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@ARTICLE{vonPolier:1042825,
      author       = {von Polier, Georg G. and Ahlers, Eike and Volkening, Julia
                      and Langner, Jörg and Patil, Kaustubh R. and Eickhoff,
                      Simon B. and Helmhold, Florian and Krautz, Agnieszka Ewa and
                      Langner, Daina},
      title        = {{E}xploring voice as a digital phenotype in adults with
                      {ADHD}},
      journal      = {Scientific reports},
      volume       = {15},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Springer Nature},
      reportid     = {FZJ-2025-02663},
      pages        = {18076},
      year         = {2025},
      abstract     = {Current diagnostic procedures for attention deficit
                      hyperactivity disorder (ADHD) are mainly subjective and
                      prone to bias. While research on potential biomarkers,
                      including EEG, brain imaging, and genetics is promising, it
                      has yet to demonstrate clinical utility. Dopaminergic
                      signaling alternations and executive functioning, crucial to
                      ADHD pathology, are closely related to voice production.
                      Consistently, previous studies point to alterations in voice
                      and speech production in ADHD. However, studies
                      investigating voice in large clinical samples allowing for
                      individual-level prediction of ADHD are lacking. Here, 387
                      ADHD patients, 204 healthy controls, and 100 psychiatric
                      controls underwent standardized diagnostic assessment.
                      Subjects provided multiple 3-minutes speech samples,
                      yielding 920 samples. Based on prosodic voice features,
                      random forest-based classifications were performed, and
                      cross-validated out-of-sample accuracy was calculated. The
                      classification of ADHD showed the best performance in young
                      female participants (AUC = 0.87) with lower performance
                      in older participants and males. Psychiatric comorbidity did
                      not alter the classification performance. Voice features
                      were associated with ADHD-symptom severity as indicated by
                      random forest regressions. In summary, prosodic features
                      seem to be promising candidates for further research into
                      voice-based digital phenotypes of ADHD.},
      cin          = {INM-7},
      ddc          = {600},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5254},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {40413201},
      UT           = {WOS:001494675200023},
      doi          = {10.1038/s41598-025-01989-x},
      url          = {https://juser.fz-juelich.de/record/1042825},
}