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Phoebe Imms

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

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YNICL Journal 2026 Journal Article

Bridging mental health, cognition and the brain in mild traumatic brain injury: A multilayer network analysis of the TRACK-TBI study

Juan F. Domínguez D.
Mervyn Singh
Lyndon Firman-Sadler
Jade Guarnera
Ivan L. Simpson-Kent
Phoebe Imms
Andrei Irimia
Karen Caeyenberghs

BACKGROUND: People with mild traumatic brain injury (mTBI) suffer from several mental health symptoms (e.g., anxiety, depressive symptoms) and cognitive deficits (e.g., attentional deficits, slowed processing speed). However, symptoms in TBI are largely investigated in isolation, using univariate approaches, ignoring interactions between symptoms and the underlying large-scale brain networks. We constructed the first multilayer network in mTBI to examine relationships between networks of cognition, mental health and structural brain measures and to identify key variables bridging relationships across these networks. METHODS: Chronic phase cross-sectional data (6-month follow-up) from 457 mTBI participants was extracted from the TRACK-TBI Longitudinal study. We selected four variables from self-report mental health questionnaires (affective layer), eight cognitive test scores from the NIH toolbox (cognitive layer), and gray matter volumes from eight brain regions of the central executive and salience networks from anatomical MRI scans (brain layer). We used a multilayer network approach to examine the relationships (edges) between all variables (nodes) across layers. We then used the bridge strength centrality metric to identify nodes that 'bridge' the affective, cognitive, and brain layers. RESULTS: In this sample of mTBI participants, across all affective and cognitive layer nodes, only impairments in insomnia were noted. Multilayer network analysis revealed insomnia severity, immediate verbal memory, somatisation and processing speed nodes exceeded an a priori 80th percentile threshold on the bridge strength scores and may therefore be regarded as key nodes potentially bridging relationships across affective, cognitive and brain layers. CONCLUSIONS: The bridging nodes identified in our multilayer network analyses may suggest targets for future studies to develop more customized, efficient, and efficacious treatments to alleviate mental health symptoms and cognitive deficits in mTBI.

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YNIMG Journal 2026 Journal Article

The evolution of T1-weighted lesion inpainting tools in patients with brain injury: A scoping review

Evelyn Deutscher
Phoebe Imms
Andrei Irimia
Emily Dennis
Juan F Domínguez D
Karen Caeyenberghs

Focal brain lesions from Acquired Brain Injuries (ABIs) present as regions of abnormal signal intensity on T1-weighted Magnetic Resonance Imaging (MRI) scans. These can disrupt automated neuroimaging processing algorithms traditionally developed on and for healthy brains. Lesion filling (or inpainting) can replace lesioned image voxels with signal intensities approximating healthy tissue. This creates a 'lesion free' brain to use as input to the image processing algorithms thus aiming to reduce the presence of lesion induced errors. This scoping review provides a detailed overview of the available inpainting tools for use in neuroimaging analysis of patients with ABI. First, we define lesion inpainting and highlight its importance for pre-processing of MRI scans. Next, we classify the papers resulting from our search (24 in total) into: (a) Traditional Methods (Local Diffusion, Global Diffusion, Search Patch-Based, a priori Patch-Based, or Low Rank Sparse Decomposition) and (b) Deep Learning methods (Convolutional Neural Networks, Generative Adversarial Networks, or Denoising Diffusion Models). We then discuss the strengths and limitations of each different inpainting method. Finally, we provide recommendations for both the use, and development of inpainting tools, to increase the adoption of lesion inpainting across ABI studies.

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YNICL Journal 2024 Journal Article

ENIGMA’s simple seven: Recommendations to enhance the reproducibility of resting-state fMRI in traumatic brain injury

Karen Caeyenberghs
Phoebe Imms
Andrei Irimia
Martin M. Monti
Carrie Esopenko
Nicola L. de Souza
Juan F. Dominguez D
Mary R. Newsome

Resting state functional magnetic resonance imaging (rsfMRI) provides researchers and clinicians with a powerful tool to examine functional connectivity across large-scale brain networks, with ever-increasing applications to the study of neurological disorders, such as traumatic brain injury (TBI). While rsfMRI holds unparalleled promise in systems neurosciences, its acquisition and analytical methodology across research groups is variable, resulting in a literature that is challenging to integrate and interpret. The focus of this narrative review is to address the primary methodological issues including investigator decision points in the application of rsfMRI to study the consequences of TBI. As part of the ENIGMA Brain Injury working group, we have collaborated to identify a minimum set of recommendations that are designed to produce results that are reliable, harmonizable, and reproducible for the TBI imaging research community. Part one of this review provides the results of a literature search of current rsfMRI studies of TBI, highlighting key design considerations and data processing pipelines. Part two outlines seven data acquisition, processing, and analysis recommendations with the goal of maximizing study reliability and between-site comparability, while preserving investigator autonomy. Part three summarizes new directions and opportunities for future rsfMRI studies in TBI patients. The goal is to galvanize the TBI community to gain consensus for a set of rigorous and reproducible methods, and to increase analytical transparency and data sharing to address the reproducibility crisis in the field.

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YNIMG Journal 2021 Journal Article

Fixel-based Analysis of Diffusion MRI: Methods, Applications, Challenges and Opportunities

Thijs Dhollander
Adam Clemente
Mervyn Singh
Frederique Boonstra
Oren Civier
Juan Dominguez Duque
Natalia Egorova
Peter Enticott

Diffusion MRI has provided the neuroimaging community with a powerful tool to acquire in-vivo data sensitive to microstructural features of white matter, up to 3 orders of magnitude smaller than typical voxel sizes. The key to extracting such valuable information lies in complex modelling techniques, which form the link between the rich diffusion MRI data and various metrics related to the microstructural organization. Over time, increasingly advanced techniques have been developed, up to the point where some diffusion MRI models can now provide access to properties specific to individual fibre populations in each voxel in the presence of multiple "crossing" fibre pathways. While highly valuable, such fibre-specific information poses unique challenges for typical image processing pipelines and statistical analysis. In this work, we review the "Fixel-Based Analysis" (FBA) framework, which implements bespoke solutions to this end. It has recently seen a stark increase in adoption for studies of both typical (healthy) populations as well as a wide range of clinical populations. We describe the main concepts related to Fixel-Based Analyses, as well as the methods and specific steps involved in a state-of-the-art FBA pipeline, with a focus on providing researchers with practical advice on how to interpret results. We also include an overview of the scope of all current FBA studies, categorized across a broad range of neuro-scientific domains, listing key design choices and summarizing their main results and conclusions. Finally, we critically discuss several aspects and challenges involved with the FBA framework, and outline some directions and future opportunities.

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