Abstract：
:Quantifying the connectivity between arbitrary surface patches in the human brain cortex can be used in studies on brain function and to characterize clinical diseases involving abnormal connectivity. Cortical regions of human brain in their natural forms can be represented in surface formats. In this paper, we present a framework to quantify connectivity using cortical surface segmentation and labeling from structural magnetic resonance images, tractography from diffusion tensor images, and nonlinear inter-subject registration. For a single subject, the connectivity intensity of any point on the cortical surface is set to unity if the point is connected and zero if it is not connected. The connectivity proportion is defined as the ratio of the total connected surface area to the total area of the surface patch. By nonlinearly registering the connectivity data of a group of normal controls into a template space, a population connectivity metric can be defined as either the average connectivity intensity of a cortical point or the average connectivity proportion of a cortical region. In the template space, a connectivity profile and a connectivity histogram of an arbitrary cortical region of interest can then be derived from these connectivity quantification values. Results from the application of these quantification metrics to a population of schizophrenia patients and normal controls are presented, revealing connectivity signatures of specified cortical regions and detecting connectivity abnormalities.
journal_name
J Neurosci Methodsjournal_title
Journal of neuroscience methodsauthors
Huang H,Prince JL,Mishra V,Carass A,Landman B,Park DC,Tamminga C,King R,Miller MI,van Zijl PC,Mori Sdoi
10.1016/j.jneumeth.2011.02.017subject
Has Abstractpub_date
2011-04-30 00:00:00pages
324-32issue
2eissn
0165-0270issn
1872-678Xpii
S0165-0270(11)00112-9journal_volume
197pub_type
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