Resting-State Regional Homogeneity Analysis on Real-Time fMRI Emotion Self-Regulation Training

Yang, Qiang; Qin, Yulong; Wang, Linyuan; Sun, Liang; Yan, Bin; Li, Tong; Zhang, Fan; Li, Zhonglin

doi:10.1109/iccais.2018.8570530

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…ReHo maps can potentially serve as a non-invasive prognosis tool for cirrhotic patients with overt hepatic encephalopathy [52], and have a high diagnostic accuracy for congenital blindness [53]. Real-time fMRI neurofeedback and associated brain function self-regulation were found to impact the ReHo scores of brain regions involved in the processing of emotions [54]. In another study investigating the test-retest reliability of ReHo [55], the authors found that this could be improved by employing a fast imaging sequence, using nuisance correction but no spatial smoothing in the preprocessing stage, and by carrying out the analysis on the surface of the brain (in a vertex-wise manner, rather than the voxel-wise implementation of Ref.…”

Section: Comparison With Rehomentioning

confidence: 99%

Local gradient analysis of human brain function using the Vogt-Bailey Index

Farrugia

Irazu

Scerri

et al. 2022

Preprint

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In this work, we take a closer look at the Vogt-Bailey (VB) index, proposed in (Bajada et al, 2020) as a tool for studying local functional homogeneity in the human cortex. We interpret the VB index in terms of the minimum ratio cut, a weighted graph cut that indicates whether a network can easily be disconnected into two parts having a comparable number of nodes. In our case, the nodes of the network consist of a brain vertex/voxel and its nearest neighbours, and a given edge is weighted according to the affinity of the nodes it connects (as reflected by the modified Pearson correlation between their fMRI time series). Consequently, the minimum ratio cut quantifies the degree of similarity in local brain activity. We compare the performance of the VB index with that of the Regional Homogeneity (ReHo) algorithm, commonly used to assess whether voxels in close proximity have synchronised fMRI signals, and find that the VB index is uniquely placed to detect sharp changes in the (local) functional organization of the human cortex.

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Section: Comparison With Rehomentioning

confidence: 99%

Local gradient analysis of human brain function using the Vogt-Bailey Index

Farrugia

Irazu

Scerri

et al. 2022

Preprint

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“…Chen et al (2012),Dong, Huang, and Du (2012),Fang et al (2013),Gnanadas, Sathishbabu, and Vijayakarthik (2017),Liang et al (2011),Liu et al (2010),Peng et al (2011),Qin et al (2017),Qing, Dong, Li, Zang, and Liu (2015),Qiu et al (2019),Shukla, Keehn, and Müller (2010),Song, Zhang, and Liu (2014),Wang, Song, Jiang, Zhang, and Yu (2011),Wu et al (2007),Wu et al (2009),Yang et al (2018),,Zang, Jiang, Lu, He, and Tian (2004),and Zeng, Pizarro, Nair, , Huang, Lin, and Biswal (2013),Dong, Guo, Zhang, Fu, and Shi (2010),Guo, Dong, Zhang, Zhang, and Yin (2010),Hu et al (2018), Khodaee, Hossein-Zadeh, and Ananloo (2015), Liang, P. (2014),,Meng, Zhang, Fan, and Li (2018),Turner et al (2012),Yan, Zhuo, Wang, and Wang (2011),,,Yu-Feng et al (2007),Zhang et al (2015),Zou et al (2008), andYu et al (2014) …”

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confidence: 99%

The use of machine learning and deep learning algorithms in functional magnetic resonance imaging—A systematic review

Rashid

Singh²,

Goyal

2020

Expert Systems

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Functional Magnetic Resonance Imaging (fMRI) is presently one of the most popular techniques for analysing the dynamic states in brain images using various kinds of algorithms. From the last decade, there is an exponential rise in the use of the machine and deep learning algorithms of artificial intelligence for analysing fMRI data. However, it is a big challenge for every researcher to choose a suitable machine or deep learning algorithm for analysing fMRI data due to the availability of a large number of algorithms in the literature. It takes much time for each researcher to know about the various approaches and algorithms which are in use for fMRI data. This paper provides a review in a systematic manner for the present literature of fMRI data that makes use of the machine and deep learning algorithms. The major goals of this review paper are to (a) identify machine learning and deep learning research trends for the implementation of fMRI; (b) identify usage of Machine Learning Algorithms and deep learning in fMRI, and (c) help new researchers based on fMRI to put their new findings appropriately in existing domain of fMRI research. The results of this systematic review identified various fMRI studies and classified them based on fMRI types, mental diseases, use of machine learning and deep learning algorithms. The authors have provided the studies with the best performance of machine learning and deep learning algorithms used in fMRI. The authors believe that this systematic review will help incoming researchers on fMRI in their future works.

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Local gradient analysis of human brain function using the Vogt-Bailey Index

Farrugia,

Galdi,

Irazu

et al. 2024

Brain Struct Funct

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In this work, we take a closer look at the Vogt-Bailey (VB) index, proposed in Bajada et al. (NeuroImage 221:117140, 2020) as a tool for studying local functional homogeneity in the human cortex. We interpret the VB index in terms of the minimum ratio cut, a scaled cut-set weight that indicates whether a network can easily be disconnected into two parts having a comparable number of nodes. In our case, the nodes of the network consist of a brain vertex/voxel and its neighbours, and a given edge is weighted according to the affinity of the nodes it connects (as reflected by the modified Pearson correlation between their fMRI time series). Consequently, the minimum ratio cut quantifies the degree of small-scale similarity in brain activity: the greater the similarity, the ‘heavier’ the edges and the more difficult it is to disconnect the network, hence the higher the value of the minimum ratio cut. We compare the performance of the VB index with that of the Regional Homogeneity (ReHo) algorithm, commonly used to assess whether voxels in close proximity have synchronised fMRI signals, and find that the VB index is uniquely placed to detect sharp changes in the (local) functional organization of the human cortex.

show abstract

Resting-State Regional Homogeneity Analysis on Real-Time fMRI Emotion Self-Regulation Training

Cited by 4 publications

References 10 publications

Local gradient analysis of human brain function using the Vogt-Bailey Index

Local gradient analysis of human brain function using the Vogt-Bailey Index

The use of machine learning and deep learning algorithms in functional magnetic resonance imaging—A systematic review

Local gradient analysis of human brain function using the Vogt-Bailey Index

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