DocumentCode :
1515113
Title :
Human Brain Connectomics: Networks, Techniques, and Applications [Life Sciences]
Author :
Yap, Pew-Thian ; Wu, Guorong ; Shen, Dinggang
Author_Institution :
Univ. of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Volume :
27
Issue :
4
fYear :
2010
fDate :
7/1/2010 12:00:00 AM
Firstpage :
131
Lastpage :
134
Abstract :
The human brain is organized into a collection of interacting networks with specialized functions to support various cognitive functions. The word "connectome" first burst on the scene with the work of Sporns et al., who urged brain researchers to advance a comprehensive structural description of the elements and connections forming the human brain. An increasing body of evidence indicates that schizophrenia, multiple sclerosis, and autism exhibit abnormal brain connections. Changes in connectivity also appear to occur as a consequence of neuron degeneration, either from natural aging or diseases such as Alzheimer\´s disease. A connectome is hence fundamentally important for understanding brain growth, aging, and abnormality. At the micro level, the brain elements consist of single neurons, the amount of which often treads the realm of hundreds of billions, and possible connections between them numbering in the order of 1015. At a more macro (and more manageable) level, the brain is parcellated into a number of regions, where each region accounts for the activity and coactivity of a population of neurons. The colossal task of constructing a connectome calls for powerful tools for handling the vast amount of information given by advanced imaging techniques. In this article, we provide an overview of the fundamental concepts involved, the necessary techniques, and applications to date.
Keywords :
brain models; diseases; neurophysiology; Alzheimer´s disease; autism; cognitive function; human brain connectomics; multiple sclerosis; neuron; schizophrenia; Aging; Alzheimer´s disease; Brain; Circuits; High-resolution imaging; Humans; Magnetic resonance imaging; Neuroimaging; Neurons; Symmetric matrices;
fLanguage :
English
Journal_Title :
Signal Processing Magazine, IEEE
Publisher :
ieee
ISSN :
1053-5888
Type :
jour
DOI :
10.1109/MSP.2010.936775
Filename :
5484178
Link To Document :
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