ApoE influences regional white-matter axonal density loss in Alzheimer's disease

Slattery, Catherine F.; Zhang, Jiaying; Paterson, Ross W.; Foulkes, Alexander; Carton, Amelia M.; Macpherson, Kirsty; Mancini, Laura; Thomas, David L.; Modat, Marc; Toussaint, Nicolas; Cash, David M.; Thornton, John S.; Henley, Susie M.D.; Crutch, Sebastian J.; Alexander, Daniel C.; Ourselin, Sébastien; Fox, Nick C.; Zhang, Hui; Schott, Jonathan M.

doi:10.1016/j.neurobiolaging.2017.04.021

Cited by 89 publications

(106 citation statements)

References 53 publications

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“…Previous works have shown that NODDI could be more sensitive than DTI for detecting white matter changes related to ageing24 or young-onset Alzheimer’s disease 25. Here, for the first time, we showed that NODDI outperformed DTI for identifying white matter abnormalities during the presymptomatic stage of a neurodegenerative disease.…”

Section: Discussionmentioning

confidence: 57%

“…This potential for greater tissue specificity has motivated application of NODDI in a few neurodegenerative disease studies 25 29–32. In young-onset Alzheimer’s disease, one study showed widespread NDI reduction and regional ODI reduction in the corpus callosum and internal capsule of patients 25. In Parkinson’s disease, another study observed reduced NDI in the substantia nigra and putamen of patients 31.…”

Section: Discussionmentioning

confidence: 99%

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Neurite density is reduced in the presymptomatic phase ofC9orf72disease

Wen

Zhang

Alexander

et al. 2018

J Neurol Neurosurg Psychiatry

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ObjectiveTo assess the added value of neurite orientation dispersion and density imaging (NODDI) compared with conventional diffusion tensor imaging (DTI) and anatomical MRI to detect changes in presymptomatic carriers of chromosome 9 open reading frame 72 (C9orf72) mutation.MethodsThe PREV-DEMALS (Predict to Prevent Frontotemporal Lobar Degeneration and Amyotrophic Lateral Sclerosis) study is a prospective, multicentre, observational study of first-degree relatives of individuals carrying the C9orf72 mutation. Sixty-seven participants (38 presymptomatic C9orf72 mutation carriers (C9+) and 29 non-carriers (C9−)) were included in the present cross-sectional study. Each participant underwent one single-shell, multishell diffusion MRI and three-dimensional T1-weighted MRI. Volumetric measures, DTI and NODDI metrics were calculated within regions of interest. Differences in white matter integrity, grey matter volume and free water fraction between C9+ and C9− individuals were assessed using linear mixed-effects models.ResultsCompared with C9−, C9+ demonstrated white matter abnormalities in 10 tracts with neurite density index and only 5 tracts with DTI metrics. Effect size was significantly higher for the neurite density index than for DTI metrics in two tracts. No tract had a significantly higher effect size for DTI than for NODDI. For grey matter cortical analysis, free water fraction was increased in 13 regions in C9+, whereas 11 regions displayed volumetric atrophy.ConclusionsNODDI provides higher sensitivity and greater tissue specificity compared with conventional DTI for identifying white matter abnormalities in the presymptomatic C9orf72 carriers. Our results encourage the use of neurite density as a biomarker of the preclinical phase.Trial registration number NCT02590276.

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Section: Discussionmentioning

confidence: 57%

Section: Discussionmentioning

confidence: 99%

Neurite density is reduced in the presymptomatic phase ofC9orf72disease

Wen

Zhang

Alexander

et al. 2018

J Neurol Neurosurg Psychiatry

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“…While such models (e.g., neurite orientation dispersion and density imaging) qualitatively demonstrate similar trends that reflect known white-matter ontogeny and pathology (e.g., Billiet et al, 2015; Slattery et al, 2017), quantitative estimates derived from various models are model-dependent and remain subject to the limitations of model assumptions, wherein accuracy is sacri- ﬁced for biological interpretability (Jelescu et al, 2015). More comprehensive diffusion data sets with higher b-values may over- come these limitations, but these acquisitions come at the expense of clinical feasibility.…”

Section: Discussionmentioning

confidence: 99%

Modeling white matter tract integrity in aging with diffusional kurtosis imaging

Benitez

Jensen

Falangola

et al. 2018

Neurobiology of Aging

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Myelin breakdown and neural ﬁber loss occur in aging. This study used white matter tract integrity metrics derived from biophysical modeling using Diffusional Kurtosis Imaging to assess loss of myelin (i.e., extraaxonal diffusivity, radial direction, De,⊥) and axonal density (i.e., axonal water fraction) in cognitively unimpaired older adults. Tract-based spatial statistics and region of interest analyses sought to identify ontogenic differences and age-related changes in white matter tracts using cross-sectional and longitudinal data analyzed with general linear and mixed-effects models. In addition to pure diffusion parameters (i.e., fractional anisotropy, mean diffusivity, mean kurtosis), we found that white matter tract integrity metrics signiﬁcantly differentiated early- from late-myelinating tracts, correlated with age in spatially distinct regions, and identiﬁed primarily extraaxonal changes over time. Percent metric changes were |0.3-0.9|% and |0.0-1.9|% per year using cross-sectional data and longitudinal data, respectively. There was accelerated decline in some late- versus early-myelinating tracts in older age. These results demonstrate that these metrics may inform further study of the transition from age-related changes to neurodegenerative decline.

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“…The popularity of NODDI is indisputable, with applications to a very large panel of brain alterations and pathologies (Adluru et al, 2014; Churchill et al, 2017; Kunz et al, 2014; Okita et al, 2017; Schneider et al, 2017; Slattery et al, 2017; Wen et al, 2015). However, there are important assumptions within NODDI related to its design that have strong implications to its specificity.…”

Section: Modelsmentioning

confidence: 99%

Design and Validation of Diffusion MRI Models of White Matter

2017

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Diffusion MRI is arguably the method of choice for characterizing white matter microstructure in vivo. Over the typical duration of diffusion encoding, the displacement of water molecules is conveniently on a length scale similar to that of the underlying cellular structures. Moreover, water molecules in white matter are largely compartmentalized which enables biologically-inspired compartmental diffusion models to characterize and quantify the true biological microstructure. A plethora of white matter models have been proposed. However, overparameterization and mathematical fitting complications encourage the introduction of simplifying assumptions that vary between different approaches. These choices impact the quantitative estimation of model parameters with potential detriments to their biological accuracy and promised specificity. First, we review biophysical white matter models in use and recapitulate their underlying assumptions and realms of applicability. Second, we present up-to-date efforts to validate parameters estimated from biophysical models. Simulations and dedicated phantoms are useful in assessing the performance of models when the ground truth is known. However, the biggest challenge remains the validation of the “biological accuracy” of estimated parameters. Complementary techniques such as microscopy of fixed tissue specimens have facilitated direct comparisons of estimates of white matter fiber orientation and densities. However, validation of compartmental diffusivities remains challenging, and complementary MRI-based techniques such as alternative diffusion encodings, compartment-specific contrast agents and metabolites have been used to validate diffusion models. Finally, white matter injury and disease pose additional challenges to modeling, which are also discussed. This review aims to provide an overview of the current state of models and their validation and to stimulate further research in the field to solve the remaining open questions and converge towards consensus.

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ApoE influences regional white-matter axonal density loss in Alzheimer's disease

Cited by 89 publications

References 53 publications

Neurite density is reduced in the presymptomatic phase ofC9orf72disease

Neurite density is reduced in the presymptomatic phase ofC9orf72disease

Modeling white matter tract integrity in aging with diffusional kurtosis imaging

Design and Validation of Diffusion MRI Models of White Matter

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