Evidence map›Paper›PMID 25964743›Full record

ArticleFrontiers in neuroanatomy2015

Parcellation of the primary cerebral cortices based on local connectivity profiles.

Qiaojun Li, Ming Song, Lingzhong Fan, Yong Liu, Tianzi Jiang

Abstract read
In one paragraph

Article in Frontiers in neuroanatomy, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Qiaojun LiBrainnetome Center, Institute of Automation, Chinese Academy of Sciences Beijing, China ; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing, China.
Ming SongBrainnetome Center, Institute of Automation, Chinese Academy of Sciences Beijing, China ; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing, China.
Lingzhong FanBrainnetome Center, Institute of Automation, Chinese Academy of Sciences Beijing, China ; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing, China.
Yong LiuBrainnetome Center, Institute of Automation, Chinese Academy of Sciences Beijing, China ; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing, China.
Tianzi JiangBrainnetome Center, Institute of Automation, Chinese Academy of Sciences Beijing, China ; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing, China ; CAS Center for Excellence in Brain Science, Institute of Automation, Chinese Academy of Sciences Beijing, China ; The Queensland Brain Institute, University of Queensland, Brisbane QLD, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Connectivity-based parcellation using diffusion MRI has been extensively used to parcellate subcortical areas and the association cortex. Connectivity profiles are vital for connectivity-based parcellation. Two categories of connectivity profiles are generally utilized, including global connectivity profiles, in which the connectivity information is from the seed to the whole brain, and long connectivity profiles, in which the connectivity information is from the seed to other brain regions after excluding the seed. However, whether global or long connectivity profiles should be applied in parcellating the primary cortex utilizing connectivity-based parcellation is unclear. Many sources of evidence have indicated that the primary cerebral cortices are composed of structurally and functionally distinct subregions. Because the primary cerebral cortices are rich in local anatomic hierarchical connections and possess high degree of local functional connectivity profiles, we proposed that local connectivity profiles, that is the connectivity information within a seed region of interest, might be used for parcellating the primary cerebral cortices. In this study, the global, long, and local connectivity profiles were separately used to parcellate the bilateral M1, A1, S1, and V1. We found that results using the three profiles were all quite consistent with reported cytoarchitectonic evidence. More importantly, the results using local connectivity profiles showed less inter-subject variability than the results using the other two, a finding which suggests that local connectivity profiles are superior to global and long connectivity profiles for parcellating the primary cerebral cortices. This also implies that, depending on the characteristics of specific areas of the cerebral cortex, different connectivity profiles may need to be adopted to parcellate different areas.

Indexed as

connectivity-based parcellationdiffusion tensor imaginglocal connectivity profilesprimary cerebral corticestractography

Identifiers

PMID25964743
PMCPMC4410601

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.