JPND: Stratification of presymptomatic amyotrophic lateral sclerosis: the development of novel imaging biomarkers
JPND: Stratification of presymptomatic amyotrophic lateral sclerosis: the development of novel imaging biomarkers
批准号:
MR/T046473/1
负责人:
Daniel Alexander
金额:
$50.47万
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2020
资助国家:
英国
项目状态:
未结题
起止时间:
2020 至 --
中文摘要
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英文摘要
Amyotrophic lateral sclerosis (ALS) is a relentlessly progressive neurodegenerative disorder with no effective disease-modifying therapies at present. The delay between symptom onset and diagnosis by current diagnostic criteria is approximately 12 months worldwide which precludes the timely inclusion of suspected patients into clinical trials. By the time patients are recruited into imaging and pharmacological studies significant pathological changes have already taken place. While the vast majority of existing ALS studies are 'post-symptomatic', the presymptomatic phase of the disease represents a unique opportunity to evaluate mechanisms of disease propagation, characterise patterns of anatomical spread, validate staging systems and appraise the comparative sensitivity profile of emerging imaging modalities. Very few spinal cord imaging studies currently exist in ALS despite their potential to characterise both the lower and upper motor neuron components of the disease. This consortium proposes to embark on a large, prospective, multicentre, longitudinal study of asymptomatic and symptomatic c9orf72 hexanucleotide carriers using a purpose-designed spinal and brain imaging protocol and comprehensive clinical, genetic, electrophysiological and neuropsychological profiling. Newly developed imaging techniques such as spinal cord NODDI, spinal fMRI, quantitative thoracic cord imaging will be implemented in addition to established spinal cord and brain imaging techniques. No accurate prognostic indicators currently exist in asymptomatic hexanucleotide mutation carriers to foretell if they will develop ALS or FTD and when symptoms are likely to manifest. Beyond the academic relevance of characterising presymptomatic propagation patterns, the study has a number of pragmatic deliverables such as the development of individualised prognostic indicators, sensitive imaging-based diagnostic protocols and novel monitoring tools which are indispensable for future biomarker-supported clinical trial designs. This proposal endeavours to capitalise on recent technological advances to develop precision imaging tools for academic applications, clinical trials, the clinical care of patients and suspected patients and the support asymptomatic relatives. The ultimate aspiration of this application is the optimisation of novel imaging protocols which will be transferable to other motor neuron diseases, dementia syndromes, and meaningfully contribute to the development of novel disease-modifying therapies.
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Machine-learning-informed parameter estimation improves the reliability of spinal cord diffusion MRI
DOI:
--
发表时间:
2023-01
期刊:
影响因子:
--
作者:
[Ting Gong;Francesco Grussu;C. Wheeler-Kingshott;D. Alexander;Hui Zhang]
通讯作者:
Ting Gong;Francesco Grussu;C. Wheeler-Kingshott;D. Alexander;Hui Zhang
MTE-NODDI made practical with learning-based acquisition and parameter-estimation acceleration
MTE-NODDI 通过基于学习的采集和参数估计加速变得实用
DOI:
--
发表时间:
2021
期刊:
影响因子:
--
作者:
[Gong T]
通讯作者:
Gong T
Combined T2-T2*-diffusion imaging enables simultaneous mapping of compartment-specific T2 and T2*
组合 T2-T2* 扩散成像可同时映射特定室的 T2 和 T2*
DOI:
--
发表时间:
2022
期刊:
影响因子:
--
作者:
[Gong T]
通讯作者:
Gong T
Rician likelihood loss for quantitative MRI using self-supervised deep learning
使用自监督深度学习进行定量 MRI 的莱斯似然损失
DOI:
10.48550/arxiv.2307.07072
发表时间:
2023
期刊:
影响因子:
--
作者:
[Parker C]
通讯作者:
Parker C
Deep-learning-informed parameter estimation improves reliability of spinal cord diffusion MRI
基于深度学习的参数估计提高了脊髓扩散 MRI 的可靠性
DOI:
--
发表时间:
2022
期刊:
影响因子:
--
作者:
[Gong T]
通讯作者:
Gong T
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负责人:Daniel Alexander
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财政年份:2017
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A biophysical simulation framework for magnetic resonance microstructure imaging
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财政年份:2016
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负责人:Daniel Alexander
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Medical image computing for next-generation healthcare technology
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财政年份:2015
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资助金额:$75.55万
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财政年份:2013
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负责人:Daniel Alexander
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依托单位:
Direct Measurements of Microstructure from MRI
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Copy of A Monte-Carlo diffusion simulation framework for diffusion MRI
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负责人:Daniel Alexander
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国内基金
海外基金
使用倾向分(Propensity Score)和主分层(Principal Stratification)进行因果推断
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批准号:10401003
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项目类别:青年科学基金项目
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资助金额:11.0万元
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批准年份:2004
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负责人:张俊妮
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依托单位: