SpExNeuro - A behavioural and brain imaging data collection of speech and executive functions
Poster No:
746
Submission Type:
Abstract Submission
Authors:
Gianna Kuhles1,2, Julia Camilleri1, Simon Eickhoff1,2, Susanne Weis1,2
Institutions:
1Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany, 2Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
First Author:
Gianna Kuhles
Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich|Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf
Jülich, Germany|Düsseldorf, Germany
Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich|Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf
Jülich, Germany|Düsseldorf, Germany
Co-Author(s):
Julia Camilleri
Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich
Jülich, Germany
Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich
Jülich, Germany
Simon Eickhoff
Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich|Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf
Jülich, Germany|Düsseldorf, Germany
Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich|Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf
Jülich, Germany|Düsseldorf, Germany
Susanne Weis
Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich|Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf
Jülich, Germany|Düsseldorf, Germany
Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich|Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf
Jülich, Germany|Düsseldorf, Germany
Introduction:
The relationship between speech and executive functions (EF) is well documented (Hagoort, 2017; Novick et al., 2005), but the validity of speech biomarkers for cognitive performance remains inconclusive (Robin et al., 2020). Moreover, the interactions between the subdomains of EF and speech are under-researched, specifically regarding how they vary between individuals. Foundational studies are needed to investigate these associations, focusing on the potential predictive power of linguistic parameters. To this end, the SpEx study (Camilleri & Volkening et al., 2024) acquired speech and EF data from 148 participants, enabling comprehensive analyses. Previous studies have used this dataset together with ML analyses to study the connection between EF and verbal fluency (Amunts et al., 2020, 2021). Adding to these analyses, we here evaluated the predictiveness of EF by prosody. However, the SpEx study only focused on behavioural data. To facilitate the investigation of neural mechanisms of EF-speech interactions, we here present the SpExNeuro study containing a larger sample and neuroimaging data.
Methods:
As part of the objectives of the SpEx study, we examined suprasegmental features as predictors of EF performance (Kuhles et al., in review). EF performance was measured using standard tests, capturing 66 variables across cognitive flexibility, working memory, inhibition, and attention domains. 264 prosodic features, including frequency, energy, spectral, and temporal dimensions, were extracted from the speech data using OpenSmile. ML analyses with 10-fold cross-validation employed RF regressors to predict EF from prosodic features, evaluated by R² metrics. Confounding effects of sex, age, and education were regressed out. Building on SpEx, SpExNeuro expands the dataset with additional behavioral and neuroimaging data (Fig. 1). During a ~2 h MRI session, subjects perform EF core domain tests and speech tasks (verbal fluency, spontaneous speech). Brain activities are measured using fMRI on a Siemens Prisma 3-T scanner with a 64-channel head coil. In addition to this task-based fMRI, RS, and DTI data are collected.
Results:
Among the predicted EF targets, reasonable model fits were found only for the EF targets TMT BTA and TMT BTB. However, deeper analyses uncovered significant confound leakage, revealing that the relationships between confounds (age, sex, and education) and the EF targets inflated prediction accuracy (Fig. 2). These findings emphasise the critical need to carefully control for confounding variables in ML pipelines and highlight significant risks in the interpretation of ML predictions.
To overcome the limitations of current datasets, a larger and more diverse validation set, including both behavioral and neuroimaging data, is urgently needed to ensure the generalisability of findings and elucidate underlying mechanisms. The ongoing SpExNeuro study addresses these challenges by capturing a comprehensive range of variables across behavioral, linguistic, neuroimaging, and neuroendocrine domains. The data will be openly accessible to maximise its utility for future research on individual differences in speech and EF.
To overcome the limitations of current datasets, a larger and more diverse validation set, including both behavioral and neuroimaging data, is urgently needed to ensure the generalisability of findings and elucidate underlying mechanisms. The ongoing SpExNeuro study addresses these challenges by capturing a comprehensive range of variables across behavioral, linguistic, neuroimaging, and neuroendocrine domains. The data will be openly accessible to maximise its utility for future research on individual differences in speech and EF.
Conclusions:
Together, our studies highlight the need for robust methodologies, both experimental and computational, to deepen our understanding of EF and speech relationships. The SpEx and SpExNeuro datasets provide a critical resource for investigating complex interactions between speech and EF, integrating behavioral, linguistic, and neuroimaging data. The data enables the study of individual differences in EF and speech and their interaction with demographic, hormonal, and neuroanatomical factors. Using our data, advanced ML analyses can uncover shared brain activation patterns, bridging behavioral and neural data, and enhancing individualized biomarkers. We encourage scientists to leverage this growing dataset for collaborative research.
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making 1
Language:
Speech Production 2
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Diffusion MRI Modeling and Analysis
Neuroinformatics and Data Sharing:
Databasing and Data Sharing
Keywords:
Acquisition
ADULTS
Cognition
Data analysis
FUNCTIONAL MRI
Language
Machine Learning
MRI
NORMAL HUMAN
Open Data
1|2Indicates the priority used for review
Abstract Information
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2. Amunts, J., Camilleri, J. A., Eickhoff, S. B., Patil, K. R., Heim, S., Von Polier, G. G., & Weis, S. (2021). Comprehensive verbal fluency features predict executive function performance. Scientific reports, 11(1), 6929.
3. Camilleri, J. A., Volkening, J., Heim, S., Mochalski, L. N., Neufeld, H., Schlothauer, N., Kuhles, G., Eickhoff, S. B. & Weis, S. (2024). SpEx: a German-language dataset of speech and executive function performance. Scientific Reports, 14(1), 9431.
4. Hagoort, P. (2017). The core and beyond in the language-ready brain. Neuroscience & Biobehavioral Reviews, 81, 194-204.
5. Kuhles, G., Hamdan, S., Heim, S., Eickhoff, S., Patil, K. R., Camilleri, J., & Weis, S. (in review). Pitfalls in using ML to predict cognitive function performance. Research Square.
6. Novick, J. M., Trueswell, J. C., & Thompson-Schill, S. L. (2005). Cognitive control and parsing: Reexamining the role of Broca’s area in sentence comprehension. Cognitive, Affective, & Behavioral Neuroscience, 5(3), 263-281.
7. Robin, J., Harrison, J. E., Kaufman, L. D., Rudzicz, F., Simpson, W., & Yancheva, M. (2020). Evaluation of speech-based digital biomarkers: review and recommendations. Digital Biomarkers, 4(3), 99-108.