Article
Neurosciences
Michiel Cottaar, Matteo Bastiani, Nikhil Boddu, Matthew F. Glasser, Suzanne Haber, David C. van Essen, Stamatios N. Sotiropoulos, Saad Jbabdi
Summary: Many brain imaging studies measure structural connectivity with diffusion tractography, but biases in the data can limit accuracy. A new algorithm reduces these biases by modeling fiber density and orientation.
Article
Neurosciences
Margaret Caroline Stapleton, Stefan Paul Koch, Devin Raine Everaldo Cortes, Samuel Wyman, Kristina E. Schwab, Susanne Mueller, Christopher Gordon McKennan, Philipp Boehm-Sturm, Yijen Lin Wu
Summary: Research suggests that the relationship between late-onset Alzheimer's disease (LOAD) and apolipoprotein-E (ApoE) may be related to changes in brain network structure. Using diffusion tensor imaging and graph theory analysis, significant differences were found in the brain networks of ApoE KO mice compared to WT mice, particularly in the networks involving the hippocampus, amygdala, and caudate putamen.
FRONTIERS IN NEUROSCIENCE
(2023)
Article
Neurosciences
Zhuopin Sun, Steven Meikle, Fernando Calamante
Summary: The CONNectome-based Non-Local Means (CONN-NLM) filter exploits synergies between dMRI-derived structural connectivity and PET intensity information to denoise PET images. The method improves PET image quality by reducing noise while preserving lesion contrasts, outperforming filters that do not use dMRI information. CONN-NLM represents a new avenue to exploit synergies between MRI and PET for more informative and accurate PET smoothing.
FRONTIERS IN NEUROSCIENCE
(2022)
Article
Neurosciences
Burke Q. Rosen, Eric Halgren
Summary: The WU-Minn Human Connectome Project (HCP) provides a dataset of advanced MRI techniques for over a thousand healthy subjects, with a focus on resting-state fMRI. A full-cortex connectome derived from probabilistic diffusion tractography revealed that connection strengths are lognormally distributed and decay exponentially with tract length, among other findings. Comparisons with existing connectivity matrices suggest that the dMRI connectome is more similar to cortico-cortico-evoked potential connectivity.
Article
Neurosciences
Scott Trinkle, Sean Foxley, Gregg Wildenberg, Narayanan Kasthuri, Patrick La Riviere
Summary: Diffusion MRI tractography is a noninvasive method for measuring the structural connectome in humans, but recent studies have shown limitations due to local uncertainties in fiber orientations. Geometry plays a larger role in determining the topology of graphs produced by tractography compared to neural tracers, underestimating weights at long distances and affecting the placement of network hubs. The role of spatial embedding in modular structure and network efficiency is explored in both modalities, with geometric biases inherent in tractography quantified for future validation efforts.
Review
Radiology, Nuclear Medicine & Medical Imaging
Chun-Hung Yeh, Derek K. Jones, Xiaoyun Liang, Maxime Descoteaux, Alan Connelly
Summary: Diffusion MRI-based tractography is commonly used for inferring the structural brain connectome, and applying graph theory to analyze these connectomes provides important opportunities to explore connectivity patterns. However, challenges exist in this framework, particularly regarding methodological and biological plausibility.
JOURNAL OF MAGNETIC RESONANCE IMAGING
(2021)
Article
Neurosciences
Xuehu Wei, Helyne Adamson, Matthias Schwendemann, Tomas Goucha, Angela D. Friederici, Alfred Anwander
Summary: The current study investigated the differences in brain white matter connections between native speakers of German and Arabic. German speakers showed stronger connectivity in a language network associated with complex syntax processing, while Arabic speakers exhibited stronger connectivity between semantic language regions and inter-hemispheric connections. These findings suggest that the structural language connectome is influenced by the linguistic characteristics of the native language.
Article
Neurosciences
Alessandro Crimi, Luca Dodero, Fabio Sambataro, Vittorio Murino, Diego Sona
Summary: This paper proposes a constrained autoregressive model to understand how brain structure modulates function and discover novel biomarkers. The model includes indirect connections and can be used with raw and deconvoluted BOLD signal, showing results closer to reality.
Article
Neurosciences
Martin Cole, Kyle Murray, Etienne St-Onge, Benjamin Risk, Jianhui Zhong, Giovanni Schifitto, Maxime Descoteaux, Zhengwu Zhang
Summary: There is increasing interest in studying the relationship between structural connectivity (SC) and functional connectivity (FC) using diffusion and functional MRI. A novel atlas-free approach called Surface-Based Connectivity Integration (SBCI) is proposed to accurately study the relationships between SC and FC. Using data from the Human Connectome Project, high-quality SC-FC coupling measures produced by SBCI were introduced and used to study sex differences in young adults, showing promise for future connectomics studies.
HUMAN BRAIN MAPPING
(2021)
Article
Neurosciences
Ahmed Radwan, Lisa Decraene, Patrick Dupont, Nicolas Leenaerts, Cristina Simon-Martinez, Katrijn Klingels, Els Ortibus, Hilde Feys, Stefan Sunaert, Jeroen Blommaert, Lisa Mailleux
Summary: This study explored the structural brain connectomes in children with spastic unilateral cerebral palsy (uCP) and its relationship to sensory-motor function using graph theory. The results showed a hyperconnectivity pattern in the CDGM-lesion group compared to the PWM-lesion group, with higher clustering coefficient, characteristic path length, and local efficiency. The CST-wiring pattern was found to be the strongest predictor for motor function. The findings highlight the potential of structural connectomics in understanding disease severity and brain development in children with uCP.
HUMAN BRAIN MAPPING
(2023)
Article
Critical Care Medicine
Timo Roine, Mehrbod Mohammadian, Jussi Hirvonen, Timo Kurki, Jussi P. Posti, Riikka S. K. Takala, Virginia F. Newcombe, Jussi Tallus, Ari J. Katila, Henna-Riikka Maanpaeae, Janek Frantzen, David Menon, Olli Tenovuo
Summary: We investigated the topology of structural brain connectivity networks and found that both global and local network properties are associated with outcome after mild traumatic brain injury. Higher normalized global efficiency, degree, and strength as well as lower small-worldness are correlated with better outcome. The local network properties of the left putamen and the left postcentral gyrus show the most prominent correlations with outcome.
JOURNAL OF NEUROTRAUMA
(2022)
Article
Multidisciplinary Sciences
Philippe Poulin, Guillaume Theaud, Francois Rheault, Etienne St-Onge, Arnaud Bore, Emmanuelle Renauld, Louis de Beaumont, Samuel Guay, Pierre-Marc Jodoin, Maxime Descoteaux
Summary: TractoInferno is the largest open-source multi-site tractography database in the world, aimed at providing standardized training datasets and evaluation protocols for machine learning tractography approaches. It includes 284 samples from different sites, covering a wide range of data and algorithms for research and evaluation.
Article
Biophysics
Jianzhong He, Shun Yao, Qingrun Zeng, Jinping Chen, Tian Sang, Lei Xie, Yiang Pan, Yuanjing Feng
Summary: The human visual pathway can be reconstructed noninvasively using diffusion MRI tractography. However, this process is challenging due to the complex fiber geometries and the skull base environment. In this paper, a unified global tractography framework is proposed to automatically reconstruct the visual pathway.
NMR IN BIOMEDICINE
(2023)
Article
Computer Science, Interdisciplinary Applications
Dogu Baran Aydogan, Yonggang Shi
Summary: Tractography is an important technique for reconstructing structural connections in the brain using diffusion MRI, but its reliability has been questioned, leading to the proposal of a novel propagation-based tracker that can generate geometrically smooth curves without increasing complexity. Extensive experiments have shown promising results visually and quantitatively, indicating the potential of the new tracker in improving the accuracy of brain connectivity mapping.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2021)
Article
Neurosciences
Hassna Irzan, Erika Molteni, Michael Hutel, Sebastien Ourselin, Neil Marlow, Andrew Melbourne
Summary: The study found significant alterations in white matter connectivity in extremely preterm young adults at both macro-and microstructural levels, with overall diminished connectivity but comparable spatial configuration of WM fibres with fewer WM fibres per voxel. These alterations are widespread throughout the brain, particularly concentrated along pathways between deep grey matter regions, frontal regions, and the cerebellum, indicating that white matter abnormalities persist into early adulthood in individuals exposed to the extrauterine environment prematurely.
Article
Neurosciences
Shania Mereen Soman, Nandita Vijayakumar, Gareth Ball, Christian Hyde, Timothy J. Silk
Summary: This study used a network-based approach to examine the developmental trajectories of functional connectivity in children with ADHD. The findings revealed differential developmental patterns in networks connecting cortical and limbic regions, as well as between visual and higher-order cognitive regions. These findings underscore the importance of investigating the longitudinal course of development to understand functional connectivity networks in children with ADHD.
BIOLOGICAL PSYCHIATRY-COGNITIVE NEUROSCIENCE AND NEUROIMAGING
(2023)
Article
Neurosciences
Sunniva Fenn-Moltu, Sean P. Fitzgibbon, Judit Ciarrusta, Michael Eyre, Lucilio Cordero-Grande, Andrew Chew, Shona Falconer, Oliver Gale-Grant, Nicholas Harper, Ralica Dimitrova, Katy Vecchiato, Daphna Fenchel, Ayesha Javed, Megan Earl, Anthony N. Price, Emer Hughes, Eugene P. Duff, Jonathan O'Muircheartaigh, Chiara Nosarti, Tomoki Arichi, Daniel Rueckert, Serena Counsell, Joseph Hajnal, A. David Edwards, Grainne McAlonan, Dafnis Batalle
Summary: The formation of the functional connectome in early life is crucial for future learning and behavior. However, our understanding of how the functional organization of brain regions matures during the early postnatal period, especially in response to adverse neurodevelopmental outcomes like preterm birth, is limited. In this study involving 366 neonates, we found that functional centrality (weighted degree) increased with age in visual regions and decreased in motor and auditory regions in term-born infants. Preterm-born infants scanned at term equivalent age showed higher functional centrality in visual regions and lower measures in motor regions. Functional centrality did not predict neurodevelopmental outcomes at 18 months old.
Article
Radiology, Nuclear Medicine & Medical Imaging
Francesco Padormo, Paul Cawley, Louise Dillon, Emer Hughes, Jennifer Almalbis, Joanna Robinson, Alessandra Maggioni, Miguel De La Fuente Botella, Dan Cromb, Anthony Price, Lori Arlinghaus, John Pitts, Tianrui Luo, Dingtian Zhang, Sean C. L. Deoni, Steve Williams, Shaihan Malik, Jonathan O'Muircheartaigh, Serena J. Counsell, Mary Rutherford, Tomoki Arichi, A. David Edwards, Joseph V. Hajnal
Summary: This study utilizes ultralow-field MRI systems to measure T-1 values in neonates and finds that these values are shorter than those previously measured at standard clinical field strengths, but longer than those of adults at ultralow-field. T-1 values decrease with postmenstrual age, making them a potential biomarker for perinatal brain development.
MAGNETIC RESONANCE IN MEDICINE
(2023)
Letter
Anesthesiology
Philippa Bridgen, Shaihan Malik, Thomas Wilkinson, John N. Cronin, Tahzeeb Bhagat, Nicholas Hart, Stuart Mc Corkell, Joanne Perkins, Shane Tibby, Sara Hanna, Richard Kirwan, Thomas Pauly, Arthur Weeks, Geoff Charles-Edwards, Francesco Padormo, David Stell, Kariem El-Boghdadly, Sebastien Ourselin, Sharon L. Giles, Anthony D. Edwards, Joseph V. Hajnal, Benjamin J. Blaise
BRITISH JOURNAL OF ANAESTHESIA
(2023)
Article
Neurosciences
Abi Fukami-Gartner, Ana A. Baburamani, Ralica Dimitrova, Prachi A. Patkee, Olatz Ojinaga-Alfageme, Alexandra F. Bonthrone, Daniel Cromb, Alena U. Uus, Serena J. Counsell, Joseph Hajnal, Jonathan O'Muircheartaigh, Mary A. Rutherford
Summary: Down syndrome (DS) is a common genetic cause of intellectual disability. In this study, researchers analyzed the brain volumes of neonates with DS using neuroimaging techniques. They found that the DS brain showed significant reductions in overall volume, cerebral white matter, and cerebellar volumes, as well as differences in relative lobar volumes. Furthermore, certain features such as enlarged deep gray matter volume and lateral ventricle enlargement were observed. Assessing phenotypic severity at the neonatal stage may help guide early interventions and improve neurodevelopmental outcomes in children with DS.
Article
Computer Science, Interdisciplinary Applications
Cheng Ouyang, Chen Chen, Surui Li, Zeju Li, Chen Qin, Wenjia Bai, Daniel Rueckert
Summary: In this work, the authors investigate the problem of training a deep network that is robust to unseen domains using only data from one source domain. They propose a causality-inspired data augmentation approach to expose the model to synthesized domain-shifted training examples. The approach is validated on three cross-domain segmentation scenarios and shows consistent performance improvements compared to competitive methods.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2023)
Article
Psychology, Developmental
Lucy Vanes, Sunniva Fenn-Moltu, Laila Hadaya, Sean Fitzgibbon, Lucilio Cordero-Grande, Anthony Price, Andrew Chew, Shona Falconer, Tomoki Arichi, Serena J. Counsell, Joseph V. Hajnal, Dafnis Batalle, David Edwards, Chiara Nosarti
Summary: Preterm birth increases the risk of adverse behavioural outcomes in later life. Our study examines the longitudinal development of neonatal brain volume and functional connectivity after preterm birth and their relationship to psychomotor outcomes and psychopathology in toddlerhood. We found that better psychomotor functioning is associated with specific brain volume and connectivity changes in the neonatal period, while increased psychopathology is related to alterations in regional subcortical volume. Additionally, socio-economic deprivation and cognitively stimulating parenting play different roles in predicting psychopathology and psychomotor outcomes. Our findings highlight the importance of longitudinal imaging and environmental influences in understanding behavioural development in preterm infants.
DEVELOPMENTAL COGNITIVE NEUROSCIENCE
(2023)
Article
Biology
Sian Wilson, Maximilian Pietsch, Lucilio Cordero-Grande, Daan Christiaens, Alena Uus, Vyacheslav R. Karolis, Vanessa Kyriakopoulou, Kathleen Colford, Anthony N. Price, Jana Hutter, Mary A. Rutherford, Emer J. Hughes, Serena J. Counsell, Jacques-Donald Tournier, Joseph Hajnal, A. David Edwards, Jonathan O'Muicheartaigh, Tomoki Arichi, Finnegan J. Calabro
Summary: In this study, high-resolution in utero diffusion magnetic resonance imaging was used to examine the development of thalamocortical white matter in 140 fetuses. The researchers delineated the thalamocortical pathways and parcellated the fetal thalamus based on its cortical connectivity. They quantified microstructural tissue components along the tracts in fetal compartments and identified changes in diffusion metrics reflecting critical neurobiological transitions. These findings provide a normative reference for further studies on developmental disruptions and their contributions to pathophysiology.
Article
Psychiatry
Laila Hadaya, Konstantina Dimitrakopoulou, Lucy D. Vanes, Dana Kanel, Sunniva Fenn-Moltu, Oliver Gale-Grant, Serena J. Counsell, A. David Edwards, Mansoor Saqi, Dafnis Batalle, Chiara Nosarti
Summary: Very preterm birth (VPT) has effects on brain development and can lead to cognitive and behavioral difficulties. This study aimed to categorize VPT children into different behavior subgroups and investigate the differences in neonatal brain structure and function between these subgroups.
TRANSLATIONAL PSYCHIATRY
(2023)
Article
Computer Science, Interdisciplinary Applications
Adam Marcus, Paul Bentley, Daniel Rueckert
Summary: The proposed study introduces a novel end-to-end multi-task transformer-based model for concurrent segmentation and age estimation of cerebral ischemic lesions. The method captures long-range dependencies using gated positional self-attention and CT-specific data augmentation, and can be effectively trained with low-data regimes in medical imaging. Experimental results demonstrate promising performance in lesion age classification, outperforming existing task-specific algorithms.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2023)
Article
Computer Science, Artificial Intelligence
Jiazhen Pan, Manal Hamdi, Wenqi Huang, Kerstin Hammernik, Thomas Kuestner, Daniel Rueckert
Summary: This article introduces a learning-based and unrolled MCMR framework that can achieve accurate and rapid CMR reconstruction, delivering artifacts-free motion estimation and high-quality reconstruction even at imaging acceleration rates up to 20x.
MEDICAL IMAGE ANALYSIS
(2024)
