Evidence map›Paper›PMID 34955572›Full record

ArticleJournal of the American Statistical Association2021

Brain Regions Identified as Being Associated with Verbal Reasoning through the Use of Imaging Regression via Internal Variation.

Long Feng, Xuan Bi, Heping Zhang

Abstract read
In one paragraph

Article in Journal of the American Statistical Association, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Multi-task Learning with High-Dimensional Noisy Images.Journal of the American Statistical Association · 2024
    Article
  3. Article
  4. Article
  5. Article
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

3 authors.

Long FengDepartment of Biostatistics, Yale University.
Xuan BiInformation and Decision Sciences, Carlson School of Management, University of Minnesota.
Heping ZhangDepartment of Biostatistics, Yale University.

Funding

Neurodevelopmental Genomics: Trajectories of Complex PhenotypesRC2MH089983 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI GUR, RAQUEL E · 2009 to 2011
$14.3M
2/2 Neurodevelopmental Genomics: Trajectories of Complex PhenotypesRC2MH089924 · NIMH · CHILDREN'S HOSP OF PHILADELPHIA · PI HAKONARSON, HAKON · 2009 to 2011
$10.7M
Statistical Methods in Genetic Studies of Substance UseR01DA016750 · NIDA · YALE UNIVERSITY · PI ZHANG, HEPING · 2004 to 2016
$3.3M
Analysis of Big Data Squared in Biomedical StudiesR01MH116527 · NIMH · YALE UNIVERSITY · PI ZHANG, HEPING · 2018 to 2022
$2.3M
Analysis of Genomic and Complex DataR01HG010171 · NHGRI · YALE UNIVERSITY · PI ZHANG, HEPING · 2019 to 2022
$1.4M
NHGRI NIH HHS R01 HG010171NIDA NIH HHS R01 DA016750NIMH NIH HHS R01 MH116527NIMH NIH HHS RC2 MH089924NIMH NIH HHS RC2 MH089983
6 · The paper itself

Abstract

Brain-imaging data have been increasingly used to understand intellectual disabilities. Despite significant progress in biomedical research, the mechanisms for most of the intellectual disabilities remain unknown. Finding the underlying neurological mechanisms has been proved difficult, especially in children due to the rapid development of their brains. We investigate verbal reasoning, which is a reliable measure of individuals' general intellectual abilities, and develop a class of high-order imaging regression models to identify brain subregions which might be associated with this specific intellectual ability. A key novelty of our method is to take advantage of spatial brain structures, and specifically the piecewise smooth nature of most imaging coefficients in the form of high-order tensors. Our approach provides an effective and urgently needed method for identifying brain subregions potentially underlying certain intellectual disabilities. The idea behind our approach is a carefully constructed concept called Internal Variation (IV). The IV employs tensor decomposition and provides a computationally feasible substitution for Total Variation (TV), which has been considered in the literature to deal with similar problems but is problematic in high order tensor regression. Before applying our method to analyze the real data, we conduct comprehensive simulation studies to demonstrate the validity of our method in imaging signal identification. Then, we present our results from the analysis of a dataset based on the Philadelphia Neurodevelopmental Cohort for which we preprocessed the data including re-orienting, bias-field correcting, extracting, normalizing and registering the magnetic resonance images from 978 individuals. Our analysis identified a subregion across the cingulate cortex and the corpus callosum as being associated with individuals' verbal reasoning ability, which, to the best of our knowledge, is a novel region that has not been reported in the literature. This finding is useful in further investigation of functional mechansims for verbal reasoning.

Indexed as

Brain ImagingInternal VariationPiecewise SmoothnessTensor RegressionVerbal Reasoning

Identifiers

PMID34955572
PMCPMC8693017

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Registered trials

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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.