Les troubles non moteurs de la maladie de Parkinson idiopathique (identification et coordination médecin généraliste-neurologue)

BORDEAUX2-BU Santé (330632101) / SudocSudocFranceF

Weight gain mecanisms in Parkinson's disease after subthalamic stimulation.

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Weight gain mecanisms in Parkinson's disease after subthalamic stimulation.

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La réponse du métabolisme de base des patients parkinsoniens aux traitements, est différente selon le sexe

La réponse du métabolisme de base des patients parkinsoniens aux traitements, est différente selon le sexe. 6. Journées Francophones de Nutrition (JFN

Enhancing stroke lesion detection and segmentation through nnU-net and multi-modal MRI Analysis

International audience1.Introduction Accurate delineation of chronic stroke lesions is crucial for many research and clinical applications. Indeed, all neuroimaging research after stroke require a segmentation process. Moreover, the segmentation is implemented in algorithms discussing prognostic elements. However, manual segmentation from T1-w MR images is time-consuming and prone to errors. 2.Objectives This work aimed to automate chronic stroke lesion detection and segmentation using the nnU-Net framework and a combination of datasets. The nnU-Net framework is widely recognized for its state-of-the-art performance in medical image segmentation. We have previously successfully adapted the nnU-Net framework to multiple sclerosis lesion segmentation within the Longiseg4ms tool.3.Materials and MethodsWe utilized the ATLAS v2.0 dataset* and an in-house dataset of T1-w and FLAIR MRI scans from patients with chronic stroke lesions. We leveraged the nnU-Net framework and incorporated both T1-w and FLAIR MRI modalities4.ResultsThe target model achieved a mean Dice score of 0.730 and a mean lesion-wise F1 score of 0.684, demonstrating superior performance compared to the baseline model. The high correlation coefficient (Pearson correlation coefficient = 0.930) between the ground truth and model output volumes indicates a strong agreement between the two segmentation methods. This correlation highlights the accuracy of our volume prediction and highlights the reliability of our automated segmentation approach.5.ConclusionOur nnU-Net-based model, trained on T1-w and FLAIR images, improved the segmentation of chronic stroke lesions, outperforming the baseline model. These findings highlight the potential of automated models for improving chronic stroke lesion segmentation and using FLAIR modality to enhance the results.*Liew S-L et al. (2022). A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms. Sci Data 9, 320

Enhancing stroke lesion detection and segmentation through nnU-net and multi-modal MRI Analysis

International audience1.Introduction Accurate delineation of chronic stroke lesions is crucial for many research and clinical applications. Indeed, all neuroimaging research after stroke require a segmentation process. Moreover, the segmentation is implemented in algorithms discussing prognostic elements. However, manual segmentation from T1-w MR images is time-consuming and prone to errors. 2.Objectives This work aimed to automate chronic stroke lesion detection and segmentation using the nnU-Net framework and a combination of datasets. The nnU-Net framework is widely recognized for its state-of-the-art performance in medical image segmentation. We have previously successfully adapted the nnU-Net framework to multiple sclerosis lesion segmentation within the Longiseg4ms tool.3.Materials and MethodsWe utilized the ATLAS v2.0 dataset* and an in-house dataset of T1-w and FLAIR MRI scans from patients with chronic stroke lesions. We leveraged the nnU-Net framework and incorporated both T1-w and FLAIR MRI modalities4.ResultsThe target model achieved a mean Dice score of 0.730 and a mean lesion-wise F1 score of 0.684, demonstrating superior performance compared to the baseline model. The high correlation coefficient (Pearson correlation coefficient = 0.930) between the ground truth and model output volumes indicates a strong agreement between the two segmentation methods. This correlation highlights the accuracy of our volume prediction and highlights the reliability of our automated segmentation approach.5.ConclusionOur nnU-Net-based model, trained on T1-w and FLAIR images, improved the segmentation of chronic stroke lesions, outperforming the baseline model. These findings highlight the potential of automated models for improving chronic stroke lesion segmentation and using FLAIR modality to enhance the results.*Liew S-L et al. (2022). A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms. Sci Data 9, 320

Deep Learning and Multi-Modal MRI for the Segmentation of Sub-Acute and Chronic Stroke Lesions Authors

International audienceBackground: Stroke is a leading cause of morbidity and mortality worldwide. Accurate segmentation of sub-acute and chronic stroke lesions using MRI is crucial for assessing brain damage and developing effective rehabilitation plans. Manual segmentation is labor-intensive and error-prone, necessitating automated approaches. This study aims at improving sub-acute and chronic stroke lesion segmentation using deep learning and multi-modal MRI data. Both models are made available to the research community.Methods: This study developed and evaluated two models for segmenting sub-acute and chronic stroke lesions using MRI: a single-modality model trained on the public ATLAS v2.0 dataset, and a dual-modality model adapted from the single-modality model by integrating T1-w and FLAIR MRI data from an internal dataset. Both models were trained using the nnU-Net framework, employing a preprocessing pipeline to improve the segmentation accuracy.Results: The single-modality model achieved a mean Dice score of 83.0% on the ATLAS v2.0 dataset, and 68.8% on the internal test set. The dual-modality model significantly improved segmentation accuracy, yielding a mean Dice score of 75.6% and an F1 score of 72.6% on the internal test set. Additionally, volumetric analysis showed a high Pearson correlation coefficient (0.94) between predicted and actual lesion volumes.Conclusions: The improved performance of the dual-modality model suggests the benefit of integrating FLAIR MRI to capture lesion characteristics in detecting and segmenting sub-acute and chronic stroke lesions. This could lead to more accurate assessment of brain damage and more effective rehabilitation plans for stroke patients. Future research should focus on larger multi-modal datasets and further investigate segmentation challenges, as well as clinical validation

3D visual fMRI using binocular stimulation

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Cortical activity and egocentric representation: An fMRI study

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