Evidence map›Paper›PMID 36672073›Full record

ArticleBrain sciences2023

Decreased Brain Structural Network Connectivity in Patients with Mild Cognitive Impairment: A Novel Fractal Dimension Analysis.

Chi Ieong Lau, Jiann-Horng Yeh, Yuh-Feng Tsai, Chen-Yu Hsiao, Yu-Te Wu, Chi-Wen Jao

Abstract read
In one paragraph

Article in Brain sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. 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

6 authors.

Chi Ieong LauInstitute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan.ORCID 0000-0003-0699-2234
Jiann-Horng YehSchool of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei 242, Taiwan.
Yuh-Feng TsaiSchool of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei 242, Taiwan.
Chen-Yu HsiaoDepartment of Diagnostic Radiology, Shin Kong Wu Ho Su Memorial Hospital, Taipei 111, Taiwan.
Yu-Te WuInstitute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan.ORCID 0000-0002-6942-0340
Chi-Wen JaoInstitute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan.

Funding

Shin Kong WHS Memorial Hospital 111GB006-0 and 111-SKH-NYCU-01
6 · The paper itself

Abstract

Mild cognitive impairment (MCI) is widely regarded to be the intermediate stage to Alzheimer's disease. Cerebral morphological alteration in cortical subregions can provide an accurate predictor for early recognition of MCI. Thirty patients with MCI and thirty healthy control subjects participated in this study. The Desikan-Killiany cortical atlas was applied to segment participants' cerebral cortex into 68 subregions. A complexity measure termed fractal dimension (FD) was applied to assess morphological changes in cortical subregions of participants. The MCI group revealed significantly decreased FD values in the bilateral temporal lobes, right parietal lobe including the medial temporal, fusiform, para hippocampal, and also the orbitofrontal lobes. We further proposed a novel FD-based brain structural network to compare network parameters, including intra- and inter-lobular connectivity between groups. The control group had five modules, and the MCI group had six modules in their brain networks. The MCI group demonstrated shrinkage of modular sizes with fewer components integrated, and significantly decreased global modularity in the brain network. The MCI group had lower intra- and inter-lobular connectivity in all lobes. Between cerebral lobes, the MCI patients may maintain nodal connections between both hemispheres to reduce connectivity loss in the lateral hemispheres. The method and results presented in this study could be a suitable tool for early detection of MCI.

Indexed as

Alzheimer’s diseasebrain structural networkcognitive functionfractal dimensionMCI

Identifiers

PMID36672073
PMCPMC9856782

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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.