Evidence map›Paper›PMID 39963290›Full record

ArticleBrain communications2025

Connectome-based prediction of future episodic memory performance for individual amnestic mild cognitive impairment patients.

Zhengsheng Zhang, Mengxue Wang, Tong Lu, Yachen Shi, Chunming Xie, Qingguo Ren, Zan Wang

Abstract read
In one paragraph

Article in Brain communications, 2025. 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

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

1 citing paper in PubMed.

  1. [Progress on prognostic assessment methods for mild cognitive impairment].Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences · 2026
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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

7 authors.

Zhengsheng ZhangDepartment of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing 210009, China.
Mengxue WangDepartment of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing 210009, China.
Tong LuDepartment of Radiology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing 210009, China.
Yachen ShiDepartment of Interventional Neurology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi 214023, China.
Chunming XieDepartment of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing 210009, China.
Qingguo RenDepartment of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing 210009, China.
Zan WangDepartment of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing 210009, China.ORCID https://orcid.org/0000-0003-4770-2803

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The amnestic mild cognitive impairment progression to probable Alzheimer's disease is a continuous phenomenon. Here we conduct a cohort study and apply machine learning to generate a model of predicting episodic memory development for individual amnestic mild cognitive impairment patient that incorporates whole-brain functional connectivity. Fifty amnestic mild cognitive impairment patients completed baseline and 3-year follow-up visits including episodic memory assessments (e.g. Rey Auditory Verbal Learning Test Delayed Recall) and resting-state functional MRI scanning. Using a multivariate analytical method known as relevance vector regression, we found that the baseline whole-brain functional connectivity features failed to predict the baseline Rey Auditory Verbal Learning Test Delayed Recall scores (

Indexed as

Alzheimer’s diseaseamnestic mild cognitive impairmentepisodic memorymachine learningmagnetic resonance imaging

Identifiers

PMID39963290
PMCPMC11831076

What OpenQuestion holds

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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.