherent morphological transitions along the groups, and are located in areas where the sulcus also exhibits differences in shape. Moreover, the bundles are also spatially correlated to changes in functional activations. These results prove the link between the brain wiring and the cortical folding pattern. Moreover, they evidence that a finer delineation of the bundles allow the detection of differences that most of the time are blurred out due to the mixing of configurations
Actas de conferencias sobre el tema "Bundle based tractography"
Chowdhury, Fahmida K., Eddie Jacobs, Jakir Hossen y Teddy Salan. "Diffusion tensor based global tractography of human brain fiber bundles". En 2014 8th International Conference on Electrical and Computer Engineering (ICECE). IEEE, 2014. http://dx.doi.org/10.1109/icece.2014.7026860.
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