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Timmins HC, Thompson AE, Kiernan MC. Diagnostic criteria for amyotrophic lateral sclerosis. Curr Opin Neurol 2024:00019052-990000000-00183. [PMID: 39037015 DOI: 10.1097/wco.0000000000001302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/23/2024]
Abstract
PURPOSE OF REVIEW The present review will discuss the evolution of diagnostic criteria for amyotrophic lateral sclerosis (ALS) and biomarker considerations. RECENT FINDINGS To address the limitations of existing ALS diagnostic criteria, a consortium of key stakeholders developed the Gold Coast consensus criteria (GCC). The GCC has similar or greater sensitivity compared with the revised El Escorial (rEEC) and Awaji criteria (AC), particularly for atypical phenotypes, maintained across disease duration, severity, and site of onset. In addition to improving diagnostic sensitivity, using the GCC in clinical trials may promote an increased enrolment of up to 50% of ALS patients who do not currently meet the full diagnostic eligibility requirements of the rEEC. Future inclusion of genetic biomarkers may mitigate some limitations of the GCC, to further improve diagnostic utility. In advance of such a process, validation of these biomarkers will be required before inclusion as additional criteria. SUMMARY The GCC are simpler to use than previous consensus criteria, with demonstrated greater sensitivity and, enabling an earlier and more definitive ALS diagnosis, thereby facilitating wider enrolment into clinical trials. Broader implementation of the GCC in clinical trial settings is currently underway, globally.
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Affiliation(s)
| | - Alexandra E Thompson
- Neuroscience Research Australia
- Department of Neurology, Royal Prince Alfred Hospital Sydney, Australia
| | - Matthew C Kiernan
- Neuroscience Research Australia
- University of New South Wales
- Department of Neurology, Prince of Wales Hospital
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2
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Wang X, Zhu Z, Sun J, Jia L, Cai L, Chen Q, Yang W, Wang Y, Zhang Y, Guo S, Liu W, Yang Z, Zhao P, Wang Z, Lv H. Changes in iron load in specific brain areas lead to neurodegenerative diseases of the central nervous system. Prog Neuropsychopharmacol Biol Psychiatry 2024; 129:110903. [PMID: 38036035 DOI: 10.1016/j.pnpbp.2023.110903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 11/24/2023] [Accepted: 11/24/2023] [Indexed: 12/02/2023]
Abstract
The causes of neurodegenerative diseases remain largely elusive, increasing their personal and societal impacts. To reveal the causal effects of iron load on Parkinson's disease (PD), Alzheimer's disease (AD), amyotrophic lateral sclerosis and multiple sclerosis, we used Mendelian randomisation and brain imaging data from a UK Biobank genome-wide association study of 39,691 brain imaging samples (predominantly of European origin). Using susceptibility-weighted images, which reflect iron load, we analysed genetically significant brain regions. Inverse variance weighting was used as the main estimate, while MR Egger and weighted median were used to detect heterogeneity and pleiotropy. Nine clear associations were obtained. For AD and PD, an increased iron load was causative: the right pallidum for AD and the right caudate, left caudate and right accumbens for PD. However, a reduced iron load was identified in the right and left caudate for multiple sclerosis, the bilateral hippocampus for mixed vascular dementia and the left thalamus and bilateral accumbens for subcortical vascular dementia. Thus, changes in iron load in different brain regions have causal effects on neurodegenerative diseases. Our results are crucial for understanding the pathogenesis and investigating the treatment of these diseases.
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Affiliation(s)
- Xinghao Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing 100050, People's Republic of China
| | - Zaimin Zhu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, People's Republic of China
| | - Jing Sun
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing 100050, People's Republic of China
| | - Li Jia
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing 100050, People's Republic of China
| | - Linkun Cai
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing 100050, People's Republic of China; School of Biological Science and Medical Engineering, Beihang University, No.37 XueYuan Road, Beijing 100191, People's Republic of China
| | - Qian Chen
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing 100050, People's Republic of China
| | - Wenbo Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing 100050, People's Republic of China
| | - Yiling Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing 100050, People's Republic of China
| | - Yufan Zhang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing 100050, People's Republic of China
| | - Sihui Guo
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing 100050, People's Republic of China
| | - Wenjuan Liu
- Department of Radiology, Aerospace Center Hospital, Beijing, People's Republic of China; Peking University Aerospace School of Clinical Medicine, Beijing 100049, People's Republic of China
| | - Zhenghan Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing 100050, People's Republic of China
| | - Pengfei Zhao
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing 100050, People's Republic of China
| | - Zhenchang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing 100050, People's Republic of China.
| | - Han Lv
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing 100050, People's Republic of China.
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3
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Natsumeda M, Matsuzawa H, Watanabe M, Motohashi K, Gabdulkhaev R, Tsukamoto Y, Kanemaru Y, Watanabe J, Ogura R, Okada M, Kurabe S, Okamoto K, Kakita A, Igarashi H, Fujii Y. SWI by 7T MR Imaging for the Microscopic Imaging Diagnosis of Astrocytic and Oligodendroglial Tumors. AJNR Am J Neuroradiol 2022; 43:1575-1581. [PMID: 36229164 PMCID: PMC9731250 DOI: 10.3174/ajnr.a7666] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 08/21/2022] [Indexed: 02/01/2023]
Abstract
BACKGROUND AND PURPOSE Despite advances in molecular imaging, preoperative diagnosis of astrocytomas and oligodendrogliomas can be challenging. In the present study, we assessed whether 7T SWI can be used to distinguish astrocytomas and oligodendrogliomas and whether malignant grading of gliomas is possible. MATERIALS AND METHODS 7T SWI was performed on 21 patients with gliomas before surgery with optimization for sharp visualization of the corticomedullary junction. Scoring for cortical thickening and displacement of medullary vessels, characteristic of oligodendroglial tumors, and cortical tapering, characteristic of astrocytic tumors, was performed. Additionally, characteristics of malignancy, including thickening of the medullary veins, the presence of microbleeds, and/or necrosis were scored. RESULTS Scoring for oligodendroglial (highest possible score, +3) and astrocytic (lowest score possible, -3) characteristics yielded a significant difference between astrocytomas and oligodendrogliomas (mean, -1.93 versus +1.71, P < .01). Scoring for malignancy was significantly different among the World Health Organization grade II (n = 10), grade III (n = 4), and grade IV (n = 7) tumors (mean, 0.20 versus 1.38 versus 2.79). Cortical thickening was observed significantly more frequently in oligodendrogliomas (P < .02), with a sensitivity of 71.4% and specificity of 85.7%; observation of tapering of the cortex was higher in astrocytomas (P < .01) with a sensitivity of 85.7% and specificity of 100%. CONCLUSIONS Visualization of the corticomedullary junction by 7T SWI was useful in distinguishing astrocytomas and oligodendrogliomas. Observation of tapering of the cortex was most sensitive and specific for diagnosing astrocytomas. Reliably predicting malignant grade was also possible by 7T SWI.
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Affiliation(s)
- M Natsumeda
- From the Department of Neurosurgery (M.N., K.M., Y.T., Y.K., J.W., R.O., M.O., S.K., Y.F.)
| | - H Matsuzawa
- Center for Integrated Human Brain Science (H.M., M.W., H.I.)
| | - M Watanabe
- Center for Integrated Human Brain Science (H.M., M.W., H.I.)
| | - K Motohashi
- From the Department of Neurosurgery (M.N., K.M., Y.T., Y.K., J.W., R.O., M.O., S.K., Y.F.)
| | | | - Y Tsukamoto
- From the Department of Neurosurgery (M.N., K.M., Y.T., Y.K., J.W., R.O., M.O., S.K., Y.F.)
| | - Y Kanemaru
- From the Department of Neurosurgery (M.N., K.M., Y.T., Y.K., J.W., R.O., M.O., S.K., Y.F.)
| | - J Watanabe
- From the Department of Neurosurgery (M.N., K.M., Y.T., Y.K., J.W., R.O., M.O., S.K., Y.F.)
| | - R Ogura
- From the Department of Neurosurgery (M.N., K.M., Y.T., Y.K., J.W., R.O., M.O., S.K., Y.F.)
| | - M Okada
- From the Department of Neurosurgery (M.N., K.M., Y.T., Y.K., J.W., R.O., M.O., S.K., Y.F.)
| | - S Kurabe
- From the Department of Neurosurgery (M.N., K.M., Y.T., Y.K., J.W., R.O., M.O., S.K., Y.F.)
| | - K Okamoto
- Department of Translational Research (K.O.), Brain Research Institute, Niigata University, Niigata, Japan
| | - A Kakita
- Department of Pathology (R.G., A.K.)
| | - H Igarashi
- Center for Integrated Human Brain Science (H.M., M.W., H.I.)
| | - Y Fujii
- From the Department of Neurosurgery (M.N., K.M., Y.T., Y.K., J.W., R.O., M.O., S.K., Y.F.)
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4
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Sarioglu FC, Sarioglu O, Guleryuz H, Deliloglu B, Tuzun F, Duman N, Ozkan H. The role of MRI-based texture analysis to predict the severity of brain injury in neonates with perinatal asphyxia. Br J Radiol 2022; 95:20210128. [PMID: 34919441 PMCID: PMC9153720 DOI: 10.1259/bjr.20210128] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVE To evaluate the efficacy of the MRI-based texture analysis (TA) of the basal ganglia and thalami to distinguish moderate-to-severe hypoxic-ischemic encephalopathy (HIE) from mild HIE in neonates. METHODS This study included 68 neonates (15 with mild, 20 with moderate-to-severe HIE, and 33 control) were born at 37 gestational weeks or later and underwent MRI in first 10 days after birth. The basal ganglia and thalami were delineated for TA on the apparent diffusion coefficient (ADC) maps, T1-, and T2 weighted images. The basal ganglia, thalami, and the posterior limb of the internal capsule (PLIC) were also evaluated visually on diffusion-weighted imaging and T1 weighted sequence. Receiver operating characteristic curve and logistic regression analyses were used. RESULTS Totally, 56 texture features for the basal ganglia and 46 features for the thalami were significantly different between the HIE groups on the ADC maps, T2-, and T2 weighted sequences. Using a Histogram_entropy log-10 value as >1.8 from the basal ganglia on the ADC maps (p < 0.001; OR, 266) and the absence of hyperintensity of the PLIC on T1 weighted images (p = 0.012; OR, 17.11) were found as independent predictors for moderate-to-severe HIE. Using only a Histogram_entropy log-10 value had an equal diagnostic yield when compared to its combination with other texture features and imaging findings. CONCLUSION The Histogram_entropy log-10 value can be used as an indicator to differentiate from moderate-to-severe to mild HIE. ADVANCES IN KNOWLEDGE MRI-based TA may provide quantitative findings to indicate different stages in neonates with perinatal asphyxia.
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Affiliation(s)
- Fatma Ceren Sarioglu
- Division of Pediatric Radiology, Department of Radiology, Dokuz Eylul University School of Medicine, İzmir, Turkey
| | - Orkun Sarioglu
- Department of Radiology, Izmir Democracy University School of Medicine, Izmir, Turkey
| | - Handan Guleryuz
- Division of Pediatric Radiology, Department of Radiology, Dokuz Eylul University School of Medicine, İzmir, Turkey
| | - Burak Deliloglu
- Division of Neonatology, Department of Pediatrics, Dokuz Eylul University School of Medicine, Izmir, Turkey
| | - Funda Tuzun
- Division of Neonatology, Department of Pediatrics, Dokuz Eylul University School of Medicine, Izmir, Turkey
| | - Nuray Duman
- Division of Neonatology, Department of Pediatrics, Dokuz Eylul University School of Medicine, Izmir, Turkey
| | - Hasan Ozkan
- Division of Neonatology, Department of Pediatrics, Dokuz Eylul University School of Medicine, Izmir, Turkey
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Zheng S, Wang H, Han F, Chu J, Zhang F, Zhang X, Shi Y, Zhang L. Detection of Microstructural Medial Prefrontal Cortex Changes Using Magnetic Resonance Imaging Texture Analysis in a Post-Traumatic Stress Disorder Rat Model. Front Psychiatry 2022; 13:805851. [PMID: 35530016 PMCID: PMC9068999 DOI: 10.3389/fpsyt.2022.805851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 03/23/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Radiomics is characterized by high-throughput extraction of texture features from medical images and the mining of information that can potentially be used to define neuroimaging markers in many neurological or psychiatric diseases. However, there have been few studies concerning MRI radiomics in post-traumatic stress disorder (PTSD). The study's aims were to appraise changes in microstructure of the medial prefrontal cortex (mPFC) in a PTSD animal model, specifically single-prolonged stress (SPS) rats, by using MRI texture analysis. The feasibility of using a radiomics approach to classify PTSD rats was examined. METHODS Morris water maze and elevated plus maze were used to assess behavioral changes in the rats. Two hundred and sixty two texture features were extracted from each region of interest in T2-weighted images. Stepwise discriminant analysis (SDA) and LASSO regression were used to perform feature selection and radiomics signature building to identify mPFC radiomics signatures consisting of optimal features, respectively. Receiver operating characteristic curve plots were used to evaluate the classification performance. Immunofluorescence techniques were used to examine the expression of glial fibrillary acidic protein (GFAP) and neuronal nuclei (NeuN) in the mPFC. Nuclear pycnosis was detected using 4',6-diamidino-2-phenylindole (DAPI) staining. RESULTS Behavioral results indicated decreased learning and spatial memory performance and increased anxiety-like behavior after SPS stimulation. SDA analysis showed that the general non-cross-validated and cross-validated discrimination accuracies were 86.5% and 80.4%. After LASSO dimensionality reduction, 10 classification models were established. For classifying PTSD rats between the control and each SPS group, these models achieved AUCs of 0.944, 0.950, 0.959, and 0.936. Among four SPS groups, the AUCs were 0.927, 0.943, 0.967, 0.916, 0.932, and 0.893, respectively. The number of GFAP-positive cells and intensity of GFAP-IR within the mPFC increased 1 day after SPS treatment, and then decreased. The intensity of NeuN-IR and number of NeuN-positive cells significantly decreased from 1 to 14 days after SPS stimulation. The brightness levels of DAPI-stained nuclei increased in SPS groups. CONCLUSION Non-invasive MRI radiomics features present an efficient and sensitive way to detect microstructural changes in the mPFC after SPS stimulation, and they could potentially serve as a novel neuroimaging marker in PTSD diagnosis.
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Affiliation(s)
- Shilei Zheng
- Department of Radiology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
| | - Han Wang
- Medical Imaging Center, Taian Central Hospital, Taian, China
| | - Fang Han
- Post-Traumatic Stress Disorder Laboratory, Department of Histology and Embryology, Basic Medical Sciences College, China Medical University, Shenyang, China
| | - Jianyi Chu
- Department of Radiology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
| | - Fan Zhang
- Department of Neurology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
| | - Xianglin Zhang
- Department of Radiology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
| | - Yuxiu Shi
- Post-Traumatic Stress Disorder Laboratory, Department of Histology and Embryology, Basic Medical Sciences College, China Medical University, Shenyang, China
| | - Lili Zhang
- Department of Stomatology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
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6
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Ishaque A, Ta D, Khan M, Zinman L, Korngut L, Genge A, Dionne A, Briemberg H, Luk C, Yang YH, Beaulieu C, Emery D, Eurich DT, Frayne R, Graham S, Wilman A, Dupré N, Kalra S. Distinct patterns of progressive gray and white matter degeneration in amyotrophic lateral sclerosis. Hum Brain Mapp 2021; 43:1519-1534. [PMID: 34908212 PMCID: PMC8886653 DOI: 10.1002/hbm.25738] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 11/22/2021] [Accepted: 11/25/2021] [Indexed: 01/17/2023] Open
Abstract
Progressive cerebral degeneration in amyotrophic lateral sclerosis (ALS) remains poorly understood. Here, three-dimensional (3D) texture analysis was used to study longitudinal gray and white matter cerebral degeneration in ALS from routine T1-weighted magnetic resonance imaging (MRI). Participants were included from the Canadian ALS Neuroimaging Consortium (CALSNIC) who underwent up to three clinical assessments and MRI at four-month intervals, up to 8 months after baseline (T0 ). Three-dimensional maps of the texture feature autocorrelation were computed from T1-weighted images. One hundred and nineteen controls and 137 ALS patients were included, with 81 controls and 84 ALS patients returning for at least one follow-up. At baseline, texture changes in ALS patients were detected in the motor cortex, corticospinal tract, insular cortex, and bilateral frontal and temporal white matter compared to controls. Longitudinal comparison of texture maps between T0 and Tmax (last follow-up visit) within ALS patients showed progressive texture alterations in the temporal white matter, insula, and internal capsule. Additionally, when compared to controls, ALS patients had greater texture changes in the frontal and temporal structures at Tmax than at T0 . In subgroup analysis, slow progressing ALS patients had greater progressive texture change in the internal capsule than the fast progressing patients. Contrastingly, fast progressing patients had greater progressive texture changes in the precentral gyrus. These findings suggest that the characteristic longitudinal gray matter pathology in ALS is the progressive involvement of frontotemporal regions rather than a worsening pathology within the motor cortex, and that phenotypic variability is associated with distinct progressive spatial pathology.
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Affiliation(s)
- Abdullah Ishaque
- Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada.,Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada
| | - Daniel Ta
- Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada.,Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada
| | - Muhammad Khan
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada
| | - Lorne Zinman
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, Canada
| | - Lawrence Korngut
- Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Angela Genge
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, Montreal, Canada
| | - Annie Dionne
- Département des Sciences Neurologiques, Hôpital de l'Enfant-Jésus, CHU de Québec, Quebec City, Canada
| | - Hannah Briemberg
- Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, Canada
| | - Collin Luk
- Division of Neurology, Department of Medicine, University of Alberta, Edmonton, Canada
| | - Yee-Hong Yang
- Department of Computing Science, University of Alberta, Edmonton
| | - Christian Beaulieu
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
| | - Derek Emery
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Canada
| | - Dean T Eurich
- School of Public Health, University of Alberta, Edmonton, Canada
| | - Richard Frayne
- Department of Radiology, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada.,Seaman Family MR Research Centre, Foothills Medical Centre, Alberta Health Services, Calgary, Canada
| | - Simon Graham
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Alan Wilman
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
| | - Nicolas Dupré
- Neuroscience Axis, CHU de Québec, Université Laval, Quebec City, Canada.,Department of Medicine, Faculty of Medicine, Université Laval, Quebec City, Canada
| | - Sanjay Kalra
- Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada.,Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada.,Division of Neurology, Department of Medicine, University of Alberta, Edmonton, Canada
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7
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Bhattarai A, Egan GF, Talman P, Chua P, Chen Z. Magnetic Resonance Iron Imaging in Amyotrophic Lateral Sclerosis. J Magn Reson Imaging 2021; 55:1283-1300. [PMID: 33586315 DOI: 10.1002/jmri.27530] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 01/05/2021] [Accepted: 01/08/2021] [Indexed: 01/18/2023] Open
Abstract
Amyotrophic lateral sclerosis (ALS) results in progressive impairment of upper and lower motor neurons. Increasing evidence from both in vivo and ex vivo studies suggest that iron accumulation in the motor cortex is a neuropathological hallmark in ALS. An in vivo neuroimaging marker of iron dysregulation in ALS would be useful in disease diagnosis and prognosis. Magnetic resonance imaging (MRI), with its unique capability to generate a variety of soft tissue contrasts, provides opportunities to image iron distribution in the human brain with millimeter to sub-millimeter anatomical resolution. Conventionally, MRI T1-weighted, T2-weighted, and T2*-weighted images have been used to investigate iron dysregulation in the brain in vivo. Susceptibility weighted imaging has enhanced contrast for para-magnetic materials that provides superior sensitivity to iron in vivo. Recently, the development of quantitative susceptibility mapping (QSM) has realized the possibility of using quantitative assessments of magnetic susceptibility measures in brain tissues as a surrogate measurement of in vivo brain iron. In this review, we provide an overview of MRI techniques that have been used to investigate iron dysregulation in ALS in vivo. The potential uses, strengths, and limitations of these techniques in clinical trials, disease diagnosis, and prognosis are presented and discussed. We recommend further longitudinal studies with appropriate cohort characterization to validate the efficacy of these techniques. We conclude that quantitative iron assessment using recent advances in MRI including QSM holds great potential to be a sensitive diagnostic and prognostic marker in ALS. The use of multimodal neuroimaging markers in combination with iron imaging may also offer improved sensitivity in ALS diagnosis and prognosis that could make a major contribution to clinical care and treatment trials. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Anjan Bhattarai
- Department of Psychiatry, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia.,Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
| | - Gary F Egan
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
| | - Paul Talman
- Department of Neuroscience, Barwon Health, Geelong, Victoria, Australia
| | - Phyllis Chua
- Department of Psychiatry, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia.,Statewide Progressive Neurological Services, Calvary Health Care Bethlehem, Melbourne, Victoria, Australia
| | - Zhaolin Chen
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
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Bede P. The histological correlates of imaging metrics: postmortem validation of in vivo findings. Amyotroph Lateral Scler Frontotemporal Degener 2019; 20:457-460. [PMID: 31293187 DOI: 10.1080/21678421.2019.1639195] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Peter Bede
- Computational Neuroimaging Group, Trinity College Dublin , Dublin , Ireland
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