Brain network reorganization after targeted attack at a hub region

Tu, Wenyu; Ma, Zilu; Zhang, Nanyin

doi:10.1016/j.neuroimage.2021.118219

Cited by 28 publications

(32 citation statements)

References 76 publications

(100 reference statements)

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“…5 ). These spatiotemporal coactivation patterns triggered by dHP calcium spikes clearly suggest that DMN is indeed a functional network with coordinated activity between separate nodes ( Tu et al, 2021b , 2021a ).…”

Section: Discussionmentioning

confidence: 86%

Gaining insight into the neural basis of resting-state fMRI signal

Zhang

et al. 2022

NeuroImage

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Section: Discussionmentioning

confidence: 86%

Gaining insight into the neural basis of resting-state fMRI signal

Zhang

et al. 2022

NeuroImage

Self Cite

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“…This approach was recently utilized by an awake rat resting-state fMRI database and derived similar results ( Liu et al, 2020 ). The metric was built on robust cingulate and RSC FC, as both are considered to be key nodes of the DMN ( Tu et al, 2021a ; Tu et al, 2021b ; Peeters at al., 2020 ; Belloy et al, 2021 ; Coletta et al, 2020 ; Grandjean et al, 2020 ; Paasonen et al, 2018 ; Lu et al, 2012 ), whereas the cingulate and somatosensory FC is usually low ( Gozzi and Schwarz, 2016 ), or often in opposing states ( Belloy et al, 2018 ; Gutierrez-Barragan et al, 2019 ). Nevertheless, emerging studies have shown dichotomous involvement of cingulate cortex in both the DMN and the SN ( Tsai et al, 2020 ; Mandino et al, 2019b ).…”

Section: Discussionmentioning

confidence: 99%

An isotropic EPI database and analytical pipelines for rat brain resting-state fMRI

et al. 2021

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Resting-state functional magnetic resonance imaging (fMRI) has drastically expanded the scope of brain research by advancing our knowledge about the topologies, dynamics, and interspecies translatability of functional brain networks. Several databases have been developed and shared in accordance with recent key initiatives in the rodent fMRI community to enhance the transparency, reproducibility, and interpretability of data acquired at various sites. Despite these pioneering efforts, one notable challenge preventing efficient standardization in the field is the customary choice of anisotropic echo planar imaging (EPI) schemes with limited spatial coverage. Imaging with anisotropic resolution and/or reduced brain coverage has significant shortcomings including reduced registration accuracy and increased deviation in brain feature detection. Here we proposed a high-spatial-resolution (0.4 mm), isotropic, whole-brain EPI protocol for the rat brain using a horizontal slicing scheme that can maintain a functionally relevant repetition time (TR), avoid high gradient duty cycles, and offer unequivocal whole-brain coverage. Using this protocol, we acquired resting-state EPI fMRI data from 87 healthy rats under the widely used dexmedetomidine sedation supplemented with low-dose isoflurane on a 9.4 T MRI system. We developed an EPI template that closely approximates the Paxinos and Watson’s rat brain coordinate system and demonstrated its ability to improve the accuracy of group-level approaches and streamline fMRI data pre-processing. Using this database, we employed a multi-scale dictionary-learning approach to identify reliable spatiotemporal features representing rat brain intrinsic activity. Subsequently, we performed k-means clustering on those features to obtain spatially discrete, functional regions of interest (ROIs). Using Euclidean-based hierarchical clustering and modularity-based partitioning, we identified the topological organizations of the rat brain. Additionally, the identified group-level FC network appeared robust across strains and sexes. The “triple-network” commonly adapted in human fMRI were resembled in the rat brain. Through this work, we disseminate raw and pre-processed isotropic EPI data, a rat brain EPI template, as well as identified functional ROIs and networks in standardized rat brain coordinates. We also make our analytical pipelines and scripts publicly available, with the hope of facilitating rat brain resting-state fMRI study standardization.

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“…The qEEG z-NF protocol in children with developmental dyslexia was combined with training for the various visual deficits in dyslexia, and it could positively affect the functional connectivity of a specific frequency network that is also involved in the reading process. The selective inhibition of the excitatory neurons in a brain hub (region) can change the network integration [57]. Reduced network integration can result from the selective suppression of the excitatory neurons in a hub.…”

Section: Introductionmentioning

confidence: 99%

Improving Functional Connectivity in Developmental Dyslexia through Combined Neurofeedback and Visual Training

Taskov

Dushanova

2022

Symmetry

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This study examined the effects of combined neurofeedback (NF) and visual training (VT) on children with developmental dyslexia (DD). Although NF is the first noninvasive approach to support neurological disorders, the mechanisms of its effects on the brain functional connectivity are still unclear. A key question is whether the functional connectivities of the EEG frequency networks change after the combined NF–VT training of DD children (postD). NF sessions of voluntary α/θ rhythm control were applied in a low-spatial-frequency (LSF) illusion contrast discrimination, which provides feedback with visual cues to improve the brain signals and cognitive abilities in DD children. The measures of connectivity, which are defined by small-world propensity, were sensitive to the properties of the brain electrical oscillations in the quantitative EEG-NF training. In the high-contrast LSF illusion, the z-NF reduced the α/θ scores in the frontal areas, and in the right ventral temporal, occipital–temporal, and middle occipital areas in the postD (vs. the preD) because of their suppression in the local hub θ-network and the altered global characteristics of the functional θ-frequency network. In the low-contrast condition, the z-NF stimulated increases in the α/θ scores, which induced hubs in the left-side α-frequency network of the postD, and changes in the global characteristics of the functional α-frequency network. Because of the anterior, superior, and middle temporal deficits affecting the ventral and occipital–temporal pathways, the z-NF–VT compensated for the more ventral brain regions, mainly in the left hemispheres of the postD group in the low-contrast LSF illusion. Compared to pretraining, the NF–VT increased the segregation of the α, β (low-contrast), and θ networks (high-contrast), as well as the γ2-network integration (both contrasts) after the termination of the training of the children with developmental dyslexia. The remediation compensated more for the dorsal (prefrontal, premotor, occipital–parietal connectivities) dysfunction of the θ network in the developmental dyslexia in the high-contrast LSF illusion. Our findings provide neurobehavioral evidence for the exquisite brain functional plasticity and direct effect of NF–VT on cognitive disabilities in DD children.

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Brain network reorganization after targeted attack at a hub region

Cited by 28 publications

References 76 publications

Gaining insight into the neural basis of resting-state fMRI signal

Gaining insight into the neural basis of resting-state fMRI signal

An isotropic EPI database and analytical pipelines for rat brain resting-state fMRI

Improving Functional Connectivity in Developmental Dyslexia through Combined Neurofeedback and Visual Training

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