Extended Network-Based Statistics for Measuring Altered Directed Connectivity Components in the Human Brain

Ge, Yunxiang; Yang, Zhihan; Yuan, Feng; Pan, Yu; Dou, Wei

doi:10.1109/bibm52615.2021.9669502

Cited by 4 publications

(13 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For functional network analysis, we utilized the extended network-based statistics (extended-NBS) ( Zalesky et al, 2010 ; Ge et al, 2021 ) to evaluate functional connectivity changes in MSA patients. Firstly, the same connection was selected from the two groups and two-sample t-test was performed.…”

Section: Methodsmentioning

confidence: 99%

Altered Brain Volume, Microstructure Metrics and Functional Connectivity Features in Multiple System Atrophy

Zheng

et al. 2022

Front. Aging Neurosci.

Self Cite

View full text Add to dashboard Cite

In order to deeply understand the specific patterns of volume, microstructure, and functional changes in Multiple System Atrophy patients with cerebellar ataxia syndrome (MSA-c), we perform the current study by simultaneously applying structural (T1-weighted imaging), Diffusion tensor imaging (DTI), functional (BOLD fMRI) and extended Network-Based Statistics (extended-NBS) analysis. Twenty-nine MSA-c type patients and twenty-seven healthy controls (HCs) were involved in this study. First, we analyzed the whole brain changes of volume, microstructure, and functional connectivity (FC) in MSA-c patients. Then, we explored the correlations between significant multimodal MRI features and the total Unified Multiple System Atrophy Rating Scale (UMSARS) scores. Finally, we searched for sensitive imaging biomarkers for the diagnosis of MSA-c using support vector machine (SVM) classifier. Results showed significant grey matter atrophy in cerebellum and white matter microstructural abnormalities in cerebellum, left fusiform gyrus, right precentral gyrus and lingual gyrus. Extended-NBS analysis found two significant different connected components, featuring altered functional connectivity related to left and right cerebellar sub-regions, respectively. Moreover, the reduced fiber bundle counts at right Cerebellum_3 (Cbe3) and decreased fractional anisotropy (FA) values at bilateral Cbe9 were negatively associated with total UMSARS scores. Finally, the significant features at left Cbe9, Cbe1, and Cbe7b were found to be useful as sensitive biomarkers to differentiate MSA-c from HCs according to the SVM analysis. These findings advanced our understanding of the neural pathophysiological mechanisms of MSA from the perspective of multimodal neuroimaging.

show abstract

Section: Methodsmentioning

confidence: 99%

Altered Brain Volume, Microstructure Metrics and Functional Connectivity Features in Multiple System Atrophy

Zheng

et al. 2022

Front. Aging Neurosci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…research (Ge et al, 2021), we considered that SCI alterations to brain networks may involve a large number of edges that may satisfy the implicit assumptions of both the TFNOS-TT and TFNOS-PC procedures. Given the sample size of the fMRI dataset, it implies that only the stronger effect can be detected with adequate power (Section 3.1.3).…”

Section: Experiments Iii: Test In the Fmri Datasetmentioning

confidence: 99%

“…Finally, we investigated the group-level difference in P-FC 814 and CCM-EC between patients with SCI and HC, as illustra-815 tive examples of our TFNOS framework. Based on previous 816 research (Ge et al, 2021), we considered that SCI alterations 817 to brain networks may involve a large number of edges that 818 may satisfy the implicit assumptions of both the TFNOS-TT 819 and TFNOS-PC procedures. Given the sample size of the fMRI 820 dataset, it implies that only the stronger effect can be detected 821 with adequate power (Section 3.1.3).…”

Section: Benchmarking Cluster-level Inference Proceduresmentioning

confidence: 99%

Threshold-Free Network-Oriented Statistics in Neuroscience

Hao,

Wang,

Xia

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Several amendments have been proposed to reduce the computation requirement of DCM recently [45,46], but the model complexity is still challenging for clinical applications. We proposed a method based on convergent cross-mapping (CCM) that can reflect the interactions between regions in a dynamic, nonlinear, and deterministic way, which is not covered by GCA [47]. The method overview, together with the extended network-based statistic, is shown in Figure 3.…”

Section: Directed Connectivitymentioning

confidence: 99%

“…Based on directed connectivity, we proposed the extended-NBS (e-NBS) to search for altered connected components in groups of directed networks [47]. The method overview is shown in Figure 3.…”

Section: Network-based Statisticsmentioning

confidence: 99%

Resting-State Brain Network Analysis Methods and Applications

Ge¹,

Dou²

2022

Neurophysiology - Networks, Plasticity, Pathophysiology and Behavior

Self Cite

View full text Add to dashboard Cite

Resting-state fMRI has been widely applied in clinical research. Brain networks constructed by functional connectivity can reveal alterations related to disease and treatment. One of the major concerns of brain network application under clinical situations is how to analyze groups of data to find the potential biomarkers that can aid in diagnosis. In this paper, we briefly review common methods to construct brain networks from resting-state fMRI data, including different ways of the node definition and edge calculation. We focus on using a brain atlas to define nodes and estimate edges by static and dynamic functional connectivity. The directed connectivity method is also mentioned. We then discuss the challenges and pitfalls when analyzing groups of brain networks, including functional connectivity alterations, graph theory attributes analysis, and network-based statistics. Finally, we review the clinical application of resting-state fMRI in neurorehabilitation of spinal cord injury patients and stroke patients, the research on the mechanism and early diagnosis of neurodegenerative diseases, such as multiple system atrophy, as well as the research on brain functional network alteration of glioma patients.

show abstract

Extended Network-Based Statistics for Measuring Altered Directed Connectivity Components in the Human Brain

Cited by 4 publications

References 21 publications

Altered Brain Volume, Microstructure Metrics and Functional Connectivity Features in Multiple System Atrophy

Altered Brain Volume, Microstructure Metrics and Functional Connectivity Features in Multiple System Atrophy

Threshold-Free Network-Oriented Statistics in Neuroscience

Resting-State Brain Network Analysis Methods and Applications

Contact Info

Product

Resources

About