Large-scale brain networks in cognition: emerging methods and principles.

By Steven L. Bressler, Vinod Menon. Trends in Cognitive Sciences, 2010

2,271 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/0b0d195c2524538c1338a823a2765af478c113ca

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Olaf Sporns
2. D. Bassett
3. Yong He
4. E. Bullmore
5. Alan C. Evans
6. Vinod Menon
7. Jinhui Wang
8. Stephen M. Smith

In Radiology, Nuclear Medicine and Imaging

1. Olaf Sporns
2. Yong He
3. D. Bassett
4. Alan C. Evans
5. Jinhui Wang
6. E. Bullmore
7. Vinod Menon
8. Stephen M. Smith

Citations in the corpus

• High school teachers’ experience of the educational potential of outdoor teaching and learning (2014, background): “From a neurological perspective, cognitive functions result from dynamic interaction between brain areas that operates in large-scale networks (Bressler & Menon, 2010).”
• Multiple components of developmental dyscalculia (2013, background): “More broadly, it is becoming increasingly clear that cognitive skills such as mathematical problem solving depend crucially on interactions within and between large-scale brain networks [11].”
• EEG Based Emotion Recognition by Combining Functional Connectivity Network and Local Activations (2019, background): “In recent years, increasing evidences have confirmed the crucial role of the interaction between multiple brain areas in the cognition process [14], [15], indicating potential network connection patterns among multiple regions for information processing.”
• Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data (2018, neutral): “Functional connectivity and network measures derived from fMRI data have facilitated the study of the brain’s function during development, in aging (Bressler & Menon, 2010; Fox & Raichle, 2007; Raichle 2015), and in the context of various neurological disorders (Bullmore & Sporns, 2009, 2012;…”
• Structural connectomics in brain diseases (2013, positive): “Specific brain functions like executive functions, language, cognition or affect are encoded in specific subnetworks (Bressler and Menon, 2010).”
• A Weighted and Directed Interareal Connectivity Matrix for Macaque Cerebral Cortex (2012, background): “Large-scale brain networks can be characterized at multiple levels of organization (Bressler and Menon 2010).”
• Interactive specialization: a domain-general framework for human functional brain development? (2011, positive): “In this respect, IS follows recent trends in the adult functional neuroimaging literature (Bressler and Menon, 2010) in which the response properties of a cortical region are determined by its patterns of connectivity to other regions as well as by their own current activity.”
• Modular Brain Networks (2015, background): “In recent years, the convergence of two major scientific developments has prompted a new network-based perspective on brain function (Bressler & Menon 2010, Bullmore & Sporns 2009, Park & Friston 2013, Sporns 2014, Sporns et al. 2004).”
• Groupwise whole-brain parcellation from resting-state fMRI data for network node identification (2013, positive): “…interest in applying network theory methods to the analysis of functional connectivity data in order to characterize the connectivity between nodes at the brain system level (Achard et al., 2006; Bressler and Menon, 2010; Bullmore and Sporns, 2009; Hagmann et al., 2010; Rubinov and Sporns, 2010).”
• Self-awareness, self-regulation, and self-transcendence (S-ART): a framework for understanding the neurobiological mechanisms of mindfulness (2012, background): “between the self-specifying networks and the NS network may be a significant contributor to emotion dysregulation (Bressler and Menon, 2010), and affective biased attention (Todd et al.”
• From everyday emotions to aesthetic emotions: towards a unified theory of musical emotions. (2013, background): “Rather, each mechanism might involve a particular ‘network’ of dynamically interacting brain regions (cf. Bressler & Menon, 2010), some of which might overlap for different mechanisms.”
• Discovering dynamic brain networks from big data in rest and task (2017, background): “Either case, being able to characterise the temporal trajectories of whole-brain network activity at different time scales is, considering the complex and deeply integrated nature of the brain, crucial to understand the ultimate underpinnings of cognition (Buzs aki and Draguhn, 2004; Bressler and Me”
• Informatic parcellation of the network involved in the computation of subjective value. (2014, methodology): “Similarly, our methods did not allow us to dissociate the coding of value and salience signals (Seeley et al., 2007; Bressler and Menon, 2010), which are difficult to tease apart in many decision experiments.”