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DICCCOL specifications

Information


Unique identifier OMICS_16353
Name DICCCOL
Alternative name Dense Individualized and Common Connectivity-based Cortical Landmarks
Restrictions to use None
Community driven No
Data access File download, Browse
User data submission Not allowed
Maintained Yes

Maintainer


  • person_outline Tianming Liu

Publications for Dense Individualized and Common Connectivity-based Cortical Landmarks

DICCCOL citations

 (4)
library_books

Compensation through Functional Hyperconnectivity: A Longitudinal Connectome Assessment of Mild Traumatic Brain Injury

2015
PMCID: 4706919
PMID: 26819765
DOI: 10.1155/2016/4072402

[…] A series of steps were taken to analyze the data: (1) localizing DICCCOL nodes of each subject using the DICCCOL framework and computing FC between the time series of each pair of DICCCOLs; (2) performing a longitudinal statistical analysis using mixed 2 × 2 design […]

library_books

Construction of Multi Scale Consistent Brain Networks: Methods and Applications

2015
PLoS One
PMCID: 4395249
PMID: 25876038
DOI: 10.1371/journal.pone.0118175

[…] ruction was performed []. The streamline fiber tracking was performed using the MEDINRIA (http://www-sop.inria.fr/asclepios/software/MedINRIA) (FA threshold: 0.2; minimum fiber length: 20). Then, the DICCCOL landmarks were predicted according to the steps in []. Finally, in order to find all fibers connecting cortical landmarks, we prolonged or shortened the fibers to make them reach the gray matt […]

library_books

A Comparative Study of Theoretical Graph Models for Characterizing Structural Networks of Human Brain

2013
PMCID: 3863486
PMID: 24369454
DOI: 10.1155/2013/201735

[…] e cortical parcellation based on image/surface registration, whose limitations have been comprehensively discussed in [, ].In this paper, we apply our recently developed brain reference system, named Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL) [] which discovers 358 consistent and corresponding ROIs across subjects based on diffusion tensor imaging (DTI) data, t […]

library_books

Increased Cortical Limbic Anatomical Network Connectivity in Major Depression Revealed by Diffusion Tensor Imaging

2012
PLoS One
PMCID: 3458828
PMID: 23049910
DOI: 10.1371/journal.pone.0045972

[…] There are several other types of automatic cortical parcellation methods, such as DICCCOL (Dense Individualized and Common Connectivity-Based Cortical Landmarks) , automatic labeling in the Freesurfer , random parcellation and the graph theory . Given the lack of a gold standard f […]

Citations

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DICCCOL funding source(s)
This work was supported by the NIH K01 EB 006878, NIH R01 HL087923-03S2, and The University of Georgia start-up research funding, by the NWPU Foundation for Fundamental Research, by the China Government Scholarship, by The National Natural Science Foundation of China (30830046) and The National 973 Program of China (2009 CB918303), by the Paul B. Beeson Career Developmental Awards (K23-AG028982) and National Alliance for Research in Schizophrenia and Depression Young Investigator Award.

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