User‐independent diffusion tensor imaging analysis pipelines in a rat model presenting ventriculomegalia: A comparison study

Akakpo, Luis; Pierre, Wyston C.; Jin, Chen; Londono, Irène; Pouliot, Philippe; Lodygensky, Gregory A.

doi:10.1002/nbm.3793

Cited by 6 publications

(9 citation statements)

References 47 publications

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“…In accordance to our results, animal studies showed that adverse events during the perinatal period can lead to sustained alteration of the brain methylome up to 1 year after the initial insult. 38,40 Furthermore, using the microtubule-associated protein 1 expressed in axons, Fan et al have confirmed that axonal fragmentation, already present 3 days after LPS exposure, was still there 70 days later. 61 Our datasets revealed that major portion of methylation changes occurred in intergenic and intronic regions and on CpG within the open sea.…”

Section: F I G U R E 4 Methylation Levels Of Genes Of Interest Associmentioning

confidence: 92%

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Alteration of the brain methylation landscape following postnatal inflammatory injury in rat pups

et al. 2019

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This is an open access article under the terms of the Creat ive Commo ns Attri butio n-NonCo mmercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. contributed equally to this study.Abbreviations: ASD, autism spectrum disorder; CpG, cytosine followed by guanine; DMR, differentially methylated region; DOHaD, developmental origins of health and disease; E. coli, Escherichia coli; GO, gene ontology; LPS, lipopolysaccharide; P3, postnatal day 3; P4, postnatal day 4; P24, postnatal day 24; PBS, phosphate-buffered saline; PFA, paraformaldehyde; RRBS, reduced representation bisulfite sequencing; WMI, white matter injury. AbstractPreterm infants are vulnerable to inflammation-induced white matter injury (WMI), which is associated with neurocognitive impairment and increased risk of neuropsychiatric diseases in adulthood. Epigenetic mechanisms, particularly DNA methylation, play a role in normal development and modulate the response to pathological challenges. Our aims were to determine how WMI triggered DNA methylation alterations in brains of neonatal rats and if such changes persisted over time. We used a robust model of WMI by injecting lipopolysaccharide (LPS) or sterile saline in the corpus callosum of 3-day-old (P3) rat pups. Brains were collected 24 hours (P4) and 21 days post-injection (P24). We extracted genomic DNA from the brain to establish genome-wide quantitative DNA methylation profiles using reduced representation bisulfite sequencing. Neonatal LPS exposure induced a persistent increased methylation of genes related to nervous system development and a reduced methylation of genes associated with inflammatory pathways. These findings suggest that early-life neuroinflammatory exposure impacts the cerebral methylation landscape with determining widespread epigenetic modifications especially in genes related to neurodevelopment. K E Y W O R D SDNA methylation, epigenetics, lipopolysaccharide, periventricular leukomalacia

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Section: F I G U R E 4 Methylation Levels Of Genes Of Interest Associmentioning

confidence: 92%

“…Using this robust animal model of inflammatory WMI, 10,11,[38][39][40] we hypothesized that postnatal inflammatory exposure induced significant cerebral epigenetics modifications similarly to recent epidemiological findings in former preterm infants.…”

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confidence: 94%

Alteration of the brain methylation landscape following postnatal inflammatory injury in rat pups

et al. 2019

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“…Whole‐brain deterministic fiber tractography was performed with the continuous tracking algorithm . The algorithm computed fiber trajectories between network nodes continuously unless the fiber made a turn greater than 70° or met a voxel with an FA value less than 0.4 to determine the strong connections . The weight of a network edge was calculated as the number of fiber tracts between two regions normalized by the mean volume of the two regions.…”

Section: Methodsmentioning

confidence: 99%

“…19 The algorithm computed fiber trajectories between network nodes continuously unless the fiber made a turn greater than 70 or met a voxel with an FA value less than 0.4 to determine the strong connections. 20,21 The weight of a network edge was calculated as the number of fiber tracts between two regions normalized by the mean volume of the two regions. And last, a matrix with 64 × 64 elements were constructed, representing the structural network of each subject.…”

Section: Network Construction and Wm Integrity Evaluationmentioning

confidence: 99%

Changes of white matter integrity and structural network connectivity in nondemented cerebral small‐vessel disease

Liu

Zou

Tang

et al. 2019

Magnetic Resonance Imaging

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Background Previous studies have found widespread impairment of white matter (WM) integrity and disruption of structural network connectivity in cerebral small‐vessel disease, but have not evaluated these changes jointly in nondemented patients. Purpose To jointly investigate the microstructural impairment of WM and the related alterations of structural network topology in nondemented cerebral small‐vessel disease (CSVD‐ND). Study Type Prospective. Population Thirty‐seven CSVD‐ND patients and 34 elderly controls, who were age‐, sex‐, and education‐matched. Field Strength/Sequence 3.0T/diffusion tensor imaging (DTI). Assessment Clinical characteristics, lacunar infarct, and white matter hyperintensity (WMH) was assessed. A multiatlas likelihood fusion (MALF) algorithm was used for DTI‐based brain segmentation and network node defining. Then the alterations of WM integrity and structural network topology were investigated jointly. Statistical Tests Student's t‐test, chi‐square test, Mann–Whitney U‐test, linear regression, Pearson correlation, and multiple comparison correction. Results Decreased fractional anisotropy and increased trace values were observed in predefined structures (P < 0.05, familywise error rate‐corrected), including major commissural fibers, projection fibers, and some association fibers. Topologically, both groups showed small‐worldness. CSVD‐ND patients showed reduced global and local efficiency (P < 0.001). Despite widespread impairment of WM integrity, CSVD‐ND patients only showed reduced nodal efficiency in the right superior occipital gyrus and the right lingual gyrus (P < 0.05, familywise error rate‐corrected). The nodal local efficiency of the right precuneus was associated with the processing speed after adjusting the effect of lacunar infarct and WMH (r = –0.499, P = 0.038). Data Conclusion WM integrity was widely impaired in nondemented CSVD patients but structural network connectivity was relatively preserved. DTI may potentially provide information for the pathophysiology of CSVD in the nondemented phase. Level of Evidence: 1 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:1162–1169.

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“…One observation that has drawn growing interest is the differential behavior of the diffusion signal at low b-values, which is denominated intravoxel incoherent motion (IVIM). [1][2][3][4][5][6] At low b-values, the signal decays faster. This decay is described through the pseudo-diffusion coefficient (D * ) in area per unit time, through the associated signal fraction (f also called volume fraction of incoherently flowing blood in tissues) and through the diffusion coefficient (D) according to Eq.…”

Section: Introductionmentioning

confidence: 99%

Impact of <i>b</i>-Value Sampling Scheme on Brain IVIM Parameter Estimation in Healthy Subjects

et al. 2020

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Purpose: Intravoxel incoherent motion (IVIM) analysis has attracted the interest of the clinical community due to its close relationship with microperfusion. Nevertheless, there is no clear reference protocol for its implementation; one of the questions being which b-value distribution to use. This study aimed to stress the importance of the sampling scheme and to show that an optimized b-value distribution decreases the variance associated with IVIM parameters in the brain with respect to a regular distribution in healthy volunteers.Methods: Ten volunteers were included in this study; images were acquired on a 1.5T MR scanner. Two distributions of 16 b-values were used: one considered 'regular' due to its close association with that used in other studies, and the other considered 'optimized' according to previous studies. IVIM parameters were adjusted according to the bi-exponential model, using two-step method. Analysis was undertaken in ROI defined using in the Automated Anatomical Labeling atlas, and parameters distributions were compared in a total of 832 ROI. Results:Maps with fewer speckles were obtained with the 'optimized' distribution. Coefficients of variation did not change significantly for the estimation of the diffusion coefficient D but decreased by approximately 39% for the pseudo-diffusion coefficient estimation and by 21% for the perfusion fraction. Distributions of adjusted parameters were found significantly different in 50% of the cases for the perfusion fraction, in 80% of the cases for the pseudo-diffusion coefficient and 17% of the cases for the diffusion coefficient. Observations across brain areas show that the range of average values for IVIM parameters is smaller in the 'optimized' case. Conclusion:Using an optimized distribution, data are sampled in a way that the IVIM signal decay is better described and less variance is obtained in the fitted parameters. The increased precision gained could help to detect small variations in IVIM parameters.

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User‐independent diffusion tensor imaging analysis pipelines in a rat model presenting ventriculomegalia: A comparison study

Cited by 6 publications

References 47 publications

Alteration of the brain methylation landscape following postnatal inflammatory injury in rat pups

Alteration of the brain methylation landscape following postnatal inflammatory injury in rat pups

Changes of white matter integrity and structural network connectivity in nondemented cerebral small‐vessel disease

Impact of <i>b</i>-Value Sampling Scheme on Brain IVIM Parameter Estimation in Healthy Subjects

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