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A Connectivity-Based Psychometric Prediction Framework for Brain-Behavior Relationship Studies.

ABSTRACT: The recent availability of population-based studies with neuroimaging and behavioral measurements opens promising perspectives to investigate the relationships between interindividual variability in brain regions' connectivity and behavioral phenotypes. However, the multivariate nature of connectivity-based prediction model severely limits the insight into brain-behavior patterns for neuroscience. To address this issue, we propose a connectivity-based psychometric prediction framework based on individual regions' connectivity profiles. We first illustrate two main applications: 1) single brain region's predictive power for a range of psychometric variables and 2) single psychometric variable's predictive power variation across brain region. We compare the patterns of brain-behavior provided by these approaches to the brain-behavior relationships from activation approaches. Then, capitalizing on the increased transparency of our approach, we demonstrate how the influence of various data processing and analyses can directly influence the patterns of brain-behavior relationships, as well as the unique insight into brain-behavior relationships offered by this approach.

SUBMITTER: Wu J

PROVIDER: S-EPMC8660224 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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A Connectivity-Based Psychometric Prediction Framework for Brain-Behavior Relationship Studies.

Wu Jianxiao J Eickhoff Simon B SB Hoffstaedter Felix F Patil Kaustubh R KR Schwender Holger H Yeo B T Thomas BTT Genon Sarah S

Cerebral cortex (New York, N.Y. : 1991) 20210701 8

The recent availability of population-based studies with neuroimaging and behavioral measurements opens promising perspectives to investigate the relationships between interindividual variability in brain regions' connectivity and behavioral phenotypes. However, the multivariate nature of connectivity-based prediction model severely limits the insight into brain-behavior patterns for neuroscience. To address this issue, we propose a connectivity-based psychometric prediction framework based on i ...[more]

PMID: 33884421

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Project description:BackgroundTo be able to design effective strategies to improve healthcare professionals' implementation behaviors, a valid and reliable questionnaire is needed to assess potential implementation determinants. The present study describes the development of the Determinants of Implementation Behavior Questionnaire (DIBQ) and investigates the reliability and validity of this Theoretical Domains Framework (TDF)-based questionnaire.MethodsThe DIBQ was developed to measure the potential behavioral determinants of the 12-domain version of the TDF (Michie et al., 2005). We identified existing questionnaires including items assessing constructs within TDF domains and developed new items where needed. Confirmatory factor analysis was used to examine whether the predefined structure of the TDF-based questionnaire was supported by the data. Cronbach's alpha was calculated to assess internal consistency reliability of the questionnaire, and domains' discriminant validity was investigated.ResultsWe developed an initial questionnaire containing 100 items assessing 12 domains. Results obtained from confirmatory factor analysis and Cronbach's alpha resulted in the final questionnaire consisting of 93 items assessing 18 domains, explaining 63.3% of the variance, and internal consistency reliability values ranging from .68 to .93. Domains demonstrated good discriminant validity, although the domains 'Knowledge' and 'Skills' and the domains 'Skills' and 'Social/professional role and identity' were highly correlated.ConclusionsWe have developed a valid and reliable questionnaire that can be used to assess potential determinants of healthcare professional implementation behavior following the theoretical domains of the TDF. The DIBQ can be used by researchers and practitioners who are interested in identifying determinants of implementation behaviors in order to be able to develop effective strategies to improve healthcare professionals' implementation behaviors. Furthermore, the findings provide a novel validation of the TDF and indicate that the domain 'Environmental context and resources' might be divided into several environment-related domains.

Dataset Information

A Connectivity-Based Psychometric Prediction Framework for Brain-Behavior Relationship Studies.

Publications

A Connectivity-Based Psychometric Prediction Framework for Brain-Behavior Relationship Studies.

Similar Datasets

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