Identification and characterization of Huntington related pathology: An in vivo DKI imaging study

Blockx, Ines; Verhoye, Marleen; Audekerke, Johan Van; Bergwerf, Irene; Kane, Jack X; Palacios, Rafael Delgado y; Veraart, Jelle; Jeurissen, Ben; Raber, Kerstin; Hörsten, Stephan von; Ponsaerts, Peter; Sijbers, Jan; Leergaard, Trygve B.; Linden, Annemie Van der

doi:10.1016/j.neuroimage.2012.06.032

Cited by 37 publications

(41 citation statements)

References 60 publications

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“…The combined use of DTI and DKI has acknowledged the increased sensitivity of DKI over DTI parameters, and emphasized the complementary information provided by DK metrics in assessing microstructural changes (Blockx et al , 2012a; Blockx et al , 2012b; Cheung et al , 2012; Delgado y Palacios et al , 2011; Delgado y Palacios et al , 2014; Umesh Rudrapatna et al , 2014; Vanhoutte et al , 2013; Veraart et al , 2011; Zhu et al , 2015; Zhuo et al , 2012). In addition, white matter tract integrity (WMTI) metrics that model the diffusion in white matter by two non-exchanging compartments have been recently introduced that can be extracted from DKI measures in white matter, and aim to provide more specific information about axonal integrity and extra-axonal surroundings (Fieremans et al , 2011).…”

Section: Introductionmentioning

confidence: 99%

Diffusion kurtosis imaging probes cortical alterations and white matter pathology following cuprizone induced demyelination and spontaneous remyelination

et al. 2016

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Although MRI is the gold standard for the diagnosis and monitoring of multiple sclerosis (MS), current conventional MRI techniques often fail to detect cortical alterations and provide little information about gliosis, axonal damage and myelin status of lesioned areas. Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) provide sensitive and complementary measures of the neural tissue microstructure. Additionally, specific white matter tract integrity (WMTI) metrics modelling the diffusion in white matter were recently derived. In the current study we used the well-characterized cuprizone mouse model of central nervous system demyelination to assess the temporal evolution of diffusion tensor (DT), diffusion kurtosis tensor (DK) and WMTI-derived metrics following acute inflammatory demyelination and spontaneous remyelination. While DT-derived metrics were unable to detect cuprizone induced cortical alterations, the mean kurtosis (MK) and radial kurtosis (RK) were found decreased under cuprizone administration, as compared to age-matched controls, in both the motor and somatosensory cortices. The MK remained decreased in the motor cortices at the end of the recovery period, reflecting long lasting impairment of myelination. In white matter, DT, DK and WMTI-derived metrics enabled the detection of cuprizone induced changes differentially according to the stage and the severity of the lesion. More specifically, MK, RK and the axonal water fraction (AWF) were the most sensitive for the detection of cuprizone induced changes in the genu of the corpus callosum, a region less affected by cuprizone administration. Additionally, microgliosis was associated with an increase of MK and RK during the acute inflammatory demyelination phase. In regions undergoing severe demyelination, namely the body and splenium of the corpus callosum, DT-derived metrics, notably the mean diffusion (MD) and radial diffusion (RD), were among the best discriminators between cuprizone and control groups, hence highlighting their ability to detect both acute and long lasting changes. Interestingly, WMTI-derived metrics showed the aptitude to distinguish between the different stage of the disease. Both the intra-axonal diffusivity (Da) and the AWF were found to be decreased in the cuprizone treated group, Da specifically decreased during the acute inflammatory demyelinating phase whereas the AWF decrease was associated to the spontaneous remyelination and the recovery period. Altogether our results demonstrate that DKI is sensitive to alterations of cortical areas and provides, along with WMTI metrics, information that is complementary to DT-derived metrics for the characterization of demyelination in both white and grey matter and subsequent inflammatory processes associated with a demyelinating event.

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Section: Introductionmentioning

confidence: 99%

Diffusion kurtosis imaging probes cortical alterations and white matter pathology following cuprizone induced demyelination and spontaneous remyelination

et al. 2016

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“…In addition, it has been shown that the extra information provided by DKI can be used to resolve intravoxel fiber crossings [15, 16], which is not possible with DTI. Although a relatively new method, DKI is already yielding promising preliminary results for several brain diseases including stroke [17–19], brain cancer [20,21], attention-deficit hyperactivity disorder [22], traumatic brain injury [23,24], Huntington’s disease [25], epilepsy [26], as well as normal aging [27]. …”

Section: Introductionmentioning

confidence: 99%

Non-Gaussian diffusion MRI assessment of brain microstructure in mild cognitive impairment and Alzheimer’s disease

Falangola

Jensen

Tabesh

et al. 2013

Magnetic Resonance Imaging

107

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We report the first application of a novel diffusion-based MRI method, called diffusional kurtosis imaging (DKI), to investigate changes in brain tissue microstructure in patients with mild cognitive impairment (MCI) and AD and in cognitively intact controls. The subject groups were characterized and compared in terms of DKI-derived metrics for selected brain regions using analysis of covariance with a Tukey multiple comparison correction. Receiver operating characteristic (ROC) and binary logistic regression analyses were used to assess the utility of regional diffusion measures, alone and in combination, to discriminate each pair of subject groups. ROC analyses identified mean and radial kurtoses in the anterior corona radiata as the best individual discriminators of MCI from controls, with the measures having an area under the ROC curve (AUC) of 0.80 and 0.82, respectively. The next best discriminators of MCI from controls were diffusivity and kurtosis (both mean and radial) in the prefrontal white matter (WM), with each measure having an AUC between 0.77 and 0.79. Finally, the axial diffusivity in the hippocampus was the best overall discriminator of MCI from AD, having an AUC of 0.90. These preliminary results suggest that non-Gaussian diffusion MRI may be beneficial in the assessment of microstructural tissue damage at the early stage of MCI and may be useful in developing biomarkers for the clinical staging of AD.

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“…Recently, DKI has been shown to be a good potential biomarker for Alzheimer's disease [58], [59]. In animal studies, significant differences, however, no larger than 10%, were reported, for example in the detection of Huntington disease [60]. Again, few of these studies included noise correction in their data processing and the SNR is not known.…”

Section: Discussionmentioning

confidence: 99%

Influence of Noise Correction on Intra- and Inter-Subject Variability of Quantitative Metrics in Diffusion Kurtosis Imaging

et al. 2014

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Diffusion kurtosis imaging (DKI) is a promising extension of diffusion tensor imaging, giving new insights into the white matter microstructure and providing new biomarkers. Given the rapidly increasing number of studies, DKI has a potential to establish itself as a valuable tool in brain diagnostics. However, to become a routine procedure, DKI still needs to be improved in terms of robustness, reliability, and reproducibility. As it requires acquisitions at higher diffusion weightings, results are more affected by noise than in diffusion tensor imaging. The lack of standard procedures for post-processing, especially for noise correction, might become a significant obstacle for the use of DKI in clinical routine limiting its application. We considered two noise correction schemes accounting for the noise properties of multichannel phased-array coils, in order to improve the data quality at signal-to-noise ratio (SNR) typical for DKI. The SNR dependence of estimated DKI metrics such as mean kurtosis (MK), mean diffusivity (MD) and fractional anisotropy (FA) is investigated for these noise correction approaches in Monte Carlo simulations and in in vivo human studies. The intra-subject reproducibility is investigated in a single subject study by varying the SNR level and SNR spatial distribution. Then the impact of the noise correction on inter-subject variability is evaluated in a homogeneous sample of 25 healthy volunteers. Results show a strong impact of noise correction on the MK estimate, while the estimation of FA and MD was affected to a lesser extent. Both intra- and inter-subject SNR-related variability of the MK estimate is considerably reduced after correction for the noise bias, providing more accurate and reproducible measures. In this work, we have proposed a straightforward method that improves accuracy of DKI metrics. This should contribute to standardization of DKI applications in clinical studies making valuable inferences in group analysis and longitudinal studies.

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Identification and characterization of Huntington related pathology: An in vivo DKI imaging study

Cited by 37 publications

References 60 publications

Diffusion kurtosis imaging probes cortical alterations and white matter pathology following cuprizone induced demyelination and spontaneous remyelination

Diffusion kurtosis imaging probes cortical alterations and white matter pathology following cuprizone induced demyelination and spontaneous remyelination

Non-Gaussian diffusion MRI assessment of brain microstructure in mild cognitive impairment and Alzheimer’s disease

Influence of Noise Correction on Intra- and Inter-Subject Variability of Quantitative Metrics in Diffusion Kurtosis Imaging

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