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Safri AA, Nassir CMNCM, Iman IN, Mohd Taib NH, Achuthan A, Mustapha M. Diffusion tensor imaging pipeline measures of cerebral white matter integrity: An overview of recent advances and prospects. World J Clin Cases 2022; 10:8450-8462. [PMID: 36157806 PMCID: PMC9453345 DOI: 10.12998/wjcc.v10.i24.8450] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 06/20/2022] [Accepted: 07/17/2022] [Indexed: 02/05/2023] Open
Abstract
Cerebral small vessel disease (CSVD) is a leading cause of age-related microvascular cognitive decline, resulting in significant morbidity and decreased quality of life. Despite a progress on its key pathophysiological bases and general acceptance of key terms from neuroimaging findings as observed on the magnetic resonance imaging (MRI), key questions on CSVD remain elusive. Enhanced relationships and reliable lesion studies, such as white matter tractography using diffusion-based MRI (dMRI) are necessary in order to improve the assessment of white matter architecture and connectivity in CSVD. Diffusion tensor imaging (DTI) and tractography is an application of dMRI that provides data that can be used to non-invasively appraise the brain white matter connections via fiber tracking and enable visualization of individual patient-specific white matter fiber tracts to reflect the extent of CSVD-associated white matter damage. However, due to a lack of standardization on various sets of software or image pipeline processing utilized in this technique that driven mostly from research setting, interpreting the findings remain contentious, especially to inform an improved diagnosis and/or prognosis of CSVD for routine clinical use. In this minireview, we highlight the advances in DTI pipeline processing and the prospect of this DTI metrics as potential imaging biomarker for CSVD, even for subclinical CSVD in at-risk individuals.
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Affiliation(s)
- Amanina Ahmad Safri
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kubang Kerian 16150, Kelantan, Malaysia
| | - Che Mohd Nasril Che Mohd Nassir
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kubang Kerian 16150, Kelantan, Malaysia
| | - Ismail Nurul Iman
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kubang Kerian 16150, Kelantan, Malaysia
| | - Nur Hartini Mohd Taib
- Department of Radiology, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kubang Kerian 16150, Kelantan, Malaysia
| | - Anusha Achuthan
- School of Computer Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia
| | - Muzaimi Mustapha
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kubang Kerian 16150, Kelantan, Malaysia
- Department of Neurosciences, Hospital Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
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Morgan CA, Roberts RP, Chaffey T, Tahara-Eckl L, van der Meer M, Günther M, Anderson TJ, Cutfield NJ, Dalrymple-Alford JC, Kirk IJ, Rose Addis D, Tippett LJ, Melzer TR. Reproducibility and repeatability of magnetic resonance imaging in dementia. Phys Med 2022; 101:8-17. [PMID: 35849909 DOI: 10.1016/j.ejmp.2022.06.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/09/2022] [Accepted: 06/27/2022] [Indexed: 01/01/2023] Open
Abstract
PURPOSE Individualised predictive models of cognitive decline require disease-monitoring markers that are repeatable. For wide-spread adoption, such markers also need to be reproducible at different locations. This study assessed the repeatability and reproducibility of MRI markers derived from a dementia protocol. METHODS Six participants were scanned at three different sites with a 3T MRI scanner. The protocol employed: T1-weighted (T1w) imaging, resting state functional MRI (rsfMRI), arterial spin labelling (ASL), diffusion-weighted imaging (DWI), T2-weighted fluid attenuation inversion recovery (FLAIR), T2-weighted (T2w) imaging, and susceptibility weighted imaging (SWI). Participants were scanned repeatedly, up to six times over a maximum period of five years. One participant was also scanned a further three times on sequential days on one scanner. Fifteen derived metrics were computed from the seven different modalities. RESULTS Reproducibility (coefficient of variation; CoV, across sites) was best for T1w derived grey matter, white matter and hippocampal volume (CoV < 1.5%), compared to rsfMRI and SWI derived metrics (CoV, 19% and 21%). For a given metric, long-term repeatability (CoV across time) was comparable to reproducibility, with short-term repeatability considerably better. CONCLUSIONS Reproducibility and repeatability were assessed for a suite of markers calculated from a dementia MRI protocol. In general, structural markers were less variable than functional MRI markers. Variability over time on the same scanner was comparable to variability measured across different scanners. Overall, the results support the viability of multi-site longitudinal studies for monitoring cognitive decline.
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Affiliation(s)
- Catherine A Morgan
- School of Psychology and Centre for Brain Research, The University of Auckland, Auckland, New Zealand; Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand; Centre for Advanced MRI, Auckland UniServices Limited, Auckland, New Zealand.
| | - Reece P Roberts
- School of Psychology and Centre for Brain Research, The University of Auckland, Auckland, New Zealand; Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand
| | - Tessa Chaffey
- School of Psychology and Centre for Brain Research, The University of Auckland, Auckland, New Zealand
| | - Lenore Tahara-Eckl
- School of Psychology and Centre for Brain Research, The University of Auckland, Auckland, New Zealand
| | - Meghan van der Meer
- School of Psychology and Centre for Brain Research, The University of Auckland, Auckland, New Zealand
| | - Matthias Günther
- Fraunhofer Institute for Digital Medicine and University of Bremen, Bremen, Germany
| | - Timothy J Anderson
- Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand; Department of Medicine, University of Otago, Christchurch, New Zealand; NZ Brain Research Institute, Christchurch, New Zealand
| | - Nicholas J Cutfield
- Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand; Department of Medicine, University of Otago, Dunedin, New Zealand
| | - John C Dalrymple-Alford
- Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand; Department of Medicine, University of Otago, Christchurch, New Zealand; NZ Brain Research Institute, Christchurch, New Zealand; School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, New Zealand
| | - Ian J Kirk
- School of Psychology and Centre for Brain Research, The University of Auckland, Auckland, New Zealand; Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand
| | - Donna Rose Addis
- School of Psychology and Centre for Brain Research, The University of Auckland, Auckland, New Zealand; Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand; Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada; Department of Psychology, University of Toronto, Toronto, Canada
| | - Lynette J Tippett
- School of Psychology and Centre for Brain Research, The University of Auckland, Auckland, New Zealand; Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand
| | - Tracy R Melzer
- Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand; Department of Medicine, University of Otago, Christchurch, New Zealand; NZ Brain Research Institute, Christchurch, New Zealand; School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, New Zealand
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Pomilio AB, Vitale AA, Lazarowski AJ. Neuroproteomics Chip-Based Mass Spectrometry and Other Techniques for Alzheimer´S Disease Biomarkers – Update. Curr Pharm Des 2022; 28:1124-1151. [DOI: 10.2174/1381612828666220413094918] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 02/25/2022] [Indexed: 11/22/2022]
Abstract
Background:
Alzheimer's disease (AD) is a progressive neurodegenerative disease of growing interest given that there is cognitive damage and symptom onset acceleration. Therefore, it is important to find AD biomarkers for early diagnosis, disease progression, and discrimination of AD and other diseases.
Objective:
To update the relevance of mass spectrometry for the identification of peptides and proteins involved in AD useful as discriminating biomarkers.
Methods:
Proteomics and peptidomics technologies that show the highest possible specificity and selectivity for AD biomarkers are analyzed, together with the biological fluids used. In addition to positron emission tomography and magnetic resonance imaging, MALDI-TOF mass spectrometry is widely used to identify proteins and peptides involved in AD. The use of protein chips in SELDI technology and electroblotting chips for peptides makes feasible small amounts (L) of samples for analysis.
Results:
Suitable biomarkers are related to AD pathology, such as intracellular neurofibrillary tangles; extraneuronal senile plaques; neuronal and axonal degeneration; inflammation and oxidative stress. Recently, peptides were added to the candidate list, which are not amyloid-b or tau fragments, but are related to coagulation, brain plasticity, and complement/neuroinflammation systems involving the neurovascular unit.
Conclusion:
The progress made in the application of mass spectrometry and recent chip techniques is promising for discriminating between AD, mild cognitive impairment, and matched healthy controls. The application of this technique to blood samples from patients with AD has shown to be less invasive and fast enough to determine the diagnosis, stage of the disease, prognosis, and follow-up of the therapeutic response.
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Affiliation(s)
- Alicia B. Pomilio
- Departamento de Bioquímica Clínica, Área Hematología, Hospital de Clínicas “José de San Martín”, Universidad de Buenos Aires, Av. Córdoba 2351, C1120AAF Buenos Aires, Argentina
| | - Arturo A. Vitale
- Departamento de Bioquímica Clínica, Área Hematología, Hospital de Clínicas “José de San Martín”, Universidad de Buenos Aires, Av. Córdoba 2351, C1120AAF Buenos Aires, Argentina
| | - Alberto J. Lazarowski
- Departamento de Bioquímica Clínica, Facultad de Farmacia y Bioquímica, Instituto de Fisiopatología y Bioquímica Clínica (INFIBIOC), Universidad de Buenos Aires, Córdoba 2351, C1120AAF Buenos Aires, Argentina
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DiBella EVR, Sharma A, Richards L, Prabhakaran V, Majersik JJ, HashemizadehKolowri SK. Beyond Diffusion Tensor MRI Methods for Improved Characterization of the Brain after Ischemic Stroke: A Review. AJNR Am J Neuroradiol 2022; 43:661-669. [PMID: 35272983 PMCID: PMC9089249 DOI: 10.3174/ajnr.a7414] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 11/08/2021] [Indexed: 12/22/2022]
Abstract
Ischemic stroke is a worldwide problem, with 15 million people experiencing a stroke annually. MR imaging is a valuable tool for understanding and assessing brain changes after stroke and predicting recovery. Of particular interest is the use of diffusion MR imaging in the nonacute stage 1-30 days poststroke. Thousands of articles have been published on the use of diffusion MR imaging in stroke, including several recent articles reviewing the use of DTI for stroke. The goal of this work was to survey and put into context the recent use of diffusion MR imaging methods beyond DTI, including diffusional kurtosis, generalized fractional anisotropy, spherical harmonics methods, and neurite orientation and dispersion models, in patients poststroke. Early studies report that these types of beyond-DTI methods outperform DTI metrics either in being more sensitive to poststroke changes or by better predicting outcome motor scores. More and larger studies are needed to confirm the improved prediction of stroke recovery with the beyond-DTI methods.
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Affiliation(s)
- E V R DiBella
- From the Departments of Radiology and Imaging Sciences (E.V.R.D., A.S., S.K.H.)
| | - A Sharma
- From the Departments of Radiology and Imaging Sciences (E.V.R.D., A.S., S.K.H.)
| | - L Richards
- Occupational and Recreational Therapies (L.R.)
| | - V Prabhakaran
- Department of Radiology (V.P.), University of Wisconsin, Madison, Wisconsin
| | - J J Majersik
- Neurology (J.J.M.), University of Utah, Salt Lake City, Utah
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Bergamino M, Schiavi S, Daducci A, Walsh RR, Stokes AM. Analysis of Brain Structural Connectivity Networks and White Matter Integrity in Patients With Mild Cognitive Impairment. Front Aging Neurosci 2022; 14:793991. [PMID: 35173605 PMCID: PMC8842680 DOI: 10.3389/fnagi.2022.793991] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 01/06/2022] [Indexed: 11/13/2022] Open
Abstract
White matter integrity and structural connectivity may be altered in mild cognitive impairment (MCI), and these changes may closely reflect decline in specific cognitive domains. Multi-shell diffusion data in healthy control (HC, n = 31) and mild cognitive impairment (MCI, n = 19) cohorts were downloaded from the ADNI3 database. The data were analyzed using an advanced approach to assess both white matter microstructural integrity and structural connectivity. Compared with HC, lower intracellular compartment (IC) and higher isotropic (ISO) values were found in MCI. Additionally, significant correlations were found between IC and Montreal Cognitive Assessment (MoCA) scores in the MCI cohort. Network analysis detected structural connectivity differences between the two groups, with lower connectivity in MCI. Additionally, significant differences between HC and MCI were observed for global network efficiency. Our results demonstrate the potential of advanced diffusion MRI biomarkers for understanding brain changes in MCI.
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Affiliation(s)
- Maurizio Bergamino
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, AZ, United States
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | | | - Ryan R. Walsh
- Muhammad Ali Parkinson Center, Barrow Neurological Institute, Phoenix, AZ, United States
| | - Ashley M. Stokes
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, AZ, United States
- *Correspondence: Ashley M. Stokes,
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