A multimodal study of multiscale individual structural-connectivity-based parcellations
Poster No:
1647
Submission Type:
Abstract Submission
Authors:
Clément Langlet1, Denis Rivière1, Bastien Herlin1, Ivy Uszynski1, Cyril Poupon1, Jean-François Mangin1
Institutions:
1BAOBAB, NeuroSpin, Université Paris-Saclay, CNRS, CEA, Gif-sur-Yvette, France
First Author:
Co-Author(s):
Introduction:
Mapping the brain is a long standing goal of the neuroscience community as cerebral maps provide a spatial referential crucial to understand the human brain. An additional application of these cerebral maps is the dimension reduction that they provide for further genetics and machine learning analyses. Two main strategies coexist to yield these maps: defining anatomical atlases or generating data-driven parcellations. However, these maps are often average templates that are projected onto the individual via an alignment driven by cortical landmarks, and fail to represent the anatomical peculiarities of the individuals (Glasser, 2016) (Mangin, 2019). Individual parcellations have already been shown to improve the study of cortical thickness heritability in some regions of the brain (Langlet, 2024), yet a better understanding across other phenotypes remains of interest for the brain mapping community. In this work, we generated multiscale individual parcellations using a data-driven algorithm based on structural connectivity profiles and studied their relevance using multimodal MRI data.
Methods:
First, we used a group-based clustering algorithm - Constellation (Lefranc and Roca, 2015) - to generate averaged subdivisions of the Desikan atlas (Desikan, 2006) based on structural connectivity profiles derived from the diffusion MRI of the HCP dataset (Van Essen, 2013) [Figure 1]. In each region, from 2 to 12 subdivisions were generated. These multiscale subdivisions were then projected onto 1004 subjects of the dataset using their individual structural connectivity profiles (Langlet, 2023). We then estimated the probability density function of each parcel - using a gaussian Kernel Density Estimation - on various phenotypes such as myelin, cortical thickness and cortical thickness corrected by curvature, grey matter volume, DTI measures (Fractional Anisotropy and Mean Diffusivity) and NODDI model (Zhang, 2012) indexes (Neurite Density Index and Orientation Dispersion Index). To study the impact of the number of subdivisions for each Desikan region and each modality, we used a Generalized Contrast-to-Noise ratio (gCNR) (Rodriguez-Molares, 2020) defined as an inverse function of the overlap between each subdivision and the neighbouring parcels. This process was applied on tractograms stemming from the FSL probabilistic tractography algorithm and the MRtrix algorithm using SIFT2 yielding two sets of results.
·Constellation algorithm
Results:
We obtained the gCNR for multiscale subdivisions of Desikan regions for each studied phenotype and two tractography algorithms. Although the evolution of this score seems to follow a general trend across the number of subdivisions (kmax), it can be different according to the phenotype [Figure 2]. The evolution of the contrast score is fairly reproducible between the two tractography algorithms. These results suggest that, from a data processing point of view, there may be no such thing as one relevant number of parcels and this may depend on several factors such as the phenotype or the region of the brain.
·Generalized Contrast-to-Noise ratio on multiple phenotypes
Conclusions:
The present study gives insights into the relevance of individual parcellations for data processing purposes through a multimodal evaluation. Although the generalized Contrast-to-Noise ratio is not a standard score in the MRI community to study parcellations, the recent use of probability density estimates in MIND networks (Sebenius, 2023) has yielded promising results, therefore further investigations on the multiscale individual parcellations will be conducted. These multiscale individual parcellations will be provided to the community for replication and further studies.
Modeling and Analysis Methods:
Segmentation and Parcellation 1
Univariate Modeling 2
Keywords:
Data analysis
Morphometrics
MRI
Statistical Methods
STRUCTURAL MRI
Tractography
Univariate
White Matter
Other - Parcellations, Connectivity
1|2Indicates the priority used for review
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Provide references using APA citation style.
the human cerebral cortex on mri scans into gyral based regions of interest.
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