Changes in functional connectivity dynamics associated with vigilance network in taxi drivers

Shen, Hui; Li, Zhenfeng; Qin, Jian; Liu, Qiang; Wang, Lubin; Zeng, Ling‐Li; Li, Hong; Hu, Dewen

doi:10.1016/j.neuroimage.2015.09.010

Cited by 66 publications

(65 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On one hand, previous research has shown that dynamic network reconfigurations are associated with a genetic liability for schizophrenia (Braun et al, ). On the other hand, the individual chronnectome has prominent plasticity; the patterns of DFC can change with improvements in individual skills, such as motor learning tasks (Bassett et al, ) and daily driving (Shen et al, ). Therefore, genetics and acquired experience may influence or even shape the individual chronnectome.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to divergent abilities in individual identification and cognition prediction, previous studies have reported that the mean strength and variability of functional connectivity showed divergent alterations in skill learning (Shen et al, ) and in neurological disorders, such as mild traumatic brain injury (Mayer et al, ). Specifically, Shen et al () found that the variability in DFC, instead of the mean connectivity strength, could effectively distinguish taxi drivers from nondrivers. Mayer et al () demonstrated that the variability, rather than the strength, of functional connectivity across resting‐state functional systems had a decreasing trend in patients with mild traumatic brain injury.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns

Liu

Liao

Xia

et al. 2017

Human Brain Mapping

183

190

View full text Add to dashboard Cite

The human brain is a large, interacting dynamic network, and its architecture of coupling among brain regions varies across time (termed the "chronnectome"). However, very little is known about whether and how the dynamic properties of the chronnectome can characterize individual uniqueness, such as identifying individuals as a "fingerprint" of the brain. Here, we employed multiband resting-state functional magnetic resonance imaging data from the Human Connectome Project (N = 105) and a sliding time-window dynamic network analysis approach to systematically examine individual time-varying properties of the chronnectome. We revealed stable and remarkable individual variability in three dynamic characteristics of brain connectivity (i.e., strength, stability, and variability), which was mainly distributed in three higher order cognitive systems (i.e., default mode, dorsal attention, and fronto-parietal) and in two primary systems (i.e., visual and sensorimotor). Intriguingly, the spatial patterns of these dynamic characteristics of brain connectivity could successfully identify individuals with high accuracy and could further significantly predict individual higher cognitive performance (e.g., fluid intelligence and executive function), which was primarily contributed by the higher order cognitive systems. Together, our findings highlight that the chronnectome captures inherent functional dynamics of individual brain networks and provides implications for individualized characterization of health and disease.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns

Liu

Liao

Xia

et al. 2017

Human Brain Mapping

183

190

View full text Add to dashboard Cite

show abstract

“…Specifically, individuals with more years of driving experience demonstrated less increase in left parietal cortex and less increase in yaw rate when completing the turn in the incongruent condition. Previous driving experience is an important variable to consider (Shen et al, 2016). Our results suggest that with more driving experience a driver is better equipped to handle changes in a vehicle’s behavior.…”

Section: Discussionmentioning

confidence: 99%

Mind over motor mapping: Driver response to changing vehicle dynamics

Bruno

Baker

Gundran

et al. 2018

Human Brain Mapping

View full text Add to dashboard Cite

Improvements in vehicle safety require understanding of the neural systems that support the complex, dynamic task of real-world driving. We used functional near infrared spectroscopy (fNIRS) and pupilometry to quantify cortical and physiological responses during a realistic, simulated driving task in which vehicle dynamics were manipulated. Our results elucidate compensatory changes in driver behavior in response to changes in vehicle handling. We also describe associated neural and physiological responses under different levels of mental workload. The increased cortical activation we observed during the late phase of the experiment may indicate motor learning in prefrontal-parietal networks. Finally, relationships among cortical activation, steering control, and individual personality traits suggest that individual brain states and traits may be useful in predicting a driver's response to changes in vehicle dynamics. Results such as these will be useful for informing the design of automated safety systems that facilitate safe and supportive driver-car communication.

show abstract

“…The most common approach for extracting recurring FC patterns is temporal clustering, which assumes that each time instance or window is associated with a unique brain state 30,32,33,40,71–76 . The spatial patterns within each cluster, as well as the accompanying temporal information (e.g., the occurrence rates of different clusters, and patterns of transitions amongst different patterns) can be exploited to characterize brain dynamics in clinical applications such schizophrenia 38,39 .…”

Section: Summarizing Brain Dynamicsmentioning

confidence: 99%

Methods and Considerations for Dynamic Analysis of Functional MR Imaging Data

Chen

Rubinov

Chang

2017

Neuroimaging Clinics of North America

View full text Add to dashboard Cite

Synopsis Functional MRI studies have recently begun to examine spontaneous changes in inter-regional interactions (functional connectivity) over seconds to minutes, and their relation to natural shifts in cognitive and physiological states. This practice opens the potential for uncovering structured, transient configurations of coordinated brain activity whose features may provide novel cognitive and clinical biomarkers. However, the analysis of these time-varying phenomena requires careful differentiation between neural and non-neural contributions to the fMRI signal, as well as thorough validation and statistical testing. In this article, we present an overview of methodological and interpretational considerations in this emerging field.

show abstract

Changes in functional connectivity dynamics associated with vigilance network in taxi drivers

Cited by 66 publications

References 64 publications

Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns

Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns

Mind over motor mapping: Driver response to changing vehicle dynamics

Methods and Considerations for Dynamic Analysis of Functional MR Imaging Data

Contact Info

Product

Resources

About