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Article: Factorisation-Based Image Labelling.

Yan, Yu / Balbastre, Yaël / Brudfors, Mikael / Ashburner, John

2022 Volume 15, Page(s) 818604

Abstract: Segmentation of brain magnetic resonance images (MRI) into anatomical regions is a useful task in neuroimaging. Manual annotation is time consuming and expensive, so having a fully automated and general purpose brain segmentation algorithm is highly ... ...

Abstract	Segmentation of brain magnetic resonance images (MRI) into anatomical regions is a useful task in neuroimaging. Manual annotation is time consuming and expensive, so having a fully automated and general purpose brain segmentation algorithm is highly desirable. To this end, we propose a patched-based labell propagation approach based on a generative model with latent variables. Once trained, our Factorisation-based Image Labelling (FIL) model is able to label target images with a variety of image contrasts. We compare the effectiveness of our proposed model against the state-of-the-art using data from the
Language	English
Publishing date	2022-01-17
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2411902-7
ISSN	1662-453X ; 1662-4548
ISSN (online)	1662-453X
ISSN	1662-4548
DOI	10.3389/fnins.2021.818604
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Article ; Online: Voxel-wise multivariate analysis of brain-psychosocial associations in adolescents reveals six latent dimensions of cognition and psychopathology.

Adams, Rick A / Zor, Cemre / Mihalik, Agoston / Tsirlis, Konstantinos / Brudfors, Mikael / Chapman, James / Ashburner, John / Paulus, Martin P / Mourão-Miranda, Janaina

Biological psychiatry. Cognitive neuroscience and neuroimaging

2024

Abstract: Background: Adolescence heralds the onset of much psychopathology, which may be conceptualized as an emergence of altered covariation between symptoms and brain measures. Multivariate methods can detect such modes of covariation or latent dimensions, ... ...

Abstract	Background: Adolescence heralds the onset of much psychopathology, which may be conceptualized as an emergence of altered covariation between symptoms and brain measures. Multivariate methods can detect such modes of covariation or latent dimensions, but none specifically relating to psychopathology have yet been found using population-level structural brain data. Using voxel-wise (instead of parcellated) brain data may strengthen latent dimensions' brain-psychosocial relationships, but this creates computational challenges. Methods: We obtained voxel-wise grey matter density and psychosocial variables from the baseline (aged 9-10 years) Adolescent Brain and Cognitive Development cohort (n=11288), and employed a state-of-the-art segmentation method, sparse partial least squares, and a rigorous machine learning framework to prevent overfitting. Results: We found six latent dimensions, four pertaining specifically to mental health. The mental health dimensions related to overeating, anorexia/internalizing, oppositional symptoms (all p<0.002) and ADHD symptoms (p=0.03). ADHD related to increased and internalizing related to decreased grey matter density in dopaminergic and serotonergic midbrain areas, whereas oppositional symptoms related to increased grey matter in a noradrenergic nucleus. Internalizing related to increased and oppositional symptoms to reduced grey matter density in insula, cingulate and auditory cortices. Striatal regions featured strongly, with reduced caudate nucleus grey matter in ADHD, and reduced putamen grey matter in oppositional/conduct problems. Voxel-wise grey matter density generated stronger brain-psychosocial correlations than brain parcellations. Conclusions: Voxel-wise brain data strengthen latent dimensions of brain-psychosocial covariation and sparse multivariate methods increase their psychopathological specificity. Internalizing and externalizing are associated with opposite grey matter changes in similar cortical and subcortical areas.
Language	English
Publishing date	2024-04-06
Publishing country	United States
Document type	Journal Article
ZDB-ID	2879089-3
ISSN	2451-9030 ; 2451-9022
ISSN (online)	2451-9030
ISSN	2451-9022
DOI	10.1016/j.bpsc.2024.03.006
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Article ; Online: Correcting inter-scan motion artifacts in quantitative R

Balbastre, Yaël / Aghaeifar, Ali / Corbin, Nadège / Brudfors, Mikael / Ashburner, John / Callaghan, Martina F

Magnetic resonance in medicine

2022 Volume 88, Issue 1, Page(s) 280–291

Abstract: Purpose: Inter-scan motion is a substantial source of error in : Theory: The proposed methods use coil-combined magnitude images to obtain the relative coil sensitivities. The first method efficiently computes the relative sensitivities via a simple ... ...

Abstract	Purpose: Inter-scan motion is a substantial source of error in Theory: The proposed methods use coil-combined magnitude images to obtain the relative coil sensitivities. The first method efficiently computes the relative sensitivities via a simple ratio; the second by fitting a more sophisticated generative model. Methods: R Results: At 3T, the proposed methods outperform the baseline method. Inter-scan motion artifacts were also reduced at 7T. However, at 7T reproducibility only converged on that of the no motion condition if position-specific transmit field effects were also incorporated. Conclusion: The proposed methods simplify inter-scan motion correction of
MeSH term(s)	Artifacts ; Magnetic Resonance Imaging/methods ; Motion ; Radionuclide Imaging ; Reproducibility of Results
Language	English
Publishing date	2022-03-21
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	605774-3
ISSN	1522-2594 ; 0740-3194
ISSN (online)	1522-2594
ISSN	0740-3194
DOI	10.1002/mrm.29216
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Article ; Online: An algorithm for learning shape and appearance models without annotations.

Ashburner, John / Brudfors, Mikael / Bronik, Kevin / Balbastre, Yaël

Medical image analysis

2019 Volume 55, Page(s) 197–215

Abstract: This paper presents a framework for automatically learning shape and appearance models for medical (and certain other) images. The algorithm was developed with the aim of eventually enabling distributed privacy-preserving analysis of brain image data, ... ...

Abstract	This paper presents a framework for automatically learning shape and appearance models for medical (and certain other) images. The algorithm was developed with the aim of eventually enabling distributed privacy-preserving analysis of brain image data, such that shared information (shape and appearance basis functions) may be passed across sites, whereas latent variables that encode individual images remain secure within each site. These latent variables are proposed as features for privacy-preserving data mining applications. The approach is demonstrated qualitatively on the KDEF dataset of 2D face images, showing that it can align images that traditionally require shape and appearance models trained using manually annotated data (manually defined landmarks etc.). It is applied to the MNIST dataset of handwritten digits to show its potential for machine learning applications, particularly when training data is limited. The model is able to handle "missing data", which allows it to be cross-validated according to how well it can predict left-out voxels. The suitability of the derived features for classifying individuals into patient groups was assessed by applying it to a dataset of over 1900 segmented T1-weighted MR images, which included images from the COBRE and ABIDE datasets.
MeSH term(s)	Algorithms ; Brain/diagnostic imaging ; Face/diagnostic imaging ; Humans ; Image Processing, Computer-Assisted/methods ; Imaging, Three-Dimensional/methods ; Machine Learning ; Magnetic Resonance Imaging/methods
Language	English
Publishing date	2019-04-30
Publishing country	Netherlands
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1356436-5
ISSN	1361-8423 ; 1361-8431 ; 1361-8415
ISSN (online)	1361-8423 ; 1361-8431
ISSN	1361-8415
DOI	10.1016/j.media.2019.04.008
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Article ; Online: Model-based multi-parameter mapping.

Balbastre, Yaël / Brudfors, Mikael / Azzarito, Michela / Lambert, Christian / Callaghan, Martina F / Ashburner, John

Medical image analysis

2021 Volume 73, Page(s) 102149

Abstract: Quantitative MR imaging is increasingly favoured for its richer information content and standardised measures. However, computing quantitative parameter maps, such as those encoding longitudinal relaxation rate ( ... ...

Abstract	Quantitative MR imaging is increasingly favoured for its richer information content and standardised measures. However, computing quantitative parameter maps, such as those encoding longitudinal relaxation rate (R
MeSH term(s)	Algorithms ; Humans ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging
Language	English
Publishing date	2021-06-29
Publishing country	Netherlands
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1356436-5
ISSN	1361-8423 ; 1361-8431 ; 1361-8415
ISSN (online)	1361-8423 ; 1361-8431
ISSN	1361-8415
DOI	10.1016/j.media.2021.102149
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Book ; Online: Groupwise Multimodal Image Registration using Joint Total Variation

Brudfors, Mikael / Balbastre, Yaël / Ashburner, John

2020

Abstract: In medical imaging it is common practice to acquire a wide range of modalities (MRI, CT, PET, etc.), to highlight different structures or pathologies. As patient movement between scans or scanning session is unavoidable, registration is often an ... ...

Abstract	In medical imaging it is common practice to acquire a wide range of modalities (MRI, CT, PET, etc.), to highlight different structures or pathologies. As patient movement between scans or scanning session is unavoidable, registration is often an essential step before any subsequent image analysis. In this paper, we introduce a cost function based on joint total variation for such multimodal image registration. This cost function has the advantage of enabling principled, groupwise alignment of multiple images, whilst being insensitive to strong intensity non-uniformities. We evaluate our algorithm on rigidly aligning both simulated and real 3D brain scans. This validation shows robustness to strong intensity non-uniformities and low registration errors for CT/PET to MRI alignment. Our implementation is publicly available at https://github.com/brudfors/coregistration-njtv.
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition
Subject code	004
Publishing date	2020-05-06
Publishing country	us
Document type	Book ; Online
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Article ; Online: Voxel-based dysconnectomic brain morphometry with computed tomography in Down syndrome.

Sánchez-Moreno, Beatriz / Zhang, Linda / Mateo, Gloria / Moldenhauer, Fernando / Brudfors, Mikael / Ashburner, John / Nachev, Parashkev / de Asúa, Diego Real / Strange, Bryan A

Annals of clinical and translational neurology

2023 Volume 11, Issue 1, Page(s) 143–155

Abstract: Objective: Alzheimer's disease (AD) is a major health concern for aging adults with Down syndrome (DS), but conventional diagnostic techniques are less reliable in those with severe baseline disability. Likewise, acquisition of magnetic resonance ... ...

Abstract	Objective: Alzheimer's disease (AD) is a major health concern for aging adults with Down syndrome (DS), but conventional diagnostic techniques are less reliable in those with severe baseline disability. Likewise, acquisition of magnetic resonance imaging to evaluate cerebral atrophy is not straightforward, as prolonged scanning times are less tolerated in this population. Computed tomography (CT) scans can be obtained faster, but poor contrast resolution limits its function for morphometric analysis. We implemented an automated analysis of CT scans to characterize differences across dementia stages in a cross-sectional study of an adult DS cohort. Methods: CT scans of 98 individuals were analyzed using an automatic algorithm. Voxel-based correlations with clinical dementia stages and AD plasma biomarkers (phosphorylated tau-181 and neurofilament light chain) were identified, and their dysconnectomic patterns delineated. Results: Dementia severity was negatively correlated with gray (GM) and white matter (WM) volumes in temporal lobe regions, including parahippocampal gyri. Dysconnectome analysis revealed an association between WM loss and temporal lobe GM volume reduction. AD biomarkers were negatively associated with GM volume in hippocampal and cingulate gyri. Interpretation: Our automated algorithm and novel dysconnectomic analysis of CT scans successfully described brain morphometric differences related to AD in adults with DS, providing a new avenue for neuroimaging analysis in populations for whom magnetic resonance imaging is difficult to obtain.
MeSH term(s)	Adult ; Humans ; Down Syndrome/diagnostic imaging ; Down Syndrome/pathology ; Cross-Sectional Studies ; Brain/diagnostic imaging ; Brain/pathology ; Alzheimer Disease/diagnostic imaging ; Alzheimer Disease/pathology ; Magnetic Resonance Imaging/methods ; Biomarkers
Chemical Substances	Biomarkers
Language	English
Publishing date	2023-12-29
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2740696-9
ISSN	2328-9503 ; 2328-9503
ISSN (online)	2328-9503
ISSN	2328-9503
DOI	10.1002/acn3.51940
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Article ; Online: Predicting mortality in acutely hospitalised older patients: the impact of model dimensionality.

Tsui, Alex / Tudosiu, Petru-Daniel / Brudfors, Mikael / Jha, Ashwani / Cardoso, Jorge / Ourselin, Sebastien / Ashburner, John / Rees, Geraint / Davis, Daniel / Nachev, Parashkev

BMC medicine

2023 Volume 21, Issue 1, Page(s) 10

Abstract: Background: The prediction of long-term mortality following acute illness can be unreliable for older patients, inhibiting the delivery of targeted clinical interventions. The difficulty plausibly arises from the complex, multifactorial nature of the ... ...

Abstract	Background: The prediction of long-term mortality following acute illness can be unreliable for older patients, inhibiting the delivery of targeted clinical interventions. The difficulty plausibly arises from the complex, multifactorial nature of the underlying biology in this population, which flexible, multimodal models based on machine learning may overcome. Here, we test this hypothesis by quantifying the comparative predictive fidelity of such models in a large consecutive sample of older patients acutely admitted to hospital and characterise their biological support. Methods: A set of 804 admission episodes involving 616 unique patients with a mean age of 84.5 years consecutively admitted to the Acute Geriatric service at University College Hospital were identified, in whom clinical diagnoses, blood tests, cognitive status, computed tomography of the head, and mortality within 600 days after admission were available. We trained and evaluated out-of-sample an array of extreme gradient boosted trees-based predictive models of incrementally greater numbers of investigational modalities and modelled features. Both linear and non-linear associations with investigational features were quantified. Results: Predictive models of mortality showed progressively increasing fidelity with greater numbers of modelled modalities and dimensions. The area under the receiver operating characteristic curve rose from 0.67 (sd = 0.078) for age and sex to 0.874 (sd = 0.046) for the most comprehensive model. Extracranial bone and soft tissue features contributed more than intracranial features towards long-term mortality prediction. The anterior cingulate and angular gyri, and serum albumin, were the greatest intracranial and biochemical model contributors respectively. Conclusions: High-dimensional, multimodal predictive models of mortality based on routine clinical data offer higher predictive fidelity than simpler models, facilitating individual level prognostication and interventional targeting. The joint contributions of both extracranial and intracranial features highlight the potential importance of optimising somatic as well as neural functions in healthy ageing. Our findings suggest a promising path towards a high-fidelity, multimodal index of frailty.
MeSH term(s)	Humans ; Aged ; Aged, 80 and over ; Hospitalization ; ROC Curve ; Frailty/diagnosis ; Retrospective Studies ; Hospital Mortality
Language	English
Publishing date	2023-01-08
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2131669-7
ISSN	1741-7015 ; 1741-7015
ISSN (online)	1741-7015
ISSN	1741-7015
DOI	10.1186/s12916-022-02698-2
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Book ; Online: Empirical Bayesian Mixture Models for Medical Image Translation

Brudfors, Mikael / Ashburner, John / Nachev, Parashkev / Balbastre, Yael

2019

Abstract: Automatically generating one medical imaging modality from another is known as medical image translation, and has numerous interesting applications. This paper presents an interpretable generative modelling approach to medical image translation. By ... ...

Abstract	Automatically generating one medical imaging modality from another is known as medical image translation, and has numerous interesting applications. This paper presents an interpretable generative modelling approach to medical image translation. By allowing a common model for group-wise normalisation and segmentation of brain scans to handle missing data, the model allows for predicting entirely missing modalities from one, or a few, MR contrasts. Furthermore, the model can be trained on a fairly small number of subjects. The proposed model is validated on three clinically relevant scenarios. Results appear promising and show that a principled, probabilistic model of the relationship between multi-channel signal intensities can be used to infer missing modalities -- both MR contrasts and CT images. Comment: Accepted to the Simulation and Synthesis in Medical Imaging (SASHIMI) workshop at MICCAI 2019
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2019-08-16
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: A Tool for Super-Resolving Multimodal Clinical MRI

Brudfors, Mikael / Balbastre, Yael / Nachev, Parashkev / Ashburner, John

2019

Abstract: We present a tool for resolution recovery in multimodal clinical magnetic resonance imaging (MRI). Such images exhibit great variability, both biological and instrumental. This variability makes automated processing with neuroimaging analysis software ... ...

Abstract	We present a tool for resolution recovery in multimodal clinical magnetic resonance imaging (MRI). Such images exhibit great variability, both biological and instrumental. This variability makes automated processing with neuroimaging analysis software very challenging. This leaves intelligence extractable only from large-scale analyses of clinical data untapped, and impedes the introduction of automated predictive systems in clinical care. The tool presented in this paper enables such processing, via inference in a generative model of thick-sliced, multi-contrast MR scans. All model parameters are estimated from the observed data, without the need for manual tuning. The model-driven nature of the approach means that no type of training is needed for applicability to the diversity of MR contrasts present in a clinical context. We show on simulated data that the proposed approach outperforms conventional model-based techniques, and on a large hospital dataset of multimodal MRIs that the tool can successfully super-resolve very thick-sliced images. The implementation is available from https://github.com/brudfors/spm_superres.
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition
Subject code	004
Publishing date	2019-09-03
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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