Superficial white matter bundle atlas based on hierarchical fiber clustering over probabilistic tractography data

Román, Claudio; Hernández, Cecilia; Figueroa, Miguel; Houenou, Josselin; Poupon, Cyril; Mangin, Jean‐François; Guevara, Pamela

doi:10.1016/j.neuroimage.2022.119550

Cited by 20 publications

(24 citation statements)

References 77 publications

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“…We used the distance matrix obtained in section 2.2.2 to calculate an affinity graph, which have been successfully used to group short fibers with similar shape. [13][14][15]18 The affinity is calculated using a ij = e −dij /σ 2 , where d ij is the d N E distance between aligned centroids i and j. The parameter σ determines the similarity scale (manually adjusted to σ = 25mm).…”

Section: Step 3: Hierarchical Clustering Over Centroid Affinity Graphmentioning

confidence: 99%

“…The distance threshold was calculated using the mean length of the atlas bundle fibers. 18 We applied the automatic segmentation method 28, 29 using the original and filtered atlas bundles, over 25 subjects of the HCP database. We quantified the reduction in the dispersion of the extracted bundles by calculating the number of segmented fibers with a d N E distance higher than 15mm to the bundle centroid (computed in the same manner as described in section 2.2.1) and averaged the results for all subjects.…”

Section: Automatic Bundle Segmentation Based On Multi-subject Atlasmentioning

confidence: 99%

“…8 Despite these limitations, their clinical relevance has been assessed in different brain disorders, e.g., Autism, 9 Epilepsy, 10 Alzheimer's Disease 11 and Multiple Sclerosis. 12 Atlas of SWM are still under development [13][14][15][16][17][18] and efforts are being made towards better segmentation methods of short bundles, which are harder to extract due to their complex form. [19][20][21] Regardless of the recent advances in mapping short association bundles, the literature on the shape of SWM fibers is still relatively scarce.…”

Section: Introductionmentioning

confidence: 99%

“…8 Recent multi-subject bundle atlases based on inter-subject clustering strategies reveal a high number of short bundles. [14][15][16][17][18] However, they lack a characterization of the different configurations present across bundles. This paper proposes a clustering strategy to group bundles of an SWM multi-subject atlas 18 according to their main shape.…”

Section: Introductionmentioning

confidence: 99%

“…[14][15][16][17][18] However, they lack a characterization of the different configurations present across bundles. This paper proposes a clustering strategy to group bundles of an SWM multi-subject atlas 18 according to their main shape. First, for each atlas bundle, we calculate its representative centroid, describing its overall form.…”

Section: Introductionmentioning

confidence: 99%

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Superficial white matter shape characterization using hierarchical clustering and a multi-subject bundle atlas

Mendoza

Román

Molina

et al. 2023

18th International Symposium on Medical Information Processing and Analysis

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The description of the superficial white matter (SWM) functional and structural organization is still an unachieved task. In particular, their shape has not been assessed in detail using diffusion Magnetic Resonance Imaging (dMRI) tractography. This work aims to characterize the different shapes of the short-range association connections present in an SWM multi-subject bundle atlas derived from probabilistic dMRI tractography datasets. First, we calculated a representative centroid shape for each atlas bundle. Next, we computed a distance matrix that encodes the similarity between every pair of centroids. For the distance matrix computation, centroids were first aligned using a streamline-based registration, reducing the 3D spatial separation effect and allowing us to focus only on shape differences. Then, we applied a hierarchical clustering algorithm over the affinity graph derived from the distance matrix. As a result, we obtained ten classes with distinctive shapes, ranging from a straight line form to U and C arrangements. The most predominant shapes were: (i) short open U, (ii) short closed U, and (iii) short C. Moreover, we used the shape information to filter out noisy streamlines in the atlas bundles and applied an automatic segmentation algorithm to 25 subjects of the HCP database. Our results show that the filtering steps help to segment more dense bundles with fewer outliers, improving the identification of the brain's short fibers.

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Section: Step 3: Hierarchical Clustering Over Centroid Affinity Graphmentioning

confidence: 99%

Section: Automatic Bundle Segmentation Based On Multi-subject Atlasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Superficial white matter shape characterization using hierarchical clustering and a multi-subject bundle atlas

Mendoza

Román

Molina

et al. 2023

18th International Symposium on Medical Information Processing and Analysis

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Assessment of the Depiction of Superficial White Matter Using Ultra‐High‐Resolution Diffusion MRI

Zhang,

Chen,

Ning

et al. 2024

Human Brain Mapping

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The superficial white matter (SWM) consists of numerous short‐range association fibers connecting adjacent and nearby gyri and plays an important role in brain function, development, aging, and various neurological disorders. Diffusion MRI (dMRI) tractography is an advanced imaging technique that enables in vivo mapping of the SWM. However, detailed imaging of the small, highly‐curved fibers of the SWM is a challenge for current clinical and research dMRI acquisitions. This work investigates the efficacy of mapping the SWM using in vivo ultra‐high‐resolution dMRI data. We compare the SWM mapping performance from two dMRI acquisitions: a high‐resolution 0.76‐mm isotropic acquisition using the generalized slice‐dithered enhanced resolution (gSlider) protocol and a lower resolution 1.25‐mm isotropic acquisition obtained from the Human Connectome Project Young Adult (HCP‐YA) database. Our results demonstrate significant differences in the cortico‐cortical anatomical connectivity that is depicted by these two acquisitions. We perform a detailed assessment of the anatomical plausibility of these results with respect to the nonhuman primate (macaque) tract‐tracing literature. We find that the high‐resolution gSlider dataset is more successful at depicting a large number of true positive anatomical connections in the SWM. An additional cortical coverage analysis demonstrates significantly higher cortical coverage in the gSlider dataset for SWM streamlines under 40 mm in length. Overall, we conclude that the spatial resolution of the dMRI data is one important factor that can significantly affect the mapping of SWM. Considering the relatively long acquisition time, the application of dMRI tractography for SWM mapping in future work should consider the balance of data acquisition efforts and the efficacy of SWM depiction.

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TractCloud: Registration-Free Tractography Parcellation with a Novel Local-Global Streamline Point Cloud Representation

Xue,

Chen,

Zhang

et al. 2023

Lecture Notes in Computer Science

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Superficial white matter bundle atlas based on hierarchical fiber clustering over probabilistic tractography data

Cited by 20 publications

References 77 publications

Superficial white matter shape characterization using hierarchical clustering and a multi-subject bundle atlas

Superficial white matter shape characterization using hierarchical clustering and a multi-subject bundle atlas

Assessment of the Depiction of Superficial White Matter Using Ultra‐High‐Resolution Diffusion MRI

TractCloud: Registration-Free Tractography Parcellation with a Novel Local-Global Streamline Point Cloud Representation

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