Evidence map›Paper›PMID 39290876›Full record

ArticleInternational journal of clinical and health psychology : IJCHP

Imaging body-mind crosstalk in young adults.

Qian Yu, Zhaowei Kong, Liye Zou, Fabian Herold, Sebastian Ludyga, Zhihao Zhang, Meijun Hou, Arthur F Kramer, Kirk I Erickson, Marco Taubert and 12 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of clinical and health psychology : IJCHP. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

22 authors.

Qian YuFaculty of Education, University of Macau, Macao, 999078, China.
Zhaowei KongFaculty of Education, University of Macau, Macao, 999078, China.
Liye ZouBody-Brain-Mind Laboratory, School of Psychology, Shenzhen University, Shenzhen, 518060, China.
Fabian HeroldBody-Brain-Mind Laboratory, School of Psychology, Shenzhen University, Shenzhen, 518060, China.
Sebastian LudygaDepartment of Sport, Exercise and Health, Sport Science Section, University of Basel, Grosse Allee 6, Basel, CH, 4052, Switzerland.
Zhihao ZhangBody-Brain-Mind Laboratory, School of Psychology, Shenzhen University, Shenzhen, 518060, China.
Meijun HouBody-Brain-Mind Laboratory, School of Psychology, Shenzhen University, Shenzhen, 518060, China.
Arthur F KramerCenter for Cognitive and Brain Health, Northeastern University, Boston, MA, 02115, USA.
Kirk I EricksonAdventHealth Research Institute, Neuroscience, Orlando, FL, 32101, USA.
Marco TaubertDepartment Sport Science, Institute III, Faculty for Humanities, Center for Behavioral and Brain Sciences, Otto von Guericke University, Magdeburg, 39106, Germany.
Charles H HillmanCenter for Cognitive and Brain Health, Northeastern University, Boston, MA, 02115, USA.
Sean P MullenBeckman Institute, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA.
Markus GerberDepartment of Sport, Exercise and Health, Sport Science Section, University of Basel, Grosse Allee 6, Basel, CH, 4052, Switzerland.
Notger G MüllerResearch Group Degenerative and Chronic Diseases, Movement, Faculty of Health Sciences Brandenburg, University of Potsdam, Potsdam, 14476, Germany.
Keita KamijoFaculty of Liberal Arts and Sciences, Chukyo University, Nagoya, 466-8666, Japan.
Toru IshiharaGraduate School of Human Development and Environment, Kobe University, Kobe, 657-8501, Japan.
Robert SchinkeSchool of Kinesiology and Health Sciences, Laurentian University, Sudbury, ON P3E 2C6, Canada.
Boris ChevalSwiss Center for Affective Sciences, University of Geneva, Geneva CH-12114, Switzerland.
Terry McMorrisDepartment Sport and Exercise Science, Institute for Sport, University of Chichester, College Lane, West Sussex, Chichester, PO19 6PE, United Kingdom.
Ka Kit WongFaculty of Education, University of Macau, Macao, 999078, China.
Qingde ShiFaculty of Health Sciences and Sports, Macao Polytechnic University, 999078, Macao, China.
Jinlei NieFaculty of Health Sciences and Sports, Macao Polytechnic University, 999078, Macao, China.

Funding

Mapping the Human Connectome: Structure, Function, and HeritabilityU54MH091657 · NIMH · WASHINGTON UNIVERSITY · PI UGURBIL, KAMIL, VAN ESSEN, DAVID C · 2010 to 2014
$34.7M
NIMH NIH HHS U54 MH091657
6 · The paper itself

Abstract

Objective: There is evidence that complex relationships exist between motor functions, brain structure, and cognitive functions, particularly in the aging population. However, whether such relationships observed in older adults could extend to other age groups (e.g., younger adults) remains to be elucidated. Thus, the current study addressed this gap in the literature by investigating potential associations between motor functions, brain structure, and cognitive functions in a large cohort of young adults. Methods: In the current study, data from 910 participants (22-35 yr) were retrieved from the Human Connectome Project. Interactions between motor functions (i.e., cardiorespiratory fitness, gait speed, hand dexterity, and handgrip strength), brain structure (i.e., cortical thickness, surface area, and subcortical volumes), and cognitive functions were examined using linear mixed-effects models and mediation analyses. The performance of different machine-learning classifiers to discriminate young adults at three different levels (related to each motor function) was compared. Results: Cardiorespiratory fitness and hand dexterity were positively associated with fluid and crystallized intelligence in young adults, whereas gait speed and handgrip strength were correlated with specific measures of fluid intelligence (e.g., inhibitory control, flexibility, sustained attention, and spatial orientation; false discovery rate [FDR] corrected, Conclusions: In summary, similar to observations in older adults, the current study provides empirical evidence (i) that motor functions in young adults are positively related to specific measures of cognitive functions, and (ii) that such relationships are at least partially mediated by distinct brain structures. Furthermore, our analyses suggest that machine-learning classifier has a promising potential to be used as a classification tool and decision support for identifying populations with below-average motor and cognitive functions.

Indexed as

Brain structureFluid and crystallized intelligenceMachine learningMotor functionYoung adults

Identifiers

PMID39290876
PMCPMC11407095

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.