Homotopic local-global parcellation of the human cerebral cortex from resting-state functional connectivity

Yan, Xiaoxuan; Kong, Ru; Xue, Aihuiping; Yang, Qing; Orban, Csaba; An, Lijun; Holmes, Avram J.; Qian, Xing; Chen, Jianzhong; Zuo, Xi‐Nian; Zhou, Juan; Fortier, Marielle V.; Tan, Ai Peng; Gluckman, Peter D.; Chong, Yap Seng; Meaney, Micheal; Bzdok, Danilo; Eickhoff, Simon B.; Yeo, B.T. Thomas

doi:10.1101/2022.10.25.513788

Cited by 3 publications

(4 citation statements)

References 141 publications

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“…In this study, individual parcellations were generated using the Kong et al (2019) MS-HBM algorithm. However, improved versions of this algorithm have since been published (Kong et al, 2021;Yan et al, 2023), which account for parcel distributions, spatial contiguity, local gradients, and homotopy (or the lack thereof in Schaefer parcels). Thus, future investigations using NSAR might consider implementing an updated individual parcellation algorithm.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Evidence for a Compensatory Relationship between Left- and Right-Lateralized Brain Networks

Peterson,

Braga,

Floris

et al. 2023

Preprint

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The two hemispheres of the human brain are functionally asymmetric. At the network level, the language network exhibits left-hemisphere lateralization. While this asymmetry is widely replicated, the extent to which other functional networks demonstrate lateralization remains a subject of investigation. Additionally, it is unknown how the lateralization of one functional network may affect the lateralization of other networks within individuals. We quantified lateralization for each of 17 networks by computing the relative surface area on the left and right cerebral hemispheres. After examining the ecological, convergent, and external validity and test-retest reliability of this surface area-based measure of lateralization, we addressed two hypotheses across multiple datasets (Human Connectome Project = 553, Human Connectome Project-Development = 343, Natural Scenes Dataset = 8). First, we hypothesized that networks associated with language, visuospatial attention, and executive control would show the greatest lateralization. Second, we hypothesized that relationships between lateralized networks would follow a dependent relationship such that greater left-lateralization of a network would be associated with greater right-lateralization of a different network within individuals, and that this pattern would be systematic across individuals. A language network was among the three networks identified as being significantly left-lateralized, and attention and executive control networks were among the five networks identified as being significantly right-lateralized. Next, correlation matrices, an exploratory factor analysis, and confirmatory factor analyses were used to test the second hypothesis and examine the organization of lateralized networks. We found general support for a dependent relationship between highly left-and right-lateralized networks, meaning that across subjects, greater left lateralization of a given network (such as a language network) was linked to greater right lateralization of another network (such as a ventral attention/salience network) and vice versa. These results further our understanding of brain organization at the macro-scale network level in individuals, carrying specific relevance for neurodevelopmental conditions characterized by disruptions in lateralization such as autism and schizophrenia.

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Section: Limitations and Future Directionsmentioning

confidence: 99%

Evidence for a Compensatory Relationship between Left- and Right-Lateralized Brain Networks

Peterson,

Braga,

Floris

et al. 2023

Preprint

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“…While numerous criteria exist for the parcellation of cerebral territories, the designation of regional labels predominantly relies on established anatomical or neurofunctional insights. Examples include macroanatomical landmarks, such as gyri and sulci [10-12, 16, 19, 20], cellular configurations at the microscopic scale [20], the spatial distribution of transporters or receptors [21,22], and regions characterized by vascular territories [23], as well as by functional or anatomical connectivity [5,[24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

OpenMAP-T1: A Rapid Deep Learning Approach to Parcellate 280 Anatomical Regions to Cover the Whole Brain

Nishimaki,

Onda,

Ikuta

et al. 2024

Preprint

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This study introduces OpenMAP-T1, a deep-learning-based method for rapid and accurate whole-brain parcellation in T1-weighted brain MRI, which aims to overcome the limitations of conventional normalization-to-atlas-based approaches and multi-atlas label-fusion (MALF) techniques. Brain image parcellation is a fundamental process in neuroscientific and clinical research, enabling a detailed analysis of specific cerebral regions. Normalization-to-atlas-based methods have been employed for this task, but they face limitations due to variations in brain morphology, especially in pathological conditions. The MALF teqhniques improved the accuracy of the image parcellation and robustness to variations in brain morphology, but at the cost of high computational demand that requires a lengthy processing time. OpenMAP-T1 integrates several convolutional neural network models across six phases: preprocessing; cropping; skull-stripping; parcellation; hemisphere segmentation; and final merging. This process involves standardizing MRI images, isolating the brain tissue, and parcellating it into 280 anatomical structures that cover the whole brain, including detailed gray and white matter structures, while simplifying the parcellation processes and incorporating robust training to handle various scan types and conditions. The OpenMAP-T1 was tested on eight available open resources, including real-world clinical images, demonstrating robustness across different datasets with variations in scanner types, magnetic field strengths, and image processing techniques, such as defacing. Compared to existing methods, OpenMAP-T1 significantly reduced the processing time per image from several hours to less than 90 seconds without compromising accuracy. It was particularly effective in handling images with intensity inhomogeneity and varying head positions, conditions commonly seen in clinical settings. The adaptability of OpenMAP-T1 to a wide range of MRI datasets and its robustness to various scan conditions highlight its potential as a versatile tool in neuroimaging.

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“…CC-BY-NC 4.0 International license perpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for this this version posted December 21, 2022. ; https://doi.org/10.1101/2022.12.21.521366 doi: bioRxiv preprint Zalesky et al, 2010), variability in the surface areas of regions (Van Essen et al, 2012), and inter-hemispheric (a)symmetry (Yan et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…For example, parcellations have been generated using manual segmentation based on sulcal and gyral anatomy (Desikan et al, 2006); using network models based on functional connectivity (Schaefer et al, 2018); and on multimodal combinations of anatomical, microstructural, and functional features (Genon et al, 2018;Glasser et al, 2016;Wang et al, 2015). Parcellations are also difficult to compare due to differences in the number of regions delineated (Fornito et al, 2010;Zalesky et al, 2010), variability in the surface areas of regions (Van Essen et al, 2012), and inter-hemispheric (a)symmetry (Yan et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Can hubs of the human connectome be identified consistently with diffusion MRI?

Gajwani¹,

Oldham

Pang³

et al. 2022

Preprint

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Recent years have seen a surge in the use of diffusion MRI to map connectomes in humans, paralleled by a similar increase in pre-processing and analysis choices. Yet these different steps and their effects are rarely compared systematically. Here, in a healthy young adult population (n = 294), we characterized the impact of a range of analysis pipelines on one widely studied property of the human connectome; its degree distribution. We evaluated the effects of 40 pipelines (comparing common choices of parcellation, streamline seeding, tractography algorithm, and streamline propagation constraint) and 44 group-representative connectome reconstruction schemes on highly connected hub regions. We found that hub location is highly variable between pipelines. The choice of parcellation has a major influence on hub architecture, and hub connectivity is highly correlated with regional surface area in most of the assessed pipelines (ρ > 0.70 in 69% of the pipelines), particularly when using weighted networks. Overall, our results demonstrate the need for prudent decision-making when processing diffusion MRI data, and for carefully considering how different pre-processing choices can influence connectome organization.

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Homotopic local-global parcellation of the human cerebral cortex from resting-state functional connectivity

Cited by 3 publications

References 141 publications

Evidence for a Compensatory Relationship between Left- and Right-Lateralized Brain Networks

Evidence for a Compensatory Relationship between Left- and Right-Lateralized Brain Networks

OpenMAP-T1: A Rapid Deep Learning Approach to Parcellate 280 Anatomical Regions to Cover the Whole Brain

Can hubs of the human connectome be identified consistently with diffusion MRI?

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