Automatic Segmentation of White Matter Tracts Using Multiple Brain MRI Sequences

Author(s):  
Ilya Nelkenbaum ◽  
Galia Tsarfaty ◽  
Nahum Kiryati ◽  
Eli Konen ◽  
Arnaldo Mayer
2020 ◽  
Vol 117 (18) ◽  
pp. 10035-10044
Author(s):  
Xiaojie Wang ◽  
Verginia C. Cuzon Carlson ◽  
Colin Studholme ◽  
Natali Newman ◽  
Matthew M. Ford ◽  
...  

One factor that contributes to the high prevalence of fetal alcohol spectrum disorder (FASD) is binge-like consumption of alcohol before pregnancy awareness. It is known that treatments are more effective with early recognition of FASD. Recent advances in retrospective motion correction for the reconstruction of three-dimensional (3D) fetal brain MRI have led to significant improvements in the quality and resolution of anatomical and diffusion MRI of the fetal brain. Here, a rhesus macaque model of FASD, involving oral self-administration of 1.5 g/kg ethanol per day beginning prior to pregnancy and extending through the first 60 d of a 168-d gestational term, was utilized to determine whether fetal MRI could detect alcohol-induced abnormalities in brain development. This approach revealed differences between ethanol-exposed and control fetuses at gestation day 135 (G135), but not G110 or G85. At G135, ethanol-exposed fetuses had reduced brainstem and cerebellum volume and water diffusion anisotropy in several white matter tracts, compared to controls. Ex vivo electrophysiological recordings performed on fetal brain tissue obtained immediately following MRI demonstrated that the structural abnormalities observed at G135 are of functional significance. Specifically, spontaneous excitatory postsynaptic current amplitudes measured from individual neurons in the primary somatosensory cortex and putamen strongly correlated with diffusion anisotropy in the white matter tracts that connect these structures. These findings demonstrate that exposure to ethanol early in gestation perturbs development of brain regions associated with motor control in a manner that is detectable with fetal MRI.


Author(s):  
Amira Chekir ◽  
Maxime Descoteaux ◽  
Eleftherios Garyfallidis ◽  
Marc-Alexandre Cote ◽  
Fatima Oulebsir Boumghar

2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Ayush Goyal ◽  
Sunayana Tirumalasetty ◽  
Gahangir Hossain ◽  
Rajab Challoo ◽  
Manish Arya ◽  
...  

This research presents an independent stand-alone graphical computational tool which functions as a neurological disease prediction framework for diagnosis of neurological disorders to assist neurologists or researchers in the field to perform automatic segmentation of gray and white matter regions in brain MRI images. The tool was built in collaboration with neurologists and neurosurgeons and many of the features are based on their feedback. This tool provides the user automatized functionality to perform automatic segmentation and extract the gray and white matter regions of patient brain image data using an algorithm called adapted fuzzy c-means (FCM) membership-based clustering with preprocessing using the elliptical Hough transform and postprocessing using connected region analysis. Dice coefficients for several patient brain MRI images were calculated to measure the similarity between the manual tracings by experts and automatic segmentations obtained in this research. The average Dice coefficients are 0.86 for gray matter, 0.88 for white matter, and 0.87 for total cortical matter. Dice coefficients of the proposed algorithm were also the highest when compared with previously published standard state-of-the-art brain MRI segmentation algorithms in terms of accuracy in segmenting the gray matter, white matter, and total cortical matter.


2010 ◽  
Vol 41 (01) ◽  
Author(s):  
J Faber ◽  
JC Schöne-Bake ◽  
C Melzer ◽  
M Tittgemeyer ◽  
B Weber

2019 ◽  
Author(s):  
Justin C. Hayes ◽  
Katherine L Alfred ◽  
Rachel Pizzie ◽  
Joshua S. Cetron ◽  
David J. M. Kraemer

Modality specific encoding habits account for a significant portion of individual differences reflected in functional activation during cognitive processing. Yet, little is known about how these habits of thought influence long-term structural changes in the brain. Traditionally, habits of thought have been assessed using self-report questionnaires such as the visualizer-verbalizer questionnaire. Here, rather than relying on subjective reports, we measured habits of thought using a novel behavioral task assessing attentional biases toward picture and word stimuli. Hypothesizing that verbal habits of thought are reflected in the structural integrity of white matter tracts and cortical regions of interest, we used diffusion tensor imaging and volumetric analyses to assess this prediction. Using a whole-brain approach, we show that word bias is associated with increased volume in several bilateral language regions, in both white and grey matter parcels. Additionally, connectivity within white matter tracts within an a priori speech production network increased as a function of word bias. These results demonstrate long-term structural and morphological differences associated with verbal habits of thought.


Neuroreport ◽  
2018 ◽  
Vol 29 (17) ◽  
pp. 1473-1478 ◽  
Author(s):  
Courtney R. Burton ◽  
David J. Schaeffer ◽  
Anastasia M. Bobilev ◽  
Jordan E. Pierce ◽  
Amanda L. Rodrigue ◽  
...  

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