Evidence map›Paper›PMID 40944641›Full record

ArticleHuman brain mapping2025

Flexible Reconfigurations of Brain Networks During Decisions With Predefined Versus Self-Generated Options.

Qianying Wu, Zhihao Zhang, Ming Hsu, Andrew S Kayser

Abstract read
In one paragraph

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
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

4 authors.

Qianying WuDivision of the Humanities and Social Sciences, California Institute of Technology, Pasadena, California, USA.ORCID 0000-0002-2665-2205
Zhihao ZhangDarden School of Business, University of Virginia, Charlottesville, Virginia, USA.
Ming HsuHaas School of Business, University of California, Berkeley, Berkeley, California, USA.
Andrew S KayserHelen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, USA.ORCID 0000-0001-5102-2136

Funding

Program Development (Pilot)P30AG012839 · NIA · UNIVERSITY OF CALIFORNIA BERKELEY · PI Magali Barbieri · 1999 to 2026
$18.6M
Dopaminergic Mechanisms Underlying Human Social Behavior: A Multimodal ApproachR01MH112775 · NIMH · UNIVERSITY OF CALIFORNIA BERKELEY · PI HSU, MING, KAYSER, ANDREW S · 2019 to 2023
$3.5M
Behavioral and Neural Correlates of Social Function in Alcohol Use DisordersR01AA026587 · NIAAA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KAYSER, ANDREW S · 2018 to 2022
$2.3M
Higher-Order Visual Decision Making: Networks and MechanismsR01EY024554 · NEI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KAYSER, ANDREW S · 2014 to 2018
$1.7M
Research Network on Decision Neuroscience and AgingR24AG054355 · NIA · YALE UNIVERSITY · PI SAMANEZ LARKIN, GREGORY R · 2016 to 2020
$1.4M
Caltech's T&C Chen Center for Social and Decision NeuroscienceNational Science Foundation ES1851902NEI NIH HHS R01 EY024554NIAAA NIH HHS R01 AA026587NIA NIH HHS P30 AG012839NIA NIH HHS R24 AG054355NIMH NIH HHS R01 MH112775Simons Foundation Autism Research Initiative
6 · The paper itself

Abstract

How do large-scale brain networks dynamically reorganize to support different types of decision-making with distinct yet overlapping cognitive demands? While we often make decisions by evaluating and choosing from a set of externally defined choice options, we can also generate options internally from our existing knowledge store. Such flexibility suggests the ability for decision-related brain networks to reconfigure in response to the need to recruit sensory processing and semantic retrieval modules to evaluate externally and internally generated options, respectively. Here we sought to test this hypothesis by applying graph-theoretic tools to functional neuroimaging data obtained for (i) decisions with externally provided options (external-menu choices/EMC); (ii) decisions with self-generated options (internal-menu choices/IMC); and (ii) a semantic fluency condition in which individuals generated but were not required to evaluate options (semantic fluency; SF). Using categorical multi-slice community detection, we found that variations in cognitive demands across the tasks were associated with distinct reconfigurations of hierarchically organized modular brain networks. Specifically, global network organization that differed primarily along the dimension of external visual/sensory input distinguished EMC from both of the internally oriented tasks (IMC and SF). At submodular levels, IMC was distinguished from SF by stronger interactions between presumptive semantic retrieval and valuation networks hypothesized to support the generation and evaluation of choice options. These findings are consistent with a hierarchical architecture in which modules at multiple levels interact to support adaptive decision-making.

Indexed as

BrainDecision MakingNerve NetAdultBrain MappingFemaleHumansMagnetic Resonance ImagingMaleNeural PathwaysSemanticsYoung Adultdecision makinghierarchical organizationnetwork reconfigurationsemantic retrieval

Identifiers

PMID40944641
PMCPMC12432990

What OpenQuestion holds

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Registered trials

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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.