Combining Endovascular Aneurysm Sealing with Chimney Grafts - 5 Year Follow-Up after 47 Procedures.

BACKGROUND: To evaluate longer-term results of a cohort treated with primary chimney endovascular aneurysm sealing (ChEVAS) for complex abdominal aortic aneurysms or secondary ChEVAS after failed endovascular aneurysm repair/endovascular aneurysm sealing. METHODS: A single-center study was conducted of 47 consecutive patients (mean age 72 ± 8 years, range 50-91; 38 men) treated with ChEVAS from February 2014 to November 2016 and followed through December 2021. The main outcome measures were all-cause mortality (ACM), aneurysm-related mortality, occurrence of secondary complications and conversion to open surgery. Data are presented as the median (interquartile range [IQR]) and absolute range. RESULTS: 35 patients received a primary ChEVAS (=group I) and 12 patients a secondary ChEVAS (=group II). Technical success was 97% (group I) and 92% (group II); 30-day mortality was 3% and 8%, respectively. The median proximal sealing zone length was 20.5 mm (IQR 16, 24; range 10-48) in group I and 26 mm (IQR 17.5, 30; range 8-45) in group II, respectively. During a median time of follow-up of 62 months (range 0-88), ACM amounted to 60% (group I) and 58% (group II); aneurysm mortality was 29% and 8%, respectively. An endoleak was seen in 57% (group I: 15 type Ia endoleaks, four isolated type Ib, and 1 endoleak type V) and 25% (group II: 1 endoleak type Ia, one type II, and 2 type V), aneurysm growth in 40% and 17%, migration in 40% and 17%, resulting in 20% and 25% conversions in group I and II, respectively. Overall a secondary intervention was performed in 51% (group I) and 25% (group II), respectively. The occurrence of complications did not significantly differ between the 2 groups. Neither the number of chimney grafts, nor the thrombus ratio significantly affected the occurrence of abovementioned complications. CONCLUSIONS: While initially delivering a high technical success rate, ChEVAS fails to provide acceptable longer-term results both in primary and secondary ChEVAS, resulting in high rates of complications, secondary interventions and open conversions.

Covalent Proximity Scanning of a Distal Cysteine to Target PI3Kα.

Covalent protein kinase inhibitors exploit currently noncatalytic cysteines in the adenosine 5'-triphosphate (ATP)-binding site via electrophiles directly appended to a reversible-inhibitor scaffold. Here, we delineate a path to target solvent-exposed cysteines at a distance >10 Å from an ATP-site-directed core module and produce potent covalent phosphoinositide 3-kinase α (PI3Kα) inhibitors. First, reactive warheads are used to reach out to Cys862 on PI3Kα, and second, enones are replaced with druglike warheads while linkers are optimized. The systematic investigation of intrinsic warhead reactivity (kchem), rate of covalent bond formation and proximity (kinact and reaction space volume Vr), and integration of structure data, kinetic and structural modeling, led to the guided identification of high-quality, covalent chemical probes. A novel stochastic approach provided direct access to the calculation of overall reaction rates as a function of kchem, kinact, Ki, and Vr, which was validated with compounds with varied linker lengths. X-ray crystallography, protein mass spectrometry (MS), and NanoBRET assays confirmed covalent bond formation of the acrylamide warhead and Cys862. In rat liver microsomes, compounds 19 and 22 outperformed the rapidly metabolized CNX-1351, the only known PI3Kα irreversible inhibitor. Washout experiments in cancer cell lines with mutated, constitutively activated PI3Kα showed a long-lasting inhibition of PI3Kα. In SKOV3 cells, compounds 19 and 22 revealed PI3Kß-dependent signaling, which was sensitive to TGX221. Compounds 19 and 22 thus qualify as specific chemical probes to explore PI3Kα-selective signaling branches. The proposed approach is generally suited to develop covalent tools targeting distal, unexplored Cys residues in biologically active enzymes.

Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion.

Resting-state functional magnetic resonance imaging (rs-fMRI) offers the opportunity to delineate individual-specific brain networks. A major question is whether individual-specific network topography (i.e., location and spatial arrangement) is behaviorally relevant. Here, we propose a multi-session hierarchical Bayesian model (MS-HBM) for estimating individual-specific cortical networks and investigate whether individual-specific network topography can predict human behavior. The multiple layers of the MS-HBM explicitly differentiate intra-subject (within-subject) from inter-subject (between-subject) network variability. By ignoring intra-subject variability, previous network mappings might confuse intra-subject variability for inter-subject differences. Compared with other approaches, MS-HBM parcellations generalized better to new rs-fMRI and task-fMRI data from the same subjects. More specifically, MS-HBM parcellations estimated from a single rs-fMRI session (10 min) showed comparable generalizability as parcellations estimated by 2 state-of-the-art methods using 5 sessions (50 min). We also showed that behavioral phenotypes across cognition, personality, and emotion could be predicted by individual-specific network topography with modest accuracy, comparable to previous reports predicting phenotypes based on connectivity strength. Network topography estimated by MS-HBM was more effective for behavioral prediction than network size, as well as network topography estimated by other parcellation approaches. Thus, similar to connectivity strength, individual-specific network topography might also serve as a fingerprint of human behavior.

Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI.

A central goal in systems neuroscience is the parcellation of the cerebral cortex into discrete neurobiological "atoms". Resting-state functional magnetic resonance imaging (rs-fMRI) offers the possibility of in vivo human cortical parcellation. Almost all previous parcellations relied on 1 of 2 approaches. The local gradient approach detects abrupt transitions in functional connectivity patterns. These transitions potentially reflect cortical areal boundaries defined by histology or visuotopic fMRI. By contrast, the global similarity approach clusters similar functional connectivity patterns regardless of spatial proximity, resulting in parcels with homogeneous (similar) rs-fMRI signals. Here, we propose a gradient-weighted Markov Random Field (gwMRF) model integrating local gradient and global similarity approaches. Using task-fMRI and rs-fMRI across diverse acquisition protocols, we found gwMRF parcellations to be more homogeneous than 4 previously published parcellations. Furthermore, gwMRF parcellations agreed with the boundaries of certain cortical areas defined using histology and visuotopic fMRI. Some parcels captured subareal (somatotopic and visuotopic) features that likely reflect distinct computational units within known cortical areas. These results suggest that gwMRF parcellations reveal neurobiologically meaningful features of brain organization and are potentially useful for future applications requiring dimensionality reduction of voxel-wise fMRI data. Multiresolution parcellations generated from 1489 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal).

Advanced MRI techniques to improve our understanding of experience-induced neuroplasticity.

Over the last two decades, numerous human MRI studies of neuroplasticity have shown compelling evidence for extensive and rapid experience-induced brain plasticity in vivo. To date, most of these studies have consisted of simply detecting a difference in structural or functional images with little concern for their lack of biological specificity. Recent reviews and public debates have stressed the need for advanced imaging techniques to gain a better understanding of the nature of these differences - characterizing their extent in time and space, their underlying biological and network dynamics. The purpose of this article is to give an overview of advanced imaging techniques for an audience of cognitive neuroscientists that can assist them in the design and interpretation of future MRI studies of neuroplasticity. The review encompasses MRI methods that probe the morphology, microstructure, function, and connectivity of the brain with improved specificity. We underline the possible physiological underpinnings of these techniques and their recent applications within the framework of learning- and experience-induced plasticity in healthy adults. Finally, we discuss the advantages of a multi-modal approach to gain a more nuanced and comprehensive description of the process of learning.

The impact of blood on liver metabolite profiling - a combined metabolomic and proteomic approach.

Metabolomics has entered the well-established omic sciences as it is an indispensable information resource to achieve a global picture of biological systems. The aim of the present study was to estimate the influence of blood removal from mice liver as part of sample preparation for metabolomic and proteomic studies. For this purpose, perfused mice liver tissue (i.e. with blood removed) and unperfused mice liver tissue (i.e. containing blood) were compared by two-dimensional gas chromatography time of flight mass spectrometry (GC × GC-TOFMS) for the metabolomic part, and by liquid chromatography tandem mass spectrometry (LC-MS/MS) for the proteomic part. Our data showed significant differences between the unperfused and perfused liver tissue samples. Furthermore, we also observed an overlap of blood and tissue metabolite profiles in our data, suggesting that the perfusion of liver tissue prior to analysis is beneficial for an accurate metabolic profile of this organ.

Does (mis)communication mitigate the upshot of diversity?

This paper contributes to the literature on how diversity impacts groups by exploring how communication mediates the ability of diverse individuals to work together. To do so we incorporate a communication channel into a representative model of problem-solving by teams of diverse agents that provides the foundations for one of the most widely cited analytical results in the literature on diversity and team performance: the "Diversity Trumps Ability Theorem". We extend the model to account for the fact that communication between agents is a necessary feature of team problem-solving, and we introduce the possibility that this communication occurs with error, and that this error might sometimes be correlated with how different agents are from one another. Accounting for communication does not give us reason to reject the claim associated with the theorem, that functionally diverse teams tend to outperform more homogeneous teams (even when the homogeneous teams are comprised of individuals with more task relevant expertise). However, incorporating communication into our model clarifies the role that four factors play in moderating the extent to which teams capture the benefits of functional diversity: i) the complexity of the problem, ii) the number of available approaches to solving the problem, iii) the ways of encoding or conceptualizing a problem, and iv) institutional characteristics, such as how teams work together. Specifically, we find that whether (and to what extent) teams capture the benefits of functional diversity depends on how these four factors interact with one another. Particularly important is the role institutional dynamics (like search methods) play in moderating or amplifying interpersonal frictions (like miscommunication), and notably we find that institutions that work in one setting can be counterproductive in other settings.

The strength of weak connections in the macaque cortico-cortical network.

Examination of the cortico-cortical network of mammals has unraveled key topological features and their role in the function of the healthy and diseased brain. Recent findings from social and biological networks pinpoint the significant role of weak connections in network coherence and mediation of information from segregated parts of the network. In the current study, inspired by such findings and proposed architectures pertaining to social networks, we examine the structure of weak connections in the macaque cortico-cortical network by employing a tract-tracing dataset. We demonstrate that the cortico-cortical connections as a whole, as well as connections between segregated communities of brain areas, comply with the architecture suggested by the so-called strength-of-weak-ties hypothesis. However, we find that the wiring of these connections is not optimal with respect to the aforementioned architecture. This configuration is not attributable to a trade-off with factors known to constrain brain wiring, i.e., wiring cost and efficiency. Lastly, weak connections, but not strong ones, appear important for network cohesion. Our findings relate a topological property to the strength of cortico-cortical connections, highlight the prominent role of weak connections in the cortico-cortical structural network and pinpoint their potential functional significance. These findings suggest that certain neuroimaging studies, despite methodological challenges, should explicitly take them into account and not treat them as negligible.

Interferon Gamma Counteracts the Angiogenic Switch and Induces Vascular Permeability in Dextran Sulfate Sodium Colitis in Mice.

BACKGROUND: Interferon (IFN)-γ is a central pathogenesis factor in inflammatory bowel disease (IBD) with pleiotropic effects on many different cell types. However, as yet, the immune modulatory functions of IFN-γ in IBD have been predominantly investigated. Based on previous studies showing that IFN-γ acts antiangiogenic in colorectal carcinoma, we investigated the effects of IFN-γ on the vascular system in IBD. METHODS: Colon tissues of patients with IBD and dextran sulfate sodium-induced colitis in mice were subjected to immunohistochemistry, quantitative real-time polymerase chain reactions, and in situ hybridization to quantify cell activation, angiogenesis, and immune responses. Vascular structure and permeability in mice were analyzed by ultramicroscopy and in vivo confocal laser endomicroscopy. RESULTS: We showed a significantly increased blood vessel density in IBD and dextran sulfate sodium colitis. In mice, this was associated with a disorganized blood vessel structure and profound vascular leakage. As compared with genes associated with angiogenesis, genes associated with inflammatory cell activation including IFN-γ were more strongly upregulated in colitis tissues. IFN-γ exerted direct effects on endothelial cells in IBD tissues in vivo, as indicated by the expression of IFN-γ-induced guanylate binding protein 1 (GBP-1). Neutralization of IFN-γ in the acute dextran sulfate sodium colitis model demonstrated that this cytokine exerts endogenous angiostatic activity in IBD and contributes to increased vascular permeability. CONCLUSIONS: The dissection of the pleiotropic activities of IFN-γ in IBD provides new insights to the pathological functions of this cytokine and may be of high relevance for the optimization of combination therapy approaches.

Real-time digital signal processing-based optical coherence tomography and Doppler optical coherence tomography.

We present the development and use of a real-time digital signal processing (DSP)-based optical coherence tomography (OCT) and Doppler OCT system. Images of microstructure and transient fluid-flow profiles are acquired using the DSP architecture for real-time processing of computationally intensive calculations. This acquisition system is readily configurable for a wide range of real-time signal processing and image processing applications in OCT.

Connexel visualization: a software implementation of glyphs and edge-bundling for dense connectivity data using brainGL.

The visualization of brain connectivity becomes progressively more challenging as analytic and computational advances begin to facilitate connexel-wise analyses, which include all connections between pairs of voxels. Drawing full connectivity graphs can result in depictions that, rather than illustrating connectivity patterns in more detail, obfuscate patterns owing to the data density. In an effort to expand the possibilities for visualization, we describe two approaches for presenting connexels: edge-bundling, which clarifies structure by grouping geometrically similar connections; and, connectivity glyphs, which depict a condensed connectivity map at each point on the cortical surface. These approaches can be applied in the native brain space, facilitating interpretation of the relation of connexels to brain anatomy. The tools have been implemented as part of brainGL, an extensive open-source software for the interactive exploration of structural and functional brain data.

A comparative analysis of spectral exponent estimation techniques for 1/f(ß) processes with applications to the analysis of stride interval time series.

BACKGROUND: The time evolution and complex interactions of many nonlinear systems, such as in the human body, result in fractal types of parameter outcomes that exhibit self similarity over long time scales by a power law in the frequency spectrum S(f)=1/f(ß). The scaling exponent ß is thus often interpreted as a "biomarker" of relative health and decline. NEW METHOD: This paper presents a thorough comparative numerical analysis of fractal characterization techniques with specific consideration given to experimentally measured gait stride interval time series. The ideal fractal signals generated in the numerical analysis are constrained under varying lengths and biases indicative of a range of physiologically conceivable fractal signals. This analysis is to complement previous investigations of fractal characteristics in healthy and pathological gait stride interval time series, with which this study is compared. RESULTS: The results of our analysis showed that the averaged wavelet coefficient method consistently yielded the most accurate results. COMPARISON WITH EXISTING METHODS: Class dependent methods proved to be unsuitable for physiological time series. Detrended fluctuation analysis as most prevailing method in the literature exhibited large estimation variances. CONCLUSIONS: The comparative numerical analysis and experimental applications provide a thorough basis for determining an appropriate and robust method for measuring and comparing a physiologically meaningful biomarker, the spectral index ß. In consideration of the constraints of application, we note the significant drawbacks of detrended fluctuation analysis and conclude that the averaged wavelet coefficient method can provide reasonable consistency and accuracy for characterizing these fractal time series.

Serotonergic modulation of intrinsic functional connectivity.

Serotonin functions as an essential neuromodulator that serves a multitude of roles, most prominently balancing mood. Serotonergic challenge has been observed to reduce intrinsic functional connectivity in brain regions implicated in mood regulation. However, the full scope of serotonergic action on functional connectivity in the human brain has not been explored. Here, we show evidence that a single dose of a serotonin reuptake inhibitor dramatically alters functional connectivity throughout the whole brain in healthy subjects (n = 22). Our network-centrality analysis reveals a widespread decrease in connectivity in most cortical and subcortical areas. In the cerebellum and thalamus, however, we find localized increases. These rapid and brain-encompassing connectivity changes linked to acute serotonin transporter blockade suggest a key role for the serotonin transporter in the modulation of the functional macroscale connectome.

Dynamic network participation of functional connectivity hubs assessed by resting-state fMRI.

Network studies of large-scale brain connectivity have demonstrated that highly connected areas, or "hubs," are a key feature of human functional and structural brain organization. We use resting-state functional MRI data and connectivity clustering to identify multi-network hubs and show that while hubs can belong to multiple networks their degree of integration into these different networks varies dynamically over time. The extent of the network variation was related to the connectedness of the hub. In addition, we found that these network dynamics were inversely related to positive self-generated thoughts reported by individuals and were further decreased with older age. Moreover, the left caudate varied its degree of participation between a default mode subnetwork and a limbic network. This variation was predictive of individual differences in the reports of past-related thoughts. These results support an association between ongoing thought processes and network dynamics and offer a new approach to investigate the brain dynamics underlying mental experience.

Early small vessel disease affects frontoparietal and cerebellar hubs in close correlation with clinical symptoms--a resting-state fMRI study.

Cerebral small vessel disease, mainly characterized by white matter lesions and lacunes, has a high clinical impact as it leads to vascular dementia. Recent studies have shown that this disease impairs frontoparietal networks. Here, we apply resting-state magnetic resonance imaging and data-driven whole-brain imaging analysis methods (eigenvector centrality) to investigate changes of the functional connectome in early small vessel disease. We show reduced connectivity in frontoparietal networks, whereas connectivity increases in the cerebellum. These functional changes are closely related to white matter lesions and typical neuropsychological deficits associated with small vessel disease.