Age‐related non‐Gaussian diffusion patterns in the prefrontal brain

Falangola, Maria F.; Jensen, Jens H.; Babb, James S.; Hu, Chaoqun; Castellanos, F. Xavier; Martino, Adriana Di; Ferris, Steven; Helpern, Joseph A.

doi:10.1002/jmri.21604

Cited by 221 publications

(213 citation statements)

References 23 publications

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“…Given the wide age range in our study population and the previously documented age-related changes in diffusion parameters in white matter (19), we corrected parameter values in the NAWM and PLIC for age, on the basis of the average age of the complete patient group (54 years) and the slope of the linear regression of the respective parameter value versus age in the NAWM or PLIC. Statistical analysis was performed by three authors (S.V.C., R.R.P., and F.D.K.…”

Section: Discussionmentioning

confidence: 99%

Gliomas: Diffusion Kurtosis MR Imaging in Grading

Cauter

Veraart²,

Sijbers³

et al. 2012

Radiology

315

260

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Purpose:To assess the diagnostic accuracy of diffusion kurtosis magnetic resonance imaging parameters in grading gliomas. Materials and Methods:The institutional review board approved this prospective study, and informed consent was obtained from all patients. Diffusion parameters-mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis, and radial and axial kurtosis-were compared in the solid parts of 17 high-grade gliomas and 11 low-grade gliomas (P , .05 significance level, Mann-Whitney-Wilcoxon test, Bonferroni correction). MD, FA, mean kurtosis, radial kurtosis, and axial kurtosis in solid tumors were also normalized to the corresponding values in contralateral normal-appearing white matter (NAWM) and the contralateral posterior limb of the internal capsule (PLIC) after age correction and were compared among tumor grades. Results:Mean, radial, and axial kurtosis were significantly higher in high-grade gliomas than in low-grade gliomas (P = .02, P = .015, and P = .01, respectively). FA and MD did not significantly differ between glioma grades. All values, except for axial kurtosis, that were normalized to the values in the contralateral NAWM were significantly different between high-grade and low-grade gliomas (mean kurtosis, P = .02; radial kurtosis, P = .03; FA, P = .025; and MD, P = .03). When values were normalized to those in the contralateral PLIC, none of the considered parameters showed significant differences between high-grade and low-grade gliomas. The highest sensitivity and specificity for discriminating between high-grade and low-grade gliomas were found for mean kurtosis (71% and 82%, respectively) and mean kurtosis normalized to the value in the contralateral NAWM (100% and 73%, respectively). Optimal thresholds for mean kurtosis and mean kurtosis normalized to the value in the contralateral NAWM for differentiating high-grade from low-grade gliomas were 0.52 and 0.51, respectively. Conclusion:There were significant differences in kurtosis parameters between high-grade and low-grade gliomas; hence, better separation was achieved with these parameters than with conventional diffusion imaging parameters.q RSNA, 2012 1

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Section: Discussionmentioning

confidence: 99%

Gliomas: Diffusion Kurtosis MR Imaging in Grading

Cauter

Veraart²,

Sijbers³

et al. 2012

Radiology

315

260

View full text Add to dashboard Cite

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“…To this aim, more advanced methods such as Kurtosis Imaging [204][205][206][207], Diffusion Spectrum Imaging [208], higher-order tensor models [209], compartment models [210,211], and anomalous diffusion [212,213] have been introduced in order to augment their suitability in a clinical setting [214]. These upcoming techniques have been succesfully used in some pathologies, including AD, to enhance information of earlier microstructural tissue alterations linked to disease progression [215,216]. Among these, Kurtosis Imaging seems to be the most promising developing modality in relation of its easy setup/ optimization and relatively short time acquisition in clinical MR scanners.…”

Section: Diffusion Tensor Imagingmentioning

confidence: 99%

Perspective on future role of biological markers in clinical therapy trials of Alzheimer's disease: A long-range point of view beyond 2020

Hampel

Lista

Teipel

et al. 2014

Biochemical Pharmacology

102

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Recent advances in understanding the molecular mechanisms underlying various paths toward the pathogenesis of Alzheimer's disease (AD) has begun to provide new insight for interventions to modify disease progression. The evolving knowledge gained from multidisciplinary basic research has begun to identify new concepts for treatments and distinct classes of therapeutic targets; as well as putative disease-modifying compounds that are now being tested in clinical trials. There is a mounting consensus that such disease modifying compounds and/or interventions are more likely to be effectively administered as early as possible in the cascade of pathogenic processes preceding and underlying the clinical expression of AD. The budding sentiment is that "treatments" need to be applied before various molecular mechanisms converge into an irreversible pathway leading to morphological, metabolic and functional alterations that characterize the pathophysiology of AD. In light of this, biological indicators of pathophysiological mechanisms are desired to chart and detect AD throughout the asymptomatic early molecular stages into the prodromal and early dementia phase. A major conceptual development in the clinical AD research field was the recent proposal of new diagnostic criteria, which specifically incorporate the use of biomarkers as defining criteria for preclinical stages of AD. This paradigm shift in AD definition, conceptualization, operationalization, detection and diagnosis represents novel fundamental opportunities for the modification of interventional trial designs. This perspective summarizes not only present knowledge regarding biological markers but also unresolved questions on the status of surrogate indicators for detection of the disease in asymptomatic people and diagnosis of AD

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“…In contrast, the use of DKI to evaluate structural changes in gray matter has been reported in relatively small numbers of subjects. 51,52 Therefore, DKI may be useful in the evaluation of NAGM in patients with multiple sclerosis. Brain MR imaging demonstrated multiple cerebral infarctions, including a subacute lesion in the right frontal deep white matter, which appeared as a high signal on diŠusion-weighted imaging (DWI) (a) and a low signal on the calculated apparent diŠusion coe‹cient (ADC) map (b).…”

Section: Multiple Sclerosismentioning

confidence: 99%

Visualizing Non-Gaussian Diffusion: Clinical Application of q-Space Imaging and Diffusional Kurtosis Imaging of the Brain and Spine

Hori

Fukunaga

Masutani

et al. 2012

MRMS

104

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Recently, non-Gaussian diŠusion-weighted imaging (DWI) techniques, including qspace imaging (QSI) and diŠusional kurtosis imaging (DKI), have emerged as advanced methods to evaluate tissue microstructure in vivo using water diŠusion. QSI and DKI have shown promising results in clinical applications, such as in the evaluation of brain tumors (e.g., grading gliomas), degenerative diseases (e.g., speciˆc diagnosis of Parkinson disease), demyelinating diseases (e.g., assessment of normal-appearing tissue of multiple sclerosis), and cerebrovascular diseases (e.g., assessment of the microstructural environment of fresh infarctions). Representative metrics in clinical use are the full width at half maximum, also known as the mean displacement of the probability density function curve, which is derived from QSI, and diŠusional kurtosis, which is derived from DKI. These new metrics may provide information on tissue structure in addition to that provided by conventional Gaussian DWI investigations that use the apparent diŠusion coe‹cient and fractional anisotropy, recognized indices for evaluating disease and normal development in the brain and spine. In some clinical situations, sensitivity for detecting pathological conditions is higher using QSI and DKI than conventional DWI and diŠusion tensor imaging (DTI) because DWI and DTI calculations are based on the assumption that water molecules follow a Gaussian distribution, whereas hindrance of the distribution of water molecules by complex and restricted structures in actual neural tissues produces distributions that are far from Gaussian. We review the technical aspects and clinical applications of QSI and DKI, focusing on clinical use and in vivo studies and highlighting diŠerences from conventional diŠusional metrics.

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Age‐related non‐Gaussian diffusion patterns in the prefrontal brain

Cited by 221 publications

References 23 publications

Gliomas: Diffusion Kurtosis MR Imaging in Grading

Gliomas: Diffusion Kurtosis MR Imaging in Grading

Perspective on future role of biological markers in clinical therapy trials of Alzheimer's disease: A long-range point of view beyond 2020

Visualizing Non-Gaussian Diffusion: Clinical Application of q-Space Imaging and Diffusional Kurtosis Imaging of the Brain and Spine

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