The emergence of large-scale brain networks in cognition is a relatively new field of research that is rapidly growing. This field is driven by the development of new methods and principles that allow researchers to explore the interactions between different brain regions and to measure the changes in connectivity between them. These methods include functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and electroencephalography (EEG). These techniques allow researchers to measure the changes in the connectivity of different brain regions as a result of cognitive processes such as learning, decision-making, and memory.

One of the most important principles in this field is the concept of “small-world” networks. This concept states that the brain is composed of a large number of highly interconnected regions that can be used to explain the behavior of the brain. This concept has been used to explain the neural basis of various cognitive processes, including memory and decision-making.

Another principle used in the study of large-scale brain networks is the concept of “modularity.” Modularity refers to the idea that different cognitive processes are organized into distinct modules or networks that can be studied independently. This concept has been used to explain the structure of various cognitive processes, such as language and attention.

Finally, the concept of “dynamic connectivity” has been used to explain the changes in the connectivity of different brain regions over time. This concept suggests that the connectivity of different brain regions can change over time as a result of the cognitive processes that are occurring. This dynamic connectivity has been used to explain the changes in the connectivity of different brain regions during different stages of learning and memory.

These methods and principles are used to study the interactions between different brain regions and to measure the changes in connectivity between them. This field of research has the potential to provide new insights into the neural basis of various cognitive processes and to improve our understanding of how the brain works.

Large-scale brain network

Collections of brain regions

Large-scale brain networks (also known as intrinsic brain networks) are collections of widespread brain regions showing functional connectivity by statistical analysis of the fMRI BOLD signal or other recording methods such as EEG, PET and MEG. An emerging paradigm in neuroscience is that cognitive tasks are performed not by individual brain regions working in isolation but by networks consisting of several discrete brain regions that are said to be “functionally connected”. Functional connectivity networks may be found using algorithms such as cluster analysis, spatial independent component analysis (ICA), seed based, and others. Synchronized brain regions may also be identified using long-range synchronization of the EEG, MEG, or other dynamic brain signals.

The set of identified brain areas that are linked together in a large-scale network varies with cognitive function. When the cognitive state is not explicit (i.e., the subject is at “rest”), the large-scale brain network is a resting state network (RSN). As a physical system with graph-like properties, a large-scale brain network has both nodes and edges and cannot be identified simply by the co-activation of brain areas. In recent decades, the analysis of brain networks was made feasible by advances in imaging techniques as well as new tools from graph theory and dynamical systems.

Large-scale brain networks are identified by their function and provide a coherent framework for understanding cognition by offering a neural model of how different cognitive functions emerge when different sets of brain regions join together as self-organized coalitions. The number and composition of the coalitions will vary with the algorithm and parameters used to identify them. In one model, there is only the default mode network and the task-positive network, but most current analyses show several networks, from a small handful to 17. The most common and stable networks are enumerated below. The regions participating in a functional network may be dynamically reconfigured.

Disruptions in activity in various networks have been implicated in neuropsychiatric disorders such as depression, Alzheimer’s, autism spectrum disorder, schizophrenia, ADHD and bipolar disorder.

Core networks

An example that identified 10 large-scale brain networks from resting state fMRI activity through independent component analysis.

Because brain networks can be identified at various different resolutions and with various different neurobiological properties, there is currently no universal atlas of brain networks that fits all circumstances. The Organization for Human Brain Mapping has the Workgroup for HArmonized Taxonomy of NETworks (WHATNET) group to work towards a consensus regarding network nomenclature. While the work continues, Uddin, Yeo, and Spreng proposed in 2019 that the following six networks should be defined as core networks based on converging evidences from multiple studies to facilitate communication between researchers.

Default Mode (Medial frontoparietal)

Main article: Default mode network
  • The default mode network is active when an individual is awake and at rest. It preferentially activates when individuals focus on internally-oriented tasks such as daydreaming, envisioning the future, retrieving memories, and theory of mind. It is negatively correlated with brain systems that focus on external visual signals. It is the most widely researched network.

Salience (Midcingulo-Insular)

Main article: Salience network
  • The salience network consists of several structures, including the anterior (bilateral) insula, dorsal anterior cingulate cortex, and three subcortical structures which are the ventral striatum, substantia nigra/ventral tegmental region. It plays the key role of monitoring the salience of external inputs and internal brain events. Specifically, it aids in directing attention by identifying important biological and cognitive events.
  • This network includes the ventral attention network, which primarily includes the temporoparietal junction and the ventral frontal cortex of the right hemisphere. These areas respond when behaviorally relevant stimuli occur unexpectedly. The ventral attention network is inhibited during focused attention in which top-down processing is being used, such as when visually searching for something. This response may prevent goal-driven attention from being distracted by non-relevant stimuli. It becomes active again when the target or relevant information about the target is found.

Attention (Dorsal frontoparietal)

  • This network is involved in the voluntary, top-down deployment of attention. Within the dorsal attention network, the intraparietal sulcus and frontal eye fields influence the visual areas of the brain. These influencing factors allow for the orientation of attention.

Control (Lateral frontoparietal)

  • This network initiates and modulates cognitive control and comprises 18 sub-regions of the brain. There is a strong correlation between fluid intelligence and the involvement of the fronto-parietal network with other networks.
  • Versions of this network have also been called the central executive (or executive control) network and the cognitive control network.

Sensorimotor or Somatomotor (Pericentral)

Main article: Sensorimotor network
  • This network processes somatosensory information and coordinates motion. The auditory cortex may be included.

Visual (Occipital)

Further information: Visual cortex
  • This network handles visual information processing.

Other networks

Different methods and data have identified several other brain networks, many of which greatly overlap or are subsets of more well-characterized core networks.

  • Limbic
  • Auditory
  • Right/left executive
  • Cerebellar
  • Spatial attention
  • Language
  • Lateral visual
  • Temporal
  • Visual perception/imagery

See also

References


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