Test-retest reliability of structural brain networks from diffusion MRI

Structural brain networks constructed from diffusion MRI (dMRI) and tractography have been demonstrated in healthy volunteers and more recently in various disorders affecting brain connectivity. However, few studies have addressed the reproducibility of the resulting networks. We measured the test–retest properties of such networks by varying several factors affecting network construction using ten healthy volunteers who underwent a dMRI protocol at 1.5 T on two separate occasions. Each T1-weighted brain was parcellated into 84 regions-of-interest and network connections were identified using dMRI and two alternative tractography algorithms, two alternative seeding strategies, a white matter waypoint constraint and three alternative network weightings. In each case, four common graph-theoretic measures were obtained. Network properties were assessed both node-wise and per network in terms of the intraclass correlation coefficient (ICC) and by comparing within- and between-subject differences. Our findings suggest that test–retest performance was improved when: 1) seeding from white matter, rather than grey; and 2) using probabilistic tractography with a two-fibre model and sufficient streamlines, rather than deterministic tensor tractography. In terms of network weighting, a measure of streamline density produced better test–retest performance than tract-averaged diffusion anisotropy, although it remains unclear which is a more accurate representation of the underlying connectivity. For the best performing configuration, the global within-subject differences were between 3.2% and 11.9% with ICCs between 0.62 and 0.76. The mean nodal within-subject differences were between 5.2% and 24.2% with mean ICCs between 0.46 and 0.62. For 83.3% (70/84) of nodes, the within-subject differences were smaller than between-subject differences. Overall, these findings suggest that whilst current techniques produce networks capable of characterising the genuine between-subject differences in connectivity, future work must be undertaken to improve network reliability

Adaptive thresholding for reliable topological inference in single subject fMRI analysis

Single subject fMRI has proved to be a useful tool for mapping functional areas in clinical procedures such as tumour resection. Using fMRI data, clinicians assess the risk, plan and execute such procedures based on thresholded statistical maps. However, because current thresholding methods were developed mainly in the context of cognitive neuroscience group studies, most single subject fMRI maps are thresholded manually to satisfy specific criteria related to single subject analyses. Here, we propose a new adaptive thresholding method which combines Gamma-Gaussian mixture modelling with topological thresholding to improve cluster delineation. In a series of simulations we show that by adapting to the signal and noise properties, the new method performs well in terms of the trade-off between false negative and positive cluster error rates as well as in terms of over and underestimation of the true activation border. We also show through simulations and a motor test-retest study on ten volunteer subjects that adaptive thresholding improves reliability, mainly by accounting for the global signal variance. This in turn increases the likelihood that the true activation pattern can be determined

Mitochondrial cyclophilin D promotes disease tolerance by licensing NK cell development and IL-22 production against influenza virus

Severity of pulmonary viral infections, including influenza A virus (IAV), is linked to excessive immunopathology, which impairs lung function. Thus, the same immune responses that limit viral replication can concomitantly cause lung damage that must be countered by largely uncharacterized disease tolerance mechanisms. Here, we show that mitochondrial cyclophilin D (CypD) protects against IAV via disease tolerance. Cyp

Cluster-based computational methods for mass univariate analyses of event-related brain potentials/fields:A simulation study

Background In recent years, analyses of event related potentials/fields have moved from the selection of a few components and peaks to a mass-univariate approach in which the whole data space is analyzed. Such extensive testing increases the number of false positives and correction for multiple comparisons is needed. Method Here we review all cluster-based correction for multiple comparison methods (cluster-height, cluster-size, cluster-mass, and threshold free cluster enhancement – TFCE), in conjunction with two computational approaches (permutation and bootstrap). Results Data driven Monte-Carlo simulations comparing two conditions within subjects (two sample Student's t-test) showed that, on average, all cluster-based methods using permutation or bootstrap alike control well the family-wise error rate (FWER), with a few caveats. Conclusions (i) A minimum of 800 iterations are necessary to obtain stable results; (ii) below 50 trials, bootstrap methods are too conservative; (iii) for low critical family-wise error rates (e.g. p = 1%), permutations can be too liberal; (iv) TFCE controls best the type 1 error rate with an attenuated extent parameter (i.e. power < 1)

The genetics-BIDS extension: Easing the search for genetic data associated with human brain imaging

Metadata are what makes databases searchable. Without them, researchers would have difficulty finding data with features they are interested in. Brain imaging genetics is at the intersection of two disciplines, each with dedicated dictionaries and ontologies facilitating data search and analysis. Here, we present the genetics Brain Imaging Data Structure extension, consisting of metadata files for human brain imaging data to which they are linked, and describe succinctly the genomic and transcriptomic data associated with them, which may be in different databases. This extension will facilitate identifying micro-scale molecular features that are linked to macro-scale imaging repositories, facilitating data aggregation across studies

M. tuberculosis Reprograms Hematopoietic Stem Cells to Limit Myelopoiesis and Impair Trained Immunity

A greater understanding of hematopoietic stem cell (HSC) regulation is required for dissecting protective versus detrimental immunity to pathogens that cause chronic infections such as Mycobacterium tuberculosis (Mtb). We have shown that systemic administration of Bacille Calmette-Guérin (BCG) or ß-glucan reprograms HSCs in the bone marrow (BM) via a type II interferon (IFN-II) or interleukin-1 (IL1) response, respectively, which confers protective trained immunity against Mtb. Here, we demonstrate that, unlike BCG or ß-glucan, Mtb reprograms HSCs via an IFN-I response that suppresses myelopoiesis and impairs development of protective trained immunity to Mtb. Mechanistically, IFN-I signaling dysregulates iron metabolism, depolarizes mitochondrial membrane potential, and induces cell death specifically in myeloid progenitors. Additionally, activation of the IFN-I/iron axis in HSCs impairs trained immunity to Mtb infection. These results identify an unanticipated immune evasion strategy of Mtb in the BM that controls the magnitude and intrinsic anti-microbial capacity of innate immunity to infection

Human alveolar macrophage metabolism is compromised during Mycobacterium tuberculosis infection

Pulmonary macrophages have two distinct ontogenies: long-lived embryonically-seeded alveolar macrophages (AM) and bone marrow-derived macrophages (BMDM). Here, we show that after infection with a virulent strain of Mycobacterium tuberculosis (H37Rv), primary murine AM exhibit a unique transcriptomic signature characterized by metabolic reprogramming distinct from conventional BMDM. In contrast to BMDM, AM failed to shift from oxidative phosphorylation (OXPHOS) to glycolysis and consequently were unable to control infection with an avirulent strain (H37Ra). Importantly, healthy human AM infected with H37Ra equally demonstrated diminished energetics, recapitulating our observation in the murine model system. However, the results from seahorse showed that the shift towards glycolysis in both AM and BMDM was inhibited by H37Rv. We further demonstrated that pharmacological (e.g. metformin or the iron chelator desferrioxamine) reprogramming of AM towards glycolysis reduced necrosis and enhanced AM capacity to control H37Rv growth. Together, our results indicate that the unique bioenergetics of AM renders these cells a perfect target for Mtb survival and that metabolic reprogramming may be a viable host targeted therapy against TB

Separation and Determination of Fatty Acids from Lipid Fractions by High-Performance Liquid Chromatography: Cholesterol Esters of Umbilical Cord Arteries

Preeclampsia is accompanied by an extensive remodeling of the extracellular matrix of umbilical cord. It is associated with an increase in collagen content in the umbilical cord artery. Furthermore, preeclampsia distinctly reduces proteolytic and gelatinolytic activity, especially after activation with various agents

Testing for the Dual-Route Cascade Reading Model in the Brain: An fMRI Effective Connectivity Account of an Efficient Reading Style

Neuropsychological data about the forms of acquired reading impairment provide a strong basis for the theoretical framework of the dual-route cascade (DRC) model which is predictive of reading performance. However, lesions are often extensive and heterogeneous, thus making it difficult to establish precise functional anatomical correlates. Here, we provide a connective neural account in the aim of accommodating the main principles of the DRC framework and to make predictions on reading skill. We located prominent reading areas using fMRI and applied structural equation modeling to pinpoint distinct neural pathways. Functionality of regions together with neural network dissociations between words and pseudowords corroborate the existing neuroanatomical view on the DRC and provide a novel outlook on the sub-regions involved. In a similar vein, congruent (or incongruent) reliance of pathways, that is reliance on the word (or pseudoword) pathway during word reading and on the pseudoword (or word) pathway during pseudoword reading predicted good (or poor) reading performance as assessed by out-of-magnet reading tests. Finally, inter-individual analysis unraveled an efficient reading style mirroring pathway reliance as a function of the fingerprint of the stimulus to be read, suggesting an optimal pattern of cerebral information trafficking which leads to high reading performance

A systematic review on the use of quantitative imaging to detect cancer therapy adverse effects in normal-appearing brain tissue

Cancer therapy for both central nervous system (CNS) and non-CNS tumors has been previously associated with transient and long-term cognitive deterioration, commonly referred to as ‘chemo fog’. This therapy-related damage to otherwise normal-appearing brain tissue is reported using post-mortem neuropathological analysis. Although the literature on monitoring therapy effects on structural magnetic resonance imaging (MRI) is well established, such macroscopic structural changes appear relatively late and irreversible. Early quantitative MRI biomarkers of therapy-induced damage would potentially permit taking these treatment side effects into account, paving the way towards a more personalized treatment planning. This systematic review (PROSPERO number 224196) provides an overview of quantitative tomographic imaging methods, potentially identifying the adverse side effects of cancer therapy in normal-appearing brain tissue. Seventy studies were obtained from the MEDLINE and Web of Science databases. Studies reporting changes in normal-appearing brain tissue using MRI, PET, or SPECT quantitative biomarkers, related to radio-, chemo-, immuno-, or hormone therapy for any kind of solid, cystic, or liquid tumor were included. The main findings of the reviewed studies were summarized, providing also the risk of bias of each study assessed using a modified QUADAS-2 tool. For each imaging method, this review provides the methodological background, and the benefits and shortcomings of each method from the imaging perspective. Finally, a set of recommendations is proposed to support future research