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| Hauptseite > Publikationsdatenbank > Towards Decoding Fiber Architecture in the Human Brain Using Deep Learning > print |
| Hauptseite > Publikationsdatenbank > Towards Decoding Fiber Architecture in the Human Brain Using Deep Learning > print |
| 001 | 1033600 | ||
| 005 | 20241213210708.0 | ||
| 037 | _ | _ | |a FZJ-2024-06480 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Boztoprak, Zeynep |0 P:(DE-Juel1)198947 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a INM Retreat 2024 |c Jülich |d 2024-11-19 - 2024-11-20 |w Germany |
| 245 | _ | _ | |a Towards Decoding Fiber Architecture in the Human Brain Using Deep Learning |
| 260 | _ | _ | |c 2024 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
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| 520 | _ | _ | |a Analyzing 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. 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. 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. 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. |
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