scholarly journals Dynamic Brain Connectivity Patterns in Conscious and Unconscious Brain

2016 ◽  
Author(s):  
Yuncong Ma ◽  
Christina Hamilton ◽  
Nanyin Zhang

AbstractBrain functional connectivity undergoes dynamic changes from the awake to unconscious states. However, how the dynamics of functional connectivity patterns are linked to consciousness at the behavioral level remains elusive. Here we acquired resting-state functional magnetic resonance imaging (rsfMRI) data during wakefulness and graded levels of consciousness in rats. Data were analyzed using a dynamic approach combining the sliding-window method and k-means clustering. Our results demonstrate that whole-brain networks contain several quasi-stable patterns that dynamically recurred from the awake state into anesthetized states. Remarkably, two brain connectivity states with distinct spatial similarity to the structure of anatomical connectivity were strongly biased toward high and low consciousness levels, respectively. These results provide compelling neuroimaging evidence linking the dynamics of whole-brain functional connectivity patterns and states of consciousness at the behavioral level.Conflict of interestnone.


2018 ◽  
Vol 14 (1) ◽  
pp. 100-109 ◽  
Author(s):  
Jinliang Zhang ◽  
Gaoyan Zhang ◽  
Xianglin Li ◽  
Peiyuan Wang ◽  
Bin Wang ◽  
...  


2015 ◽  
Vol 112 (28) ◽  
pp. 8762-8767 ◽  
Author(s):  
Javier Gonzalez-Castillo ◽  
Colin W. Hoy ◽  
Daniel A. Handwerker ◽  
Meghan E. Robinson ◽  
Laura C. Buchanan ◽  
...  

Functional connectivity (FC) patterns in functional MRI exhibit dynamic behavior on the scale of seconds, with rich spatiotemporal structure and limited sets of whole-brain, quasi-stable FC configurations (FC states) recurring across time and subjects. Based on previous evidence linking various aspects of cognition to group-level, minute-to-minute FC changes in localized connections, we hypothesized that whole-brain FC states may reflect the global, orchestrated dynamics of cognitive processing on the scale of seconds. To test this hypothesis, subjects were continuously scanned as they engaged in and transitioned between mental states dictated by tasks. FC states computed within windows as short as 22.5 s permitted robust tracking of cognition in single subjects with near perfect accuracy. Accuracy dropped markedly for subjects with the lowest task performance. Spatially restricting FC information decreased accuracy at short time scales, emphasizing the distributed nature of whole-brain FC dynamics, beyond univariate magnitude changes, as valuable markers of cognition.



2021 ◽  
Vol 11 (3) ◽  
pp. 310
Author(s):  
Xiaoxuan Fan ◽  
Yujia Wu ◽  
Lei Cai ◽  
Jingwen Ma ◽  
Ning Pan ◽  
...  

Cantonese-Mandarin bilinguals are logographic-logographic bilinguals that provide a unique population for bilingual studies. Whole brain functional connectivity analysis makes up for the deficiencies of previous bilingual studies on the seed-based approach and helps give a complete picture of the brain connectivity profiles of logographic-logographic bilinguals. The current study is to explore the effect of the long-term logographic-logographic bilingual experience on the functional connectivity of the whole-brain network. Thirty Cantonese-Mandarin bilingual and 30 Mandarin monolingual college students were recruited in the study. Resting state functional magnetic resonance imaging (rs-fMRI) was performed to investigate the whole-brain functional connectivity differences by network-based statistics (NBS), and the differences in network efficiency were investigated by graph theory between the two groups (false discovery rate corrected for multiple comparisons, q = 0.05). Compared with the Mandarin monolingual group, Cantonese-Mandarin bilinguals increased functional connectivity between the bilateral frontoparietal and temporal regions and decreased functional connectivity in the bilateral occipital cortex and between the right sensorimotor region and bilateral prefrontal cortex. No significant differences in network efficiency were found between the two groups. Compared with the Mandarin monolinguals, Cantonese-Mandarin bilinguals had no significant discrepancies in network efficiency. However, the Cantonese-Mandarin bilinguals developed a more strongly connected subnetwork related to language control, inhibition, phonological and semantic processing, and memory retrieval, whereas a weaker connected subnetwork related to visual and phonology processing, and speech production also developed.



2018 ◽  
Author(s):  
Paulina Kieliba ◽  
Sasidhar Madugula ◽  
Nicola Filippini ◽  
Eugene P. Duff ◽  
Tamar R. Makin

AbstractMeasuring whole-brain functional connectivity patterns based on task-free (‘restingstate’) spontaneous fluctuations in the functional MRI (fMRI) signal is a standard approach to probing habitual brain states, independent of task-specific context. This view is supported by spatial correspondence between task- and rest-derived connectivity networks. Yet, it remains unclear whether intrinsic connectivity observed in a resting-state acquisitions is persistent during task. Here, we sought to determine how changes in ongoing brain activation, elicited by task performance, impact the integrity of whole-brain functional connectivity patterns. We employed a ‘steadystates’ paradigm, in which participants continuously executed a specific task (without baseline periods). Participants underwent separate task-based (visual, motor and visuomotor) or task-free (resting) steady-state scans, each performed over a 5-minute period. This unique design allowed us to apply a set of traditional resting-state analyses to various task-states. In addition, a classical fMRI block-design was employed to identify individualized brain activation patterns for each task, allowing to characterize how differing activation patterns across the steady-states impact whole-brain intrinsic connectivity patterns. By examining correlations across segregated brain regions (nodes) and the whole brain (using independent component analysis), we show that the whole-brain network architecture characteristic of the resting-state is robustly preserved across different steady-task states, despite striking inter-task changes in brain activation (signal amplitude). Subtler changes in functional connectivity were detected locally, within the active networks. Together, we show that intrinsic connectivity underlying the canonical resting-state networks is relatively stable even when participants are engaged in different tasks and is not limited to the resting-state.New and NoteworthyDoes intrinsic functional connectivity (FC) reflect the canonical or transient state of the brain? We tested the consistency of the intrinsic connectivity networks across different task-conditions. We show that despite local changes in connectivity, at the whole-brain level there is little modulation in FC patterns, despite profound and large-scale activation changes. We therefore conclude that intrinsic FC largely reflects the a priori habitual state of the brain, independent of the specific cognitive context.



2021 ◽  
Author(s):  
Limei Song ◽  
Huadong Yang ◽  
Mingdong Yang ◽  
Dianmei Liu ◽  
Yanming Ge ◽  
...  

Abstract Previous studies have revealed changed functional connectivity patterns between brain areas in chess players using resting-state functional magnetic resonance imaging (rs-fMRI). However, how to exactly characterize the voxel-wise whole brain functional connectivity pattern changes in chess players remains unclear, which could provide more convincing evidence for establishing the relationship between long-term chess practice and brain function changes. In this study, we employed newly developed whole brain functional connectivity pattern homogeneity (FcHo) method to identify the voxel-wise changes of functional connectivity patterns in 28 chess master players and 27 healthy novices. Seed-based functional connectivity analysis was used to identify the alteration of corresponding functional couplings. FcHo analysis revealed significantly increased whole brain functional connectivity pattern similarity in anterior cingulate cortex (ACC), anterior middle temporal gyrus (aMTG), primary visual cortex (V1), and decreased FcHo in thalamus and precentral gyrus in chess players. Resting-state functional connectivity analyses identified chess players showed decreased functional connections between V1 and precentral gyrus. Besides, a linear support vector machine (SVM) based classification achieved an accuracy of 85.45%, a sensitivity of 85.71% and a specificity of 85.19% to differentiate chess players from novices by leave-one-out cross-validation. Finally, correlation analyses revealed that the mean FcHo values of thalamus were significantly negatively correlated with the training time. Our findings provide new evidences for the important roles of ACC, aMTG, V1, thalamus and precentral gyrus in chess players and indicate that long-term professional chess training may enhance the semantic and episodic processing, efficiency of visual-motor transformation, and cognitive ability.



2018 ◽  
Author(s):  
Jin Yan ◽  
Yingying Zhu

AbstractFunctional brain network has been widely studied in many previous work for brain disorder diagnosis and brain network analysis. However, most previous work focus on static dynamic brain network research. Lots of recent work reveals that the brain shows dynamic activity even in resting state. Such dynamic brain functional connectivity reveals discriminative patterns for identifying many brain disorders. Current sliding window based dynamic brain connectivity framework are not easy to be applied to real clinical applications due to many issues: First, how to set up the optimal sliding window size and how to determine the threshold for the brain connectivity patterns. Secondly, how to represent the high dimensional dynamic brain connectivity pattern in a low dimensional representations for diagnosis purpose. Last, how to deal with the different length dynamic brain network patterns especially when the raw data are of different length. In order to address all those above issues, we proposed a new framework, which employs multiple scale sliding windows and automatically learns a sparse and low ran dynamic brain functional connectivity patterns from raw fMRI data. Furthermore, we are able to measure different length dynamic brain functional connectivity patterns in an equal space by learning a sparse coded convolutional filters. We have evaluated our method with state of the art dynamic brain network methods and the results demonstrated the strong potential of our methods for brain disorder diagnosis in real clinical applications.



PLoS ONE ◽  
2013 ◽  
Vol 8 (1) ◽  
pp. e54512 ◽  
Author(s):  
Delong Zhang ◽  
Bo Liu ◽  
Jun Chen ◽  
Xiaoling Peng ◽  
Xian Liu ◽  
...  


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