Brain changes associated with Alzheimer's disease (AD) begin decades before disease diagnosis. While β-amyloid plaques and neurofibrillary tangles are defining features of AD, neuronal loss and ...synaptic pathology are closely related to the cognitive dysfunction. Brain imaging methods that are tuned to assess degeneration of myelinated nerve fibers in the brain (collectively called white matter) include diffusion tensor imaging (DTI) and related techniques, and are expected to shed light on disease-related loss of structural connectivity. Participants (N = 70, ages 47-76 years) from the Wisconsin Registry for Alzheimer's Prevention study underwent DTI and hybrid diffusion imaging to determine a free-water elimination (FWE-DTI) model. The study assessed the extent to which preclinical AD pathology affects brain white matter. Preclinical AD pathology was determined using cerebrospinal fluid (CSF) biomarkers. The sample was enriched for AD risk (APOE ε4 and parental history of AD). AD pathology assessed by CSF analyses was significantly associated with altered microstructure on both DTI and FWE-DTI. Affected regions included frontal, parietal, and especially temporal white matter. The f-value derived from the FWE-DTI model appeared to be the most sensitive to the relationship between the CSF AD biomarkers and microstructural alterations in white matter. These findings suggest that white matter degeneration is an early pathological feature of AD that may have utility both for early disease detection and as outcome measures for clinical trials. More complex models of microstructural diffusion properties including FWE-DTI may provide increased sensitivity to early brain changes associated with AD over standard DTI.
Many chemotherapeutic drugs kill only a fraction of cancer cells, limiting their efficacy. We used live-cell imaging to investigate the role of p53 dynamics in fractional killing of colon cancer ...cells in response to chemotherapy. We found that both surviving and dying cells reach similar levels of p53, indicating that cell death is not determined by a fixed p53 threshold. Instead, a cell’s probability of death depends on the time and levels of p53. Cells must reach a threshold level of p53 to execute apoptosis, and this threshold increases with time. The increase in p53 apoptotic threshold is due to drug-dependent induction of anti-apoptotic genes, predominantly in the inhibitors of apoptosis (IAP) family. Our study underlines the importance of measuring the dynamics of key players in response to chemotherapy to determine mechanisms of resistance and optimize the timing of combination therapy.
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•In response to a drug, single cells show different fates and rates of p53 induction•The p53 threshold required to enact apoptosis rises with time•Drug efficacy is enhanced by accelerating the rate of p53 induction•Inhibition of anti-apoptotic proteins reduces the increase in threshold over time
Upregulation of p53 in response to chemotherapy leads to apoptosis in some cells while other cells survive. Cells with rapid induction of p53 enact apoptosis while cells with slow yet high levels of p53 survive due to the upregulation of IAP proteins. Cell death can be enhanced by either increasing the rate of p53 accumulation or flattening the p53 apoptotic threshold.
Microstructural changes in human brain white matter of young to middle-aged adults were studied using advanced diffusion Magnetic Resonance Imaging (dMRI). Multiple shell diffusion-weighted data were ...acquired using the Hybrid Diffusion Imaging (HYDI). The HYDI method is extremely versatile and data were analyzed using Diffusion Tensor Imaging (DTI), Neurite Orientation Dispersion and Density Imaging (NODDI), and q-space imaging approaches. Twenty-four females and 23 males between 18 and 55years of age were included in this study. The impact of age and sex on diffusion metrics were tested using least squares linear regressions in 48 white matter regions of interest (ROIs) across the whole brain and adjusted for multiple comparisons across ROIs. In this study, white matter projections to either the hippocampus or the cerebral cortices were the brain regions most sensitive to aging. Specifically, in this young to middle-aged cohort, aging effects were associated with more dispersion of white matter fibers while the tissue restriction and intra-axonal volume fraction remained relatively stable. The fiber dispersion index of NODDI exhibited the most pronounced sensitivity to aging. In addition, changes of the DTI indices in this aging cohort were correlated mostly with the fiber dispersion index rather than the intracellular volume fraction of NODDI or the q-space measurements. While men and women did not differ in the aging rate, men tend to have higher intra-axonal volume fraction than women. This study demonstrates that advanced dMRI using a HYDI acquisition and compartmental modeling of NODDI can elucidate microstructural alterations that are sensitive to age and sex. Finally, this study provides insight into the relationships between DTI diffusion metrics and advanced diffusion metrics of NODDI model and q-space imaging.
•Age-related white matter changes were studied using Hybrid Diffusion Imaging.•NODDI diffusion indices are more sensitive to aging and sex differences than DTI.•Aging mainly causes dispersion in white matter fibers in middle-aged adults.•In young to middle-aged adults, men have greater axonal fractions and dispersion.•Age-related changes in axial and radial diffusivity are driven by fiber dispersion.
Diffusion tensor imaging is used to measure the diffusion of water in tissue. The diffusion properties carry information about the relative organization and structure of the underlying tissue. In the ...case of a single voxel containing both tissue and a fast diffusing component such as free water, a single diffusion tensor is no longer appropriate. A two-tensor free water elimination model has previously been proposed to correct for the case of volume mixing. Here, this model was implemented in a straightforward but novel manner without the use of spatial constraints. The optimal acquisition parameters were investigated through Monte Carlo simulations and human brain imaging studies. At a signal-to-noise ratio of 40 with 64 diffusion-weighted encoding images, the most accurate estimates of fast diffusion signal were obtained with two diffusion-weighted shells (b-value in s/mm2×number of directions) of 500×32 and 1500×32. The potential bias in fractional anisotropy induced by this two-compartment model was more than an order of magnitude less than the error of using the single diffusion tensor model in the presence of partial volume effects with free water. This strategy may be useful for characterizing the diffusion of tissues adjacent to cerebral spinal fluid (CSF), tissues affected by edema, and removing artifacts from blurring and ghosting of the CSF signal.
•We implemented a diffusion model that resolves partial volume effects with CSF.•This model results in a smaller bias than DTI and better fits the measured signal.•The scan is less than 7min, shorter than the other advanced diffusion models.•An optimized acquisition was found through Monte Carlo simulations.
Alterations to myelin may be a core pathological feature of neurodegenerative diseases. Although white matter microstructural differences have been described in Parkinson's disease (PD), it is ...unknown whether such differences include alterations of the brain's myelin content. Thus, the objective of the current study is to measure and compare brain myelin content between PD patients and age-matched controls. In this cross-sectional study, 63 participants from the Longitudinal MRI in Parkinson's Disease study underwent brain MRI, Unified Parkinson's Disease Rating Scale (UPDRS) scoring, and cognitive asessments. Subjects were imaged with the mcDEPSOT (multi-component driven equilibrium single pulse observation of T1 and T2), a multicomponent relaxometry technique that quantifies longitudinal and transverse relaxation rates (R1 and R2, respectively) and the myelin water fraction (VFM), a surrogate for myelin content. A voxel-wise approach was used to compare R1, R2, and VFM measures between PD and control groups, and to evaluate relationships with age as well as disease duration, UPDRS scores, and daily levodopa equivalent dose. PD subjects had higher VFM than controls in frontal and temporal white matter and bilateral thalamus. Greater age was strongly associated with lower VFM in both groups, while an age-by-group interaction suggested a slower rate of VFM decline in the left putamen with aging in PD. Within the PD group, measures of disease severity, including UPDRS, daily levodopa equivalent dose, and disease duration, were observed to be related with myelin content in diffuse brain regions. The age-by-group interaction suggests that either PD or dopaminergic therapies allay observed age-related myelin changes. The relationships between VFM and disease severity measures suggests that VFM may provide a surrogate marker for microstructural changes related to Parkinson's disease.
Systematic differences in functional connectivity MRI metrics have been consistently observed in autism, with predominantly decreased cortico-cortical connectivity. Previous attempts at single ...subject classification in high-functioning autism using whole brain point-to-point functional connectivity have yielded about 80% accurate classification of autism vs. control subjects across a wide age range. We attempted to replicate the method and results using the Autism Brain Imaging Data Exchange (ABIDE) including resting state fMRI data obtained from 964 subjects and 16 separate international sites.
For each of 964 subjects, we obtained pairwise functional connectivity measurements from a lattice of 7266 regions of interest covering the gray matter (26.4 million "connections") after preprocessing that included motion and slice timing correction, coregistration to an anatomic image, normalization to standard space, and voxelwise removal by regression of motion parameters, soft tissue, CSF, and white matter signals. Connections were grouped into multiple bins, and a leave-one-out classifier was evaluated on connections comprising each set of bins. Age, age-squared, gender, handedness, and site were included as covariates for the classifier.
Classification accuracy significantly outperformed chance but was much lower for multisite prediction than for previous single site results. As high as 60% accuracy was obtained for whole brain classification, with the best accuracy from connections involving regions of the default mode network, parahippocampaland fusiform gyri, insula, Wernicke Area, and intraparietal sulcus. The classifier score was related to symptom severity, social function, daily living skills, and verbal IQ. Classification accuracy was significantly higher for sites with longer BOLD imaging times.
Multisite functional connectivity classification of autism outperformed chance using a simple leave-one-out classifier, but exhibited poorer accuracy than for single site results. Attempts to use multisite classifiers will likely require improved classification algorithms, longer BOLD imaging times, and standardized acquisition parameters for possible future clinical utility.
We report a hafnium-containing MOF, hcp UiO-67(Hf), which is a ligand-deficient layered analogue of the face-centered cubic fcu UiO-67(Hf). hcp UiO-67 accommodates its lower ligand:metal ratio ...compared to fcu UiO-67 through a new structural mechanism: the formation of a condensed “double cluster” (Hf12O8(OH)14), analogous to the condensation of coordination polyhedra in oxide frameworks. In oxide frameworks, variable stoichiometry can lead to more complex defect structures, e.g., crystallographic shear planes or modules with differing compositions, which can be the source of further chemical reactivity; likewise, the layered hcp UiO-67 can react further to reversibly form a two-dimensional metal–organic framework, hxl UiO-67. Both three-dimensional hcp UiO-67 and two-dimensional hxl UiO-67 can be delaminated to form metal–organic nanosheets. Delamination of hcp UiO-67 occurs through the cleavage of strong hafnium-carboxylate bonds and is effected under mild conditions, suggesting that defect-ordered MOFs could be a productive route to porous two-dimensional materials.
NODDI is widely used in parameterizing microstructural brain properties. The model includes three signal compartments: intracellular, extracellular, and free water. The neurite compartment intrinsic ...parallel diffusivity (d∥) is set to 1.7 μm2⋅ms-1, though the effects of this assumption have not been extensively explored. This work investigates the optimality of d∥ = 1.7 μm2⋅ms-1 under varying imaging protocol, age groups, sex, and tissue type in comparison to other biologically plausible values of d∥.
Model residuals were used as the optimality criterion. The model residuals were evaluated in function of d∥ over the range from 0.5 to 3.0 μm2⋅ms-1. This was done with respect to tissue type (i.e., white matter versus gray matter), sex, age (infancy to late adulthood), and diffusion-weighting protocol (maximum b-value). Variation in the estimated parameters with respect to d∥ was also explored.
Results show d∥ = 1.7 μm2⋅ms-1 is appropriate for adult brain white matter but it is suboptimal for gray matter with optimal values being significantly lower. d∥ = 1.7 μm2⋅ms-1 was also suboptimal in the infant brain for both white and gray matter with optimal values being significantly lower. Minor optimum d∥ differences were observed versus diffusion protocol. No significant sex effects were observed. Additionally, changes in d∥ resulted in significant changes to the estimated NODDI parameters.
The default (d∥) of 1.7 μm2⋅ms-1 is suboptimal in gray matter and infant brains.
Purpose
To test the performance of the MPnRAGE motion‐correction algorithm on quantitative relaxometry estimates.
Methods
Twelve children (9.4 ± 2.6 years, min = 6.5 years, max = 13.8 years) were ...imaged 3 times in a session without sedation. Stabilization padding was not used for the second and third scans. Quantitative T1 values were estimated in each voxel on images reconstructed with and without motion correction. Mean T1 values were assessed in various regions determined from automated segmentation algorithms. Statistical tests were performed on mean values and the coefficient of variation across the measurements. Accuracy of T1 estimates were determined by scanning the High Precision Devices (Boulder, CO) MRI system phantom with the same protocol.
Results
The T1 values obtained with MPnRAGE agreed within 4% of the reference values of the High Precision Devices phantom. The best fit line was T1(MPnRAGE) = 1.02 T1(reference)—0.9 ms, R2 = 0.9999. For in vivo studies, motion correction reduced the coefficients of variation of mean T1 values in whole‐brain tissue regions determined by FSL FAST by 74% ± 7%, and subcortical regions determined by FIRST and FreeSurfer by 32% ± 21% and 33% ± 26%, respectively. Across all participants, the mean coefficients of variation ranged from 0.8% to 2.0% for subcortical regions and 0.6% ± 0.5% for cortical regions when motion correction was applied.
Conclusion
The MPnRAGE technique demonstrated highly accurate values in phantom measurements. When combined with retrospective motion correction, MPnRAGE demonstrated highly reproducible T1 values, even in participants who moved during the acquisition.
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