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| Hauptseite > Online First > Fine-Grained Cytoarchitectonic Parcellation of Broca’s Region SupportsFunctional Differentiation — In Julich-Brain Atlas (EBRAINS) > print |
| Hauptseite > Online First > Fine-Grained Cytoarchitectonic Parcellation of Broca’s Region SupportsFunctional Differentiation — In Julich-Brain Atlas (EBRAINS) > print |
| 001 | 1048795 | ||
| 005 | 20251203103905.0 | ||
| 037 | _ | _ | |a FZJ-2025-04910 |
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| 100 | 1 | _ | |a Fedorchenko, Nataliia |0 P:(DE-Juel1)194187 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a EBRAINS summit 2025 |c Brüssel |d 2025-12-08 - 2025-12-11 |w Belgium |
| 245 | _ | _ | |a Fine-Grained Cytoarchitectonic Parcellation of Broca’s Region SupportsFunctional Differentiation — In Julich-Brain Atlas (EBRAINS) |
| 260 | _ | _ | |c 2025 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
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| 520 | _ | _ | |a Introduction Broca’s region plays a key role in language and action processing, yet its classical subdivision intoareas 44 and 45 does not fully capture its functional heterogeneity. Existing anatomical maps oftenlack sufficient granularity and do not adequately reflect interindividual variability. To address this, weperformed a detailed cytoarchitectonic parcellation of Broca’s region, complemented by 3Dreconstruction and layer-specific cell segmentation approaches. The resulting maps will be integratedinto the EBRAINS infrastructure to facilitate neuroimaging and brain modelling research. Methods Ten post-mortem human brains (5 female, 5 male; age range 30–80 years) were analyzed using anobserver-independent, quantitative cytoarchitectonic mapping method (Bludau et al., 2014). Thisapproach identified four subdivisions—44p, 44a, 45p, and 45a—arranged along the anterior-posterioraxis of Broca’s region. 3D probability maps (PMs), and maximum probability maps (MPMs) weregenerated in MNI Colin27 and MNI152 stereotaxic reference spaces to capture interindividual spatialvariability. Ultra-high-resolution 3D reconstructions were performed using the BigBrain (Amunts etall., 2013) dataset at 1 μm isotropic resolution following the methods of (Schiffer et al., 2021). Layerspecific cell segmentation was conducted using Contour Proposal Networks (CPN), a state-of-the-artobject instance segmentation method for biomedical images (Upschulte et al., 2022); quantitative cellcounts are pending. Lateralization was quantified by calculating Euclidean distances between left andright homologous subdivisions. Structural relationships with adjacent cortical areas were explored viahierarchical clustering and multidimensional scaling. Functional relevance was assessed by mappingfMRI activation peaks reported in key studies (Goucha & Friederici, 2015; Zaccarella & Friederici,2015; Papitto et al., 2024). Furthermore, a meta-analysis using MPMs of the four subdivisions asspeed regions identified brain areas consistently co-activated during various cognitive andsensorimotor tasks. Results The four subdivisions—44p, 44a, 45p, and 45a—were robustly identified and exhibited distinctcytoarchitectonic features. Probabilistic maps showed stable spatial distributions with measurableinterindividual variability. BigBrain-based reconstructions provided detailed visualization ofmicrostructural anatomy. Layer-specific cell segmentation delineated cortical layers, with quantitativeanalysis ongoing. Lateralization analysis revealed left-right spatial asymmetries, with medianEuclidean distances of approximately 1.5–2.5 for key subdivisions, supporting known left-hemispheredominance in language processing. Functional mapping linked 44p primarily with action and syntax, 44a with syntax, and 45p/45a with semantic processing. The meta-analysis further confirmeddifferential co- activation patterns across the four subdivisions, highlighting their functionalspecialization. Conclusions This work offers a refined cytoarchitectonic parcellation of Broca’s region with probabilistic 3D maps,supporting its functional differentiation. Integration with high-resolution 3D reconstructions and layerspecific segmentation advances the microstructural understanding of this critical language areas.Lateralization results align with known hemispheric specialization. All data and maps will be madepublicly available via the Julich-Brain Atlas (Amunts et al., 2020) on the EBRAINS platform,promoting FAIR data access and supporting future neuroimaging, brain modeling, and structurefunction investigations. Aknowledgements This work was in part funded by Max Planck School of Cognition, Leipzig, Germany, as well receiveda funding from European Union’s Horizon 2020 Research and Innovation Programme under ´GrantAgreement No. 101147319 (EBRAINS 2.0 Project) as well as from the Helmholtz Association’sInitiative and Networking Fund through the Helmholtz International BigBrain Analytics and Learninglaboratory (HIBALL) under the Helmholtz International Lab grant agreement InterLabs-0015 References
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| 536 | _ | _ | |a EBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to Advance Neuroscience and Brain Health (101147319) |0 G:(EU-Grant)101147319 |c 101147319 |f HORIZON-INFRA-2022-SERV-B-01 |x 1 |
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| 700 | 1 | _ | |a Ruland, Sabine Helene |0 P:(DE-Juel1)144196 |b 1 |u fzj |
| 700 | 1 | _ | |a Mohlberg, Hartmut |0 P:(DE-Juel1)131660 |b 2 |u fzj |
| 700 | 1 | _ | |a Bludau, Sebastian |0 P:(DE-Juel1)131636 |b 3 |u fzj |
| 700 | 1 | _ | |a Schiffer, Christian |0 P:(DE-Juel1)170068 |b 4 |u fzj |
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| 700 | 1 | _ | |a Friederici, Angela C. |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Amunts, Katrin |0 P:(DE-Juel1)131631 |b 8 |u fzj |
| 856 | 4 | _ | |u https://cdn.prod.website-files.com/67fd5fb0e5cec89dfb6b65be/692d689da7f10aeb4b0bded3_EBRAINS-Summit-2025-Book-of-Abstracts-1-Dec.pdf |
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