Connectivity gradients on tractography data: Pipeline and example applications.
Connectivity gradients on tractography data: Pipeline and example applications.
复制标题
Tractography数据的连接梯度:管道和示例应用程序。
DOI:
10.1002/hbm.25623
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发表时间:
2021-12-15
影响因子:
4.8
通讯作者:
Mars RB
中科院分区:
文献类型:
--
作者:
Blazquez Freches G;Haak KV;Beckmann CF;Mars RB
Gray matter connectivity can be described in terms of its topographical organization, but the differential role of white matter connections underlying that organization is often unknown. In this study, we propose a method for unveiling principles of organization of both gray and white matter based on white matter connectivity as assessed using diffusion magnetic ressonance imaging (MRI) tractography with spectral embedding gradient mapping. A key feature of the proposed approach is its capacity to project the individual connectivity gradients it reveals back onto its input data in the form of projection images, allowing one to assess the contributions of specific white matter tracts to the observed gradients. We demonstrate the ability of our proposed pipeline to identify connectivity gradients in prefrontal and occipital gray matter. Finally, leveraging the use of tractography, we demonstrate that it is possible to observe gradients within the white matter bundles themselves. Together, the proposed framework presents a generalized way to assess both the topographical organization of structural brain connectivity and the anatomical features driving it. In this work, we introduce a pipeline for deriving overlapping connectivity gradients on gray and white matter, according to their white matter connections. We then use the pipeline to derive connectivity gradients on the prefrontal and occipital cortices as well as the optic radiation. Finally, we backproject these gradients onto the input white matter, revealing their origins.
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影响因子:
4.8
作者:
Cerliani, Leonardo;Thomas, Rajat M.;Jbabdi, Saad;Siero, Jeroen C. W.;Nanetti, Luca;Crippa, Alessandro;Gazzola, Valeria;D'Arceuil, Helen;Keysers, Christian
通讯作者:
Keysers, Christian
影响因子:
64.8
作者:
AZZOPARDI, P;COWEY, A
通讯作者:
COWEY, A
DOI:
10.1007/978-3-319-46720-7_24
发表时间:
2016-10-01
期刊:
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
影响因子:
--
作者:
Aydogan, Dogu Baran;Shi, Yonggang
通讯作者:
Shi, Yonggang
DOI:
10.1073/pnas.1803667115
发表时间:
2018-10-02
影响因子:
11.1
作者:
Vos de Wael R;Larivière S;Caldairou B;Hong SJ;Margulies DS;Jefferies E;Bernasconi A;Smallwood J;Bernasconi N;Bernhardt BC
通讯作者:
Bernhardt BC
影响因子:
4.8
作者:
DICE, LR
通讯作者:
DICE, LR