Cortical Amyloid Beta in Cognitively Normal Elderly Adults is Associated with Decreased Network Efficiency within the Cerebro-Cerebellar System

Background: Deposition of cortical amyloid beta (Aβ) is a correlate of aging and a risk factor for Alzheimer disease (AD). While several higher order cognitive processes involve functional interactions between cortex and cerebellum, this study aims to investigate effects of cortical Aβ deposition on coupling within the cerebro-cerebellar system. Methods: We included 15 healthy elderly subjects with normal cognitive performance as assessed by neuropsychological testing. Cortical Aβ was quantified using (11)carbon-labeled Pittsburgh compound B positron-emission-tomography late frame signals. Volumes of brain structures were assessed by applying an automated parcelation algorithm to three dimensional magnetization-prepared rapid gradient-echo T1-weighted images. Basal functional network activity within the cerebro-cerebellar system was assessed using blood-oxygen-level dependent resting state functional magnetic resonance imaging at the high field strength of 7 T for measuring coupling between cerebellar seeds and cerebral gray matter. A bivariate regression approach was applied for identification of brain regions with significant effects of individual cortical Aβ load on coupling. Results: Consistent with earlier reports, a significant degree of positive and negative coupling could be observed between cerebellar seeds and cerebral voxels. Significant positive effects of cortical Aβ load on cerebro-cerebellar coupling resulted for cerebral brain regions located in inferior temporal lobe, prefrontal cortex, hippocampus, parahippocampal gyrus, and thalamus. Conclusion: Our findings indicate that brain amyloidosis in cognitively normal elderly subjects is associated with decreased network efficiency within the cerebro-cerebellar system. While the identified cerebral regions are consistent with established patterns of increased sensitivity for Aβ-associated neurodegeneration, additional studies are needed to elucidate the relationship between dysfunction of the cerebro-cerebellar system and risk for AD

Regional Fluid-Attenuated Inversion Recovery (FLAIR) at 7 Tesla correlates with amyloid beta in hippocampus and brainstem of cognitively normal elderly subjects

Background: Accumulation of amyloid beta (Aβ) may occur during healthy aging and is a risk factor for Alzheimer Disease (AD). While individual Aβ-accumulation can be measured non-invasively using Pittsburgh Compund-B positron emission tomography (PiB-PET), Fluid-attenuated inversion recovery (FLAIR) is a Magnetic Resonance Imaging (MRI) sequence, capable of indicating heterogeneous age-related brain pathologies associated with tissue-edema. In the current study cognitively normal elderly subjects were investigated for regional correlation of PiB- and FLAIR intensity. Methods: Fourteen healthy elderly subjects without known history of cognitive impairment received 11C-PiB-PET for estimation of regional Aβ-load. In addition, whole brain T1-MPRAGE and FLAIR-MRI sequences were acquired at high field strength of 7 Tesla (7T). Volume-normalized intensities of brain regions were assessed by applying an automated subcortical segmentation algorithm for spatial definition of brain structures. Statistical dependence between FLAIR- and PiB-PET intensities was tested using Spearman's rank correlation coefficient (rho), followed by Holm–Bonferroni correction for multiple testing. Results: Neuropsychological testing revealed normal cognitive performance levels in all participants. Mean regional PiB-PET and FLAIR intensities were normally distributed and independent. Significant correlation between volume-normalized PiB-PET signals and FLAIR intensities resulted for Hippocampus (right: rho = 0.86; left: rho = 0.84), Brainstem (rho = 0.85) and left Basal Ganglia vessel region (rho = 0.82). Conclusions: Our finding of a significant relationship between PiB- and FLAIR intensity mainly observable in the Hippocampus and Brainstem, indicates regional Aβ associated tissue-edema in cognitively normal elderly subjects. Further studies including clinical populations are necessary to clarify the relevance of our findings for estimating individual risk for age-related neurodegenerative processes such as AD

Pulse encoding for ZTE imaging: RF excitation without dead-time penalty

Purpose To overcome limitations in the duration of RF excitation in zero-TE (ZTE) MRI by exploiting intrinsic encoding properties of RF pulses to retrieve data missed during the dead time caused by the pulse. Methods An enhanced ZTE signal model was developed using multiple RF pulses, which enables accessing information hidden in the pulse-induced dead time via encoding intrinsically applied by the RF pulses. Such ZTE with pulse encoding was implemented by acquisition of two ZTE data sets using excitation with similar frequency-swept pulses differing only by a small off-resonance in their center frequency. In this way, the minimum scan time is doubled but each acquisition contributes equally to the SNR, as with ordinary averaging. The method was demonstrated on long-T-2 and short-T-2 phantoms as well as in in vivo experiments. Results ZTE with pulse encoding provided good image quality at unprecedented dead-time gaps, demonstrated here up to 6 Nyquist dwells. In head imaging, the ability to use longer excitation pulses led to approximately 2-fold improvements in SNR efficiency as compared with conventional ZTE and allowed the creation of T-1 contrast. Conclusion Exploiting intrinsic encoding properties of RF pulses in a new signal model enables algebraic reconstruction of ZTE data sets with large dead-time gaps. This permits larger flip angles, which can be used to achieve enhanced T-1 contrast and significant improvements in SNR efficiency in case the Ernst angle can be better approached, thus broadening the range of application of ZTE MRI.ISSN:0740-3194ISSN:1522-259

Evaluating diffusion dispersion across an extended range of b-values and frequencies: Exploiting gap-filled OGSE shapes, strong gradients, and spiral readouts

Purpose: To address the long echo times and relatively weak diffusionsensitiza-tion that typically limit oscillating gradient spin- echo (OGSE) experiments, an OGSE implementation combining spiral readouts, gap- filled oscillating gradient shapes providing stronger diffusion encoding, and a high- performance gradient system is developed here and utilized to investigate the tradeoff between b- value and maximum OGSE frequency in measurements of diffusion dispersion (i.e., the frequency dependence of diffusivity) in the in vivo human brain. In addition, to assess the effects of the marginal flow sensitivity introduced by these OGSE waveforms, flow- compensated variants are devised for experimental comparison. Methods: Using DTI sequences, OGSE acquisitions were performed on three volunteers at b- values of 300, 500, and 1000 s/mm2and frequencies up to 125, 100, and 75 Hz, respectively; scans were performed for gap- filled oscillating gradient shapes with and without flow sensitivity. Pulsed gradient spin- echo DTI acquisi-tions were also performed at each b- value. Upon reconstruction, mean diffusivity (MD) maps and maps of the diffusion dispersion rate were computed. Results: The power law diffusion dispersion model was found to fit best to MD measurements acquired at b= 1000 s/mm2despite the associated reduction of the spectral range; this observation was consistent with Monte Carlo simulations. Furthermore, diffusion dispersion rates without flow sensitivity were slightly higher than flow- sensitive measurements. Conclusion: The presented OGSE implementation provided an improved de-piction of diffusion dispersion and demonstrated the advantages of measuring dispersion at higher b- values rather than higher frequencies within the regimes employed in this study.ISSN:0740-3194ISSN:1522-259

Regimes of jetting and bubbling in a fluidized bed studied using real-time magnetic resonance imaging

Real-time magnetic resonance imaging was used to study the different flow regimes which occur in a fluidized bed containing a gas injection system. The gas flow rates through the main distributor and a central orifice were varied independently. We identified six different regimes of bubbling and jetting behavior: (1) freely bubbling, (2) permanent jet, (3) spouting, (4) pulsating jet, (5) pulsating jet with bubble collapse and (6) pulsating jet and freely bubbling. While regimes (1–4) have been described previously in the literature, regimes (5) and (6) are described here for the first time. To construct a regime map, the Froude number (Fr) and the ratio of the superficial gas velocity to the minimum fluidization velocity (U/Umf) were used to describe the system. We observed that bubbles formed predominantly, when U/Umf > 1. Further, we propose an empirical model that predicts the length of jets in the permanent jet regime as a function of Fr and background gas flow as (see equation in the version of the publisher). The proposed model is in good agreement with tomographic measurements in smaller 3D systems reported in the literature, indicating that the non-dimensionalized description of jet length using a Fr number is valid throughout a large range of system diameters. Moreover, the bubble breakoff frequency of the pulsating jet regime was assessed by Fourier analysis, demonstrating that the frequency increases with increasing Fr before plateauing.ISSN:0300-9467ISSN:1385-8947ISSN:1873-3212ISSN:0923-046

Whole-brain estimates of directed connectivity for human connectomics

Connectomics is essential for understanding large-scale brain networks but requires that individual connection estimates are neurobiologically interpretable. In particular, a principle of brain organization is that reciprocal connections between cortical areas are functionally asymmetric. This is a challenge for fMRI-based connectomics in humans where only undirected functional connectivity estimates are routinely available. By contrast, whole-brain estimates of effective (directed) connectivity are computationally challenging, and emerging methods require empirical validation. Here, using a motor task at 7T, we demonstrate that a novel generative model can infer known connectivity features in a whole-brain network (>200 regions, >40,000 connections) highly efficiently. Furthermore, graph-theoretical analyses of directed connectivity estimates identify functional roles of motor areas more accurately than undirected functional connectivity estimates. These results, which can be achieved in an entirely unsupervised manner, demonstrate the feasibility of inferring directed connections in whole-brain networks and open new avenues for human connectomics.ISSN:1053-8119ISSN:1095-957

Magnetic resonance imaging of gas-solid fluidization with liquid bridging

ISSN:1547-5905ISSN:0001-154