scholarly journals Dynamic switching between intrinsic and extrinsic mode networks as demands change from passive to active processing

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Frank Riemer ◽  
Renate Grüner ◽  
Justyna Beresniewicz ◽  
Katarzyna Kazimierczak ◽  
Lars Ersland ◽  
...  
Keyword(s):  
Large Scale ◽  
Time Course ◽  
Mental Arithmetic ◽  
Block Design ◽  
Posterior Cingulate Cortex ◽  
Visual Tasks ◽  
Time Courses ◽  
Large Scale Networks ◽  
Task Positive Network ◽  
Active Processing

AbstractIn this study we report on the relationship between default and extrinsic mode networks across alternating brief periods of rest and active task processing. Three different visual tasks were used in a classic fMRI ON–OFF block design where task (ON) blocks alternated with equal periods of rest (OFF) blocks: mental rotation, working memory and mental arithmetic. We showed the existence of a generalized task-positive network, labelled the extrinsic mode network (EMN) that is anti-correlated with the default mode network (DMN) as processing demands shifted from rest to active processing. We then identified two key regions of interest (ROIs) in the supplementary motor area (SMA) and precuneus/posterior cingulate cortex (PCC) regions as hubs for the extrinsic and intrinsic networks, and extracted the time-course from these ROIs. The results showed a close to perfect anti-correlation for the SMA and Precuneus/PCC time-courses for ON- and OFF-blocks. We suggest the existence of two large-scale networks, an extrinsic mode network and an intrinsic mode network, which are up- and down-regulated as environmental demands change from active to passive processing.

scholarly journals Dynamic switching between intrinsic and extrinsic mode networks as demands change from passive to active processing

2020 ◽  
Author(s):  
Frank Riemer ◽  
Renate Grüner ◽  
Justyna Beresniewicz ◽  
Katarzyna Kazimierczak ◽  
Lars Ersland ◽  
...  
Keyword(s):  
Large Scale ◽  
Time Course ◽  
Mental Arithmetic ◽  
Block Design ◽  
Dynamic Switching ◽  
Visual Tasks ◽  
Time Courses ◽  
Large Scale Networks ◽  
Task Positive Network ◽  
Active Processing

AbstractWe here report on the relationship between default and extrinsic mode networks across alternating brief periods of rest and active task processing. We used three different visual tasks: mental rotation, working memory and mental arithmetic in a classic fMRI ON-OFF block design where task (ON) blocks alternated with equal periods of rest (OFF) blocks. By analysing data in two ways, using an ON-OFF contrast, we showed the existence of a generalized task-positive network, labelled the extrinsic mode network (EMN) which was anti-correlated with the default mode network (DMN) as processing demands shifted from rest to active processing. We then identified two key regions of interest (ROIs) in the SMA and Precuneus/PCC regions as hubs for the extrinsic and intrinsic networks, and extracted the time-course from these ROIs. The results showed a close to perfect correlations for the SMA and Precuneus/PCC time-courses for ON-respective OFF-blocks. We suggest the existence of two large-scale networks, an extrinsic mode network and an intrinsic mode network, respectively, which are up- and down-regulated as environmental demands change from active to passive processing.

scholarly journals Capturing the spatiotemporal dynamics of task-initiated thoughts with combined EEG and fMRI

2018 ◽  
Author(s):  
Lucie Bréchet ◽  
Denis Brunet ◽  
Gwénaël Birot ◽  
Rolf Gruetter ◽  
Christoph M. Michel ◽  
...  
Keyword(s):  
Large Scale ◽  
Temporal Dynamics ◽  
Mental Arithmetic ◽  
Brain Activity ◽  
Activity Patterns ◽  
Mental States ◽  
Spatiotemporal Dynamics ◽  
Source Analysis ◽  
Eeg Source Analysis ◽  
Large Scale Networks

AbstractWhen at rest, our mind wanders from thought to thought in distinct mental states. Despite the marked importance of ongoing mental processes, it is challenging to capture and relate these states to specific cognitive contents. In this work, we employed ultra-high field functional magnetic resonance imaging (fMRI) and high-density electroencephalography (EEG) to study the ongoing thoughts of participants instructed to retrieve self-relevant past episodes for periods of 20s. These task-initiated, participant-driven activity patterns were compared to a distinct condition where participants performed serial mental arithmetic operations, thereby shifting from self-related to self-unrelated thoughts. BOLD activity mapping revealed selective activity changes in temporal, parietal and occipital areas (“posterior hot zone”), evincing their role in integrating the re-experienced past events into conscious representations during memory retrieval. Functional connectivity analysis showed that these regions were organized in two major subparts of the default mode network, previously associated to “scene-reconstruction” and “self-experience” subsystems. EEG microstate analysis allowed studying these participant-driven thoughts in the millisecond range by determining the temporal dynamics of brief periods of stable scalp potential fields. This analysis revealed selective modulation of occurrence and duration of specific microstates in both conditions. EEG source analysis revealed similar spatial distributions between the sources of these microstates and the regions identified with fMRI. These findings support growing evidence that specific fMRI networks can be captured with EEG as repeatedly occurring, integrated brief periods of synchronized neuronal activity, lasting only fractions of seconds.SignificanceWe investigated the spatiotemporal dynamics of large-scale brain networks related to specific conscious thoughts. We demonstrate here that instructing participants to direct their thoughts to either episodic autobiographic memory or to mental arithmetic modulates distinct networks both in terms of highly spatially-specific BOLD signal oscillations as well as fast sub-second dynamics of EEG microstates. The combined findings from the two modalities evince a clear link between hemodynamic and electrophysiological signatures of spontaneous brain activity by the occurrence of thoughts that last for fractions of seconds, repeatedly appearing over time as integrated coherent activities of specific large-scale networks.

Optimal Test Assembly in Practice

2013 ◽  
Vol 221 (3) ◽  
pp. 190-200 ◽  
Author(s):  
Jörg-Tobias Kuhn ◽  
Thomas Kiefer
Keyword(s):  
Particle Physics ◽  
Large Scale ◽  
Block Design ◽  
Linear Optimization ◽  
Mixed Integer ◽  
Optimal Test ◽  
Automated Test Assembly ◽  
Test Assembly ◽  
Block Level ◽  
Mixed Integer Linear Optimization

Several techniques have been developed in recent years to generate optimal large-scale assessments (LSAs) of student achievement. These techniques often represent a blend of procedures from such diverse fields as experimental design, combinatorial optimization, particle physics, or neural networks. However, despite the theoretical advances in the field, there still exists a surprising scarcity of well-documented test designs in which all factors that have guided design decisions are explicitly and clearly communicated. This paper therefore has two goals. First, a brief summary of relevant key terms, as well as experimental designs and automated test assembly routines in LSA, is given. Second, conceptual and methodological steps in designing the assessment of the Austrian educational standards in mathematics are described in detail. The test design was generated using a two-step procedure, starting at the item block level and continuing at the item level. Initially, a partially balanced incomplete item block design was generated using simulated annealing, whereas in a second step, items were assigned to the item blocks using mixed-integer linear optimization in combination with a shadow-test approach.

scholarly journals Contribution of Ionotropic Glutamatergic Receptors to Excitability and Attentional Signals in Macaque Frontal Eye Field

2021 ◽  
Author(s):  
Miguel Dasilva ◽  
Christian Brandt ◽  
Marc Alwin Gieselmann ◽  
Claudia Distler ◽  
Alexander Thiele
Keyword(s):  
Attentional Control ◽  
Large Scale ◽  
Neuronal Excitability ◽  
Cell Types ◽  
Receptor Activation ◽  
Frontal Eye Field ◽  
Control Signals ◽  
Cognitive Operations ◽  
Eye Field ◽  
Large Scale Networks

Abstract Top-down attention, controlled by frontal cortical areas, is a key component of cognitive operations. How different neurotransmitters and neuromodulators flexibly change the cellular and network interactions with attention demands remains poorly understood. While acetylcholine and dopamine are critically involved, glutamatergic receptors have been proposed to play important roles. To understand their contribution to attentional signals, we investigated how ionotropic glutamatergic receptors in the frontal eye field (FEF) of male macaques contribute to neuronal excitability and attentional control signals in different cell types. Broad-spiking and narrow-spiking cells both required N-methyl-D-aspartic acid and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor activation for normal excitability, thereby affecting ongoing or stimulus-driven activity. However, attentional control signals were not dependent on either glutamatergic receptor type in broad- or narrow-spiking cells. A further subdivision of cell types into different functional types using cluster-analysis based on spike waveforms and spiking characteristics did not change the conclusions. This can be explained by a model where local blockade of specific ionotropic receptors is compensated by cell embedding in large-scale networks. It sets the glutamatergic system apart from the cholinergic system in FEF and demonstrates that a reduction in excitability is not sufficient to induce a reduction in attentional control signals.

scholarly journals How does the position of firms in the supply chain affect their performance? An empirical study

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Siddharth Arora ◽  
Alexandra Brintrup
Keyword(s):  
Supply Chain ◽  
Empirical Data ◽  
Large Scale ◽  
Individual Performance ◽  
Supply Network ◽  
Supply Chain Networks ◽  
Focal Firm ◽  
Cooperative Competition ◽  
Large Scale Networks ◽  
The Relationship

AbstractThe relationship between a firm and its supply chain has been well studied, however, the association between the position of firms in complex supply chain networks and their performance has not been adequately investigated. This is primarily due to insufficient availability of empirical data on large-scale networks. To addresses this gap in the literature, we investigate the relationship between embeddedness patterns of individual firms in a supply network and their performance using empirical data from the automotive industry. In this study, we devise three measures that characterize the embeddedness of individual firms in a supply network. These are namely: centrality, tier position, and triads. Our findings caution us that centrality impacts individual performance through a diminishing returns relationship. The second measure, tier position, allows us to investigate the concept of tiers in supply networks because we find that as networks emerge, the boundaries between tiers become unclear. Performance of suppliers degrade as they move away from the focal firm (i.e., Toyota). The final measure, triads, investigates the effect of buying and selling to firms that supply the same customer, portraying the level of competition and cooperation in a supplier’s network. We find that increased coopetition (i.e., cooperative competition) is a performance enhancer, however, excessive complexity resulting from being involved in both upstream and downstream coopetition results in diminishing performance. These original insights help understand the drivers of firm performance from a network perspective and provide a basis for further research.

scholarly journals BFL: a node and edge betweenness based fast layout algorithm for large scale networks

2009 ◽  
Vol 10 (1) ◽  
pp. 19 ◽  
Author(s):  
Tatsunori B Hashimoto ◽  
Masao Nagasaki ◽  
Kaname Kojima ◽  
Satoru Miyano
Keyword(s):  
Large Scale ◽  
Layout Algorithm ◽  
Edge Betweenness ◽  
Large Scale Networks
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