Article in Human brain mapping, 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.
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
8 authors.
Xiaoyu ZhaoState Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.ORCID 0009-0003-0571-4790
Ruochen YinState Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.ORCID 0009-0001-6005-425X
Chuansheng ChenDepartment of Psychological Science, University of California, California, USA.
Sebastian MarkettDepartment of Psychology, Humboldt-Universität Zu Berlin, Berlin, Germany.ORCID 0000-0002-0841-3163
Xinrui WangState Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
Gui XueState Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
Qi DongState Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
Chunhui ChenState Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.ORCID 0000-0002-9592-5692
Funding
China Brain Initiative grant 2021ZD0201701China Brain Initiative grant STI 2030-Major Projects 2021ZD0200500National Institutes of Health and additional federal partners U01DA041022National Institutes of Health and additional federal partners U01DA041025National Institutes of Health and additional federal partners U01DA041028National Institutes of Health and additional federal partners U01DA041048National Institutes of Health and additional federal partners U01DA041089National Institutes of Health and additional federal partners U01DA041093National Institutes of Health and additional federal partners U01DA041106National Institutes of Health and additional federal partners U01DA041117National Institutes of Health and additional federal partners U01DA041120National Institutes of Health and additional federal partners U01DA041134National Institutes of Health and additional federal partners U01DA041148National Institutes of Health and additional federal partners U01DA041156National Institutes of Health and additional federal partners U01DA041174National Institutes of Health and additional federal partners U01DA050987National Institutes of Health and additional federal partners U01DA050988National Institutes of Health and additional federal partners U01DA050989National Institutes of Health and additional federal partners U01DA051016National Institutes of Health and additional federal partners U01DA051018National Institutes of Health and additional federal partners U01DA051037National Institutes of Health and additional federal partners U01DA051038National Institutes of Health and additional federal partners U01DA051039National Institutes of Health and additional federal partners U24DA041123National Institutes of Health and additional federal partners U24DA041147Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning CNLYB2202Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning CNLZD2102
6 · The paper itself
Abstract
Working memory (WM) plays a crucial role in human cognition. Previous candidate and genome-wide association studies have reported many genetic variations associated with WM. However, little research has examined genetic basis of WM by using transcriptome, even though it reflects gene function more directly than does the genome. Here we propose a new approach to exploring the genetic mechanisms of WM by integrating connectome, transcriptome, and genome data in a high-quality dataset comprising 481 Chinese healthy adults. First, relevance vector regression was used to define WM-related brain regions. Second, genes differentially expressed within these regions were identified using the Allen Human Brain Atlas (AHBA) dataset. Finally, two independent datasets were used to validate these genes' contributions to WM. With this method, we identified 24 novel genes and 20 of them were confirmed in the large-scale datasets of ABCD and UK Biobank. These novel genes were enriched in the cellular component of collagen-containing extracellular matrix and the CCL18 signaling pathway. Our method offers an effective approach to integrating multimodal gene discovery and demonstrates the superiority of expression data. This new method and the newly identified genes deserve more attention in the future.
Indexed as
BrainConnectomeMemory, Short-TermTranscriptomeAdultFemaleGenome-Wide Association StudyHumansMagnetic Resonance ImagingMaleMiddle Aged
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 Socratic graph.