Evidence map›Paper›PMID 40865621›Full record

ArticleNeuroImage2025

Dynamic brain states during reasoning tasks: a co-activation pattern analysis.

Fatemeh Hasanzadeh, Christian Habeck, Yaakov Stern

Abstract read
In one paragraph

Article in NeuroImage, 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

3 authors.

Fatemeh HasanzadehCognitive Neuroscience Division, Department of Neurology, Columbia University, NY, NY, USA; Gertrude H. Sergievsky Center, Columbia University, NY, NY, USA.
Christian HabeckCognitive Neuroscience Division, Department of Neurology, Columbia University, NY, NY, USA; Gertrude H. Sergievsky Center, Columbia University, NY, NY, USA; Taub Institute for Research in Alzheimer's Disease and the Aging Brain, Columbia University, NY, NY, USA.
Yaakov SternCognitive Neuroscience Division, Department of Neurology, Columbia University, NY, NY, USA; Gertrude H. Sergievsky Center, Columbia University, NY, NY, USA; Taub Institute for Research in Alzheimer's Disease and the Aging Brain, Columbia University, NY, NY, USA. Electronic address: ys11@cumc.columbia.edu.

Funding

Exploring Cognitive Aging Using Reference Ability Neural NetworksR01AG038465 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI HABECK, CHRISTIAN GEORG, STERN, YAAKOV · 2011 to 2025
$17.0M
Imaging of Cognition, Learning and Memory in AgingR01AG026158 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI STERN, YAAKOV · 2004 to 2021
$10.9M
NIA NIH HHS R01 AG026158NIA NIH HHS R01 AG038465
6 · The paper itself

Abstract

Brain activity exhibits substantial temporal variability during cognitive processes, yet traditional fMRI analyses often fail to capture these dynamic patterns. Co-activation pattern (CAP) analysis has emerged as a promising method to study brain dynamics. CAP analysis provides a powerful framework for capturing transient brain states, however, its application to cognitive tasks remains very limited, with no prior studies specifically investigating its role in reasoning performance. This study investigated CAPs during reasoning tasks, their relationship with cognitive performance, age and other individual differences. We applied CAP analysis to fMRI data from 303 participants performing three reasoning tasks-Matrix Reasoning, Letter Sets, and Paper Folding-along with resting-state data. Using K-means clustering, we identified four distinct CAPs, each exhibiting unique spatial and temporal characteristics. These CAPs were analyzed in relation to predefined resting-state networks, revealing their functional relevance to cognitive task engagement. Key temporal metrics, including fraction occupancy, dwelling time, and transition probabilities, were assessed across reasoning tasks and resting state. The results demonstrate that CAP2 and CAP3 are predominantly engaged during reasoning tasks, with CAP2 strongly overlapping with the visual network and CAP3 exhibiting concurrent default mode and sensorimotor network activations. CAP1, primarily dominant during rest, showed prolonged engagement in older individuals, while CAP4 appeared to function as a transitional state facilitating network reorganization. Regression analyses link longer dwelling times and higher fraction occupancy of CAP2 and CAP3 to superior reasoning performance, whereas excessive transitions to CAP4 negatively impacted cognitive task outcomes. Additionally, aging was associated with reduced engagement in task-relevant CAPs and an increased tendency to transition into baseline-like states. These findings underscore the critical role of dynamic brain state reconfigurations in supporting cognition specifically reasoning and highlight CAP analysis as a powerful tool for studying transient brain function and individual cognitive differences.

Indexed as

BrainBrain MappingCognitionNerve NetThinkingAdolescentAdultAgedFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedYoung AdultBrain Network DynamicsCo-activation pattern (CAP) analysisCognitive PerformanceDynamic brain statesFunctional MRI (FMRI)Reasoning Task

Identifiers

PMID40865621
PMCPMC12692046

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.