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Article: Automatic Geometry-based Estimation of the Locus Coeruleus Region on T

Aganj, Iman / Mora, Jocelyn / Fischl, Bruce / Augustinack, Jean C

bioRxiv : the preprint server for biology

2024

Abstract: The locus coeruleus (LC) is a key brain structure implicated in cognitive function and neurodegenerative disease. Automatic segmentation of the LC is a crucial step in quantitative non-invasive analysis of the LC in large MRI cohorts. Most publicly ... ...

Abstract	The locus coeruleus (LC) is a key brain structure implicated in cognitive function and neurodegenerative disease. Automatic segmentation of the LC is a crucial step in quantitative non-invasive analysis of the LC in large MRI cohorts. Most publicly available imaging databases for training automatic LC segmentation models take advantage of specialized contrast-enhancing (e.g., neuromelanin-sensitive) MRI. Segmentation models developed with such image contrasts, however, are not readily applicable to existing datasets with conventional MRI sequences. In this work, we evaluate the feasibility of using non-contrast neuroanatomical information to geometrically approximate the LC region from standard 3-Tesla T
Language	English
Publishing date	2024-01-24
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.01.23.576958
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Article: Multi-Head Graph Convolutional Network for Structural Connectome Classification.

Kazi, Anees / Mora, Jocelyn / Fischl, Bruce / Dalca, Adrian V / Aganj, Iman

Graphs in biomedical image analysis, and overlapped cell on tissue dataset for histopathology : 5th MICCAI Workshop, GRAIL 2023 and 1st MICCAI Challenge, OCELOT 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, Septembe...

2024 Volume 14373, Page(s) 27–36

Abstract: We tackle classification based on brain connectivity derived from diffusion magnetic resonance images. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brain-connectivity input graph and processes the ... ...

Abstract	We tackle classification based on brain connectivity derived from diffusion magnetic resonance images. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brain-connectivity input graph and processes the data separately through a parallel GCN mechanism with multiple heads. The proposed network is a simple design that employs different heads involving graph convolutions focused on edges and nodes, thoroughly capturing representations from the input data. To test the ability of our model to extract complementary and representative features from brain connectivity data, we chose the task of sex classification. This quantifies the degree to which the connectome varies depending on the sex, which is important for improving our understanding of health and disease in both sexes. We show experiments on two publicly available datasets: PREVENT-AD (347 subjects) and OASIS3 (771 subjects). The proposed model demonstrates the highest performance compared to the existing machine-learning algorithms we tested, including classical methods and (graph and non-graph) deep learning. We provide a detailed analysis of each component of our model.
Language	English
Publishing date	2024-03-10
Publishing country	Switzerland
Document type	Journal Article
DOI	10.1007/978-3-031-55088-1_3
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Article: Exploratory correlation of the human structural connectome with non-MRI variables in Alzheimer's disease.

Aganj, Iman / Mora, Jocelyn / Frau-Pascual, Aina / Fischl, Bruce

Alzheimer's & dementia (Amsterdam, Netherlands)

2023 Volume 15, Issue 4, Page(s) e12511

Abstract: Introduction: Discovery of the associations between brain structural connectivity and clinical and demographic variables can help to better understand the vulnerability and resilience of the brain architecture to neurodegenerative diseases and to ... ...

Abstract	Introduction: Discovery of the associations between brain structural connectivity and clinical and demographic variables can help to better understand the vulnerability and resilience of the brain architecture to neurodegenerative diseases and to discover biomarkers. Methods: We used four diffusion-MRI databases, three related to Alzheimer's disease (AD), to exploratorily correlate structural connections between 85 brain regions with non-MRI variables, while stringently correcting the significance values for multiple testing and ruling out spurious correlations via careful visual inspection. We repeated the analysis with brain connectivity augmented with multi-synaptic neural pathways. Results: We found 85 and 101 significant relationships with direct and augmented connectivity, respectively, which were generally stronger for the latter. Age was consistently linked to decreased connectivity, and healthier clinical scores were generally linked to increased connectivity. Discussion: Our findings help to elucidate which structural brain networks are affected in AD and aging and highlight the importance of including indirect connections.
Language	English
Publishing date	2023-12-17
Publishing country	United States
Document type	Journal Article
ZDB-ID	2832898-X
ISSN	2352-8729
ISSN	2352-8729
DOI	10.1002/dad2.12511
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Article: Exploratory Correlation of The Human Structural Connectome with Non-MRI Variables in Alzheimer's Disease.

Aganj, Iman / Mora, Jocelyn / Frau-Pascual, Aina / Fischl, Bruce

bioRxiv : the preprint server for biology

2023

Abstract	Introduction: Discovery of the associations between brain structural connectivity and clinical and demographic variables can help to better understand the vulnerability and resilience of the brain architecture to neurodegenerative diseases and to discover biomarkers. Methods: We used four diffusion-MRI databases, three related to Alzheimer's disease, to exploratorily correlate structural connections between 85 brain regions with non-MRI variables, while stringently correcting the significance values for multiple testing and ruling out spurious correlations via careful visual inspection. We repeated the analysis with brain connectivity augmented with multi-synaptic neural pathways. Results: We found 85 and 101 significant relationships with direct and augmented connectivity, respectively, which were generally stronger for the latter. Age was consistently linked to decreased connectivity, and healthier clinical scores were generally linked to increased connectivity. Discussion: Our findings help to elucidate which structural brain networks are affected in Alzheimer's disease and aging and highlight the importance of including indirect connections.
Language	English
Publishing date	2023-11-09
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.06.30.547308
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Article ; Online: Multi-Head Graph Convolutional Network for Structural Connectome Classification.

Kazi, Anees / Mora, Jocelyn / Fischl, Bruce / Dalca, Adrian V / Aganj, Iman

ArXiv

2023

Abstract: We tackle classification based on brain connectivity derived from diffusion magnetic resonance images. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brain connectivity input graph and processes the ... ...

Abstract	We tackle classification based on brain connectivity derived from diffusion magnetic resonance images. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brain connectivity input graph and processes the data separately through a parallel GCN mechanism with multiple heads. The proposed network is a simple design that employs different heads involving graph convolutions focused on edges and nodes, capturing representations from the input data thoroughly. To test the ability of our model to extract complementary and representative features from brain connectivity data, we chose the task of sex classification. This quantifies the degree to which the connectome varies depending on the sex, which is important for improving our understanding of health and disease in both sexes. We show experiments on two publicly available datasets: PREVENT-AD (347 subjects) and OASIS3 (771 subjects). The proposed model demonstrates the highest performance compared to the existing machine-learning algorithms we tested, including classical methods and (graph and non-graph) deep learning. We provide a detailed analysis of each component of our model.
Language	English
Publishing date	2023-09-20
Publishing country	United States
Document type	Preprint
ISSN	2331-8422
ISSN (online)	2331-8422
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Article ; Online: SynthStrip: skull-stripping for any brain image.

Hoopes, Andrew / Mora, Jocelyn S / Dalca, Adrian V / Fischl, Bruce / Hoffmann, Malte

NeuroImage

2022 Volume 260, Page(s) 119474

Abstract: The removal of non-brain signal from magnetic resonance imaging (MRI) data, known as skull-stripping, is an integral component of many neuroimage analysis streams. Despite their abundance, popular classical skull-stripping methods are usually tailored to ...

Abstract	The removal of non-brain signal from magnetic resonance imaging (MRI) data, known as skull-stripping, is an integral component of many neuroimage analysis streams. Despite their abundance, popular classical skull-stripping methods are usually tailored to images with specific acquisition properties, namely near-isotropic resolution and T1-weighted (T1w) MRI contrast, which are prevalent in research settings. As a result, existing tools tend to adapt poorly to other image types, such as stacks of thick slices acquired with fast spin-echo (FSE) MRI that are common in the clinic. While learning-based approaches for brain extraction have gained traction in recent years, these methods face a similar burden, as they are only effective for image types seen during the training procedure. To achieve robust skull-stripping across a landscape of imaging protocols, we introduce SynthStrip, a rapid, learning-based brain-extraction tool. By leveraging anatomical segmentations to generate an entirely synthetic training dataset with anatomies, intensity distributions, and artifacts that far exceed the realistic range of medical images, SynthStrip learns to successfully generalize to a variety of real acquired brain images, removing the need for training data with target contrasts. We demonstrate the efficacy of SynthStrip for a diverse set of image acquisitions and resolutions across subject populations, ranging from newborn to adult. We show substantial improvements in accuracy over popular skull-stripping baselines - all with a single trained model. Our method and labeled evaluation data are available at https://w3id.org/synthstrip.
MeSH term(s)	Adult ; Brain/diagnostic imaging ; Brain/pathology ; Contrast Media ; Head ; Humans ; Image Processing, Computer-Assisted/methods ; Infant, Newborn ; Magnetic Resonance Imaging/methods ; Skull/diagnostic imaging ; Skull/pathology
Chemical Substances	Contrast Media
Language	English
Publishing date	2022-07-13
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1147767-2
ISSN	1095-9572 ; 1053-8119
ISSN (online)	1095-9572
ISSN	1053-8119
DOI	10.1016/j.neuroimage.2022.119474
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Zs.A 3664: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
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Book ; Online: Multi-Head Graph Convolutional Network for Structural Connectome Classification

Kazi, Anees / Mora, Jocelyn / Fischl, Bruce / Dalca, Adrian V. / Aganj, Iman

2023

Abstract: We tackle classification based on brain connectivity derived from diffusion magnetic resonance images. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brain connectivity input graph and processes the ... ...

Abstract	We tackle classification based on brain connectivity derived from diffusion magnetic resonance images. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brain connectivity input graph and processes the data separately through a parallel GCN mechanism with multiple heads. The proposed network is a simple design that employs different heads involving graph convolutions focused on edges and nodes, capturing representations from the input data thoroughly. To test the ability of our model to extract complementary and representative features from brain connectivity data, we chose the task of sex classification. This quantifies the degree to which the connectome varies depending on the sex, which is important for improving our understanding of health and disease in both sexes. We show experiments on two publicly available datasets: PREVENT-AD (347 subjects) and OASIS3 (771 subjects). The proposed model demonstrates the highest performance compared to the existing machine-learning algorithms we tested, including classical methods and (graph and non-graph) deep learning. We provide a detailed analysis of each component of our model.
Keywords	Quantitative Biology - Neurons and Cognition ; Computer Science - Machine Learning
Subject code	006
Publishing date	2023-05-02
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: SynthStrip

Hoopes, Andrew / Mora, Jocelyn S. / Dalca, Adrian V. / Fischl, Bruce / Hoffmann, Malte

Skull-Stripping for Any Brain Image

2022

Abstract	The removal of non-brain signal from magnetic resonance imaging (MRI) data, known as skull-stripping, is an integral component of many neuroimage analysis streams. Despite their abundance, popular classical skull-stripping methods are usually tailored to images with specific acquisition properties, namely near-isotropic resolution and T1-weighted (T1w) MRI contrast, which are prevalent in research settings. As a result, existing tools tend to adapt poorly to other image types, such as stacks of thick slices acquired with fast spin-echo (FSE) MRI that are common in the clinic. While learning-based approaches for brain extraction have gained traction in recent years, these methods face a similar burden, as they are only effective for image types seen during the training procedure. To achieve robust skull-stripping across a landscape of imaging protocols, we introduce SynthStrip, a rapid, learning-based brain-extraction tool. By leveraging anatomical segmentations to generate an entirely synthetic training dataset with anatomies, intensity distributions, and artifacts that far exceed the realistic range of medical images, SynthStrip learns to successfully generalize to a variety of real acquired brain images, removing the need for training data with target contrasts. We demonstrate the efficacy of SynthStrip for a diverse set of image acquisitions and resolutions across subject populations, ranging from newborn to adult. We show substantial improvements in accuracy over popular skull-stripping baselines -- all with a single trained model. Our method and labeled evaluation data are available at https://w3id.org/synthstrip. Comment: 19 pages, 9 figures, 7 tables, skull stripping, brain extraction, image synthesis, MRI-contrast agnosticism, deep learning, final published version
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Physics - Medical Physics ; Quantitative Biology - Neurons and Cognition
Subject code	006
Publishing date	2022-03-18
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A blueprint for a multi-disease, multi-domain Bayesian adaptive platform trial incorporating adult and paediatric subgroups: the Staphylococcus aureus Network Adaptive Platform trial.

Mahar, Robert K / McGlothlin, Anna / Dymock, Michael / Lee, Todd C / Lewis, Roger J / Lumley, Thomas / Mora, Jocelyn / Price, David J / Saville, Benjamin R / Snelling, Tom / Turner, Rebecca / Webb, Steven A / Davis, Joshua S / Tong, Steven Y C / Marsh, Julie A

Trials

2023 Volume 24, Issue 1, Page(s) 795

Abstract: The Staphylococcus aureus Network Adaptive Platform (SNAP) trial is a multifactorial Bayesian adaptive platform trial that aims to improve the way that S. aureus bloodstream infection, a globally common and severe infectious disease, is treated. In a ... ...

Abstract	The Staphylococcus aureus Network Adaptive Platform (SNAP) trial is a multifactorial Bayesian adaptive platform trial that aims to improve the way that S. aureus bloodstream infection, a globally common and severe infectious disease, is treated. In a world first, the SNAP trial will simultaneously investigate the effects of multiple intervention modalities within multiple groups of participants with different forms of S. aureus bloodstream infection. Here, we formalise the trial structure, modelling approach, and decision rules that will be used for the SNAP trial. By summarising the statistical principles governing the design, our hope is that the SNAP trial will serve as an adaptable template that can be used to improve comparative effectiveness research efficiency in other disease areas.Trial registration NCT05137119 . Registered on 30 November 2021.
MeSH term(s)	Adult ; Child ; Humans ; Bayes Theorem ; Sepsis ; Staphylococcal Infections/diagnosis ; Staphylococcus aureus
Language	English
Publishing date	2023-12-06
Publishing country	England
Document type	Clinical Trial ; Journal Article
ZDB-ID	2040523-6
ISSN	1745-6215 ; 1468-6694 ; 1745-6215
ISSN (online)	1745-6215
ISSN	1468-6694 ; 1745-6215
DOI	10.1186/s13063-023-07718-x
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Article ; Online: A multi-site, international laboratory study to assess the performance of penicillin susceptibility testing of Staphylococcus aureus.

Henderson, Andrew / Cheng, Matthew P / Chew, Ka Lip / Coombs, Geoffrey W / Davis, Joshua S / Grant, Jennifer M / Gregson, Dan / Giulieri, Stefano G / Howden, Benjamin P / Lee, Todd C / Nguyen, Vi / Mora, Jocelyn M / Morpeth, Susan C / Robinson, James O / Tong, Steven Y C / Van Hal, Sebastiaan J

The Journal of antimicrobial chemotherapy

2023 Volume 78, Issue 6, Page(s) 1499–1504

Abstract: Objectives: There is clinical uncertainty over the optimal treatment for penicillin-susceptible Staphylococcus aureus (PSSA) infections. Furthermore, there is concern that phenotypic penicillin susceptibility testing methods are not reliably able to ... ...

Abstract	Objectives: There is clinical uncertainty over the optimal treatment for penicillin-susceptible Staphylococcus aureus (PSSA) infections. Furthermore, there is concern that phenotypic penicillin susceptibility testing methods are not reliably able to detect some blaZ-positive S. aureus. Methods: Nine S. aureus isolates, including six genetically diverse strains harbouring blaZ, were sent in triplicate to 34 participating laboratories from Australia (n = 14), New Zealand (n = 6), Canada (n = 12), Singapore (n = 1) and Israel (n = 1). We used blaZ PCR as the gold standard to assess susceptibility testing performance of CLSI (P10 disc) and EUCAST (P1 disc) methods. Very major errors (VMEs), major error (MEs) and categorical agreement were calculated. Results: Twenty-two laboratories reported 593 results according to CLSI methodology (P10 disc). Nineteen laboratories reported 513 results according to the EUCAST (P1 disc) method. For CLSI laboratories, the categorical agreement and calculated VME and ME rates were 85% (508/593), 21% (84/396) and 1.5% (3/198), respectively. For EUCAST laboratories, the categorical agreement and calculated VME and ME rates were 93% (475/513), 11% (84/396) and 1% (3/198), respectively. Seven laboratories reported results for both methods, with VME rates of 24% for CLSI and 12% for EUCAST. Conclusions: The EUCAST method with a P1 disc resulted in a lower VME rate compared with the CLSI methods with a P10 disc. These results should be considered in the context that among collections of PSSA isolates, as determined by automated MIC testing, less than 10% harbour blaZ. Furthermore, the clinical relevance of phenotypically susceptible, but blaZ-positive S. aureus, remains unclear.
MeSH term(s)	Humans ; Anti-Bacterial Agents/pharmacology ; Staphylococcus aureus/genetics ; Penicillins/pharmacology ; Microbial Sensitivity Tests ; Clinical Decision-Making ; Uncertainty ; Staphylococcal Infections
Chemical Substances	Anti-Bacterial Agents ; Penicillins
Language	English
Publishing date	2023-04-18
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	191709-2
ISSN	1460-2091 ; 0305-7453
ISSN (online)	1460-2091
ISSN	0305-7453
DOI	10.1093/jac/dkad116
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Zs.A 1197: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (1.OG) ab Jg. 2022: Lesesaal (EG)
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