MOCCA-SURVEY Database I: Assessing GW kick retention fractions for BH-BH mergers in globular clusters

Anisotropy of gravitational wave (GW) emission results in a net momentum gained by the black hole (BH) merger product, leading to a recoil velocity up to $\sim10^3\text{ km s}^{-1}$, which may kick it out of a globular cluster (GC). We estimate GW kick retention fractions of merger products assuming different models for BH spin magnitude and orientation (MS0 - random, MS1 - spin as a function of mass and metalicity, MS2 - constant value of $0.5$). We check how they depend on BH-BH merger time and properties of the cluster. We analyze the implications of GW kick retention fractions on intermediate massive BH (IMBH) formation by repeated mergers in a GC. We also calculate final spin of the merger product, and investigate how it correlates with effective spin of the binary. We used data from MOCCA (MOnte Carlo Cluster simulAtor) GC simulations to get a realistic sample of BH-BH mergers, assigned each BH spin value according to a studied model, and calculated recoil velocity and final spin based on most recent theoretical formulas. We discovered that for physically motivated models, GW kick retention fractions are about $30\%$ and display small dependence on assumptions about spin, but are much more prone to cluster properties. In particular, we discovered a strong dependence of GW kick retention fractions on cluster density. We also show that GW kick retention fractions are high in final life stages of the cluster, but low at the beginning. Finally, we derive formulas connecting final spin with effective spin for primordial binaries, and with maximal effective spin for dynamical binaries.Comment: 13 pages, 9 figures, accepted for publication in MNRA

Biophysically motivated efficient estimation of the spatially isotropic R*2 component from a single gradient‐recalled echo measurement

Purpose To propose and validate an efficient method, based on a biophysically motivated signal model, for removing the orientation‐dependent part of R*2 using a single gradient‐recalled echo (GRE) measurement. Methods The proposed method utilized a temporal second‐order approximation of the hollow‐cylinder‐fiber model, in which the parameter describing the linear signal decay corresponded to the orientation‐independent part of R*2. The estimated parameters were compared to the classical, mono‐exponential decay model for R*2 in a sample of an ex vivo human optic chiasm (OC). The OC was measured at 16 distinct orientations relative to the external magnetic field using GRE at 7T. To show that the proposed signal model can remove the orientation dependence of R*2, it was compared to the established phenomenological method for separating R*2 into orientation‐dependent and ‐independent parts. Results Using the phenomenological method on the classical signal model, the well‐known separation of R*2 into orientation‐dependent and ‐independent parts was verified. For the proposed model, no significant orientation dependence in the linear signal decay parameter was observed. Conclusions Since the proposed second‐order model features orientation‐dependent and ‐independent components at distinct temporal orders, it can be used to remove the orientation dependence of R*2 using only a single GRE measurement

More then simply iron: Macro- to microscopic cellular iron distribution in the brain determines MR contrast

Myelin and iron are the major source of MR contrast in the brain. Iron dominates R2*, R2 and QSM in the cortex as well as in subcortical areas and contributes to white matter contrast. To exploit this contrast for cortical parcellation, myeloarchitecture mapping, or iron quantification, significant theoretical and experimental efforts were devoted to the understanding of iron-induced contrast. However, the impact of the cellular and subcellular iron distribution is not well understood. Frequently, it is described by a simple linear dependence of the MRI contrast parameters on iron concentration, largely disregarding the inhomogeneous distribution of iron in the brain. A major reason for this simplification is a lack of quantitative knowledge on the cellular iron distribution. Moreover, the interplay between the microscopic iron distribution and diffusion in creating MR contrast in static de-phasing, motional narrowing or intermediate regime is not fully understood. We set out to address this lack in knowledge and modelling by combining state of the art quantitative 7T MRI with cutting-edge quantitative iron and myelin mapping on post mortem brain samples. Quantitative R2*, R2, R1 and QSM maps were obtained for the human cortex, the subcortical and the deep white matter as well as for brain nuclei before and after de-ironing. Laser Ablation Inductively Coupled Plasma Mass Spectroscopic Imaging (LA ICP MSI) yielded quantitative iron maps with a mesoscopic resolution of 60x120μm. Proton Induced X-ray Emission (PIXE) provided quantitative iron maps with a cellular resolution down to 1μm. MSI and PIXE demonstrated the inhomogenous distribution of iron in both grey and white matter at different spatial scales. In grey matter iron rich fibers, and small (1-3μm) micro-, astro- and oligodendroglia contained most of the iron and were sparsely distributed. In superficial and deep white matter, however, oligodendrocytes somas with the sizes of 5±1.5μm (distance between cells of 20±5μm) and iron rich fibers contained most of the iron. In addition, patches of enhanced iron concentration around small vessels with a typical size of 100-200μm contribute to up to 20% of R2* and QSM and their orientation dependence in white matter. A different contrast mechanism prevailed in brain nuclei where densely packed 20μm large iron loaded neurons dominated the MR contrast. These results provide an important basis for understanding the iron induced MR-contrast and its microstructural underpinnings. Based on these measured microscopic iron distributions and a Gaussian diffusion model we are now in the process of simulating the MR contrast mechanisms in different tissue types

Mapping the human lateral geniculate nucleus and its cytoarchitectonic subdivisions using quantitative MRI

International audienc

Cell specific quantitative iron mapping on brain slices by immuno-μPIXE in healthy elderly and Parkinson’s disease

Iron is essential for neurons and glial cells, playing key roles in neurotransmitter synthesis, energy production and myelination. In contrast, high concentrations of free iron can be detrimental and contribute to neurodegeneration, through promotion of oxidative stress. Particularly in Parkinson’s disease (PD) changes in iron concentrations in the substantia nigra (SN) was suggested to play a key role in degeneration of dopaminergic neurons in nigrosome 1. However, the cellular iron pathways and the mechanisms of the pathogenic role of iron in PD are not well understood, mainly due to the lack of quantitative analytical techniques for iron quantification with subcellular resolution. Here, we quantified cellular iron concentrations and subcellular iron distribution in dopaminergic neurons and different types of glial cells in the SN both in brains of PD patients and in non-neurodegenerative control brains (Co). To this end, we combined spatially resolved quantitative element mapping using micro particle induced X-ray emission (μPIXE) with nickel-enhanced immunocytochemical detection of cell type-specific antigens allowing to allocate element-related signals to specific cell types. Distinct patterns of iron accumulation were observed across different cell populations. In the control (Co) SNc, oligodendroglial and astroglial cells hold the highest cellular iron concentration whereas in PD, the iron concentration was increased in most cell types in the substantia nigra except for astroglial cells and ferritin-positive oligodendroglial cells. While iron levels in astroglial cells remain unchanged, ferritin in oligodendroglial cells seems to be depleted by almost half in PD. The highest cellular iron levels in neurons were located in the cytoplasm, which might increase the source of non-chelated Fe3+, implicating a critical increase in the labile iron pool. Indeed, neuromelanin is characterised by a significantly higher loading of iron including most probable the occupancy of low-affinity iron binding sites. Quantitative trace element analysis is essential to characterise iron in oxidative processes in PD. The quantification of iron provides deeper insights into changes of cellular iron levels in PD and may contribute to the research in iron-chelating disease-modifying drugs

Cell specific quantitative iron mapping on brain slices by immuno-µPIXE in healthy elderly and Parkinson’s disease

Iron is essential for neurons and glial cells, playing key roles in neurotransmitter synthesis, energy production and myelination. In contrast, high concentrations of free iron can be detrimental and contribute to neurodegeneration, through promotion of oxidative stress. Particularly in Parkinson's disease (PD) changes in iron concentrations in the substantia nigra (SN) was suggested to play a key role in degeneration of dopaminergic neurons in nigrosome 1. However, the cellular iron pathways and the mechanisms of the pathogenic role of iron in PD are not well understood, mainly due to the lack of quantitative analytical techniques for iron quantification with subcellular resolution. Here, we quantified cellular iron concentrations and subcellular iron distributions in dopaminergic neurons and different types of glial cells in the SN both in brains of PD patients and in non-neurodegenerative control brains (Co). To this end, we combined spatially resolved quantitative element mapping using micro particle induced X-ray emission (mu PIXE) with nickel-enhanced immunocytochemical detection of cell type-specific antigens allowing to allocate element-related signals to specific cell types. Distinct patterns of iron accumulation were observed across different cell populations. In the control (Co) SNc, oligodendroglial and astroglial cells hold the highest cellular iron concentration whereas in PD, the iron concentration was increased in most cell types in the substantia nigra except for astroglial cells and ferritin-positive oligodendroglial cells. While iron levels in astroglial cells remain unchanged, ferritin in oligodendroglial cells seems to be depleted by almost half in PD. The highest cellular iron levels in neurons were located in the cytoplasm, which might increase the source of non-chelated Fe3+, implicating a critical increase in the labile iron pool. Indeed, neuromelanin is characterised by a significantly higher loading of iron including most probable the occupancy of low-affinity iron binding sites. Quantitative trace element analysis is essential to characterise iron in oxidative processes in PD. The quantification of iron provides deeper insights into changes of cellular iron levels in PD and may contribute to the research in iron-chelating disease-modifying drugs

Early neurone loss in Alzheimer’s disease: cortical or subcortical?

Alzheimer’s disease (AD) is a degenerative disorder where the distribution of pathology throughout the brain is not random but follows a predictive pattern used for pathological staging. While the involvement of defined functional systems is fairly well established for more advanced stages, the initial sites of degeneration are still ill defined. The prevailing concept suggests an origin within the transentorhinal and entorhinal cortex (EC) from where pathology spreads to other areas. Still, this concept has been challenged recently suggesting a potential origin of degeneration in nonthalamic subcortical nuclei giving rise to cortical innervation such as locus coeruleus (LC) and nucleus basalis of Meynert (NbM). To contribute to the identification of the early site of degeneration, here, we address the question whether cortical or subcortical degeneration occurs more early and develops more quickly during progression of AD. To this end, we stereologically assesses neurone counts in the NbM, LC and EC layer-II in the same AD patients ranging from preclinical stages to severe dementia. In all three areas, neurone loss becomes detectable already at preclinical stages and is clearly manifest at prodromal AD/MCI. At more advanced AD, cell loss is most pronounced in the NbM > LC > layer-II EC. During early AD, however, the extent of cell loss is fairly balanced between all three areas without clear indications for a preference of one area. We can thus not rule out that there is more than one way of spreading from its site of origin or that degeneration even occurs independently at several sites in parallel

Biophysically motivated efficient estimation of the spatially isotropic R∗2 component from a single gradient-recalled echo measurement

Purpose To propose and validate an efficient method, based on a biophysically motivated signal model, for removing the orientation‐dependent part of R∗2 using a single gradient‐recalled echo (GRE) measurement. Methods The proposed method utilized a temporal second‐order approximation of the hollow‐cylinder‐fiber model, in which the parameter describing the linear signal decay corresponded to the orientation‐independent part of R∗2. The estimated parameters were compared to the classical, mono‐exponential decay model for R∗2 in a sample of an ex vivo human optic chiasm (OC). The OC was measured at 16 distinct orientations relative to the external magnetic field using GRE at 7T. To show that the proposed signal model can remove the orientation dependence of R∗2, it was compared to the established phenomenological method for separating R∗2 into orientation‐dependent and ‐independent parts. Results Using the phenomenological method on the classical signal model, the well‐known separation of R∗2 into orientation‐dependent and ‐independent parts was verified. For the proposed model, no significant orientation dependence in the linear signal decay parameter was observed. Conclusions Since the proposed second‐order model features orientation‐dependent and ‐independent components at distinct temporal orders, it can be used to remove the orientation dependence of R∗2 using only a single GRE measurement