Evidence map›Paper›PMID 38174734›Full record

ArticleeLife2024

Cell type-specific connectome predicts distributed working memory activity in the mouse brain.

Xingyu Ding, Sean Froudist-Walsh, Jorge Jaramillo, Junjie Jiang, Xiao-Jing Wang

Open access · goldAbstract read
In one paragraph

Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.3field-weighted citation impact, top 11% 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

15 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
  2. Article
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  4. Biophysical mechanisms of default mode network function and dysfunction.bioRxiv : the preprint server for biology · 2026
    Article
  5. Article
  6. Article
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  9. Cognitive processes are disentangled at cortex-wide scales.bioRxiv : the preprint server for biology · 2025
    Article
  10. Article
  11. Network Mechanisms Underlying the Regional Diversity of Variance and Time Scales of the Brain's Spontaneous Activity Fluctuations.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Article
  12. Article
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  14. Review
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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

5 authors at 2 institutions in 4 countries.

Xingyu Ding *Center for Neural Science, New York University, New York, United States.ORCID https://orcid.org/0000-0002-6614-5195
Sean Froudist-Walsh *Center for Neural Science, New York University, New York, United States.ORCID https://orcid.org/0000-0003-4070-067X
Jorge Jaramillo *Center for Neural Science, New York University, New York, United States.ORCID https://orcid.org/0000-0002-6666-899X
Junjie JiangCenter for Neural Science, New York University, New York, United States.ORCID https://orcid.org/0000-0003-2930-7770
Xiao-Jing WangCenter for Neural Science, New York University, New York, United States.ORCID https://orcid.org/0000-0003-3124-8474
New York University · USUniversity of Bristol · GB

Funding

Thalamus in the middle: computations in multi-regional neural circuitsU19NS123714 · NINDS · ALLEN INSTITUTE · PI Adam G Carter, Jayaram Chandrashekar · 2022 to 2026
$19.0M
Recurrent Neual Circuit Basis of Time Integration and Decision MakingR01MH062349 · NIMH · YALE UNIVERSITY · PI WANG, XIAO-JING · 2001 to 2022
$7.2M
Biotechnology and Biological Sciences Research Council BB/X013243/1NIH HHS R01MH062349NIH HHS U19NS123714NIMH NIH HHS R01 MH062349NINDS NIH HHS U19 NS123714
6 · The paper itself

Abstract

Recent advances in connectomics and neurophysiology make it possible to probe whole-brain mechanisms of cognition and behavior. We developed a large-scale model of the multiregional mouse brain for a cardinal cognitive function called working memory, the brain's ability to internally hold and process information without sensory input. The model is built on mesoscopic connectome data for interareal cortical connections and endowed with a macroscopic gradient of measured parvalbumin-expressing interneuron density. We found that working memory coding is distributed yet exhibits modularity; the spatial pattern of mnemonic representation is determined by long-range cell type-specific targeting and density of cell classes. Cell type-specific graph measures predict the activity patterns and a core subnetwork for memory maintenance. The model shows numerous attractor states, which are self-sustained internal states (each engaging a distinct subset of areas). This work provides a framework to interpret large-scale recordings of brain activity during cognition, while highlighting the need for cell type-specific connectomics.

Indexed as

ConnectomeMemory, Short-TermAnimalsBrainCognitionMagnetic Resonance ImagingMiceattractorcell-typescomputational modelconnectomeinterneuronslarge-scalemouseneuroscienceworking memory

Identifiers

PMID38174734
PMCPMC10807864
OpenAlexW4390581266

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.