| 1. |
- Clement, Patricia, et al.
(author)
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Variability of physiological brain perfusion in healthy subjects : A systematic review of modifiers. Considerations for multi-center ASL studies
- 2018
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In: Journal of Cerebral Blood Flow and Metabolism. - 0271-678X .- 1559-7016. ; 38:9, s. 1418-1437
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Research review (peer-reviewed)abstract
- Quantitative measurements of brain perfusion are influenced by perfusion-modifiers. Standardization of measurement conditions and correction for important modifiers is essential to improve accuracy and to facilitate the interpretation of perfusion-derived parameters. An extensive literature search was carried out for factors influencing quantitative measurements of perfusion in the human brain unrelated to medication use. A total of 58 perfusion modifiers were categorized into four groups. Several factors (e.g., caffeine, aging, and blood gases) were found to induce a considerable effect on brain perfusion that was consistent across different studies; for other factors, the modifying effect was found to be debatable, due to contradictory results or lack of evidence. Using the results of this review, we propose a standard operating procedure, based on practices already implemented in several research centers. Also, a theory of ' deep MRI physiotyping' is inferred from the combined knowledge of factors influencing brain perfusion as a strategy to reduce variance by taking both personal information and the presence or absence of perfusion modifiers into account. We hypothesize that this will allow to personalize the concept of normality, as well as to reach more rigorous and earlier diagnoses of brain disorders.
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| 2. |
- Mehta, Raghav, et al.
(author)
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QU-BraTS : MICCAI BraTS 2020 Challenge on QuantifyingUncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
- 2022
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In: Journal of Machine Learning for Biomedical Imaging. - 2766-905X. ; , s. 1-54
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Journal article (peer-reviewed)abstract
- Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019-2020 task on uncertainty quantification (QU-BraTS), and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions, and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentages of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, and hence highlight the need for uncertainty quantification in medical image analyses. Our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS
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| 3. |
- Singh, Laura, et al.
(author)
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The effect of optimistic expectancies on attention bias : Neural and behavioral correlates
- 2020
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In: Scientific Reports. - : Springer Nature. - 2045-2322. ; 10:1
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Journal article (peer-reviewed)abstract
- Optimism bias and positive attention bias are important features of healthy information processing. Recent findings suggest dynamic bidirectional optimism-attention interactions, but the underlying neural mechanisms remain to be identified. The current functional magnetic resonance imaging (fMRI) study, therefore, investigated the neural mechanisms underlying causal effects of optimistic expectancies on attention. We hypothesized that expectancies guide attention to confirmatory evidence in the environment, with enhanced salience and executive control network (SN/ECN) activity for unexpected information. Moreover, based on previous findings, we anticipated optimistic expectancies to more strongly impact attention and SN/ECN activity than pessimistic expectancies. Expectancies were induced with visual cues in 50 participants; subsequent attention to reward and punishment was assessed in a visual search task. As hypothesized, cues shortened reaction times to expected information, and unexpected information enhanced SN/ECN activity. Notably, these effects were stronger for optimistic than pessimistic expectancy cues. Our findings suggest that optimistic expectancies involve particularly strong predictions of reward, causing automatic guidance of attention to reward and great surprise about unexpected punishment. Such great surprise may be counteracted by visual avoidance of the punishing evidence, as revealed by prior evidence, thereby reducing the need to update (over)optimistic reward expectancies.
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