Evidence map›Paper›PMID 32743786›Full record

ArticleGeroScience2020

Acute cognitive deficits after traumatic brain injury predict Alzheimer's disease-like degradation of the human default mode network.

Andrei Irimia, Alexander S Maher, Nikhil N Chaudhari, Nahian F Chowdhury, Elliot B Jacobs, Alzheimer’s Disease Neuroimaging Initiative

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in GeroScience, 2020. 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
2.0field-weighted citation impact, top 12% 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, 22 citations in OpenAlex.

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  7. Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment.Proceedings of the National Academy of Sciences of the United States of America · 2023
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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

6 authors at 1 institution in 1 country.

Andrei IrimiaEthel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA. irimia@usc.edu.ORCID 0000-0002-9254-9388
Alexander S MaherEthel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.
Nikhil N ChaudhariEthel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.
Nahian F ChowdhuryEthel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.
Elliot B JacobsEthel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.
Alzheimer’s Disease Neuroimaging Initiative
University of Southern California · US

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
Effects of blood-brain barrier disruption upon white matter connectivity subsequent to traumatic brain injuryR01NS100973 · NINDS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI IRIMIA, ANDREI · 2017 to 2021
$1.6M
NIA NIH HHS U01 AG024904NINDS NIH HHS R01 NS100973NINDS NIH HHS R01 NS 900972
6 · The paper itself

Abstract

Traumatic brain injury (TBI) and Alzheimer's disease (AD) are prominent neurological conditions whose neural and cognitive commonalities are poorly understood. The extent of TBI-related neurophysiological abnormalities has been hypothesized to reflect AD-like neurodegeneration because TBI can increase vulnerability to AD. However, it remains challenging to prognosticate AD risk partly because the functional relationship between acute posttraumatic sequelae and chronic AD-like degradation remains elusive. Here, functional magnetic resonance imaging (fMRI), network theory, and machine learning (ML) are leveraged to study the extent to which geriatric mild TBI (mTBI) can lead to AD-like alteration of resting-state activity in the default mode network (DMN). This network is found to contain modules whose extent of AD-like, posttraumatic degradation can be accurately prognosticated based on the acute cognitive deficits of geriatric mTBI patients with cerebral microbleeds. Aside from establishing a predictive physiological association between geriatric mTBI, cognitive impairment, and AD-like functional degradation, these findings advance the goal of acutely forecasting mTBI patients' chronic deviations from normality along AD-like functional trajectories. The association of geriatric mTBI with AD-like changes in functional brain connectivity as early as ~6 months post-injury carries substantial implications for public health because TBI has relatively high prevalence in the elderly.

Indexed as

Alzheimer DiseaseBrain Injuries, TraumaticCognitive DysfunctionAgedCognitionDefault Mode NetworkHumansNerve NetAlzheimer’s diseaseDefault mode networkFunctional connectomeGeriatricsResting stateTraumatic brain injury

Identifiers

PMID32743786
PMCPMC7525415
OpenAlexW3046893005

What OpenQuestion holds

Textmetadata
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.