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, Michael J.; Bzdok, Danilo; Eickhoff, Simon B.; Yeo, B.T. Thomas

doi:10.1016/j.neuroimage.2023.120010

Cited by 41 publications

(31 citation statements)

References 133 publications

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“…Changing the ROI coverage threshold from 50% to 60% also yielded similar results (Figure S9). We repeated the analysis using a 100-region homotopic functional parcellation (63), which also yielded similar results (Figure S10). (64).…”

Section: Estimated E/i Ratio Is Sensitive To the Effect Of Benzodiaze...mentioning

confidence: 59%

In-vivo whole-cortex estimation of excitation-inhibition ratio indexes cortical maturation and cognitive ability in youth

Zhang

Larsen

Sydnor

et al. 2023

Preprint

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A balanced excitation-inhibition ratio (E/I ratio) is critical for healthy brain function, but challenging to measure in-vivo in humans. Here we present an approach to non-invasively estimate whole-cortex E/I ratio by fitting a large-scale biophysically plausible model to resting-state functional MRI (fMRI) data. We first confirm our model generates realistic brain dynamics in the Human Connectome Project. Next, we show that the estimated E/I ratio is sensitive to the GABA-agonist benzodiazepine alprazolam during fMRI. Furthermore, drug-induced E/I changes are spatially consistent with positron emission tomography measurement of benzodiazepine receptor density. We then leverage the model to investigate the hypothesis that the E/I ratio is normatively refined during development and supports improvement in cognitive ability. We find that the E/I ratio declines heterogeneously across the cerebral cortex during youth, with the greatest reduction occurring in sensorimotor systems relative to association systems. Importantly, among children with the same chronological age, a lower E/I ratio (especially in association cortex) is linked to better cognitive performance. This result is replicated across North American (8.2 to 23.0 years old) and Asian (7.2 to 7.9 years old) cohorts, suggesting that a more mature E/I ratio indexes improved cognition during normative development. Overall, our approach opens the door to studying cortex-wide E/I ratio changes across the lifespan and in neuropsychiatric disorders.

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Section: Estimated E/i Ratio Is Sensitive To the Effect Of Benzodiaze...mentioning

confidence: 59%

In-vivo whole-cortex estimation of excitation-inhibition ratio indexes cortical maturation and cognitive ability in youth

Zhang

Larsen

Sydnor

et al. 2023

Preprint

Self Cite

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“…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

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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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“…1). 314 Specifically, with regard to nodes, clusters can be defined as 315 predefined node groups, which are determined by community 316 detection (Garcia et al, 2018;Wierzbiński et al, 2023) or clus-317 tering (Thomas Yeo et al, 2011;Yan et al, 2023) algorithms, or 318 even customized by the researcher based on empirical knowl-319 edge. For edges, clusters can be defined as the set of edges 320 within or between predefined node groups.…”

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confidence: 99%

“…We configured the edges with the grouptruth effect using the EEG dataset. The "Yan2023" homotopic 572 100-area cortex atlas (Yan et al, 2023)…”

mentioning

confidence: 99%

Threshold-Free Network-Oriented Statistics in Neuroscience

Hao,

Wang,

Xia

et al. 2024

Preprint

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Network neuroscience has emerged as a powerful tool for studying brain structure and function. Currently, the network-based statistic (NBS) method is widely used to control the type I error rate for mass univariate tests on brain network edges. However, the NBS requires the selection of a cluster-forming threshold, which lacks objective guidelines. There is also an urgent need for a powerful and flexible statistical framework for analyzing brain networks in diverse contexts. Here, we introduce a permutation-based framework called “threshold-free network-oriented statistics” (TFNOS). It integrates two “threshold-free” pathways: traversing all cluster-forming thresholds (TT) and using predefined clusters (PC). The TT procedure, building upon the threshold-free network-based statistics, requires the setting of additional parameters. The PC procedures have six variants according to the way of pooling data, the form of the null distribution, and the controlled error rate. Using numerical simulations, we comprehensively evaluated the performance of the TT procedure under 600 parameter combinations, benchmarked TFNOS procedures and baselines across different topologies of effects, sample sizes, and effect sizes, and provided examples of application on real data. We offer recommended parameter values that can make the TT procedure stably maintain leading power while empirically controlling the false discovery rate (FDR) at an appropriate level. Analyses reveal that the recommended parameters available in the field are overly liberal. Furthermore, for the PC procedures, FDR-controlling variants showed improved power compared to FWER-controlling variants, some of which are simple but do not compromise power. The nonparametric PC procedures allow for the selection of any test statistics considered appropriate. Overall, we provide empirical and principled criteria for selecting statistical procedures and the TFNOS is a generalized framework for inference in various contexts on edges or nodes of undirected or directed brain networks.

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

Cited by 41 publications

References 133 publications

In-vivo whole-cortex estimation of excitation-inhibition ratio indexes cortical maturation and cognitive ability in youth

In-vivo whole-cortex estimation of excitation-inhibition ratio indexes cortical maturation and cognitive ability in youth

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

Threshold-Free Network-Oriented Statistics in Neuroscience

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