Evidence map›Paper›PMID 36896755›Full record

ArticleHuman brain mapping2023

Accumulation of network redundancy marks the early stage of Alzheimer's disease.

Maryam Ghanbari, Guoshi Li, Li-Ming Hsu, Pew-Thian Yap

Open access · goldAbstract read
In one paragraph

Article in Human brain mapping, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
3.5field-weighted citation impact, top 7% of its field
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

14 citing papers in PubMed, 20 citations in OpenAlex.

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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

4 authors at 1 institution in 1 country.

Maryam GhanbariDepartment of Radiology, University of North Carolina, Chapel Hill, North Carolina, USA.ORCID 0000-0003-2131-7122
Guoshi LiDepartment of Radiology, University of North Carolina, Chapel Hill, North Carolina, USA.ORCID 0000-0002-8984-4722
Li-Ming HsuDepartment of Radiology, University of North Carolina, Chapel Hill, North Carolina, USA.
Pew-Thian YapDepartment of Radiology, University of North Carolina, Chapel Hill, North Carolina, USA.
University of North Carolina at Chapel Hill · US

Funding

Longitudinal Mapping of Human Brain Development in the First Years of LifeR01EB008374 · NIBIB · UNIV OF NORTH CAROLINA CHAPEL HILL · PI YAP, PEW-THIAN · 2009 to 2024
$5.1M
Development of Robust Brain Measurement Tools Informed by Ultrahigh Field 7T MRIR01EB006733 · NIBIB · UNIV OF NORTH CAROLINA CHAPEL HILL · PI YAP, PEW-THIAN · 2008 to 2020
$4.5M
Computational Diffusion MRI for Studying Early Human Brain DevelopmentR01MH125479 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI YAP, PEW-THIAN · 2021 to 2025
$2.1M
Comprehensive MR Fingerprinting for Infants and Young Children at Risk for Developmental Delays.R01HD112923 · NICHD · DUKE UNIVERSITY · PI Dan Ma, DEANNE E WILSON-COSTELLO · 2024 to 2026
$1.7M
NIBIB NIH HHS R01 EB006733NIBIB NIH HHS R01 EB008374NICHD NIH HHS R01 HD112923NIMH NIH HHS R01 MH125479
6 · The paper itself

Abstract

Brain wiring redundancy counteracts aging-related cognitive decline by reserving additional communication channels as a neuroprotective mechanism. Such a mechanism plays a potentially important role in maintaining cognitive function during the early stages of neurodegenerative disorders such as Alzheimer's disease (AD). AD is characterized by severe cognitive decline and involves a long prodromal stage of mild cognitive impairment (MCI). Since MCI subjects are at high risk of converting to AD, identifying MCI individuals is essential for early intervention. To delineate the redundancy profile during AD progression and enable better MCI diagnosis, we define a metric that reflects redundant disjoint connections between brain regions and extract redundancy features in three high-order brain networks-medial frontal, frontoparietal, and default mode networks-based on dynamic functional connectivity (dFC) captured by resting-state functional magnetic resonance imaging (rs-fMRI). We show that redundancy increases significantly from normal control (NC) to MCI individuals and decreases slightly from MCI to AD individuals. We further demonstrate that statistical features of redundancy are highly discriminative and yield state-of-the-art accuracy of up to 96.8 ± 1.0% in support vector machine (SVM) classification between NC and MCI individuals. This study provides evidence supporting the notion that redundancy serves as a crucial neuroprotective mechanism in MCI.

Indexed as

Alzheimer DiseaseCognitive DysfunctionBrainBrain MappingHumansMagnetic Resonance ImagingAlzheimer's diseasedynamic functional connectivitygraph theory

Identifiers

PMID36896755
PMCPMC10171535
OpenAlexW4323812884

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.