A new open, high-resolution, multishell, diffusion-weighted imaging dataset of the living squirrel monkey

Recently, Deep Learning (DL) models have shown promising accuracy in analysis of medical images. Alzeheimer Disease (AD), a prevalent form of dementia, uses Magnetic Resonance Imaging (MRI) scans, which is then analysed via DL models. To address the model computational constraints, Cloud Computing (CC) is integrated to operate with the DL models. Recent articles on DL-based MRI have not discussed datasets specific to different diseases, which makes it difficult to build the specific DL model. Thus, the article systematically explores a tutorial approach, where we first discuss a classification taxonomy of medical imaging datasets. Next, we present a case-study on AD MRI classification using the DL methods. We analyse three distinct models-Convolutional Neural Networks (CNN), Visual Geometry Group 16 (VGG-16), and an ensemble approach-for classification and predictive outcomes. In addition, we designed a novel framework that offers insight into how various layers interact with the dataset. Our architecture comprises an input layer, a cloud-based layer responsible for preprocessing and model execution, and a diagnostic layer that issues alerts after successful classification and prediction. According to our simulations, CNN outperformed other models with a test accuracy of 99.285%, followed by VGG-16 with 85.113%, while the ensemble model lagged with a disappointing test accuracy of 79.192%. Our cloud Computing framework serves as an efficient mechanism for medical image processing while safeguarding patient confidentiality and data privacy.

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Evidence for an evolutionary continuity in social dominance: Insights from non-human primates tractography

Royo,

Orset,

Catani

et al. 2024

Preprint

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The dynamics of social dominance play a significant role in regulating access to resources and influencing reproductive success and survival in non-human primates. These dynamics are based on aggressive and submissive interactions which create distinct, hierarchically organized social structures. In humans, whose social behavior is similarly organized, the use of brain imaging based on tractography has identified key neuronal networks of the limbic system underlying social behaviour. Among them the uncinate fasciculus and the cingulum bundle which have been associated with conduct disorder and psychopathy. In this study, we have used advanced tractography to study the anatomy of connections underlying social dominance in a colony of 15 squirrel monkeys (Saimiri sciureus). We correlated biostructural properties of the uncinate fasciculus and cingulum with behavioral hierarchy measures while controlling for factors such as age, weight, handedness, brain size, and hormonal influences. The fornix, a limbic connection involved in memory was also included as control tract. Our findings indicate a significant correlation between the integrity of the right uncinate fasciculus and social dominance measures, including normalised David’s scores, aggressive behaviors, and withdrawal behaviors. Trends observed in the left uncinate fasciculus hint at potential bilateral involvement with a right hemispheric lateralisation. These results are consistent with human studies linking the uncinate fasciculus to social disorders, suggesting an evolutionary continuity in the neuro-anatomical substrates of social dominance back to at least 35 million years.

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A new open, high-resolution, multishell, diffusion-weighted imaging dataset of the living squirrel monkey

Cited by 3 publications

References 56 publications

Towards multi-modal, multi-species brain atlases: part one

Towards multi-modal, multi-species brain atlases: part one

Revolutionizing healthcare: a comparative insight into deep learning’s role in medical imaging

Evidence for an evolutionary continuity in social dominance: Insights from non-human primates tractography

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