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A concrete exemplar of the value of large-scale data sharing has been provided by the Human Brain Project.
A concrete exemplar of the value of large-scale data sharing has been provided by the Human Brain Project.


The importance of large-scale integration of brain information for new approaches to medicine has been recognized [http://www.nature.com/neuro/journal/v7/n5/full/nn0504-426.html Nature Neuroscience 2004]. Rather than relying mainly on symptom information, a combination of brain and gene information may ultimately be required for understanding what treatment is best suited to which individual person.
The importance of large-scale integration of brain information for new approaches to medicine has been recognized <ref>{{cite journal|last=Insel|first=Thomas R|coauthors=Volkow, Nora D, Landis, Story C, Li, Ting-Kai, Battey, James F, Sieving, Paul|title=Limits to growth: why neuroscience needs large-scale science|journal=Nature Neuroscience|date=2003|volume=7|issue=5|pages=426–427|doi=10.1038/nn0504-426|url=http://www.nature.com/neuro/journal/v7/n5/full/nn0504-426.html|accessdate=25 September 2011}}</ref>. Rather than relying mainly on symptom information, a combination of brain and gene information may ultimately be required for understanding what treatment is best suited to which individual person.


It provides a framework for linking the great diversity of specializations within contemporary [[neuroscience]], including
It provides a framework for linking the great diversity of specializations within contemporary [[neuroscience]], including

Revision as of 12:16, 25 September 2011

Integrative neuroscience sculptures a theoretical neuroscience with a mathematical neuroscience that is different from computational neuroscience (Poznanski).[1] Its aim is to present studies of the functional organization of particular brain systems across hierarchical levels through integrative approaches leading to species-typical behaviors under normal and pathological states. It epitomizes top-down and bottom-up phenomenological models, as well as theoretical and philosophical foundations for explicit hierarchical and functional integration in the brain.[2]

This integrative approach focuses on the brain as an adaptive system. It is concerned with how all the components of the brain are coordinated, and the principles that guide this coordination. A key organizing principle is the importance of our need to avoid potential danger or threat, and maximize safety and reward. In humans, we see this principle operating in regard to responses driven by external information, as well as internally-generated goals. As such, Integrative Neuroscience aims for a unified understanding of brain function across timescales. It draws on information from different measurement sources (including both brain and body measures) to test these unifying principles.

Motivation

Since the ‘decade of the brain’ there has been an explosion of insights into the brain and their application in most areas of medicine. With this explosion, the need for integration of data across studies, modalities and levels of understanding is increasingly recognized. A concrete exemplar of the value of large-scale data sharing has been provided by the Human Brain Project.

The importance of large-scale integration of brain information for new approaches to medicine has been recognized [3]. Rather than relying mainly on symptom information, a combination of brain and gene information may ultimately be required for understanding what treatment is best suited to which individual person.

It provides a framework for linking the great diversity of specializations within contemporary neuroscience, including

This diversity is inevitable, yet has arguably created a void: neglect of the primary role of the nervous system in enabling the animal to survive and prosper. Integrative neuroscience aims to fill this perceived void.

Evolutionary basis

Integrative neuroscience draws on the important context of our evolutionary history Charles Darwin.[4]

References

  1. ^ Poznanski, RR (2000). Biophysical Neural Networks: Foundations of Integrative Neuroscience. New York [New York]: Mary Ann Liebert. {{cite book}}: Cite has empty unknown parameter: |1= (help)
  2. ^ Chauvet, Gilbert (1996). Theoretical Systems in Biology:Hierarchal and Functional Integration. Oxford [UK]: Pergamon Press. {{cite book}}: Cite has empty unknown parameter: |1= (help)
  3. ^ Insel, Thomas R (2003). "Limits to growth: why neuroscience needs large-scale science". Nature Neuroscience. 7 (5): 426–427. doi:10.1038/nn0504-426. Retrieved 25 September 2011. {{cite journal}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
  4. ^ Barrett, Paul (1980). Metaphysics, Materialism, & the Evolution of Mind:the early writings of Charles Darwin. Chicago [Illinois]: Chicago University Press. ISBN 0-226-13659-0.