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@INPROCEEDINGS{Kuhles:1044377,
      author       = {Kuhles, Gianna and Thies, Tabea and Barbe, Michael and
                      Eickhoff, Simon and Camilleri, Julia and Weis, Susanne},
      title        = {{P}rediction of depression in {P}arkinson’s disease by
                      prosodic speech features},
      reportid     = {FZJ-2025-03152},
      year         = {2025},
      note         = {This study was supported by▪ the Deutsche
                      Forschungsgemeinschaft (DFG, GE 2835/2–1, EI 816/16-1 and
                      EI 816/21-1),▪ the National Institute of Mental Health
                      (R01-MH074457),▪ the Helmholtz Portfolio Theme
                      "Supercomputing and Modeling for the HumanBrain",▪ the
                      Virtual Brain Cloud (EU H2020, no. 826421) $\&▪$ the
                      National Institute on Aging (R01AG067103)},
      abstract     = {Introduction: Parkinson’s disease (PD) is a progressive
                      neurodegenerative disease whichmainly affects functions of
                      the motor system. Depressive disorders (DD) are one of the
                      mostcommon comorbidities in PD (Perez-Toro et al., 2022) and
                      often lead to a decrease in qualityof life (Balestrino $\&$
                      Martinez-Martin, 2017). Previous research revealed a strong
                      relationshipbetween prosodic impairment and DD (Vélez
                      Feijó et al., 2008) which is why recent studieshave
                      investigated whether speech parameters might be suitable as
                      biomarkers for the predictionof depression in PD. However,
                      the variability in feature extraction tools and
                      methodologies hasleft the most predictive prosodic
                      parameters inadequately defined (Perez-Toro et al., 2022).
                      Inour study, we aim to address this gap by identifying
                      reliable, standardised features for predictingdepression in
                      PD.Methods: Healthy subjects (n = 249) and people with PD (n
                      = 121) performed a spontaneousspeech task of 90 s duration.
                      88 acoustic features related to prosodic functions
                      wereautomatically extracted from these speech samples using
                      the toolbox OpenSMILE. Participantsalso completed the Beck
                      Depression Inventory (BDI-II) to assess depressive
                      symptoms.Prosodic features were used to classify between
                      healthy subjects and people with PD.Additionally, a machine
                      learning regression approach was employed to predict
                      individual BDI-II scores from prosodic speech features and
                      to determine the most significant predictors.Results:
                      Firstly, the classification analysis demonstrated a high
                      level of accuracy (0.997) indistinguishing between healthy
                      subjects and individuals with PD based on the prosodic
                      features.Secondly, the regression analysis revealed an R2
                      value of 0.024, indicating limited predictivepower for
                      BDI-II scores derived from prosodic features. However,
                      consistent with priorresearch, spectral parameters emerged
                      as the most significant predictors, especially the
                      MelFrequency Cepstral Coefficients, which describe the
                      perceived pitch of the frequency spectrum.Discussion:
                      Although the model demonstrated satisfactory performance in
                      classifying betweenhealthy individuals and those with
                      Parkinson's disease, the rather low accuracy in
                      predictingdepressive disorders (DD) highlights the
                      complexity of using prosodic features for this purpose.While
                      present results need to be validated in larger and diverse
                      samples, they pave the way tothe development of uniform
                      methodologies for the systematic extraction of
                      speech-relatedbiomarkers in PD, which in the long run might
                      have clinical utility for better understanding DDin people
                      with PD.Pérez-Toro PA et al. Speech Commun. (2022)
                      145:10-20.Balestrino R $\&$ Martinez-Martin P J. Neurol.
                      Sci. (2017) 374: 3-8.Vélez Feijó A et al. Neuro. Dis.
                      Treat. (2008) 4:669-674.},
      month         = {Jan},
      date          = {2025-01-26},
      organization  = {43. European Workshop on Cognitive
                       Neuropsychology, Bressanone (Italy), 26
                       Jan 2025 - 30 Jan 2025},
      subtyp        = {After Call},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.34734/FZJ-2025-03152},
      url          = {https://juser.fz-juelich.de/record/1044377},
}