Networks of the Brain

By T. Lock. 2012

305 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Plant Science

View Paper: https://www.semanticscholar.org/paper/535b680920a1f2d927c48e53a46572e0887c457d

Citations in the corpus

• A Predictive Network Model of Cerebral Cortical Connectivity Based on a Distance Rule (2013, unknown): “Investigation of the cortical graph has made important progress, showing for instance that the brain exhibits a remarkable economy in its organization (Bullmore and Sporns, 2012; Sporns, 2011).”
• Altered Structural Brain Connectivity in Healthy Carriers of the Autism Risk Gene, CNTNAP2 (2011, unknown): “Neural network complexity is typically achieved by a balance of randomness and regularity—at either extreme, you have a system less able to learn, because it is either never stable enough to remember or never flexible enough to adapt (Sporns, 2011).”
• Graph analysis of the human connectome: Promise, progress, and pitfalls (2013, unknown): “approach that positions brain network science within the broader context of complex systems (Sporns, 2010).”
• Networks of the Brain: Quantitative Analysis and Modeling (2010, unknown): “Understanding the organization of a complex network like the brain is a necessary first step to understanding its functions as an integrated system (Sporns, 2010).”
• The Metastable Brain (2014, unknown): “As a result, Coordination Dynamics seeks an understanding of brain function that cuts across scales and levels of description—from individual neurons to minds and their social fabric (see also Akil et al., 2011; Sporns, 2010).”
• Functional Magnetic Resonance Imaging Phase Synchronization as a Measure of Dynamic Functional Connectivity (2012, unknown): “Importantly, this allows shifting of intersubjectivity estimates from individual voxels or brain areas (ISC) to connections between voxels and brain areas (SBPS), in other words, from the nodes to the links of the brain network, opening new opportunities to examine the relationship between brain are”
• Towards quantitative connectivity analysis: reducing tractography biases (2014, unknown): “A growing number of connectomics studies exploit structural properties of 3 these pathways or streamlines to make ’connectivity’ comparisons between groups or individuals (Fornito et al., 2013; 4 Hagmann et al., 2008; Ng et al., 2013; Sporns, 2010).”
• Focal Brain Lesions to Critical Locations Cause Widespread Disruption of the Modular Organization of the Brain (2012, unknown): “Separate correlation matrices were constructed for each hemisphere in each participant.”
• Resting-state EEG connectome topology and social norm processing in healthy young adults (2026, unknown): “Global CC indexes the average tendency of neighbouring regions to promote local functional synchronisation, whereas CPL indexes global (dis)integration (Watts and Strogatz 1998; De Haan et al. 2009; Rubinov and Sporns 2010; Sporns 2016 ).”
• Higher-Order Knowledge Representations for Agentic Scientific Reasoning (2026, unknown): “The bottom-left quadrant depicts natural biological emergence, including neural computation [61] and swarming behavior, where bees and fish coordinate through local signaling, motion cues, and hydrodynamic sensing to generate synchronized movement and collective decision-making [62, 63].”