scholarly journals Brain Modularity Mediates the Relation between Task Complexity and Performance

2017 ◽  
Vol 29 (9) ◽  
pp. 1532-1546 ◽  
Author(s):  
Qiuhai Yue ◽  
Randi C. Martin ◽  
Simon Fischer-Baum ◽  
Aurora I. Ramos-Nuñez ◽  
Fengdan Ye ◽  
...  
Keyword(s):  
Cognitive Processing ◽  
Cognitive Neuroscience ◽  
Task Complexity ◽  
Resting State Fmri ◽  
Brain Network ◽  
Composite Measure ◽  
Brain Organization ◽  
Complex Tasks ◽  
And Performance ◽  
The Brain

Recent work in cognitive neuroscience has focused on analyzing the brain as a network, rather than as a collection of independent regions. Prior studies taking this approach have found that individual differences in the degree of modularity of the brain network relate to performance on cognitive tasks. However, inconsistent results concerning the direction of this relationship have been obtained, with some tasks showing better performance as modularity increases and other tasks showing worse performance. A recent theoretical model [Chen, M., & Deem, M. W. 2015. Development of modularity in the neural activity of children's brains. Physical Biology, 12, 016009] suggests that these inconsistencies may be explained on the grounds that high-modularity networks favor performance on simple tasks whereas low-modularity networks favor performance on more complex tasks. The current study tests these predictions by relating modularity from resting-state fMRI to performance on a set of simple and complex behavioral tasks. Complex and simple tasks were defined on the basis of whether they did or did not draw on executive attention. Consistent with predictions, we found a negative correlation between individuals' modularity and their performance on a composite measure combining scores from the complex tasks but a positive correlation with performance on a composite measure combining scores from the simple tasks. These results and theory presented here provide a framework for linking measures of whole-brain organization from network neuroscience to cognitive processing.

scholarly journals Brain Modularity Mediates the Relation between Task Complexity and Performance

2017 ◽  
Author(s):  
Qiuhai Yue ◽  
Randi Martin ◽  
Simon Fischer-Baum ◽  
Aurora I. Ramos-Nuñez ◽  
Fengdan Ye ◽  
...  
Keyword(s):  
Cognitive Processing ◽  
Cognitive Neuroscience ◽  
Task Complexity ◽  
Resting State Fmri ◽  
Brain Network ◽  
Composite Measure ◽  
Brain Organization ◽  
Complex Tasks ◽  
And Performance ◽  
The Brain

AbstractRecent work in cognitive neuroscience has focused on analyzing the brain as a network, rather than as a collection of independent regions. Prior studies taking this approach have found that individual differences in the degree of modularity of the brain network relate to performance on cognitive tasks. However, inconsistent results concerning the direction of this relationship have been obtained, with some tasks showing better performance as modularity increases and other tasks showing worse performance. A recent theoretical model (Chen & Deem, 2015) suggests that these inconsistencies may be explained on the grounds that high-modularity networks favor performance on simple tasks whereas low-modularity networks favor performance on more complex tasks. The current study tests these predictions by relating modularity from resting-state fMRI to performance on a set of simple and complex behavioral tasks. Complex and simple tasks were defined on the basis of whether they did or did not draw on executive attention. Consistent with predictions, we found a negative correlation between individuals’ modularity and their performance on a composite measure combining scores from the complex tasks but a positive correlation with performance on a composite measure combining scores from the simple tasks. These results and theory presented here provide a framework for linking measures of whole brain organization from network neuroscience to cognitive processing.

scholarly journals PENGARUH MOTIVASI, KOMPLEKSITAS TUGAS, DAN INSENTIF MONETER TERHADAP KINERJA INDIVIDU

2021 ◽  
Vol 8 (2) ◽  
pp. 67
Author(s):  
Aulia Azzardina
Keyword(s):  
University Students ◽  
Extrinsic Motivation ◽  
Task Complexity ◽  
Monetary Incentives ◽  
Complex Tasks ◽  
Intrinsic And Extrinsic Motivation ◽  
And Performance ◽  
The Given ◽  
The Relationship ◽  
And Task

This study investigates the relationship between motivation and task complexity on performance. Monetary incentives are involved in this study as a moderating variable. The motivation examined in this research is intrinsic and extrinsic motivation. A 2x2 quasi-experiment has been conducted and involving 66 university students. Two and three-way ANOVA are used for hypothetical testing. The result shows that individuals with intrinsic motivation have shown better performance than those with extrinsic motivation. After individuals have faced more complex tasks, they achieved lower scores than those who faced less complex tasks. Prior studies suggested that motivation could be destructed by monetary incentives. However, there is no interaction proof when moderating variable is involved. The relationship between motivation and performance is not influenced by monetary incentives. In line with it, the relationship between task complexity and performance is also not strengthened or weakened by the given monetary incentives information. Thus, monetary incentives failed to influence the relationship between motivation, task complexity and performance.

scholarly journals Dwelling quietly in the rich club: brain network determinants of slow cortical fluctuations

2015 ◽  
Vol 370 (1668) ◽  
pp. 20140165 ◽  
Author(s):  
Leonardo L. Gollo ◽  
Andrew Zalesky ◽  
R. Matthew Hutchison ◽  
Martijn van den Heuvel ◽  
Michael Breakspear
Keyword(s):  
Spatial Scales ◽  
Brain Regions ◽  
Brain Network ◽  
Morphological Properties ◽  
Brain Organization ◽  
Rich Club ◽  
Guiding Principle ◽  
The Rich ◽  
Cortical Regions ◽  
The Brain

For more than a century, cerebral cartography has been driven by investigations of structural and morphological properties of the brain across spatial scales and the temporal/functional phenomena that emerge from these underlying features. The next era of brain mapping will be driven by studies that consider both of these components of brain organization simultaneously—elucidating their interactions and dependencies. Using this guiding principle, we explored the origin of slowly fluctuating patterns of synchronization within the topological core of brain regions known as the rich club, implicated in the regulation of mood and introspection. We find that a constellation of densely interconnected regions that constitute the rich club (including the anterior insula, amygdala and precuneus) play a central role in promoting a stable, dynamical core of spontaneous activity in the primate cortex. The slow timescales are well matched to the regulation of internal visceral states, corresponding to the somatic correlates of mood and anxiety. In contrast, the topology of the surrounding ‘feeder’ cortical regions shows unstable, rapidly fluctuating dynamics likely to be crucial for fast perceptual processes. We discuss these findings in relation to psychiatric disorders and the future of connectomics.

scholarly journals Timescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain

2019 ◽  
Author(s):  
John Fallon ◽  
Phil Ward ◽  
Linden Parkes ◽  
Stuart Oldham ◽  
Aurina Arnatkevic̆iūtė ◽  
...  
Keyword(s):  
Resting State ◽  
Structural Connectivity ◽  
Resting State Fmri ◽  
Information Storage ◽  
Brain Organization ◽  
Temporal Properties ◽  
Temporal Features ◽  
Human Connectome Project ◽  
The Brain ◽  
Connectivity Strength

AbstractIntrinsic timescales of activity fluctuations vary hierarchically across the brain. This variation reflects a broad gradient of functional specialization in information storage and processing, with integrative association areas displaying slower timescales that are thought to reflect longer temporal processing windows. The organization of timescales is associated with cognitive function, distinctive between individuals, and disrupted in disease, but we do not yet understand how the temporal properties of activity dynamics are shaped by the brain’s underlying structural-connectivity network. Using resting-state fMRI and diffusion MRI data from 100 healthy individuals from the Human Connectome Project, here we show that the timescale of resting-state fMRI dynamics increases with structural-connectivity strength, matching recent results in the mouse brain. Our results hold at the level of individuals, are robust to parcellation schemes, and are conserved across a range of different timescale-related statistics. We establish a comprehensive BOLD dynamical signature of structural connectivity strength by comparing over 6000 time-series features, highlighting a range of new temporal features for characterizing BOLD dynamics, including measures of stationarity and symbolic motif frequencies. Our findings indicate a conserved property of mouse and human brain organization in which a brain region’s spontaneous activity fluctuations are closely related to their surrounding structural scaffold.

scholarly journals Indexing brain state-dependent pupil dynamics with simultaneous fMRI and optical fiber calcium recording

2020 ◽  
Vol 117 (12) ◽  
pp. 6875-6882 ◽  
Author(s):  
Patricia Pais-Roldán ◽  
Kengo Takahashi ◽  
Filip Sobczak ◽  
Yi Chen ◽  
Xiaoning Zhao ◽  
...  
Keyword(s):  
Cognitive Processing ◽  
Resting State Fmri ◽  
Brain Network ◽  
Cell Group ◽  
Brain State ◽  
Correlation Pattern ◽  
Activation Patterns ◽  
State Dependent ◽  
Group 5 ◽  
Global Brain

Pupillometry, a noninvasive measure of arousal, complements human functional MRI (fMRI) to detect periods of variable cognitive processing and identify networks that relate to particular attentional states. Even under anesthesia, pupil dynamics correlate with brain-state fluctuations, and extended dilations mark the transition to more arousable states. However, cross-scale neuronal activation patterns are seldom linked to brain state-dependent pupil dynamics. Here, we complemented resting-state fMRI in rats with cortical calcium recording (GCaMP-mediated) and pupillometry to tackle the linkage between brain-state changes and neural dynamics across different scales. This multimodal platform allowed us to identify a global brain network that covaried with pupil size, which served to generate an index indicative of the brain-state fluctuation during anesthesia. Besides, a specific correlation pattern was detected in the brainstem, at a location consistent with noradrenergic cell group 5 (A5), which appeared to be dependent on the coupling between different frequencies of cortical activity, possibly further indicating particular brain-state dynamics. The multimodal fMRI combining concurrent calcium recordings and pupillometry enables tracking brain state-dependent pupil dynamics and identifying unique cross-scale neuronal dynamic patterns under anesthesia.

scholarly journals Timescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain

2020 ◽  
Vol 4 (3) ◽  
pp. 788-806 ◽  
Author(s):  
John Fallon ◽  
Phillip G. D. Ward ◽  
Linden Parkes ◽  
Stuart Oldham ◽  
Aurina Arnatkevičiūtė ◽  
...  
Keyword(s):  
Resting State ◽  
Structural Connectivity ◽  
Resting State Fmri ◽  
Information Storage ◽  
Brain Organization ◽  
Temporal Properties ◽  
Temporal Features ◽  
Human Connectome Project ◽  
The Brain ◽  
Connectivity Strength

Intrinsic timescales of activity fluctuations vary hierarchically across the brain. This variation reflects a broad gradient of functional specialization in information storage and processing, with integrative association areas displaying slower timescales that are thought to reflect longer temporal processing windows. The organization of timescales is associated with cognitive function, distinctive between individuals, and disrupted in disease, but we do not yet understand how the temporal properties of activity dynamics are shaped by the brain’s underlying structural connectivity network. Using resting-state fMRI and diffusion MRI data from 100 healthy individuals from the Human Connectome Project, here we show that the timescale of resting-state fMRI dynamics increases with structural connectivity strength, matching recent results in the mouse brain. Our results hold at the level of individuals, are robust to parcellation schemes, and are conserved across a range of different timescale- related statistics. We establish a comprehensive BOLD dynamical signature of structural connectivity strength by comparing over 6,000 time series features, highlighting a range of new temporal features for characterizing BOLD dynamics, including measures of stationarity and symbolic motif frequencies. Our findings indicate a conserved property of mouse and human brain organization in which a brain region’s spontaneous activity fluctuations are closely related to their surrounding structural scaffold.

scholarly journals Focal Brain Lesions to Critical Locations Cause Widespread Disruption of the Modular Organization of the Brain

2012 ◽  
Vol 24 (6) ◽  
pp. 1275-1285 ◽  
Author(s):  
Caterina Gratton ◽  
Emi M. Nomura ◽  
Fernando Pérez ◽  
Mark D'Esposito
Keyword(s):  
Brain Damage ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Brain Network ◽  
Brain Lesions ◽  
Modular Organization ◽  
Network Organization ◽  
Functional Deficits ◽  
The Brain ◽  
Critical Locations

Although it is generally assumed that brain damage predominantly affects only the function of the damaged region, here we show that focal damage to critical locations causes disruption of network organization throughout the brain. Using resting state fMRI, we assessed whole-brain network structure in patients with focal brain lesions. Only damage to those brain regions important for communication between subnetworks (e.g., “connectors”)—but not to those brain regions important for communication within sub-networks (e.g., “hubs”)—led to decreases in modularity, a measure of the integrity of network organization. Critically, this network dysfunction extended into the structurally intact hemisphere. Thus, focal brain damage can have a widespread, nonlocal impact on brain network organization when there is damage to regions important for the communication between networks. These findings fundamentally revise our understanding of the remote effects of focal brain damage and may explain numerous puzzling cases of functional deficits that are observed following brain injury.

scholarly journals BRAPH: A Graph Theory Software for the Analysis of Brain Connectivity

2017 ◽  
Author(s):  
Mite Mijalkov ◽  
Ehsan Kakaei ◽  
Joana B. Pereira ◽  
Eric Westman ◽  
Giovanni Volpe ◽  
...  
Keyword(s):  
Graph Theory ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Large Scale ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Brain Network ◽  
Local Network ◽  
Healthy Controls ◽  
The Brain

AbstractThe brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH – BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer’s disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinson’s patients with mild cognitive impairment.

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