scholarly journals Imaging the spontaneous flow of thought: Distinct periods of cognition contribute to dynamic functional connectivity during rest

2019 ◽  
Author(s):  
Javier Gonzalez-Castillo ◽  
César Caballero-Gaudes ◽  
Natasha Topolski ◽  
Daniel A. Handwerker ◽  
Francisco Pereira ◽  
...  

AbstractBrain functional connectivity (FC) changes have been measured across seconds using fMRI. This is true for both rest and task scenarios. Moreover, it is well accepted that task engagement alters FC, and that dynamic estimates of FC during and before task events can help predict their nature and performance. Yet, when it comes to dynamic FC (dFC) during rest, there is no consensus about its origin or significance. Some argue that rest dFC reflects fluctuations in on-going cognition, or is a manifestation of intrinsic brain maintenance mechanisms, which could have predictive clinical value. Conversely, others have concluded that rest dFC is mostly the result of sampling variability, head motion or fluctuating sleep states. Here, we present novel analyses suggesting that rest dFC is influenced by short periods of distinct mental processing, and that the cognitive nature of such mental processes can be inferred blindly from the data. As such, several different behaviorally relevant whole-brain FC configurations may occur during a single rest scan even when subjects were continuously awake and displayed minimal motion. In addition, using low dimensional embeddings as visualization aids, we show how FC states—commonly used to summarize and interpret resting dFC—can accurately and robustly reveal periods of externally imposed tasks; however, they may be less effective in capturing periods of distinct cognition during rest.


2019 ◽  
Vol 9 (11) ◽  
pp. 309
Author(s):  
Yuyuan Yang ◽  
Lubin Wang ◽  
Yu Lei ◽  
Yuyang Zhu ◽  
Hui Shen

Most previous work on dynamic functional connectivity (dFC) has focused on analyzing temporal traits of functional connectivity (similar coupling patterns at different timepoints), dividing them into functional connectivity states and detecting their between-group differences. However, the coherent functional connectivity of brain activity among the temporal dynamics of functional connectivity remains unknown. In the study, we applied manifold learning of local linear embedding to explore the consistent coupling patterns (CCPs) that reflect functionally homogeneous regions underlying dFC throughout the entire scanning period. By embedding the whole-brain functional connectivity in a low-dimensional manifold space based on the Human Connectome Project (HCP) resting-state data, we identified ten stable patterns of functional coupling across regions that underpin the temporal evolution of dFC. Moreover, some of these CCPs exhibited significant neurophysiological meaning. Furthermore, we apply this method to HCP rsfMR and tfMRI data as well as sleep-deprivation data and found that the topological organization of these low-dimensional structures has high potential for predicting sleep-deprivation states (classification accuracy of 92.3%) and task types (100% identification for all seven tasks).In summary, this work provides a methodology for distilling coherent low-dimensional functional connectivity structures in complex brain dynamics that play an important role in performing tasks or characterizing specific states of the brain.



2020 ◽  
pp. 1-10
Author(s):  
Guanmao Chen ◽  
Pan Chen ◽  
JiaYing Gong ◽  
Yanbin Jia ◽  
Shuming Zhong ◽  
...  

Abstract Background Accumulating studies have found structural and functional abnormalities of the striatum in bipolar disorder (BD) and major depressive disorder (MDD). However, changes in intrinsic brain functional connectivity dynamics of striato-cortical circuitry have not been investigated in BD and MDD. This study aimed to investigate the shared and specific patterns of dynamic functional connectivity (dFC) variability of striato-cortical circuitry in BD and MDD. Methods Brain resting-state functional magnetic resonance imaging data were acquired from 128 patients with unmedicated BD II (current episode depressed), 140 patients with unmedicated MDD, and 132 healthy controls (HCs). Six pairs of striatum seed regions were selected: the ventral striatum inferior (VSi) and the ventral striatum superior (VSs), the dorsal-caudal putamen (DCP), the dorsal-rostral putamen (DRP), and the dorsal caudate and the ventral-rostral putamen (VRP). The sliding-window analysis was used to evaluate dFC for each seed. Results Both BD II and MDD exhibited increased dFC variability between the left DRP and the left supplementary motor area, and between the right VRP and the right inferior parietal lobule. The BD II had specific increased dFC variability between the right DCP and the left precentral gyrus compared with MDD and HCs. The MDD had increased dFC variability between the left VSi and the left medial prefrontal cortex compared with BD II and HCs. Conclusions The patients with BD and MDD shared common dFC alteration in the dorsal striatal-sensorimotor and ventral striatal-cognitive circuitries. The patients with MDD had specific dFC alteration in the ventral striatal-affective circuitry.



2004 ◽  
Vol 94 (3) ◽  
pp. 944-954 ◽  
Author(s):  
Kraig L. Schell ◽  
Ellen C. Melton ◽  
Abbie Woodruff ◽  
G. Brandon Corbin

This study examined how self-regulation and task-related motivation were related to the accuracy of error detection and task engagement in a simulated quality control task that mimicked prescription-checking behavior in a pharmacy. Ninety-one participants completed measures of self-regulation, task engagement, and task-related motivation and then checked 80 simulated scripts with inserted error ratios ranging from 26% to 38%. Motivation and task engagement were assessed at the beginning of the task, the midpoint of the task, and after the task was over. Performance was measured in terms of sensitivity (error detections) and specificity (false alarm responses). Results indicated that motivation was correlated with higher sensitivity, while self-regulation was correlated with lower specificity. Higher mid-task motivation and higher self-regulation were also predictive of greater task engagement at the midpoint of the task only. Results are discussed and future research directions are proposed.





2021 ◽  
Author(s):  
Nisha Chetana Sastry ◽  
Dipanjan Roy ◽  
Arpan Banerjee

Understanding brain functions as an outcome of underlying neuro-cognitive network mechanisms in rest and task requires accurate spatiotemporal characterization of the relevant functional brain networks. Recent endeavours of the Neuroimaging community to develop the notion of dynamic functional connectivity is a step in this direction. A key goal is to detect what are the important events in time that delimits how one functional brain network defined by known patterns of correlated brain activity transitions into a 'new' network. Such characterization can also lead to more accurate conceptual realization of brain states, thereby, defined in terms of time-resolved correlations. Nonetheless, identifying the canonical temporal window over which dynamic functional connectivity is operational is currently based on an ad-hoc selection of sliding windows that can certainly lead to spurious results. Here, we introduce a data-driven unsupervised approach to characterize the high dimensional dynamic functional connectivity into dynamics of lower dimensional patterns. The whole-brain dynamic functional connectivity states bearing functional significance for task or rest can be explored through the temporal correlations, both short and long range. The present study investigates the stability of such short- and long-range temporal correlations to explore the dynamic network mechanisms across resting state, movie viewing and sensorimotor action tasks requiring varied degrees of attention. As an outcome of applying our methods to the fMRI data of a healthy ageing cohort we could quantify whole-brain temporal dynamics which indicates naturalistic movie watching task is closer to resting state than the sensorimotor task. Our analysis also revealed an overall trend of highest short range temporal network stability in the sensorimotor task, followed by naturalistic movie watching task and resting state that remains similar in both young and old adults. However, the stability of neurocognitive networks in the resting state in young adults is higher than their older counterparts. Thus, healthy ageing related differences in quantification of network stability along task and rest provides a blueprint of how our approach can be used for cohort studies of mental health and neurological disorders.







2010 ◽  
Author(s):  
Sarah C. Bienkowski ◽  
Aaron M. Watson ◽  
Eric A. Surface


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