% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Domhof:907813,
      author       = {Domhof, Justin and Eickhoff, Simon and Popovych, Oleksandr},
      title        = {{R}eliability and subject specificity of personalized
                      dynamical whole-brain models},
      reportid     = {FZJ-2022-02230},
      year         = {2022},
      abstract     = {- Introduction - Dynamical whole-brain models originally
                      provided a biophysically-inspired approach to investigate
                      the relationship between the structural (SC) and functional
                      (FC) connectivity (Honey 2009). Nowadays, they are also used
                      to study the dynamical regimes of the brain and how these
                      relate to various subject traits (Bansal 2018).
                      Nevertheless, it is unclear how the modeling results perform
                      in terms of test-retest reliability and subject specificity
                      as measured through, e.g., identification accuracies. Here,
                      we systematically assess these aspects of the modeling
                      results, and examine how they relate to the reliability and
                      subject specificity of empirical data.- Methods - We used
                      the empirical SC and FC matrices of 200 healthy unrelated
                      subjects (96 males, aged 28.5 ± 3.5 years) from the Human
                      Connectome Project (Van Essen, 2012, 2013). The models were
                      built on the basis of the empirical SC matrices of
                      individuals and were either a network of neural mass models
                      (Wilson, 1972) or a system of coupled phase oscillators
                      (Kuramoto, 1984). In order to estimate how model
                      personalization could contribute to the subject specificity,
                      the latter model used region-specific natural frequencies
                      extracted from empirical data that were either subject
                      specific or the same for all subjects. The models were
                      simulated for a broad range of parameter settings to yield
                      the simulated FC matrices exhibiting the highest correlation
                      with the empirical FCs. Four empirical FC matrices (2
                      phase-encoding directions scanned on 2 days) and,
                      correspondingly, four simulated FC matrices were available
                      per subject for further analyses. We evaluated the
                      within-subject correlations of both types of FCs (empirical
                      and simulated), which served as proxies for their
                      reliability. Additionally, we calculated the between-subject
                      correlations, and used the difference between the intra- and
                      inter-subject correlations (specificity index) as a
                      characterization of the subject specificity. Finally, we
                      adapted the fingerprinting analysis from Finn et al. (2015)
                      to provide an additional measure for the subject specificity
                      of the FCs.- Results - The results show that the reliability
                      of the simulated FC can exceed that of the empirical one
                      (Fig. 1A), especially, for the structural atlases and for
                      the phase oscillator model regardless of the strategy with
                      respect to the natural frequencies (Fig. 1A, orange and
                      red), Also, the subject specificity of the simulated FC may
                      outperform that of the empirical one (Fig. 1B-C). Here
                      again, the phase oscillator model with subject-specific
                      frequencies generated FCs with a much higher subject
                      specificity than the other, less personalized modeling
                      paradigms (Fig. 1B-C, red).In addition, the atlas has a
                      larger influence on the reliability of the simulated FC than
                      on that of the empirical FC (Fig. 1A). There seems to be a
                      clear distinction between structurally- and
                      functionally-derived atlases, which result in more and less
                      reliable simulated FCs, respectively (Fig. 1A, left vs.
                      right block). Analogously, a change of parcellation affected
                      the subject specificities of the simulated FC much more than
                      those of the empirical FC (Fig. 1B-C). In particular, the
                      atlas may determine whether the subject specificities of the
                      empirical and simulated FC are at about the same or
                      different levels (Fig. 1B-C).- Conclusions - Our results
                      showed that the reliability and the subject specificity can
                      be higher for the simulated than for the empirical FC. We
                      also demonstrated the critical roles that the parcellation
                      and model implementation have on the findings. Taken
                      together, our results indicate that whole-brain dynamical
                      models can generate simulated connectomes with high
                      reliability and (subject) specificity and may outperform the
                      empirical data in this respect. In turn, this suggests that
                      these models potentially reduce the variance in the
                      empirical FC across different realizations for a single
                      subject by providing a reliable model fit for further
                      analyses.},
      month         = {Jun},
      date          = {2022-06-07},
      organization  = {The 28th Annual Meeting of the
                       Organization for Human Brain Mapping,
                       Virtual (Virtual), 7 Jun 2022 - 8 Jun
                       2022},
      subtyp        = {After Call},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5232 - Computational Principles (POF4-523)},
      pid          = {G:(DE-HGF)POF4-5232},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/907813},
}