001033600 001__ 1033600
001033600 005__ 20241213210708.0
001033600 037__ $$aFZJ-2024-06480
001033600 041__ $$aEnglish
001033600 1001_ $$0P:(DE-Juel1)198947$$aBoztoprak, Zeynep$$b0$$eCorresponding author$$ufzj
001033600 1112_ $$aINM Retreat 2024$$cJülich$$d2024-11-19 - 2024-11-20$$wGermany
001033600 245__ $$aTowards Decoding Fiber Architecture in the Human Brain Using Deep Learning
001033600 260__ $$c2024
001033600 3367_ $$033$$2EndNote$$aConference Paper
001033600 3367_ $$2BibTeX$$aINPROCEEDINGS
001033600 3367_ $$2DRIVER$$aconferenceObject
001033600 3367_ $$2ORCID$$aCONFERENCE_POSTER
001033600 3367_ $$2DataCite$$aOutput Types/Conference Poster
001033600 3367_ $$0PUB:(DE-HGF)24$$2PUB:(DE-HGF)$$aPoster$$bposter$$mposter$$s1734067768_20224$$xAfter Call
001033600 520__ $$aAnalyzing the structural organization of the human brain involves the study of nerve fibers that connect neurons or whole brain regions. Characterizing the arrangement of these anatomical connections, also known as fiber architecture, contributes to a better understanding of how the brain processes information. Three-dimensional Polarized Light Imaging (3D-PLI) allows the visualization of these short- and long-range single nerve fibers and fiber bundles, providing detailed images of fiber organization at the microscopic level. However, analyzing and interpreting the large amounts of complex data is a time-consuming task that requires expert knowledge and a lot of computational power, making manual engineering difficult and leading us to use a data-driven approach to automatically learn descriptive features of nerve fibers.<br><br>Previous work has demonstrated the ability of deep learning methods in identifying characteristics of nerve fibers in the vervet monkey brain, such as boundaries between gray and white matter and specific U-fibers. Building upon these promising results, our present study extends this research to focus on the adult human brain.<br><br>We train a deep neural network to map high-resolution image patches extracted from 3D-PLI sections to feature vectors that encode the fiber architectonic properties encoded in the patch. Exploiting the fact that spatially close locations in the brain tend to share architectonic properties, we train the neural network using a contrastive learning objective that promotes the mapping of spatially close image patches to similar feature representations. The model is trained on 30.000 patches (size 2048x2048 px at 2.66 px/m) from 26 3D-PLI sections. Learned features are then analyzed by visualizing their corresponding U-MAP embeddings in 2D.<br><br>Our analysis shows that features from patches sampled from different sections tend to cluster based on section number rather than architectural similarity. In particular, features of patches from distant sections are also distant in feature space, suggesting that the network captures section-specific features rather than the underlying brain architecture. We hypothesize that this effect is due to the uneven distribution of the available PLI-scanned sections in certain parts of the brain (non-isotropic), which affects the spatial consistency of the data. As we do not observe clustering that reflects anatomical features, suggesting that the current model fails to capture these structural properties, future work will address this challenge by exploring data augmentation techniques to introduce variability and counteract differences between sections and sampling strategies. These approaches aim to encourage the network to focus on architectural features of the brain rather than section-specific features.
001033600 536__ $$0G:(DE-HGF)POF4-5251$$a5251 - Multilevel Brain Organization and Variability (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001033600 536__ $$0G:(DE-HGF)POF4-5254$$a5254 - Neuroscientific Data Analytics and AI (POF4-525)$$cPOF4-525$$fPOF IV$$x1
001033600 536__ $$0G:(DE-HGF)ZT-I-PF-4-061$$aX-BRAIN (ZT-I-PF-4-061)$$cZT-I-PF-4-061$$x2
001033600 536__ $$0G:(DE-HGF)InterLabs-0015$$aHIBALL - Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)$$cInterLabs-0015$$x3
001033600 536__ $$0G:(DE-Juel-1)E.40401.62$$aHelmholtz AI - Helmholtz Artificial Intelligence  Coordination Unit – Local Unit FZJ (E.40401.62)$$cE.40401.62$$x4
001033600 7001_ $$0P:(DE-Juel1)131631$$aAmunts, Katrin$$b1$$ufzj
001033600 7001_ $$0P:(DE-Juel1)165746$$aDickscheid, Timo$$b2$$ufzj
001033600 7001_ $$0P:(DE-Juel1)170068$$aSchiffer, Christian$$b3$$ufzj
001033600 909CO $$ooai:juser.fz-juelich.de:1033600$$pVDB
001033600 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)198947$$aForschungszentrum Jülich$$b0$$kFZJ
001033600 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131631$$aForschungszentrum Jülich$$b1$$kFZJ
001033600 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)165746$$aForschungszentrum Jülich$$b2$$kFZJ
001033600 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)170068$$aForschungszentrum Jülich$$b3$$kFZJ
001033600 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5251$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x0
001033600 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5254$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x1
001033600 9141_ $$y2024
001033600 920__ $$lyes
001033600 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
001033600 980__ $$aposter
001033600 980__ $$aVDB
001033600 980__ $$aI:(DE-Juel1)INM-1-20090406
001033600 980__ $$aUNRESTRICTED