In the last decade, considerable efforts have been directed towards identifying neural networks among brain regions with a functional significance. There have been hypotheses linking neural networks with functions such as learning, memory, attention, executive control, and facets of language, as well as for “resting states”. Several network structures have also been proposed to differentiate neurotypical populations from various clinical conditions. This work represents a shift in emphasis and ambition from identifying specific or unique brain regions associated with tasks or cognitive capacities to the attempt of identifying mechanistic network features which includes all possible functional regions that may support cognitive capacities. There has been an important growth in publications on brain networks and connectivity over the last 20 years. Most of this work is based on studies of functional magnetic resonance (fMRI) records of time series of blood oxygenation level (BOLD) signals.
This is a moment in the development and modeling of brain networks that leverages urgent interest in identifying networks and novel computational approaches that can model connectivity, hierarchical control, and feedback. We believe there are a small number of focused areas where dramatic progress can be made, providing potential translational paths for the identification of brain networks that affect risk for addiction, learning disabilities, and diagnostic biomarkers for mental illness.
Can we reliably and validly identify brain networks supporting cognitive function using various neuroimaging measures including fMRI, MEG, EEG, & TMS, that lead to network identification of its underlying dynamics and complexity? In this Research Topic, we will try to address this question, focusing on the limitations within the existing scientific literature and the opportunities that the state of the art brings. We will focus on three main areas:
• Network Communication. Hubs and Cognitive Function: Brain networks depend critically on how each network internally communicates. If hub structures vary in terms of connectivity or order (the hub connection number) the efficiency of the network will be affected.
• The Need for Network Cognitive Neuroscience. Bitter experience should teach that simulation alone can mislead. Real data is often much more complex but can also be considerably simpler to model, assuming we get one or two of our simulation assumptions wrong. We welcome papers that apply models to real data with various network theories and graph models, including competitive graph models, identification of known specific pathways (e.g., dual routes) or intervention modeling.
• The nature of network communication: Dynamics. Underlying much of the focus on mental functions and their covariates has always been the implicit belief that cognitive function and learning can be dynamically modeled in the brain.
In the last decade, considerable efforts have been directed towards identifying neural networks among brain regions with a functional significance. There have been hypotheses linking neural networks with functions such as learning, memory, attention, executive control, and facets of language, as well as for “resting states”. Several network structures have also been proposed to differentiate neurotypical populations from various clinical conditions. This work represents a shift in emphasis and ambition from identifying specific or unique brain regions associated with tasks or cognitive capacities to the attempt of identifying mechanistic network features which includes all possible functional regions that may support cognitive capacities. There has been an important growth in publications on brain networks and connectivity over the last 20 years. Most of this work is based on studies of functional magnetic resonance (fMRI) records of time series of blood oxygenation level (BOLD) signals.
This is a moment in the development and modeling of brain networks that leverages urgent interest in identifying networks and novel computational approaches that can model connectivity, hierarchical control, and feedback. We believe there are a small number of focused areas where dramatic progress can be made, providing potential translational paths for the identification of brain networks that affect risk for addiction, learning disabilities, and diagnostic biomarkers for mental illness.
Can we reliably and validly identify brain networks supporting cognitive function using various neuroimaging measures including fMRI, MEG, EEG, & TMS, that lead to network identification of its underlying dynamics and complexity? In this Research Topic, we will try to address this question, focusing on the limitations within the existing scientific literature and the opportunities that the state of the art brings. We will focus on three main areas:
• Network Communication. Hubs and Cognitive Function: Brain networks depend critically on how each network internally communicates. If hub structures vary in terms of connectivity or order (the hub connection number) the efficiency of the network will be affected.
• The Need for Network Cognitive Neuroscience. Bitter experience should teach that simulation alone can mislead. Real data is often much more complex but can also be considerably simpler to model, assuming we get one or two of our simulation assumptions wrong. We welcome papers that apply models to real data with various network theories and graph models, including competitive graph models, identification of known specific pathways (e.g., dual routes) or intervention modeling.
• The nature of network communication: Dynamics. Underlying much of the focus on mental functions and their covariates has always been the implicit belief that cognitive function and learning can be dynamically modeled in the brain.
