Inter-Network High-Order Functional Connectivity (IN-HOFC) and its Alteration in Patients with Mild Cognitive Impairment

Zhang, Han; Giannakopoulos, Pantéleimon; Haller, Sven; Lee, Seong Whan; Qiu, Shijun; Shen, Dinggang

doi:10.1007/s12021-018-9413-x

Cited by 11 publications

(13 citation statements)

References 78 publications

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“…Our method achieved generally acceptable performance even with a stringent cross-center-validation. Zhang, Giannakopoulos, et al, 2019). From another viewpoint, only dHOFC utilizes dynamic FC, while all other methods still focus on static FC.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, if users want to use proved reliable network construction methods, PC, tHOFC, and aHOFC are suggested. Compared to PC, tHOFC and aHOFC are more robust to noise and, most importantly, they could provide supplementary information to PC (Zhang, Chen, et al, ; Zhang, Giannakopoulos, et al, ). From another viewpoint, only dHOFC utilizes dynamic FC, while all other methods still focus on static FC.…”

Section: Discussionmentioning

confidence: 99%

“…Due to the LOFC features rather than the BOLD signals are used in the tHOFC calculation, the result is essentially different from that of LOFC between the same ROIs. It has been shown that tHOFC could provide supplementary information to the conventional LOFC and help revealing additional group differences between mild cognitive impairment (MCI) subjects and cognitively normal controls (Zhang, Chen, et al, 2016;Zhang, Giannakopoulos, et al, 2019).…”

Section: Pairwise Fc-based Network Construction Methodsmentioning

confidence: 99%

“…Therefore, if users want to use a simple yet reliable network construction method, PC, tHOFC, and aHOFC are suggested. Compared to PC, tHOFC and aHOFC is more robust to noise yet interpretable, and most importantly, they could provide supplementary information to PC (Zhang et al, 2019a(Zhang et al, , 2016a. Therefore, they are suggested in such a scenario.…”

Section: Algorithm Choosing Determinationmentioning

confidence: 99%

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A toolbox for brain network construction and classification (BrainNetClass)

Zhou

Chen

Zhang

et al. 2020

Human Brain Mapping

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A B S T R A C TBrain functional network has become an increasingly used approach in understanding brain functions and diseases. Many network construction methods have been developed, whereas the majority of the studies still used static pairwise Pearson's correlation-based functional connectivity. The goal of this work is to introduce a toolbox namely "Brain Network Construction and Classification" (Brain-NetClass) to the field to promote more advanced brain network construction methods. It comprises various brain network construction methods, including some state-of-the-art methods that were recently developed to capture more complex interactions among brain regions along with connectome feature extraction, reduction, parameter optimization towards network-based individualized classification. BrainNetClass is a MATLAB-based, open-source, cross-platform toolbox with graphical user-friendly interfaces for cognitive and clinical neuroscientists to perform rigorous computer-aided diagnosis with interpretable result presentations even though they do not possess neuroimage computing and machine learning knowledge. We demonstrate the implementations of this toolbox on real datasets. BrainNetClass (v1.0) can be downloaded from https://github.com/zzstefan/BrainNetClass.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Pairwise Fc-based Network Construction Methodsmentioning

confidence: 99%

Section: Algorithm Choosing Determinationmentioning

confidence: 99%

See 2 more Smart Citations

A toolbox for brain network construction and classification (BrainNetClass)

Zhou

Chen

Zhang

et al. 2020

Human Brain Mapping

Self Cite

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“…Then, tHOFC is measured as the similarity of LOFC profiles between each pair of brain regions. The tHOFC can offer supplementary information to the conventional LOFC and introduce more differences between groups (Zhou et al, 2020;Zhang et al, 2019).…”

Section: Functional Connectivity Matrixmentioning

confidence: 99%

A Comparison Study between Autism Spectrum Disorder and Typically Control in Graph Frequency Bands Using Graph and Triadic Interaction Metrics

Jafadideh

Asl²

2021

Preprint

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Many researchers using many different approaches have attempted to find features discriminating between autism spectrum disorder (ASD) and typically control (TC) subjects. In this study, this attempt has been continued by analyzing global metrics of functional graphs and metrics of functional triadic interactions of the brain in the low, middle, and high-frequency bands (LFB, MFB, and HFB) of the structural graph. The graph signal processing (GSP) provided the combinatorial usage of the functional graph of resting-state fMRI and structural graph of DTI. In comparison to TCs, ASDs had significantly higher clustering coefficients in the MFB, higher efficiencies and strengths in the MFB and HFB, and lower small-world propensity in the HFB. These results show over-connectivity, more global integration, and probably decreased local specialization in ASDs compared to TCs. Triadic analysis showed that the numbers of unbalanced triads were significantly lower for ASDs in the MFB. This finding may show the reason for restricted and repetitive behavior in ASDs. Also, in the MFB and HFB, the numbers of balanced triads and the energies of triadic interactions were significantly higher and lower for ASDs, respectively. These findings may reflect the disruption of the optimum balance between functional integration and specialization. All of these results demonstrated that the significant differences between ASDs and TCs existed in the MFB and HFB of the structural graph when analyzing the global metrics of the functional graph and triadic interaction metrics. In conclusion, the results demonstrate the promising perspective of GSP for attaining discriminative features and new knowledge, especially in the case of ASD. Graphical Abstract

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