scholarly journals Histologically derived fiber response functions for diffusion MRI vary across white matter fibers - an ex vivo validation study in the squirrel monkey brain

2018 ◽  
Author(s):  
Kurt G Schilling ◽  
Yurui Gao ◽  
Iwona Stepniewska ◽  
Vaibhav Janve ◽  
Bennett A Landman ◽  
...  

AbstractUnderstanding the relationship between the diffusion-weighted MRI signal and the arrangement of white matter fibers is fundamental for accurate voxel-wise reconstruction of the fiber orientation distribution (FOD) and subsequent fiber tractography. Spherical deconvolution reconstruction techniques model the diffusion signal as the convolution of the FOD with a response function which represents the signal profile of a single fiber orientation. Thus, given the signal and a fiber response function, the FOD can be estimated in every imaging voxel by deconvolution. However, the selection of the appropriate response function remains relatively un-studied, and requires further validation. In this work, using 3D histologically-defined FODs and the corresponding diffusion signal from three ex vivo squirrel monkey brains, we derive the ground truth response functions. We find that the histologically-derived response functions differ from those conventionally used. Next, we find that response functions statistically vary across brain regions, which suggests that the practice of using the same kernel throughout the brain is not optimal. Additionally, response functions vary significantly across subjects. We show that different kernels lead to different FOD reconstructions, which in turn can lead to different tractography results depending on algorithmic parameters, with large variations in the accuracy of resulting reconstructions. Together, this suggests that there is room for improvement in estimating and understanding the relationship between the diffusion signal and the underlying FOD.

2019 ◽  
Author(s):  
Fenghua Guo ◽  
Chantal M.W. Tax ◽  
Alberto De Luca ◽  
Max A. Viergever ◽  
Anneriet Heemskerk ◽  
...  

AbstractDiffusion MRI of the brain enables to quantify white matter fiber orientations noninvasively. Several approaches have been proposed to estimate such characteristics from diffusion MRI data with spherical deconvolution being one of the most widely used methods. Constrained spherical deconvolution requires to define – or derive from the data – a response function, which is used to compute the fiber orientation distribution (FOD). This definition or derivation is not unequivocal and can thus result in different characteristics of the response function which are expected to affect the FOD computation and the subsequent fiber tracking. In this work, we explored the effects of inaccuracies in the shape and scaling factors of the response function on the FOD characteristics. With simulations, we show that underestimation of the shape factor in the response functions has a larger effect on the FOD peaks than overestimation of the shape factor, whereas the latter will cause more spurious peaks. Moreover, crossing fiber populations with a smaller separation angle were more sensitive to the response function inaccuracy than fiber populations with more orthogonal separation angles. Furthermore, the FOD characteristics show deviations as a result of modified shape and scaling factors of the response function. Results with the in vivo data demonstrate that the deviations of the FODs and spurious peaks can further deviate the termination of propagation in fiber tracking. This work highlights the importance of proper definition of the response function and how specific calibration factors can affect the FOD and fiber tractography results.


2019 ◽  
Vol 32 (6) ◽  
pp. e4090 ◽  
Author(s):  
Kurt G. Schilling ◽  
Yurui Gao ◽  
Iwona Stepniewska ◽  
Vaibhav Janve ◽  
Bennett A. Landman ◽  
...  

2020 ◽  
Vol 79 (OCE2) ◽  
Author(s):  
Sussanne Reyes ◽  
Patricio Peirano ◽  
Betsy Lozoff ◽  
Cecilia Algarin

Abstract IntroductionObesity has been associated with lower white matter integrity (WMI) in limbic brain regions, including the fornix. Both early decrease of WMI in the fornix (WMIf) and midlife obesity have been related to dementia incidence with advancing age. No studies have explored early cognitive predictors of WMIf in overweight-obese (OO) adults. Aim of this study was to compare OO and normal-weight (NW) participants with respect to (a) WMIf in adulthood and (b) the relationship between cognitive performance at school-age and in adolescence with WMIf in adulthood.MethodsParticipants were part of a cohort followed since infancy who underwent magnetic resonance imaging studies in adulthood (22.3 ± 1.3 years). Diffusion tensor imaging was performed and Tract Based Spatial Statistics (TBSS) was used to obtain fractional anisotropy (FA) skeleton; increased FA relates to greater WMI. A mask for the fornix was created (JHU-ICBM DTI-81 Atlas) and then used to extract the average FA for each individual. Participants also performed neurocognitive tasks: (a) school-age (10.3 ± 1.0 years): the trail making test comprises two conditions and time difference between conditions reflects cognitive flexibility; (b) adolescence (15.6 ± 0.5 years): incentive task that test the effect of incentives (reward, loss avoidance or neutral) on inhibitory control performance (correct responses latency). In adulthood, BMI was categorized as NW (≥ 18.5 to < 25.0 kg/m2) and OO (≥ 25.0 kg/m2) groups. A t-test and univariate GLM were conducted. Analysis were adjusted by sex and age-specific BMI z-scores.ResultsParticipants were 27 NW (41% female) and 41 OO (49% female). Compared to NW, OO participants showed decreased FA in the fornix (0.585 vs. 0.618, p < 0.05), i.e. lower WMIf. Differences were apparent in the relationship between cognitive flexibility at school-age (F = 2.9, p = 0.06) and loss avoidance latency in adolescence (F = 3.5, p < 0.05) with FA in the fornix in adulthood. Increased cognitive flexibility at school-age (β = 0.335, p < 0.05) and decreased loss avoidance latency in adolescence (β = -0.581, p < 0.001) were related to higher FA in the fornix in OO adults. No relationship resulted significant in NW adults.DiscussionPerformance in neurocognitive tasks at earlier developmental stages were related with WMIf only in OO adults, group characterized by decreased WMIf. Our results provide evidence regarding specific neurocognitive tasks with predictive value for WMIf alterations. Further, they could contribute to the understanding of neural mechanisms underlying obesity and also provide insight relative to neurodegenerative risk with advancing age.SupportFondecyt 11160671 and NIH HD33487.


2021 ◽  
Author(s):  
Victor Nozais ◽  
Stephanie Forkel ◽  
Chris Foulon ◽  
Laurent Petit ◽  
Michel Thiebaut de Schotten

Abstract In recent years, the field of functional neuroimaging has moved from a pure localisationist approach of isolated functional brain regions to a more integrated view of those regions within functional networks. The methods used to investigate such networks, however, rely on local signals in grey matter and are limited in identifying anatomical circuitries supporting the interaction between brain regions. Mapping the brain circuits mediating the functional signal between brain regions would propel forward our understanding of the brain’s functional signatures and dysfunctions. We developed a novel method to unravel the relationship between brain circuits and functions: The Functionnectome. The Functionectome combines the functional signal from fMRI with the anatomy of white matter brain circuits to unlock and chart the first maps of functional white matter. To showcase the versatility of this new method, we provide the first functional white matter maps revealing the joint contribution of connected areas to motor, working memory, and language functions. The Functionnectome comes with an open source companion software and opens new avenues into studying functional networks by applying the method to already existing dataset and beyond task fMRI.


2019 ◽  
Vol 40 (3) ◽  
pp. 611-621 ◽  
Author(s):  
Bastian Cheng ◽  
Philipp Dietzmann ◽  
Robert Schulz ◽  
Marlene Boenstrup ◽  
Lutz Krawinkel ◽  
...  

Following acute ischemic stroke, isolated subcortical lesions induce gray matter atrophy in anatomically connected, yet distant cortical brain regions. We expand on previous studies by analyzing cortical thinning in contralesional, homologous regions indirectly linked to primary stroke lesions via ipsilesional cortical areas. For this purpose, stroke patients were serially studied by magnetic resonance imaging (diffusion tensor imaging and high-resolution anatomical imaging) in the acute (days 3–5) and late chronic stage one year after stroke. We analyzed changes of gray and white matter integrity in 18 stroke patients (median age 68 years) with subcortical stroke. We applied probabilistic fiber tractography to identify brain regions connected to stroke lesions and contralesional homologous areas. Cortical thickness was quantified by semi-automatic measurements, and fractional anisotropy was analyzed. One year after stroke, significant decrease of cortical thickness was detected in areas connected to ischemic lesions (mean −0.15 mm; 95% CI −0.23 to −0.07 mm) as well as homologous contralateral brain regions (mean −0.13 mm; 95% CI −0.07 to −0.19 mm). We detected reduced white matter integrity of inter- and intrahemispheric fiber tracts. There were no significant associations with clinical recovery. Our results indicate that impact of subcortical lesions extends to homologous brain areas via transcallosal diaschisis.


2021 ◽  
Author(s):  
Fenghua Guo ◽  
Chantal M. W. Tax ◽  
Alberto De Luca ◽  
Max A. Viergever ◽  
Anneriet Heemskerk ◽  
...  

2020 ◽  
Author(s):  
Aidana Massalimova ◽  
Ruiqing Ni ◽  
Roger M. Nitsch ◽  
Marco Reisert ◽  
Dominik von Elverfeldt ◽  
...  

AbstractIntroductionIncreased expression of hyperphosphorylated tau and the formation of neurofibrillary tangles are associated with neuronal loss and white matter damage. Using high resolution ex vivo diffusion tensor imaging (DTI), we investigated microstructural changes in the white and grey matter in the P301L mouse model of human tauopathy at 8.5 months-of-age. For unbiased computational analysis, we implemented a pipeline for voxel-based analysis (VBA) and atlas-based analysis (ABA) of DTI mouse brain data.MethodsHemizygous and homozygous transgenic P301L mice and non-transgenic littermates were used. DTI data were acquired for generation of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD) maps. VBA on the entire brain were performed using SPM8 and SPM Mouse toolbox. Initially, all DTI maps were co-registered with Allen mouse brain atlas to bring them to one common coordinate space. In VBA, co-registered DTI maps were normalized and smoothed in order to perform two-sample t-tests to compare hemizygotes with non-transgenic littermates, homozygotes with non-transgenic littermates, hemizygotes with homozygotes on each DTI parameter map. In ABA, the average values for selected regions-of-interests were computed with co-registered DTI maps and labels in Allen mouse brain atlas. After, the same two-sample t-tests were executed on the estimated average values.ResultsWe made reconstructed DTI data and VBA and ABA pipeline publicly available. With VBA, we found microstructural changes in the white matter in hemizygous P301L mice compared to non-transgenic littermates. In contrast, more pronounced and brain-wide spread changes were observed in VBA when comparing homozygous P301L mice with non-transgenic littermates. Statistical comparison of DTI metrics in selected brain regions by ABA corroborated findings from VBA. FA was found to be decreased in most brain regions, while MD, RD and AD were increased compared to hemizygotes and non-transgenic littermates.Discussion/ConclusionHigh resolution ex vivo DTI demonstrated brain-wide microstructural changes in the P301L mouse model of human tauopathy. The comparison between hemizygous and homozygous P301L mice revealed a gene-dose dependent effect on DTI metrics. The publicly available computational data analysis pipeline can provide a platform for future mechanistic and longitudinal studies.


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