Diffusion Kurtosis Imaging Detects Microstructural Alterations in Brain of α-Synuclein Overexpressing Transgenic Mouse Model of Parkinson’s Disease: A Pilot Study

Khairnar, Amit; Latta, Peter; Dražanová, Eva; Szabó, Nikoletta; Arab, Anas; Hutter‐Paier, Birgit; Havas, Daniel; Windisch, Manfred; Šulcová, Alexandra; Starčuk, Zenon; Rektorová, Irena

doi:10.1007/s12640-015-9537-9

Cited by 17 publications

(33 citation statements)

References 42 publications

(54 reference statements)

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“…We have recently reported similar DKI results obtained from 9‐month‐old mice (Khairnar et al . ), where the thalamus was one of the most affected regions. In this study, we detected some additional changes with diffusivity parameters; these changes were not present in the younger age group.…”

Section: Discussionmentioning

confidence: 99%

“…Thalamic α‐syn pathology may contribute to the phenotype of both motor (Khairnar et al . ) and memory impairment (Magen et al . ) in TNWT‐61 mice.…”

Section: Discussionmentioning

confidence: 99%

“…All the TNWT‐61 mice manifested typical Parkinsonism‐like motor symptoms but at this age were physically unable to perform the tests for motor abilities as reported earlier (Khairnar et al . ). The animals were housed individually, paired, or in groups of three at the Central Animal Facility of Masaryk University, Brno, Czech Republic, following the housing organization from the breeder (QPS Austria).…”

Section: Methodsmentioning

confidence: 97%

“…) and on our pilot results with 9‐month‐old TNWT‐61 mice (Khairnar et al . ). The ROI selection on b = 0 images was drawn manually according to the mouse brain atlas (Paxinos and Franklin ) with the help of FA maps using ImageJ ® software (NIH, Bethesda, MD, USA) for various brain regions.…”

Section: Methodsmentioning

confidence: 97%

“…We observed the increase in kurtosis metrics preferentially in regions which are known to show high over‐expression of α‐syn (except the hippocampus) but because of a lack of immunohistochemical evaluation of α‐syn accumulation in the study we could not correlate it with α‐syn (Khairnar et al . ). Therefore, the aim of this study was to use DKI in TNWT‐61 mice in the late stage of pathology (14 months old), and to correlate the changes in DKI in regions of interest (ROI) with the amount of α‐syn, measured by immunohistochemistry, in the same animals.…”

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confidence: 97%

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Late‐stage α‐synuclein accumulation in TNWT‐61 mouse model of Parkinson's disease detected by diffusion kurtosis imaging

Khairnar

Dražanová

Szabó

et al. 2016

Journal of Neurochemistry

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Diffusion kurtosis imaging (DKI) by measuring non-Gaussian diffusion allows an accurate estimation of the distribution of water molecule displacement and may correctly characterize microstructural brain changes caused by neurodegeneration. The aim of this study was to evaluate the ability of DKI to detect changes induced by a-synuclein (a-syn) accumulation in a-syn over-expressing transgenic mice (TNWT-61) in both gray matter (GM) and white matter (WM) using region of interest (ROI) and tract-based spatial statistics analyses, respectively, and to explore the relationship between a-syn accumulation and DKI metrics in our regions of interest. Fourteen-month-old TNWT-61 mice and wild-type (WT) littermates underwent in vivo DKI scanning using the Bruker Avance 9.4 Tesla magnetic resonance imaging system. ROI analysis in the GM regions substantia nigra, striatum, hippocampus, sensorimotor cortex, and thalamus and tractbased spatial statistics analysis in WM were performed. Immunohistochemistry for a-syn was performed in TNWT-61 mice and correlated with DKI findings. We found increased kurtosis and decreased diffusivity values in GM regions such as the thalamus and sensorimotor cortex, and in WM regions such as the external and internal capsule, mamillothalamic tract, anterior commissure, cingulum, and corpus callosum in TNWT-61 mice as compared to WT mice. Furthermore, we report for the first time that a-syn accumulation is positively Received October 3, 2015; revised manuscript received November 9, 2015; accepted December 10, 2015.Address correspondence and reprint requests to Prof. Irena Rektorova, Applied Neuroscience Research Group, CEITEC Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic. E-mail: irena.rektorova@ ceitec.muni.czAbbreviations used: AD, axial diffusivity; AK, axial kurtosis; DKI, diffusion kurtosis imaging; DTI, diffusion tensor imaging; FA, fractional anisotropy; FLASH, fast low angle shot; FOV, field of view; GM, gray matter; GRAPPA, generalized auto-calibrating partially parallel acquisitions; MD, mean diffusivity; MK, mean kurtosis; MRI, magnetic resonance imaging; PD, Parkinson's disease; PET, positron emission tomography; RD, radial diffusivity; RK, radial kurtosis; ROI, region of interest; SE-EPI, spin-echo echo-planar-imaging; SN, substantia nigra; SPECT, single photon emission computed tomography; TBSS, tractbased spatial statistics; TNWT-61, transgenic mice over-expressing asyn; TR, repetition time; WM, white matter; a-syn, a-synuclein. correlated with kurtosis and negatively correlated with diffusivity in the thalamus. The study provides evidence of an association between the amount of a-syn and the magnitude of DKI metric changes in the ROIs, with the potential of improving the clinical diagnosis of Parkinson's disease.

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Section: Discussionmentioning

confidence: 99%

“…Thalamic α‐syn pathology may contribute to the phenotype of both motor (Khairnar et al . ) and memory impairment (Magen et al . ) in TNWT‐61 mice.…”

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 97%

Section: Methodsmentioning

confidence: 97%

mentioning

confidence: 97%

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Late‐stage α‐synuclein accumulation in TNWT‐61 mouse model of Parkinson's disease detected by diffusion kurtosis imaging

Khairnar

Dražanová

Szabó

et al. 2016

Journal of Neurochemistry

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Gray Matter Abnormalities in Idiopathic Parkinson's Disease: Evaluation by Diffusional Kurtosis Imaging and Neurite Orientation Dispersion and Density Imaging

Kamagata

Zalesky

Hatano

et al. 2017

Human Brain Mapping

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Mapping gray matter (GM) pathology in Parkinson's disease (PD) with conventional MRI is challenging, and the need for more sensitive brain imaging techniques is essential to facilitate early diagnosis and assessment of disease severity. GM microstructure was assessed with GM-based spatial statistics applied to diffusion kurtosis imaging (DKI) and neurite orientation dispersion imaging (NODDI) in 30 participants with PD and 28 age- and gender-matched controls. These were compared with currently used assessment methods such as diffusion tensor imaging (DTI), voxel-based morphometry (VBM), and surface-based cortical thickness analysis. Linear discriminant analysis (LDA) was also used to test whether subject diagnosis could be predicted based on a linear combination of regional diffusion metrics. Significant differences in GM microstructure were observed in the striatum and the frontal, temporal, limbic, and paralimbic areas in PD patients using DKI and NODDI. Significant correlations between motor deficits and GM microstructure were also noted in these areas. Traditional VBM and surface-based cortical thickness analyses failed to detect any GM differences. LDA indicated that mean kurtosis (MK) and intra cellular volume fraction (ICVF) were the most accurate predictors of diagnostic status. In conclusion, DKI and NODDI can detect cerebral GM abnormalities in PD in a more sensitive manner when compared with conventional methods. Hence, these methods may be useful for the diagnosis of PD and assessment of motor deficits. Hum Brain Mapp 00:000-000, 2017. © 2017 Wiley Periodicals, Inc.

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Optimization of quasi‐diffusion magnetic resonance imaging for quantitative accuracy and time‐efficient acquisition

Spilling

Howe

Barrick

2022

Magnetic Resonance in Med

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Quasi-diffusion MRI (QDI) is a novel quantitative technique based on the continuous time random walk model of diffusion dynamics. QDI provides estimates of the diffusion coefficient, D 1,2 in mm 2 s −1 and a fractional exponent, α, defining the non-Gaussianity of the diffusion signal decay. Here, the b-value selection for rapid clinical acquisition of QDI tensor imaging (QDTI) data is optimized. Methods: Clinically appropriate QDTI acquisitions were optimized in healthy volunteers with respect to a multi-b-value reference (MbR) dataset comprising 29 diffusion-sensitized images arrayed between b = 0 and 5000 s mm −2 . The effects of varying maximum b-value (b max ), number of b-value shells, and the effects of Rician noise were investigated. Results: QDTI measures showed b max dependence, most significantly for α in white matter, which monotonically decreased with higher b max leading to improved tissue contrast. Optimized 2 b-value shell acquisitions showed small systematic differences in QDTI measures relative to MbR values, with overestimation of D 1,2 and underestimation of α in white matter, and overestimation of D 1,2 and α anisotropies in gray and white matter. Additional shells improved the accuracy, precision, and reliability of QDTI estimates with 3 and 4 shells at b max = 5000 s mm −2 , and 4 b-value shells at b max = 3960 s mm −2 , providing minimal bias in D 1,2 and α compared to the MbR. Conclusion: A highly detailed optimization of non-Gaussian dMRI for in vivo brain imaging was performed. QDI provided robust parameterization of non-Gaussian diffusion signal decay in clinically feasible imaging times with high reliability, accuracy, and precision of QDTI measures.

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Diffusion Kurtosis Imaging Detects Microstructural Alterations in Brain of α-Synuclein Overexpressing Transgenic Mouse Model of Parkinson’s Disease: A Pilot Study

Cited by 17 publications

References 42 publications

Late‐stage α‐synuclein accumulation in TNWT‐61 mouse model of Parkinson's disease detected by diffusion kurtosis imaging

Late‐stage α‐synuclein accumulation in TNWT‐61 mouse model of Parkinson's disease detected by diffusion kurtosis imaging

Gray Matter Abnormalities in Idiopathic Parkinson's Disease: Evaluation by Diffusional Kurtosis Imaging and Neurite Orientation Dispersion and Density Imaging

Optimization of quasi‐diffusion magnetic resonance imaging for quantitative accuracy and time‐efficient acquisition

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