Neurofibromatosis type 1: Diffusion weighted imaging findings of brain

Alkan, Alpay; Sigirci, Ahmet; Kutlu, Ramazan; Özcan, Hamdi; Erdem, Gülnur; Aslan, Mehmet; Ateş, Özkan; Yakıncı, Cengiz; Eğri, Mücahit

doi:10.1016/j.ejrad.2005.05.008

Cited by 38 publications

(27 citation statements)

References 13 publications

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“…These findings confirm those of previous reports. [6][7][8][9]14 However, the ADC values found in this study are not as high as the ADC values found previously. An explanation of lower ADC values may be found in the shorter effective TE and the different b-value that were used in our scanning protocol compared with reported DWI protocols.…”

Section: Discussioncontrasting

confidence: 54%

“…The DTI images were obtained in 25 gradient directions with a sensitivity of b ϭ 1000 s/mm 2 (Fig 1). 8 All region-of-interest placement was done on the b ϭ 0 s/mm 2 images because anatomic detail was better on these images than on the computed maps, ensuring anatomic precision. Regions of interest were automatically superimposed on the functional maps by the software that was used in this study.…”

Section: Image Acquisitionmentioning

confidence: 99%

“…On the basis of the high ADC values, a widespread myelin disorder was suggested to be present in patients with NF-1. [6][7][8][9] However, ADC reflects only the magnitude of the diffusion of water molecules. Although high ADC values might suggest increased water content of the brain, with ADC alone, it is not possible to examine the microstructural integrity of the parenchyma.…”

mentioning

confidence: 99%

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Quantitative Differentiation Between Healthy and Disordered Brain Matter in Patients with Neurofibromatosis Type I Using Diffusion Tensor Imaging

Engelen¹,

Krab²,

Moll³

et al. 2008

AJNR Am J Neuroradiol

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Section: Discussioncontrasting

confidence: 54%

Section: Image Acquisitionmentioning

confidence: 99%

mentioning

confidence: 99%

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Quantitative Differentiation Between Healthy and Disordered Brain Matter in Patients with Neurofibromatosis Type I Using Diffusion Tensor Imaging

Engelen¹,

Krab²,

Moll³

et al. 2008

AJNR Am J Neuroradiol

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“…Hence, they are ideally suited to be evaluated by diffusion tensor imaging (DTI). The small number of scientific papers evaluating NBOs by diffusion-weighted imaging have mostly focused on studying the appar-ent diffusion coefficient (ADC) values of NBOs [11][12][13][14] . Few studies have evaluated the fractional anisotropy (FA) values and the corresponding eigenvalues of these lesions [15][16][17][18][19] .…”

Section: Introductionmentioning

confidence: 99%

Diffusion Tensor Imaging of Neurofibromatosis Bright Objects in Children with Neurofibromatosis Type 1

et al. 2014

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SUMMARY -Neurofibromatosis bright objects (NBOs) are poorly understood. This article aimed to investigate: 1) differences in fractional anisotropy (FA) between NBOs based in gray matter (GM) and white matter (WM), and 2) the relationship between NBOs and the affected white matter tracts. Fourteen NF1 patients were included in this study. Apparent diffusion coefficient (ADC), FA, radial diffusivity (RD) and eigenvalues were used to compare NBOs and matching contralateral normalappearing sites (NAS). Diffusion tensor imaging scalars were also compared with age-matched healthy controls. Fiber tractography was performed to assess NBO-induced changes in WM trajectories. ADC values were higher for GM and WM NBOs than for NAS and controls. FA values were lower in GM and

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“…Deviation in posterior fiber tract course, ↓ lateralization of fiber coherence (Marenco et al, 2007); ↑ FA in right superior longitudinal fasciculus (Hoeft et al, 2007) ↑ ADC values ,particularly in UBOs (Alkan et al, 2005;Tognini et al, 2005); ↑ ADC values, particularly in globus pallidus (Eastwood et al, 2001) * Gray Matter +White Matter CTL=Control; SZ=schizophrenia; WM=white matter; GM=gray matter FA = Fractional Anisotropy; ADC=Apparent Diffusion Coefficient pM=premutation; fM=full mutation…”

mentioning

confidence: 99%

Neural phenotypes of common and rare genetic variants

Bearden

Glahn

Lee

et al. 2008

Biological Psychology

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Neuroimaging methods offer a powerful way to bridge the gaps between genes, neurobiology and behavior. Such investigations may be further empowered by complementary strategies involving chromosomal abnormalities associated with particular neurobehavioral phenotypes, which can help to localize causative genes and better understand the genetics of complex traits in the general population. Here we review the evidence from studies using these convergent approaches to investigate genetic influences on brain structure: 1) Studies of common genetic variation associated with particular neuroanatomic phenotypes, and 2) Studies of possible 'genetic subtypes' of neuropsychiatric disorders with very high penetrance, with a focus on neuroimaging studies using novel computational brain mapping algorithms. Finally, we discuss the contribution of behavioral neurogenetics research to our understanding of the genetic basis of neuropsychiatric disorders in the broader population.

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Neurofibromatosis type 1: Diffusion weighted imaging findings of brain

Cited by 38 publications

References 13 publications

Quantitative Differentiation Between Healthy and Disordered Brain Matter in Patients with Neurofibromatosis Type I Using Diffusion Tensor Imaging

Quantitative Differentiation Between Healthy and Disordered Brain Matter in Patients with Neurofibromatosis Type I Using Diffusion Tensor Imaging

Diffusion Tensor Imaging of Neurofibromatosis Bright Objects in Children with Neurofibromatosis Type 1

Neural phenotypes of common and rare genetic variants

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