Gast ::
| Hauptseite > Publikationsdatenbank > Reliability and subject specificity of personalized dynamical whole-brain models > print |
| Hauptseite > Publikationsdatenbank > Reliability and subject specificity of personalized dynamical whole-brain models > print |
| 001 | 907813 | ||
| 005 | 20230109132012.0 | ||
| 024 | 7 | _ | |a 2128/31209 |2 Handle |
| 037 | _ | _ | |a FZJ-2022-02230 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Domhof, Justin |0 P:(DE-Juel1)179582 |b 0 |u fzj |
| 111 | 2 | _ | |a The 28th Annual Meeting of the Organization for Human Brain Mapping |g OHBM2022 |c Virtual |d 2022-06-07 - 2022-06-08 |w Virtual |
| 245 | _ | _ | |a Reliability and subject specificity of personalized dynamical whole-brain models |
| 260 | _ | _ | |c 2022 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a CONFERENCE_POSTER |2 ORCID |
| 336 | 7 | _ | |a Output Types/Conference Poster |2 DataCite |
| 336 | 7 | _ | |a Poster |b poster |m poster |0 PUB:(DE-HGF)24 |s 1673261873_7573 |2 PUB:(DE-HGF) |x After Call |
| 520 | _ | _ | |a - 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. |
| 536 | _ | _ | |a 5232 - Computational Principles (POF4-523) |0 G:(DE-HGF)POF4-5232 |c POF4-523 |f POF IV |x 0 |
| 700 | 1 | _ | |a Eickhoff, Simon |0 P:(DE-Juel1)131678 |b 1 |u fzj |
| 700 | 1 | _ | |a Popovych, Oleksandr |0 P:(DE-Juel1)131880 |b 2 |e Corresponding author |u fzj |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/907813/files/Poster.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:907813 |p openaire |p open_access |p VDB |p driver |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)179582 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)131678 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)131880 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-523 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Neuromorphic Computing and Network Dynamics |9 G:(DE-HGF)POF4-5232 |x 0 |
| 914 | 1 | _ | |y 2022 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-7-20090406 |k INM-7 |l Gehirn & Verhalten |x 0 |
| 980 | _ | _ | |a poster |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)INM-7-20090406 |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | 1 | _ | |a FullTexts |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|