Diffusion MRI (dMRI) tractography is a uniquely powerful tool capable of demonstrating structural brain network abnormalities across a range of psychiatric disorders; however, it is not currently clinically useful. This is because limitations on sensitivity effectively restrict its application to scientific studies of cohorts, rather than individual patients. Recent improvements in dMRI hardware, acquisition, processing and analysis techniques may, however, overcome these measurement limitations. We therefore acquired the highest-ever angular resolution in vivo tractographic data set, and used these data to ask the question: 'is cutting-edge, optimised dMRI now sensitive enough to measure brain network abnormalities at a level that may enable personalised psychiatry?' The fibre tracking performance of this 'gold standard' data set of 1150 unique directions (11 shells) was compared to a conventional 64-direction protocol (single shell) and a clinically practical, highly optimised and accelerated 9-min protocol of 140 directions (3 shells). Three major tracts of relevance to psychiatry were evaluated: the cingulate bundle, the uncinate fasciculus and the corticospinal tract. We found up to a 34-fold improvement in tracking accuracy using the 1150-direction data set compared to the 64-direction data set, while 140-direction data offered a maximum 17-fold improvement. We also observed between 20 and 50% improvements in tracking efficiency for the 140-direction data set, a finding we then replicated in a normal cohort (n = 53). We found evidence that lower angular resolution data may introduce systematic anatomical biases. These data highlight the imminent potential of dMRI as a clinically meaningful technique at a personalised level, and should inform current practice in clinical studies.
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http://dx.doi.org/10.1038/s41398-018-0140-8 | DOI Listing |
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January 2025
Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, UK.
Electron backscatter diffraction (EBSD) has developed over the last few decades into a valuable crystallographic characterisation method for a wide range of sample types. Despite these advances, issues such as the complexity of sample preparation, relatively slow acquisition, and damage in beam-sensitive samples, still limit the quantity and quality of interpretable data that can be obtained. To mitigate these issues, here we propose a method based on the subsampling of probe positions and subsequent reconstruction of an incomplete data set.
View Article and Find Full Text PDFDatabase (Oxford)
January 2025
European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, CB10 1SD, UK.
The HoloFood project used a hologenomic approach to understand the impact of host-microbiota interactions on salmon and chicken production by analysing multiomic data, phenotypic characteristics, and associated metadata in response to novel feeds. The project's raw data, derived analyses, and metadata are deposited in public, open archives (BioSamples, European Nucleotide Archive, MetaboLights, and MGnify), so making use of these diverse data types may require access to multiple resources. This is especially complex where analysis pipelines produce derived outputs such as functional profiles or genome catalogues.
View Article and Find Full Text PDFPsychiatry Clin Neurosci
January 2025
Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Aim: Autistic traits exhibit neurodiversity with varying behaviors across developmental stages. Brain complexity theory, illustrating the dynamics of neural activity, may elucidate the evolution of autistic traits over time. Our study explored the patterns of brain complexity in autistic individuals from childhood to adulthood.
View Article and Find Full Text PDFClin Chem
January 2025
Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, United States.
Background: Multianalyte machine learning (ML) models can potentially identify previously undetectable wrong blood in tube (WBIT) errors, improving upon current single-analyte delta check methodology. However, WBIT detection model performance has not been assessed in a real-world, low-prevalence context. To estimate real-world positive predictive values, we propose a methodology to assess WBIT detection models by evaluating the impact of missing data and by using a "low prevalence" validation data set.
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