Novel Effective Connectivity Network Inference for MCI Identification.

Yang Li, Hao Yang, Ke Li, Pew-Thian Yap, Minjeong Kim, Chong-Yaw Wee, Dinggang Shen

Abstract read

In one paragraph

Article in Machine learning in medical imaging. MLMI (Workshop), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Yang LiSchool of Automation Sciences and Electrical Engineering, Beihang University, Beijing, China.

Hao YangSchool of Automation Sciences and Electrical Engineering, Beihang University, Beijing, China.

Ke LiSchool of Aeronautic Science and Engineering, Beihang University, Beijing, China.

Pew-Thian YapDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA.

Minjeong KimDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA.

Chong-Yaw WeeDepartment of Biomedical Engineering, National University of Singapore, Singapore, Singapore.

Dinggang ShenDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA.

Funding

Quantifying Brain Abnormality by Multimodality Neuroimage Analysis,R01AG041721 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LIU, MINGXIA · 2012 to 2019

$3.0M

NIA NIH HHS R01 AG041721

6 · The paper itself

Abstract

Inferring effective brain connectivity network is a challenging task owing to perplexing noise effects, the curse of dimensionality, and inter-subject variability. However, most existing network inference methods are based on correlation analysis and consider the datum points individually, revealing limited information of the neuron interactions and ignoring the relations amongst the derivatives of the data. Hence, we proposed a novel ultra group-constrained sparse linear regression model for effective connectivity inference. This model utilizes not only the discrepancy between observed signals and the model prediction, but also the discrepancy between the associated weak derivatives of the observed and the model signals for a more accurate effective connectivity inference. What's more, a group constraint is applied to minimize the inter-subject variability and the proposed modeling was validated on a mild cognitive impairment dataset with superior results achieved.

Identifiers

PMID29911207

DOI10.1007/978-3-319-67389-9_37

PMCPMC5999330

What OpenQuestion holds

Textmetadata

LicenceTDM

Read underepoch 390

Registered trials

None linked

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.