ORCID Profile
0000-0003-4735-5776
Current Organisation
University of Nottingham
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Publisher: Elsevier BV
Date: 10-2013
Publisher: Wiley
Date: 06-07-2020
DOI: 10.1002/NBM.4348
Publisher: Elsevier BV
Date: 07-2018
Publisher: Cold Spring Harbor Laboratory
Date: 11-06-2019
DOI: 10.1101/661348
Abstract: Diffusion MRI has the potential to provide important information about the connectivity and microstructure of the human brain during normal and abnormal development, non-invasively and in vivo. Recent developments in MRI hardware and reconstruction methods now permit the acquisition of large amounts of data within relatively short scan times. This makes it possible to acquire more informative multi-shell data, with diffusion-sensitisation applied along many directions over multiple b -value shells. Such schemes are characterised by the number of shells acquired, and the specific b -value and number of directions s led for each shell. However, there is currently no clear consensus as to how to optimise these parameters. In this work, we propose a means of optimising multi-shell acquisition schemes by estimating the information content of the diffusion MRI signal, and optimising the acquisition parameters for sensitivity to the observed effects, in a manner agnostic to any particular diffusion analysis method that might subsequently be applied to the data. This method was used to design the acquisition scheme for the neonatal diffusion MRI sequence used in the developing Human Connectome Project, which aims to acquire high quality data and make it freely available to the research community. The final protocol selected by the algorithm, and currently in use within the dHCP, consists of b = 0, 400, 1000, 2600 s/mm 2 with 20, 64, 88 & 128 DW directions per shell respectively. A data driven method is presented to design multi-shell diffusion MRI acquisition schemes ( b -values and no. directions). This method optimises the multi-shell scheme for maximum sensitivity to the information content in the signal. When applied in neonates, the data suggest that a b =0 + 3 shell strategy is appropriate
Publisher: Cold Spring Harbor Laboratory
Date: 23-09-2021
DOI: 10.1101/2021.09.21.21263298
Abstract: Cerebral microbleeds (CMBs) appear as small, circular, well defined hypointense lesions of a few mm in size on T2*-weighted gradient recalled echo (T2*-GRE) images and appear enhanced on susceptibility weighted images (SWI). Due to their small size, contrast variations and other mimics (e.g. blood vessels), CMBs are highly challenging to detect automatically. In large datasets (e.g. the UK Biobank dataset), exhaustively labelling CMBs manually is difficult and time consuming. Hence it would be useful to preselect candidate CMB subjects in order to focus on those for manual labelling, which is essential for training and testing automated CMB detection tools on these datasets. In this work, we aim to detect CMB candidate subjects from a larger dataset, UK Biobank, using a machine learning-based, computationally light pipeline. For our evaluation, we used 3 different datasets, with different intensity characteristics, acquired with different scanners. They include the UK Biobank dataset and two clinical datasets with different pathological conditions. We developed and evaluated our pipelines on different types of images, consisting of SWI or GRE images. We also used the UK Biobank dataset to compare our approach with alternative CMB preselection methods using non-imaging factors and/or imaging data. Finally, we evaluated the pipeline’s generalisability across datasets. Our method provided subject-level detection accuracy 80% on all the datasets (withindataset results), and showed good generalisability across datasets, providing a consistent accuracy of over 80%, even when evaluated across different modalities.
Publisher: Cold Spring Harbor Laboratory
Date: 19-06-2023
DOI: 10.1101/2023.06.16.545260
Abstract: Despite the huge potential of magnetic resonance imaging (MRI) in mapping and exploring the brain, MRI measures can often be limited in their consistency, reproducibility and accuracy which subsequently restricts their quantifiability. Nuisance nonbiological factors, such as hardware, software, calibration differences between scanners, and post-processing options can contribute to, or drive trends in, neuroimaging features to an extent that interferes with biological variability. Such lack of consistency, known as lack of harmonisation, across neuroimaging datasets poses a great challenge for our capabilities in quantitative MRI. Here, we build a new resource for comprehensively mapping the extent of the problem and objectively evaluating neuroimaging harmonisation approaches. We use a travelling-heads paradigm consisting of multimodal MRI data of 10 travelling subjects, each scanned at 5 different sites on 6 different 3T scanners from all the 3 major vendors and using 5 neuroimaging modalities, providing more comprehensive coverage than before. We also acquire multiple within-scanner repeats for a subset of subjects, setting baselines for multi-modal scan-rescan variability. Having extracted hundreds of image-derived features, we compare three forms of variability: (i) between-scanner, (ii) within-scanner (within-subject), and (iii) biological (between-subject). We characterise the reliability of features across scanners and use our resource as a testbed to enable new investigations that until now have been relatively unexplored. Specifically, we identify optimal pipeline processing steps that minimise between-scanner variability in extracted features (implicit harmonisation). We also test the performance of post-processing harmonisation tools (explicit harmonisation) and specifically check their efficiency in reducing between-scanner variability against baseline standards provided by our data. Our explorations allow us to come up with good practice suggestions on processing steps and sets of features where results are more consistent, while our publicly-released datasets establish references for future studies in this field.
Publisher: Elsevier BV
Date: 05-2015
Publisher: Elsevier BV
Date: 02-2019
DOI: 10.1016/J.NEUROIMAGE.2018.10.079
Abstract: Functional networks obtained from magnetoencephalography (MEG) from different frequency bands show distinct spatial patterns. It remains to be elucidated how distinct spatial patterns in MEG networks emerge given a single underlying structural network. Recent work has suggested that the eigenmodes of the structural network might serve as a basis set for functional network patterns in the case of functional MRI. Here, we take this notion further in the context of frequency band specific MEG networks. We show that a selected set of eigenmodes of the structural network can predict different frequency band specific networks in the resting state, ranging from delta (1-4 Hz) to the high gamma band (40-70 Hz). These predictions outperform predictions based from surrogate data, suggesting a genuine relationship between eigenmodes of the structural network and frequency specific MEG networks. We then show that the relevant set of eigenmodes can be excited in a network of neural mass models using linear stability analysis only by including delays. Excitation of an eigenmode in this context refers to a dynamic instability of a network steady state to a spatial pattern with a corresponding coherent temporal oscillation. Simulations verify the results from linear stability analysis and suggest that theta, alpha and beta band networks emerge very near to the bifurcation. The delta and gamma bands in the resting state emerges further away from the bifurcation. These results show for the first time how delayed interactions can excite the relevant set of eigenmodes that give rise to frequency specific functional connectivity patterns.
Publisher: Elsevier BV
Date: 10-2013
Publisher: Cold Spring Harbor Laboratory
Date: 09-2021
DOI: 10.1101/2021.09.01.458571
Abstract: Exposure to enriched environments (EE) throughout a lifetime, providing so called reserve, protects against cognitive decline in later years. It has been hypothesised that high levels of alertness necessitated by EE might strengthen the right fronto-parietal networks (FPN) to facilitate this neurocognitive resilience. We have previously shown that EE offset age-related deficits in selective attention by preserving grey matter within right fronto-parietal regions. Here, using neurite orientation dispersion and density imaging (NODDI), we examined the relationship between EE, microstructural properties of fronto-parietal white matter association pathways (three branches of the superior longitudinal fasciculus, SLF), structural brain health (atrophy), and attention (alertness, orienting and executive control) in a group of older adults. We show that EE is associated with a lower orientation dispersion index (ODI) within the right SLF1 which in turn mediates the relationship between EE and alertness, as well as grey- and white-matter atrophy. This suggests that EE may induce white matter plasticity (and prevent age-related dispersion of axons) within the right FPN to facilitate the preservation of neurocognitive health in later years.
Publisher: Elsevier BV
Date: 12-2018
Publisher: Oxford University Press (OUP)
Date: 03-2022
DOI: 10.1093/BRAINCOMMS/FCAC080
Abstract: Exposure to enriched environments throughout a lifetime, providing so-called reserve, protects against cognitive decline in later years. It has been hypothesized that high levels of alertness necessitated by enriched environments might strengthen the right fronto-parietal networks to facilitate this neurocognitive resilience. We have previously shown that enriched environments offset age-related deficits in selective attention by preserving grey matter within right fronto-parietal regions. Here, using neurite orientation dispersion and density imaging, we examined the relationship between enriched environments, microstructural properties of fronto-parietal white matter association pathways (three branches of the superior longitudinal fasciculus), structural brain health (atrophy), and attention (alertness, orienting and executive control) in a group of older adults. We show that exposure to enriched environments is associated with a lower orientation dispersion index within the right superior longitudinal fasciculus 1 which in turn mediates the relationship between enriched environments and alertness, as well as grey and white matter atrophy. This suggests that enriched environments may induce white matter plasticity (and prevent age-related dispersion of axons) within the right fronto-parietal networks to facilitate the preservation of neurocognitive health in later years.
Publisher: Cold Spring Harbor Laboratory
Date: 24-04-2017
DOI: 10.1101/130385
Abstract: UK Biobank is a large-scale prospective epidemiological study with all data accessible to researchers worldwide. It is currently in the process of bringing back 100,000 of the original participants for brain, heart and body MRI, carotid ultrasound and low-dose bone/fat x-ray. The brain imaging component covers 6 modalities (T1, T2 FLAIR, susceptibility weighted MRI, Resting fMRI, Task fMRI and Diffusion MRI). Raw and processed data from the first 10,000 imaged subjects has recently been released for general research access. To help convert this data into useful summary information we have developed an automated processing and QC (Quality Control) pipeline that is available for use by other researchers. In this paper we describe the pipeline in detail, following a brief overview of UK Biobank brain imaging and the acquisition protocol. We also describe several quantitative investigations carried out as part of the development of both the imaging protocol and the processing pipeline.
Publisher: Public Library of Science (PLoS)
Date: 23-02-2018
Publisher: Springer Science and Business Media LLC
Date: 30-05-2014
Publisher: Cold Spring Harbor Laboratory
Date: 31-07-2017
DOI: 10.1101/170779
Abstract: The relationship between structure and function in the human brain is well established, but not yet well characterised. Large-scale biophysical models allow us to investigate this relationship, by leveraging structural information ( e.g . derived from diffusion tractography) in order to couple dynamical models of local neuronal activity into networks of interacting regions distributed across the cortex. In practice however, these models are difficult to parametrise, and their simulation is often delicate and computationally expensive. This undermines the experimental aspect of scientific modelling, and stands in the way of comparing different parametrisations, network architectures, or models in general, with confidence. Here, we advocate the use of Bayesian optimisation for assessing the capabilities of biophysical network models, given a set of desired properties ( e.g . band-specific functional connectivity) and in turn the use of this assessment as a principled basis for incremental modelling and model comparison. We adapt an optimisation method designed to cope with costly, high-dimensional, non-convex problems, and demonstrate its use and effectiveness. We find that this method is able to converge to regions of high functional similarity with real MEG data, with very few s les given the number of parameters, without getting stuck in local extrema, and while building and exploiting a map of uncertainty defined smoothly across the parameter space. We compare the results obtained using different methods of structural connectivity estimation from diffusion tractography, and find that one method leads to better simulations.
Publisher: Wiley
Date: 30-05-2018
DOI: 10.1002/MRM.26765
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Stamatios Sotiropoulos.