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The accurate estimation of fiber orientation distribution functions (fODFs) in diffusion magnetic resonance imaging (MRI) is crucial for understanding early brain development and its potential disruptions. Although supervised deep learning (DL) models have shown promise in fODF estimation from neonatal diffusion MRI (dMRI) data, the out‐of‐domain (OOD) performance of these models remains largely unexplored, especially under diverse domain shift scenarios. This study evaluated the robustness of three state‐of‐the‐art DL architectures: multilayer perceptron (MLP), transformer, and U‐Net/convolutional neural network (CNN) on fODF predictions derived from dMRI data. Using 488 subjects from the developing Human Connectome Project (dHCP) and the Baby Connectome Project (BCP) datasets, we reconstructed reference fODFs from the full dMRI series using single‐shell three‐tissue constrained spherical deconvolution (SS3T‐CSD) and multi‐shell multi‐tissue CSD (MSMT‐CSD) to generate reference fODF reconstructions for model training, and systematically assessed the impact of age, scanner/protocol differences, and input dimensionality on model performance. Our findings reveal that U‐Net consistently outperformed other models when fewer diffusion gradient directions were used, particularly with the SS3T‐CSD‐derived ground truth, which showed superior performance in capturing crossing fibers. However, as the number of input diffusion gradient directions increased, MLP and the transformer‐based model exhibited steady gains in accuracy. Nevertheless, performance nearly plateaued from 28 to 45 input directions in all models. Age‐related domain shifts showed asymmetric patterns, being less pronounced in late developmental stages (late neonates, and babies), with SS3T‐CSD demonstrating greater robustness to variability compared to MSMT‐CSD. To address inter‐site domain shifts, we implemented two adaptation strategies: the Method of Moments (MoM) and fine‐tuning. Both strategies achieved significant improvements (p<0.05 $$ p<0.05 $$ ) in over 95% of tested configurations, with fine‐tuning consistently yielding superior results and U‐Net benefiting the most from increased target subjects. This study represents the first systematic evaluation of OOD settings in DL applications to fODF estimation, providing critical insights into model robustness and adaptation strategies for diverse clinical and research applications. We quantify domain‐shift impacts of three state‐of‐the‐art deep learning models for fiber orientation estimation in dMRI of neonatal and baby brains, across age, scanner, input variations, target output ground truths, and demonstrate how fine‐tuning and data harmonization strategies improve model robustness for clinical and research applications.

Diffusion Magnetic Resonance Imaging (dMRI) is a noninvasive method for depicting brain microstructure . Fiber orientation distributions (FODs) are mathematical representations extensively used to map white matter fiber configurations. Recently, FOD estimation with deep neural networks has seen growing success, in particular, those of neonates estimated with fewer diffusion measurements. These methods are mostly trained on target FODs reconstructed with (MSMT-CSD), which might not be the ideal ground truth for developing brains. Here, we investigate this hypothesis by training a state-of-the-art model based on the U-Net architecture on both MSMT-CSD and (SS3T-CSD). Our results suggest that SS3T-CSD might be more suited for neonatal brains, given that the ratio between single and multiple fiber-estimated voxels with SS3T-CSD is more realistic compared to MSMT-CSD. Additionally, increasing the number of input gradient directions significantly improves performance with SS3T-CSD over MSMT-CSD. Finally, in an age domain-shift setting, SS3T-CSD maintains robust performance across age groups, indicating its potential for more accurate neonatal brain imaging.

Deep learning models have shown great promise in estimating tissue microstructure from limited diffusion magnetic resonance imaging data. However, these models face domain shift challenges when test and train data are from different scanners and protocols, or when the models are applied to data with inherent variations such as the developing brains of infants and children scanned at various ages. Several techniques have been proposed to address some of these challenges, such as data harmonization or domain adaptation in the adult brain. However, those techniques remain unexplored for the estimation of fiber orientation distribution functions in the rapidly developing brains of infants. In this work, we extensively investigate the age effect and domain shift within and across two different cohorts of 201 newborns and 165 babies using the Method of Moments and fine-tuning strategies. Our results show that reduced variations in the microstructural development of babies in comparison to newborns directly impact the deep learning models' cross-age performance. We also demonstrate that a small number of target domain samples can significantly mitigate domain shift problems.

Deep learning models have shown great promise in estimating tissue microstructure from limited diffusion magnetic resonance imaging data. However, these models face domain shift challenges when test and train data are from different scanners and protocols, or when the models are applied to data with inherent variations such as the developing brains of infants and children scanned at various ages. Several techniques have been proposed to address some of these challenges, such as data harmonization or domain adaptation in the adult brain. However, those techniques remain unexplored for the estimation of fiber orientation distribution functions in the rapidly developing brains of infants. In this work, we extensively investigate the age effect and domain shift within and across two different cohorts of 201 newborns and 165 babies using the Method of Moments and fine-tuning strategies. Our results show that reduced variations in the microstructural development of babies in comparison to newborns directly impact the deep learning models' cross-age performance. We also demonstrate that a small number of target domain samples can significantly mitigate domain shift problems.

The accurate estimation of fiber orientation distributions (fODFs) in diffusion magnetic resonance imaging (MRI) is crucial for understanding early brain development and its potential disruptions. While deep learning (DL) models have shown promise in fODF estimation from neonatal diffusion MRI (dMRI) data, the out-of-domain (OOD) performance of these models remains largely unexplored, especially under diverse domain shift scenarios. This study evaluated the robustness of three state-of-the-art DL architectures—MLP-based, Transformer-based, and U-Net/CNN-based—on fODF predictions derived from diffusion MRI data. Using 488 subjects from the developing Human Connectome Project (dHCP) and the Baby Connectome Project (BCP) datasets, we reconstructed reference fODFs from the full dMRI series using single-shell three-tissue constrained spherical deconvolution (SS3T-CSD) and multi-shell multi-tissue CSD (MSMT-CSD) to generate reference fODF reconstructions for model training, and systematically assessed the impact of age, scanner/protocol differences, and input dimensionality on model performance. Our findings reveal that U-Net consistently outperformed other models when fewer diffusion gradient directions were used, particularly with the SS3T-CSD-derived ground truth, which showed superior performance in capturing crossing fibers. However, as the number of input diffusion gradient directions increased, MLP and the transformer-based model exhibited steady gains in accuracy. Nevertheless, performance nearly plateaued from 28 to 45 input directions in all models. Age-related domain shifts were found to be less pronounced in late developmental stages (late neonates, and babies), with SS3T-CSD demonstrating greater robustness to variability compared to MSMT-CSD. To address inter-site domain shifts, we implemented two adaptation strategies: the Method of Moments (MoM) and fine-tuning. Fine-tuning consistently yielded superior results, with U-Net benefiting the most from increased target subjects. This study represents the first systematic evaluation of OOD settings in deep learning applications to fODF estimation, providing critical insights into model robustness and adaptation strategies for diverse clinical and research applications.

The Para rubber tree ( Hevea brasiliensis ) is an economically important tropical tree species that produces natural rubber, an essential industrial raw material. Here we present a high-quality genome assembly of this species (1.37 Gb, scaffold N50 = 1.28 Mb) that covers 93.8% of the genome (1.47 Gb) and harbours 43,792 predicted protein-coding genes. A striking expansion of the REF/SRPP (rubber elongation factor/small rubber particle protein) gene family and its divergence into several laticifer-specific isoforms seem crucial for rubber biosynthesis. The REF/SRPP family has isoforms with sizes similar to or larger than SRPP1 (204 amino acids) in 17 other plants examined, but no isoforms with similar sizes to REF1 (138 amino acids), the predominant molecular variant. A pivotal point in Hevea evolution was the emergence of REF1, which is located on the surface of large rubber particles that account for 93% of rubber in the latex (despite constituting only 6% of total rubber particles, large and small). The stringent control of ethylene synthesis under active ethylene signalling and response in laticifers resolves a longstanding mystery of ethylene stimulation in rubber production. Our study, which includes the re-sequencing of five other Hevea cultivars and extensive RNA-seq data, provides a valuable resource for functional genomics and tools for breeding elite Hevea cultivars. A high-quality rubber tree genome reveals insights into the evolution of rubber biosynthesis and ethylene stimulation in rubber production. Together with transcriptome data, this study provides valuable data for the research and breeding of rubber trees.