Evidence map›Paper›PMID 35811304›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2022

A Neuroimaging Signature of Cognitive Aging from Whole-Brain Functional Connectivity.

Rongtao Jiang, Dustin Scheinost, Nianming Zuo, Jing Wu, Shile Qi, Qinghao Liang, Dongmei Zhi, Na Luo, Young-Chul Chung, Sha Liu and 3 more

Open access · goldAbstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 1 pooled it
6.2field-weighted citation impact, top 3% 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

33 citing papers in PubMed, 1 synthesis or guideline pooled it, 61 citations in OpenAlex.

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  12. Plasma biomarkers as core mediators in functional network abnormalities: Evidence from a two-cohort study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
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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

13 authors at 8 institutions in 3 countries.

Rongtao JiangDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA.ORCID 0000-0003-4657-0079
Dustin ScheinostDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA.
Nianming ZuoBrainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, P. R. China.
Jing WuDepartment of Medical Oncology, Beijing You-An Hospital, Capital Medical University, Beijing, 100069, P. R. China.
Shile QiCollege of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, P. R. China.
Qinghao LiangDepartment of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA.
Dongmei ZhiState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100088, P. R. China.
Na LuoBrainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, P. R. China.
Young-Chul ChungDepartment of Psychiatry, Jeonbuk National University Medical School, Jeonju, 54907, Republic of Korea.
Sha LiuDepartment of Psychiatry and MDT Center for Cognitive Impairment and Sleep Disorders, First Hospital, First Clinical Medical College of Shanxi Medical University, Taiyuan, 030001, P. R. China.
Yong XuDepartment of Psychiatry and MDT Center for Cognitive Impairment and Sleep Disorders, First Hospital, First Clinical Medical College of Shanxi Medical University, Taiyuan, 030001, P. R. China.
Jing SuiState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100088, P. R. China.ORCID 0000-0001-6837-5966
Vince CalhounTri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Emory University and Georgia State University, Atlanta, GA, 30303, USA.
Yale University · USChinese Academy of Sciences · CNGeorgia Institute of Technology · USShanxi Medical University · CNBeijing Normal University · CNCapital Medical University · CNJeonbuk National University Hospital · KRNanjing University of Aeronautics and Astronautics · CN

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Mining the Genomewide Scan: Genetic Profiles of Structural Loss in Schizophrenia: AD/ADRD supplementR01MH094524 · NIMH · THE MIND RESEARCH NETWORK · PI CALHOUN, VINCE D, TURNER, JESS · 2012 to 2021
$6.3M
Data-driven approaches to identify biomarkers from multimodal imaging big dataR01MH117107 · NIMH · GEORGIA STATE UNIVERSITY · PI CALHOUN, VINCE D · 2019 to 2022
$1.6M
China Natural Science Foundation 61773380China Natural Science Foundation 82001450China Natural Science Foundation 82022035China Postdoctoral Science Foundation BX20200364National Science Foundation 2112455NCATS NIH HHS UL1 TR001863NIH HHS 1R01MH094524NIH HHS 1R01MH117107NIMH NIH HHS R01 MH094524NIMH NIH HHS R01 MH117107
6 · The paper itself

Abstract

Cognitive decline is amongst one of the most commonly reported complaints during normal aging. Despite evidence that age and cognition are linked with similar neural correlates, no previous studies have directly ascertained how these two constructs overlap in the brain in terms of neuroimaging-based prediction. Based on a long lifespan healthy cohort (CamCAN, aged 19-89 years, n = 567), it is shown that both cognitive function (domains spanning executive function, emotion processing, motor function, and memory) and human age can be reliably predicted from unique patterns of functional connectivity, with models generalizable in two external datasets (n = 533 and n = 453). Results show that cognitive decline and normal aging both manifest decrease within-network connections (especially default mode and ventral attention networks) and increase between-network connections (somatomotor network). Whereas dorsal attention network is an exception, which is highly predictive on cognitive ability but is weakly correlated with aging. Further, the positively weighted connections in predicting fluid intelligence significantly mediate its association with age. Together, these findings offer insights into why normal aging is often associated with cognitive decline in terms of brain network organization, indicating a process of neural dedifferentiation and compensational theory.

Indexed as

Cognitive AgingAgingBrainHumansMagnetic Resonance ImagingNeuroimagingbiomarkerbrain agecognition agingindividualized predictionpredictive neuroimaging

Identifiers

PMID35811304
PMCPMC9403648
OpenAlexW4285014711

What OpenQuestion 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 OpenQuestion graph.