Evidence map›Paper›PMID 31102167›Full record

ArticleBrain imaging and behavior2020

Gender differences in cortical morphological networks.

Ahmed Nebli, Islem Rekik

Abstract read
In one paragraph

Article in Brain imaging and behavior, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Classification of multiple sclerosis patients based on structural disconnection: A robust feature selection approach.Journal of neuroimaging : official journal of the American Society of Neuroimaging · 2022
    Article
  5. Article
  6. 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

2 authors.

Ahmed NebliBASIRA Lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey.
Islem RekikBASIRA Lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey. irekik@itu.edu.tr.ORCID http://orcid.org/0000-0001-5595-6673

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cortical morphological networks (CMN), where each network models the relationship in morphology between different cortical brain regions quantified using a specific measurement (e.g., cortical thickness), have not been investigated with respect to gender differences in the human brain. Cortical processes are expected to involve complex interactions between different brain regions, univariate methods thus might overlook informative gender markers. Hence, by leveraging machine learning techniques with the potential to highlight multivariate interacting effects, we found that the most discriminative CMN connections between males and females were derived from the left hemisphere using the mean sulcal depth as measurement. However, for both left and right hemispheres, the first most discriminative morphological connection revealed across all cortical attributes involved (entorhinal cortex ↔ caudal anterior cingulate cortex) and (entorhinal cortex ↔ transverse temporal cortex) respectively, which gives us new insights into behavioral gender differences from an omics perspective and might explain why males and females learn differently.

Indexed as

Sex CharacteristicsBrain MappingCerebral CortexFemaleHumansMagnetic Resonance ImagingMaleYoung AdultBrain connectivityCortical morphological networksCortical morphologyFeature selectionGender differencesSulcal depthT1-weighted MRI

Identifiers

PMID31102167
PMCPMC7572349

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.