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@INPROCEEDINGS{Kuhles:1043736,
      author       = {Kuhles, Gianna and Hoffstaedter, Felix and Eickhoff, Simon
                      and Weis, Susanne and Camilleri, Julia},
      title        = {{S}p{E}x{N}euro – {A} behavioural and brain imaging data
                      collection of speech and executive functions},
      reportid     = {FZJ-2025-03018},
      year         = {2025},
      abstract     = {IntroductionThe relationship between speech and executive
                      functions (EF) is well documented (Hagoort,2017; Novick et
                      al., 2005), but the validity of speech biomarkers for
                      cognitive performanceremains inconclusive (Robin et al.,
                      2020). Moreover, the interactions between the subdomainsof
                      EF and speech are under-researched, specifically regarding
                      how they vary betweenindividuals. Foundational studies are
                      needed to investigate these associations, focusing on
                      thepotential predictive power of linguistic parameters. To
                      this end, the SpEx study (Camilleri $\&Volkening$ et al.,
                      2024) acquired speech and EF data from 148 participants,
                      enablingcomprehensive analyses. Previous studies have used
                      this dataset together with ML analysesto study the
                      connection between EF and verbal fluency (Amunts et al.,
                      2020, 2021). Adding tothese analyses, we here evaluated the
                      predictiveness of EF by prosody. However, the SpExstudy only
                      focused on behavioural data. To facilitate the investigation
                      of neural mechanismsof EF-speech interactions, we here
                      present the SpExNeuro study containing a larger sampleand
                      neuroimaging data.MethodsAs part of the objectives of the
                      SpEx study, we examined suprasegmental features aspredictors
                      of EF performance (Kuhles et al., in review). EF performance
                      was measured usingstandard tests, capturing 66 variables
                      across cognitive flexibility, working memory, inhibition,and
                      attention domains. 264 prosodic features, including
                      frequency, energy, spectral, andtemporal dimensions, were
                      extracted from the speech data using OpenSmile. ML
                      analyseswith 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 andneuroimaging data
                      (Fig. 1). During a ~2 h MRI session, subjects perform EF
                      core domain testsand speech tasks (verbal fluency,
                      spontaneous speech). Brain activities are measured usingfMRI
                      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.ResultsAmong the predicted EF targets, reasonable
                      model fits were found only for the EF targets TMTBTA and TMT
                      BTB. However, deeper analyses uncovered significant confound
                      leakage,revealing that the relationships between confounds
                      (age, sex, and education) and the EFtargets inflated
                      prediction accuracy (Fig. 2). These findings emphasise the
                      critical need tocarefully control for confounding variables
                      in ML pipelines and highlight significant risks in
                      theinterpretation 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 thegeneralisability of
                      findings and elucidate underlying mechanisms. The ongoing
                      SpExNeurostudy addresses these challenges by capturing a
                      comprehensive range of variables acrossbehavioral,
                      linguistic, neuroimaging, and neuroendocrine domains. The
                      data will be openlyaccessible to maximise its utility for
                      future research on individual differences in speech
                      andEF.ConclusionsTogether, our studies highlight the need
                      for robust methodologies, both experimental
                      andcomputational, to deepen our understanding of EF and
                      speech relationships. The SpEx andSpExNeuro datasets provide
                      a critical resource for investigating complex interactions
                      betweenspeech and EF, integrating behavioral, linguistic,
                      and neuroimaging data. The data enablesthe study of
                      individual differences in EF and speech and their
                      interaction with demographic,hormonal, and neuroanatomical
                      factors. Using our data, advanced ML analyses can
                      uncovershared brain activation patterns, bridging behavioral
                      and neural data, and enhancingindividualized biomarkers. We
                      encourage scientists to leverage this growing dataset
                      forcollaborative research.},
      month         = {Jun},
      date          = {2025-06-23},
      organization  = {Organization for Human Brain Mapping,
                       Brisbane (Australia), 23 Jun 2025 - 28
                       Jun 2025},
      subtyp        = {After Call},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / 5252 - Brain Dysfunction and Plasticity
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(DE-HGF)POF4-5252},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.34734/FZJ-2025-03018},
      url          = {https://juser.fz-juelich.de/record/1043736},
}