Altered brain network topology in left-behind children: A resting-state functional magnetic resonance imaging study

Zhao, Youjin; Du, Meimei; Gao, Xin; Xiao, Yuan; Shah, Chandan; Sun, Huaiqiang; Chen, Fuqin; Yang, Lili; Yan, Zhihan; Fu, Yuchuan; Lui, Su

doi:10.1016/j.chiabu.2016.10.013

Cited by 14 publications

(9 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As reported in Figures 3-8, SS results are highly variable among different network metrics. The best SS results were obtained for Characteristic path length, Density, and Transitivity (all VF values below 1%), thus supporting findings reported for these widely used metrics (Salvador et al, 2005;Tomasi and Volkow, 2010;Wee et al, 2014;Prasad et al, 2015;Thomas et al, 2015;Kocevar et al, 2016;Zhao et al, 2016;Zhuo et al, 2017). Moreover, the result of good SS for these measures is further confirmed by recent studies showing excellent test/re-test reliability (TRT) for transitivity and characteristic path length values (Aarabi and Huppert, 2019;Ran et al, 2020;.…”

Section: Spatial Stabilitysupporting

confidence: 86%

Spatial Stability of Functional Networks: A Measure to Assess the Robustness of Graph-Theoretical Metrics to Spatial Errors Related to Brain Parcellation

et al. 2022

View full text Add to dashboard Cite

There is growing interest in studying human brain connectivity and in modelling the brain functional structure as a network. Brain network creation requires parcellation of the cerebral cortex to define nodes. Parcellation might be affected by possible errors due to inter- and intra-subject variability as a consequence of brain structural and physiological characteristics and shape variations related to ageing and diseases, acquisition noise, and misregistration. These errors could induce a knock-on effect on network measure variability. The aim of this study was to investigate spatial stability, a measure of functional connectivity variations induced by parcellation errors. We simulated parcellation variability with random small spatial changes and evaluated its effects on twenty-seven graph-theoretical measures. The study included subjects from three public online datasets. Two brain parcellations were performed using FreeSurfer with geometric atlases. Starting from these, 100 new parcellations were created by increasing the area of 30% of parcels, reducing the area of neighbour parcels, with a rearrangement of vertices. fMRI data were filtered with linear regression, CompCor, and motion correction. Adjacency matrices were constructed with 0.1, 0.2, 0.3, and 0.4 thresholds. Differences in spatial stability between datasets, atlases, and threshold were evaluated. The higher spatial stability resulted for Characteristic-path-length, Density, Transitivity, and Closeness-centrality, and the lower spatial stability resulted for Bonacich and Katz. Multivariate analysis showed a significant effect of atlas, datasets, and thresholds. Katz and Bonacich centrality, which was subject to larger variations, can be considered an unconventional graph measure, poorly implemented in the clinical field and not yet investigated for reliability assessment. Spatial stability (SS) is affected by threshold, and it decreases with increasing threshold for several measures. Moreover, SS seems to depend on atlas choice and scanning parameters. Our study highlights the importance of paying close attention to possible parcellation-related spatial errors, which may affect the reliability of functional connectivity measures.

show abstract

Section: Spatial Stabilitysupporting

confidence: 86%

Spatial Stability of Functional Networks: A Measure to Assess the Robustness of Graph-Theoretical Metrics to Spatial Errors Related to Brain Parcellation

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The minimum threshold was set at 24.5%. Previous studies indicated that human cognition was related to integration of different brain regions, and brain regions were not isolated in whole brain network 20 , 46 . We found that at the minimum threshold of 24.5%, all participants’ network was a full-connected network and each brain region was at least connected to another one in the network.…”

Section: Methodsmentioning

confidence: 99%

The Effects of Long-term Abacus Training on Topological Properties of Brain Functional Networks

Weng

Xie

Wang

et al. 2017

Sci Rep

View full text Add to dashboard Cite

Previous studies in the field of abacus-based mental calculation (AMC) training have shown that this training has the potential to enhance a wide variety of cognitive abilities. It can also generate specific changes in brain structure and function. However, there is lack of studies investigating the impact of AMC training on the characteristics of brain networks. In this study, utilizing graph-based network analysis, we compared topological properties of brain functional networks between an AMC group and a matched control group. Relative to the control group, the AMC group exhibited higher nodal degrees in bilateral calcarine sulcus and increased local efficiency in bilateral superior occipital gyrus and right cuneus. The AMC group also showed higher nodal local efficiency in right fusiform gyrus, which was associated with better math ability. However, no relationship was significant in the control group. These findings provide evidence that long-term AMC training may improve information processing efficiency in visual-spatial related regions, which extend our understanding of training plasticity at the brain network level.

show abstract

“…Scans were performed in 44 of the 45 study participants; one participant was excluded due to dental braces that produce large artifacts during scanning. Anatomical T1-weighted images were collected using a high-resolution 3D magnetization-prepared rapid gradient echo sequence with 360 1-mm-thick sagittal slices (echo time [ [43][44][45] , which consisted of 200 volumes plus 5 initial unscored dummy volumes acquired in 6.83 min.…”

Section: Neuroimagingmentioning

confidence: 99%

Closed-loop digital meditation for neurocognitive and behavioral development in adolescents with childhood neglect

et al. 2020

View full text Add to dashboard Cite

Adverse childhood experiences are linked to poor attentive behaviors during adolescence, as well as increased risk for mental health disorders in adults. However, no study has yet tested targeted interventions to optimize neurocognitive processes in this population. Here, we investigated closed-loop digital interventions in a double-blind randomized controlled study in adolescents with childhood neglect, and evaluated the outcomes using multimodal assessments of neuroimaging, cognitive, behavioral, and academic evaluations. In the primary neuroimaging results, we demonstrate that a closed-loop digital meditation intervention can strengthen functional connectivity of the dorsal anterior cingulate cortex (dACC) in the cingulo-opercular network, which is critically developing during the adolescent period. Second, this intervention enhanced sustained attention and interference-resolution abilities, and also reduced behavioral hyperactivity at a 1-year follow-up. Superior academic performance was additionally observed in adolescents who underwent the digital meditation intervention. Finally, changes in dACC functional connectivity significantly correlated with improvements in sustained attention, hyperactivity, and academic performance. This first study demonstrates that closed-loop digital meditation practice can facilitate development of important aspects of neurocognition and real-life behaviors in adolescents with early childhood neglect.

show abstract

Altered brain network topology in left-behind children: A resting-state functional magnetic resonance imaging study

Cited by 14 publications

References 52 publications

Spatial Stability of Functional Networks: A Measure to Assess the Robustness of Graph-Theoretical Metrics to Spatial Errors Related to Brain Parcellation

Spatial Stability of Functional Networks: A Measure to Assess the Robustness of Graph-Theoretical Metrics to Spatial Errors Related to Brain Parcellation

The Effects of Long-term Abacus Training on Topological Properties of Brain Functional Networks

Closed-loop digital meditation for neurocognitive and behavioral development in adolescents with childhood neglect

Contact Info

Product

Resources

About