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Kaļva K, Zdanovskis N, Šneidere K, Kostiks A, Karelis G, Platkājis A, Stepens A. Whole Brain and Corpus Callosum Fractional Anisotropy Differences in Patients with Cognitive Impairment. Diagnostics (Basel) 2023; 13:3679. [PMID: 38132263 PMCID: PMC10742911 DOI: 10.3390/diagnostics13243679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 11/20/2023] [Accepted: 12/14/2023] [Indexed: 12/23/2023] Open
Abstract
Diffusion tensor imaging (DTI) is an MRI analysis method that could help assess cognitive impairment (CI) in the ageing population more accurately. In this research, we evaluated fractional anisotropy (FA) of whole brain (WB) and corpus callosum (CC) in patients with normal cognition (NC), mild cognitive impairment (MCI), and moderate/severe cognitive impairment (SCI). In total, 41 participants were included in a cross-sectional study and divided into groups based on Montreal Cognitive Assessment (MoCA) scores (NC group, nine participants, MCI group, sixteen participants, and SCI group, sixteen participants). All participants underwent an MRI examination that included a DTI sequence. FA values between the groups were assessed by analysing FA value and age normative percentile. We did not find statistically significant differences between the groups when analysing CC FA values. Both approaches showed statistically significant differences in WB FA values between the MCI-SCI and MCI-NC groups, where the MCI group participants showed the highest mean FA and highest mean FA normative percentile results in WB.
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Affiliation(s)
- Kalvis Kaļva
- Department of Radiology, Riga Stradins University, LV-1007 Riga, Latvia; (K.K.)
- Department of Radiology, Riga East Clinical University Hospital, LV-1038 Riga, Latvia
| | - Nauris Zdanovskis
- Department of Radiology, Riga Stradins University, LV-1007 Riga, Latvia; (K.K.)
- Department of Radiology, Riga East Clinical University Hospital, LV-1038 Riga, Latvia
- Military Medicine Research and Study Centre, Riga Stradins University, LV-1007 Riga, Latvia
| | - Kristīne Šneidere
- Military Medicine Research and Study Centre, Riga Stradins University, LV-1007 Riga, Latvia
- Department of Health Psychology and Paedagogy, Riga Stradins University, LV-1007 Riga, Latvia
| | - Andrejs Kostiks
- Department of Neurology and Neurosurgery, Riga East University Hospital, LV-1038 Riga, Latvia; (A.K.)
| | - Guntis Karelis
- Department of Neurology and Neurosurgery, Riga East University Hospital, LV-1038 Riga, Latvia; (A.K.)
- Department of Infectology, Riga Stradins University, LV-1007 Riga, Latvia
| | - Ardis Platkājis
- Department of Radiology, Riga Stradins University, LV-1007 Riga, Latvia; (K.K.)
- Department of Radiology, Riga East Clinical University Hospital, LV-1038 Riga, Latvia
| | - Ainārs Stepens
- Military Medicine Research and Study Centre, Riga Stradins University, LV-1007 Riga, Latvia
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Nasrallah F, Bellapart J, Walsham J, Jacobson E, To XV, Manzanero S, Brown N, Meyer J, Stuart J, Evans T, Chandra SS, Ross J, Campbell L, Senthuran S, Newcombe V, McCullough J, Fleming J, Pollard C, Reade M. PREdiction and Diagnosis using Imaging and Clinical biomarkers Trial in Traumatic Brain Injury (PREDICT-TBI) study protocol: an observational, prospective, multicentre cohort study for the prediction of outcome in moderate-to-severe TBI. BMJ Open 2023; 13:e067740. [PMID: 37094888 PMCID: PMC10151972 DOI: 10.1136/bmjopen-2022-067740] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 03/31/2023] [Indexed: 04/26/2023] Open
Abstract
INTRODUCTION Traumatic brain injury (TBI) is a heterogeneous condition with a broad spectrum of injury severity, pathophysiological processes and variable outcomes. For moderate-to-severe TBI survivors, recovery is often protracted and outcomes can range from total dependence to full recovery. Despite advances in medical treatment options, prognosis remains largely unchanged. The objective of this study is to develop a machine learning predictive model for neurological outcomes at 6 months in patients with a moderate-to-severe TBI, incorporating longitudinal clinical, multimodal neuroimaging and blood biomarker predictor variables. METHODS AND ANALYSIS A prospective, observational, cohort study will enrol 300 patients with moderate-to-severe TBI from seven Australian hospitals over 3 years. Candidate predictors including demographic and general health variables, and longitudinal clinical, neuroimaging (CT and MRI), blood biomarker and patient-reported outcome measures will be collected at multiple time points within the acute phase of injury. The predictor variables will populate novel machine learning models to predict the Glasgow Outcome Scale Extended 6 months after injury. The study will also expand on current prognostic models by including novel blood biomarkers (circulating cell-free DNA), and the results of quantitative neuroimaging such as Quantitative Susceptibility Mapping and Dynamic Contrast Enhanced MRI as predictor variables. ETHICS AND DISSEMINATION Ethical approval has been obtained by the Royal Brisbane and Women's Hospital Human Research Ethics Committee, Queensland. Participants or their substitute decision-maker/s will receive oral and written information about the study before providing written informed consent. Study findings will be disseminated by peer-review publications and presented at national and international conferences and clinical networks. TRIAL REGISTRATION NUMBER ACTRN12620001360909.
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Affiliation(s)
- Fatima Nasrallah
- The Queensland Brain Institute, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Judith Bellapart
- Intensive Care Unit, Royal Brisbane and Women's Hospital, Metro North Health Service District, Herston, Queensland, Australia
| | - James Walsham
- Intensive Care Unit, Princess Alexandra Hospital, Woolloongabba, Queensland, Australia
- Faculty of Medicine, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Esther Jacobson
- Jamieson Trauma Institute, Metro North Health Service District, Herston, Queensland, Australia
| | - Xuan Vinh To
- The Queensland Brain Institute, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Silvia Manzanero
- Jamieson Trauma Institute, Metro North Health Service District, Herston, Queensland, Australia
| | - Nathan Brown
- Intensive Care Unit, Royal Brisbane and Women's Hospital, Metro North Health Service District, Herston, Queensland, Australia
- Faculty of Medicine, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Jason Meyer
- Intensive Care Unit, Princess Alexandra Hospital, Woolloongabba, Queensland, Australia
| | - Janine Stuart
- Intensive Care Unit, Royal Brisbane and Women's Hospital, Metro North Health Service District, Herston, Queensland, Australia
| | - Tracey Evans
- The Queensland Brain Institute, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Shekhar S Chandra
- School of Information Technology and Electrical Engineering, Architecture and Information Technology, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Jason Ross
- Health and Biosecurity, CSIRO, Westmead, New South Wales, Australia
| | - Lewis Campbell
- Intensive Care Unit, Royal Darwin Hospital, Casuarina, Darwin, Australia
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
| | - Siva Senthuran
- Intensive Care Unit, Townsville Hospital and Health Service, Townsville, Queensland, Australia
| | - Virginia Newcombe
- University Division of Anaesthesia, University of Cambridge, Cambridge, UK
| | - James McCullough
- Intensive Care Unit, Gold Coast Hospital and Health Service, Gold Coast, Queensland, Australia
| | - Jennifer Fleming
- School of Health and Rehabilitation Sciences, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Clifford Pollard
- School of Information Technology and Electrical Engineering, Architecture and Information Technology, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Michael Reade
- Intensive Care Unit, Royal Brisbane and Women's Hospital, Metro North Health Service District, Herston, Queensland, Australia
- Faculty of Medicine, The University of Queensland, Saint Lucia, Queensland, Australia
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3
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Pani J, Eikenes L, Reitlo LS, Stensvold D, Wisløff U, Håberg AK. Effects of a 5-Year Exercise Intervention on White Matter Microstructural Organization in Older Adults. A Generation 100 Substudy. Front Aging Neurosci 2022; 14:859383. [PMID: 35847676 PMCID: PMC9278017 DOI: 10.3389/fnagi.2022.859383] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 05/25/2022] [Indexed: 12/13/2022] Open
Abstract
Aerobic fitness and exercise could preserve white matter (WM) integrity in older adults. This study investigated the effect on WM microstructural organization of 5 years of exercise intervention with either supervised moderate-intensity continuous training (MICT), high-intensity interval training (HIIT), or following the national physical activity guidelines. A total of 105 participants (70-77 years at baseline), participating in the randomized controlled trial Generation 100 Study, volunteered to take part in this longitudinal 3T magnetic resonance imaging (MRI) study. The HIIT group (n = 33) exercised for four intervals of 4 min at 90% of peak heart rate two times a week, the MICT group (n = 24) exercised continuously for 50 min at 70% peak heart rate two times a week, and the control group (n = 48) followed the national guidelines of ≥30 min of physical activity almost every day. At baseline and at 1-, 3-, and 5-year follow-ups, diffusion tensor imaging (DTI) scans were performed, cardiorespiratory fitness (CRF) was measured as peak oxygen uptake (VO2peak) with ergospirometry, and information on exercise habits was collected. There was no group*time or group effect on any of the DTI indices at any time point during the intervention. Across all groups, CRF was positively associated with fractional anisotropy (FA) and axial diffusivity (AxD) at the follow-ups, and the effect became smaller with time. Exercise intensity was associated with mean diffusivity (MD)/FA, with the greatest effect at 1-year and no effect at 5-year follow-up. There was an association between exercise duration and FA and radial diffusivity (RD) only after 1 year. Despite the lack of group*time interaction or group effect, both higher CRF and exercise intensity was associated with better WM microstructural organization throughout the intervention, but the effect became attenuated over time. Different aspects of exercising affected the WM metrics and WM tracts differently with the greatest and most overlapping effects in the corpus callosum. The current study indicates not only that high CRF and exercise intensity are associated with WM microstructural organization in aging but also that exercise's positive effects on WM may decline with increasing age.
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Affiliation(s)
- Jasmine Pani
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St Olav’s University Hospital, Trondheim, Norway
| | - Live Eikenes
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Line S. Reitlo
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Dorthe Stensvold
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ulrik Wisløff
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- School of Human Movement and Nutrition Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, Brisbane, QLD, Australia
| | - Asta Kristine Håberg
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St Olav’s University Hospital, Trondheim, Norway
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de Brito Robalo BM, Biessels GJ, Chen C, Dewenter A, Duering M, Hilal S, Koek HL, Kopczak A, Yin Ka Lam B, Leemans A, Mok V, Onkenhout LP, van den Brink H, de Luca A. Diffusion MRI harmonization enables joint-analysis of multicentre data of patients with cerebral small vessel disease. Neuroimage Clin 2021; 32:102886. [PMID: 34911192 PMCID: PMC8609094 DOI: 10.1016/j.nicl.2021.102886] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 11/16/2021] [Indexed: 01/10/2023]
Abstract
OBJECTIVES Acquisition-related differences in diffusion magnetic resonance imaging (dMRI) hamper pooling of multicentre data to achieve large sample sizes. A promising solution is to harmonize the raw diffusion signal using rotation invariant spherical harmonic (RISH) features, but this has not been tested in elderly subjects. Here we aimed to establish if RISH harmonization effectively removes acquisition-related differences in multicentre dMRI of elderly subjects with cerebral small vessel disease (SVD), while preserving sensitivity to disease effects. METHODS Five cohorts of patients with SVD (N = 397) and elderly controls (N = 175) with 3 Tesla MRI on different systems were included. First, to establish effectiveness of harmonization, the RISH method was trained with data of 13 to 15 age and sex-matched controls from each site. Fractional anisotropy (FA) and mean diffusivity (MD) were compared in matched controls between sites using tract-based spatial statistics (TBSS) and voxel-wise analysis, before and after harmonization. Second, to assess sensitivity to disease effects, we examined whether the contrast (effect sizes of FA, MD and peak width of skeletonized MD - PSMD) between patients and controls within each site remained unaffected by harmonization. Finally, we evaluated the association between white matter hyperintensity (WMH) burden, FA, MD and PSMD using linear regression analyses both within individual cohorts as well as with pooled scans from multiple sites, before and after harmonization. RESULTS Before harmonization, significant differences in FA and MD were observed between matched controls of different sites (p < 0.05). After harmonization these site-differences were removed. Within each site, RISH harmonization did not alter the effect sizes of FA, MD and PSMD between patients and controls (relative change in Cohen's d = 4 %) nor the strength of association with WMH volume (relative change in R2 = 2.8 %). After harmonization, patient data of all sites could be aggregated in a single analysis to infer the association between WMH volume and FA (R2 = 0.62), MD (R2 = 0.64), and PSMD (R2 = 0.60). CONCLUSIONS We showed that RISH harmonization effectively removes acquisition-related differences in dMRI of elderly subjects while preserving sensitivity to SVD-related effects. This study provides proof of concept for future multicentre SVD studies with pooled datasets.
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Affiliation(s)
- Bruno M de Brito Robalo
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands; Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
| | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Christopher Chen
- Memory, Aging and Cognition Center, Department of Pharmacology, National University of Singapore, Singapore.
| | - Anna Dewenter
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany.
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany; Medical Image Analysis Center (MIAC AG) and qbig, Department of Biomedical Engineering, University of Basel, Basel, Switzerland.
| | - Saima Hilal
- Memory, Aging and Cognition Center, Department of Pharmacology, National University of Singapore, Singapore.
| | - Huiberdina L Koek
- Department of Geriatric Medicine, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Anna Kopczak
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany.
| | - Bonnie Yin Ka Lam
- Division of Neurology, Department of Medicine and Therapeutics, Gerald Choa Neuroscience Centre, Faculty of Medicine, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region.
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
| | - Vincent Mok
- Division of Neurology, Department of Medicine and Therapeutics, Gerald Choa Neuroscience Centre, Faculty of Medicine, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region.
| | - Laurien P Onkenhout
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Hilde van den Brink
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Alberto de Luca
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands; Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
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Steyerberg EW, Wiegers E, Sewalt C, Buki A, Citerio G, De Keyser V, Ercole A, Kunzmann K, Lanyon L, Lecky F, Lingsma H, Manley G, Nelson D, Peul W, Stocchetti N, von Steinbüchel N, Vande Vyvere T, Verheyden J, Wilson L, Maas AIR, Menon DK. Case-mix, care pathways, and outcomes in patients with traumatic brain injury in CENTER-TBI: a European prospective, multicentre, longitudinal, cohort study. Lancet Neurol 2020; 18:923-934. [PMID: 31526754 DOI: 10.1016/s1474-4422(19)30232-7] [Citation(s) in RCA: 288] [Impact Index Per Article: 72.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 05/21/2019] [Accepted: 05/23/2019] [Indexed: 11/18/2022]
Abstract
BACKGROUND The burden of traumatic brain injury (TBI) poses a large public health and societal problem, but the characteristics of patients and their care pathways in Europe are poorly understood. We aimed to characterise patient case-mix, care pathways, and outcomes of TBI. METHODS CENTER-TBI is a Europe-based, observational cohort study, consisting of a core study and a registry. Inclusion criteria for the core study were a clinical diagnosis of TBI, presentation fewer than 24 h after injury, and an indication for CT. Patients were differentiated by care pathway and assigned to the emergency room (ER) stratum (patients who were discharged from an emergency room), admission stratum (patients who were admitted to a hospital ward), or intensive care unit (ICU) stratum (patients who were admitted to the ICU). Neuroimages and biospecimens were stored in repositories and outcome was assessed at 6 months after injury. We used the IMPACT core model for estimating the expected mortality and proportion with unfavourable Glasgow Outcome Scale Extended (GOSE) outcomes in patients with moderate or severe TBI (Glasgow Coma Scale [GCS] score ≤12). The core study was registered with ClinicalTrials.gov, number NCT02210221, and with Resource Identification Portal (RRID: SCR_015582). FINDINGS Data from 4509 patients from 18 countries, collected between Dec 9, 2014, and Dec 17, 2017, were analysed in the core study and from 22 782 patients in the registry. In the core study, 848 (19%) patients were in the ER stratum, 1523 (34%) in the admission stratum, and 2138 (47%) in the ICU stratum. In the ICU stratum, 720 (36%) patients had mild TBI (GCS score 13-15). Compared with the core cohort, the registry had a higher proportion of patients in the ER (9839 [43%]) and admission (8571 [38%]) strata, with more than 95% of patients classified as having mild TBI. Patients in the core study were older than those in previous studies (median age 50 years [IQR 30-66], 1254 [28%] aged >65 years), 462 (11%) had serious comorbidities, 772 (18%) were taking anticoagulant or antiplatelet medication, and alcohol was contributory in 1054 (25%) TBIs. MRI and blood biomarker measurement enhanced characterisation of injury severity and type. Substantial inter-country differences existed in care pathways and practice. Incomplete recovery at 6 months (GOSE <8) was found in 207 (30%) patients in the ER stratum, 665 (53%) in the admission stratum, and 1547 (84%) in the ICU stratum. Among patients with moderate-to-severe TBI in the ICU stratum, 623 (55%) patients had unfavourable outcome at 6 months (GOSE <5), similar to the proportion predicted by the IMPACT prognostic model (observed to expected ratio 1·06 [95% CI 0·97-1·14]), but mortality was lower than expected (0·70 [0·62-0·76]). INTERPRETATION Patients with TBI who presented to European centres in the core study were older than were those in previous observational studies and often had comorbidities. Overall, most patients presented with mild TBI. The incomplete recovery of many patients should motivate precision medicine research and the identification of best practices to improve these outcomes. FUNDING European Union 7th Framework Programme, the Hannelore Kohl Stiftung, OneMind, and Integra LifeSciences Corporation.
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Affiliation(s)
- Ewout W Steyerberg
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands; Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands
| | - Eveline Wiegers
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Charlie Sewalt
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Andras Buki
- Department of Neurosurgery, Medical School, University of Pécs, Pécs, Hungary; Neurotrauma Research Group, János Szentágothai Research Centre, University of Pécs, Pécs, Hungary
| | - Giuseppe Citerio
- NeuroIntensive Care, ASST di Monza, Monza, Italy; School of Medicine and Surgery, Università Milano Bicocca, Milan, Italy
| | | | - Ari Ercole
- Division of Anaesthesia, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
| | - Kevin Kunzmann
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Linda Lanyon
- International Neuroinformatics Coordinating Facility, Karolinska Institute, Stockholm, Sweden
| | - Fiona Lecky
- Centre for Urgent and Emergency Care Research, Health Services Research Section, School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Hester Lingsma
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Geoffrey Manley
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
| | - David Nelson
- Department of Physiology and Pharmacology, Section of Perioperative Medicine and Intensive Care, Karolinska Institute, Stockholm, Sweden
| | - Wilco Peul
- Leiden University Medical Centre and Haaglanden Medical Centre, University Neurosurgical Centre Holland, The Hague and Leiden, Netherlands
| | - Nino Stocchetti
- Department of Pathophysiology and Transplantation, Milan University, Milan, Italy; Neuroscience Intensive Care Unit, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Nicole von Steinbüchel
- Institute of Medical Psychology and Medical Sociology, Universitätsmedizin Göttingen, Göttingen, Germany
| | - Thijs Vande Vyvere
- Department of Radiology, Antwerp University Hospital, Edegem, Belgium; Division of Psychology, University of Stirling, Stirling, UK
| | | | - Lindsay Wilson
- Division of Psychology, University of Stirling, Stirling, UK
| | - Andrew I R Maas
- Department of Neurosurgery, Antwerp University Hospital, Edegem, Belgium; University of Antwerp, Edegem, Belgium.
| | - David K Menon
- Division of Anaesthesia, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
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Pinto MS, Paolella R, Billiet T, Van Dyck P, Guns PJ, Jeurissen B, Ribbens A, den Dekker AJ, Sijbers J. Harmonization of Brain Diffusion MRI: Concepts and Methods. Front Neurosci 2020; 14:396. [PMID: 32435181 PMCID: PMC7218137 DOI: 10.3389/fnins.2020.00396] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 03/30/2020] [Indexed: 11/13/2022] Open
Abstract
MRI diffusion data suffers from significant inter- and intra-site variability, which hinders multi-site and/or longitudinal diffusion studies. This variability may arise from a range of factors, such as hardware, reconstruction algorithms and acquisition settings. To allow a reliable comparison and joint analysis of diffusion data across sites and over time, there is a clear need for robust data harmonization methods. This review article provides a comprehensive overview of diffusion data harmonization concepts and methods, and their limitations. Overall, the methods for the harmonization of multi-site diffusion images can be categorized in two main groups: diffusion parametric map harmonization (DPMH) and diffusion weighted image harmonization (DWIH). Whereas DPMH harmonizes the diffusion parametric maps (e.g., FA, MD, and MK), DWIH harmonizes the diffusion-weighted images. Defining a gold standard harmonization technique for dMRI data is still an ongoing challenge. Nevertheless, in this paper we provide two classification tools, namely a feature table and a flowchart, which aim to guide the readers in selecting an appropriate harmonization method for their study.
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Affiliation(s)
- Maíra Siqueira Pinto
- Department of Radiology, Antwerp University Hospital, University of Antwerp, Antwerp, Belgium.,imec-Vision Lab, University of Antwerp, Antwerp, Belgium
| | - Roberto Paolella
- imec-Vision Lab, University of Antwerp, Antwerp, Belgium.,Icometrix, Leuven, Belgium
| | | | - Pieter Van Dyck
- Department of Radiology, Antwerp University Hospital, University of Antwerp, Antwerp, Belgium
| | | | - Ben Jeurissen
- imec-Vision Lab, University of Antwerp, Antwerp, Belgium
| | | | | | - Jan Sijbers
- imec-Vision Lab, University of Antwerp, Antwerp, Belgium
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Navarro-Lozoya M, Kennedy MS, Dean D, Rodriguez-Devora JI. Development of Phantom Material that Resembles Compression Properties of Human Brain Tissue for Training Models. MATERIALIA 2019; 8:10.1016/j.mtla.2019.100438. [PMID: 32064462 PMCID: PMC7021247 DOI: 10.1016/j.mtla.2019.100438] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
There is a need to quantify and reproduce the mechanical behavior of brain tissue for a variety of applications from designing proper training models for surgeons to enabling research on the effectiveness of personal protective gear, such as football helmets. The mechanical response of several candidate phantom materials, including hydrogels and emulsions, was characterized and compared to porcine brain tissue under similar strains and strain rates. Some candidate materials were selected since their compositions were similar to brain tissue, such as emulsions that mimic the high content of lipids. Others, like silicone, were included since these are currently used as phantom materials. The mechanical response of the emulsion was closer to that of the native porcine brain tissue than the other candidates. The emulsions, created by addition of oil to a hydrogel, were able to withstand compressive strain greater than 40%. The addition of lipids in the emulsions also prevented the syneresis typically seen with hydrogel materials. This allowed the emulsion material to undergo freeze-thaw cycles with no significant change in their mechanical properties.
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Affiliation(s)
| | - Marian S Kennedy
- Department of Materials Science & Engineering, Clemson University, Clemson, SC
| | - Delphine Dean
- Department of Bioengineering, Clemson University, Clemson, SC
| | - Jorge I Rodriguez-Devora
- Department of Bioengineering, Clemson University, Clemson, SC
- Department of Mechanical Engineering, Clemson University, Clemson, SC
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