Carmen Tur1, Francesco Grussu2, Ferran Prados3, Thalis Charalambous1, Sara Collorone1, Baris Kanber3, Niamh Cawley1, Daniel R Altmann4, Sébastien Ourselin5, Frederik Barkhof6, Jonathan D Clayden7, Ahmed T Toosy1, Claudia Am Gandini Wheeler-Kingshott8, Olga Ciccarelli9. 1. Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London (UCL), London, UK. 2. Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London (UCL), London, UK/Centre for Medical Image Computing, Department of Computer Science, University College London (UCL), London, UK. 3. Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London (UCL), London, UK/Translational Imaging Group, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London (UCL), London, UK. 4. Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London (UCL), London, UK/Department of Medical Statistics, London School of Hygiene and Tropical Medicine, University of London, London, UK. 5. Translational Imaging Group, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London (UCL), London, UK/School of Biomedical Engineering & Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK. 6. Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London (UCL), London, UK/Translational Imaging Group, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London (UCL), London, UK/Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands/National Institute for Health Research University College London Hospitals Biomedical Research Centre, London, UK. 7. UCL Great Ormond Street Institute of Child Health, University College London (UCL), London, UK. 8. Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London (UCL), London, UK/Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy; Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy. 9. Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London (UCL), London, UK/National Institute for Health Research University College London Hospitals Biomedical Research Centre, London, UK.
Abstract
BACKGROUND: The potential of multi-shell diffusion imaging to produce accurate brain connectivity metrics able to unravel key pathophysiological processes in multiple sclerosis (MS) has scarcely been investigated. OBJECTIVE: To test, in patients with a clinically isolated syndrome (CIS), whether multi-shell imaging-derived connectivity metrics can differentiate patients from controls, correlate with clinical measures, and perform better than metrics obtained with conventional single-shell protocols. METHODS: Nineteen patients within 3 months from the CIS and 12 healthy controls underwent anatomical and 53-direction multi-shell diffusion-weighted 3T images. Patients were cognitively assessed. Voxel-wise fibre orientation distribution functions were estimated and used to obtain network metrics. These were also calculated using a conventional single-shell diffusion protocol. Through linear regression, we obtained effect sizes and standardised regression coefficients. RESULTS: Patients had lower mean nodal strength (p = 0.003) and greater network modularity than controls (p = 0.045). Greater modularity was associated with worse cognitive performance in patients, even after accounting for lesion load (p = 0.002). Multi-shell-derived metrics outperformed single-shell-derived ones. CONCLUSION: Connectivity-based nodal strength and network modularity are abnormal in the CIS. Furthermore, the increased network modularity observed in patients, indicating microstructural damage, is clinically relevant. Connectivity analyses based on multi-shell imaging can detect potentially relevant network changes in early MS.
BACKGROUND: The potential of multi-shell diffusion imaging to produce accurate brain connectivity metrics able to unravel key pathophysiological processes in multiple sclerosis (MS) has scarcely been investigated. OBJECTIVE: To test, in patients with a clinically isolated syndrome (CIS), whether multi-shell imaging-derived connectivity metrics can differentiate patients from controls, correlate with clinical measures, and perform better than metrics obtained with conventional single-shell protocols. METHODS: Nineteen patients within 3 months from the CIS and 12 healthy controls underwent anatomical and 53-direction multi-shell diffusion-weighted 3T images. Patients were cognitively assessed. Voxel-wise fibre orientation distribution functions were estimated and used to obtain network metrics. These were also calculated using a conventional single-shell diffusion protocol. Through linear regression, we obtained effect sizes and standardised regression coefficients. RESULTS:Patients had lower mean nodal strength (p = 0.003) and greater network modularity than controls (p = 0.045). Greater modularity was associated with worse cognitive performance in patients, even after accounting for lesion load (p = 0.002). Multi-shell-derived metrics outperformed single-shell-derived ones. CONCLUSION: Connectivity-based nodal strength and network modularity are abnormal in the CIS. Furthermore, the increased network modularity observed in patients, indicating microstructural damage, is clinically relevant. Connectivity analyses based on multi-shell imaging can detect potentially relevant network changes in early MS.
Authors: Massimo Filippi; Martijn P van den Heuvel; Alexander Fornito; Yong He; Hilleke E Hulshoff Pol; Federica Agosta; Giancarlo Comi; Maria A Rocca Journal: Lancet Neurol Date: 2013-10-11 Impact factor: 44.182
Authors: Thalis Charalambous; Carmen Tur; Ferran Prados; Baris Kanber; Declan T Chard; Sebastian Ourselin; Jonathan D Clayden; Claudia A M Gandini Wheeler-Kingshott; Alan J Thompson; Ahmed T Toosy Journal: J Neurol Neurosurg Psychiatry Date: 2018-11-22 Impact factor: 10.154
Authors: N Colgan; B Siow; J M O'Callaghan; I F Harrison; J A Wells; H E Holmes; O Ismail; S Richardson; D C Alexander; E C Collins; E M Fisher; R Johnson; A J Schwarz; Z Ahmed; M J O'Neill; T K Murray; H Zhang; M F Lythgoe Journal: Neuroimage Date: 2015-10-23 Impact factor: 6.556
Authors: Tommy A A Broeders; Linda Douw; Anand J C Eijlers; Iris Dekker; Bernard M J Uitdehaag; Frederik Barkhof; Hanneke E Hulst; Christiaan H Vinkers; Jeroen J G Geurts; Menno M Schoonheim Journal: Brain Commun Date: 2022-04-12
Authors: Thalis Charalambous; Jonathan D Clayden; Elizabeth Powell; Ferran Prados; Carmen Tur; Baris Kanber; Declan Chard; Sebastien Ourselin; Claudia A M Gandini Wheeler-Kingshott; Alan J Thompson; Ahmed T Toosy Journal: Sci Rep Date: 2020-02-27 Impact factor: 4.379