Non-parametric statistical thresholding for sparse magnetoencephalography source reconstructions.

Uncovering brain activity from magnetoencephalography (MEG) data requires solving an ill-posed inverse problem, greatly confounded by noise, interference, and correlated sources. Sparse reconstruction algorithms, such as Champagne, show great promise in that they provide focal brain activations robust to these confounds. In this paper, we address the technical considerations of statistically thresholding brain images obtained from sparse reconstruction algorithms. The source power distribution of sparse algorithms makes this class of algorithms ill-suited to "conventional" techniques. We propose two non-parametric resampling methods hypothesized to be compatible with sparse algorithms. The first adapts the maximal statistic procedure to sparse reconstruction results and the second departs from the maximal statistic, putting forth a less stringent procedure that protects against spurious peaks. Simulated MEG data and three real data sets are utilized to demonstrate the efficacy of the proposed methods. Two sparse algorithms, Champagne and generalized minimum-current estimation (G-MCE), are compared to two non-sparse algorithms, a variant of minimum-norm estimation, sLORETA, and an adaptive beamformer. The results, in general, demonstrate that the already sparse images obtained from Champagne and G-MCE are further thresholded by both proposed statistical thresholding procedures. While non-sparse algorithms are thresholded by the maximal statistic procedure, they are not made sparse. The work presented here is one of the first attempts to address the problem of statistically thresholding sparse reconstructions, and aims to improve upon this already advantageous and powerful class of algorithm

Cognitive Impairments in Schizophrenia as Assessed Through Activation and Connectivity Measures of Magnetoencephalography (MEG) Data

The cognitive dysfunction present in patients with schizophrenia is thought to be driven in part by disorganized connections between higher-order cortical fields. Although studies utilizing electroencephalography (EEG), PET and fMRI have contributed significantly to our understanding of these mechanisms, magnetoencephalography (MEG) possesses great potential to answer long-standing questions linking brain interactions to cognitive operations in the disorder. Many experimental paradigms employed in EEG and fMRI are readily extendible to MEG and have expanded our understanding of the neurophysiological architecture present in schizophrenia. Source reconstruction techniques, such as adaptive spatial filtering, take advantage of the spatial localization abilities of MEG, allowing us to evaluate which specific structures contribute to atypical cognition in schizophrenia. Finally, both bivariate and multivariate functional connectivity metrics of MEG data are useful for understanding how these interactions in the brain are impaired in schizophrenia, and how cognitive and clinical outcomes are affected as a result. We also present here data from our own laboratory that illustrates how some of these novel functional connectivity measures, specifically imaginary coherence (IC), are quite powerful in relating disconnectivity in the brain to characteristic behavioral findings in the disorder

Improving our understanding of metal implant failures: Multiscale chemical imaging of exogenous metals in ex-vivo biological tissues

Biological exposures to micro- and nano-scale exogenous metal particles generated as a consequence of in-service degradation of orthopaedic prosthetics can result in severe adverse tissues reactions. However, individual reactions are highly variable and are not easily predicted, due to in part a lack of understanding of the speciation of the metal-stimuli which dictates cellular interactions and toxicity. Investigating the chemistry of implant derived metallic particles in biological tissue samples is complicated by small feature sizes, low concentrations and often a heterogeneous speciation and distribution. These challenges were addressed by developing a multi-scale two-dimensional X-ray absorption spectroscopic (XAS) mapping approach to discriminate sub-micron changes in particulate chemistry within ex-vivo tissues associated with failed CoCrMo total hip replacements (THRs). As a result, in the context of THRs, we demonstrate much greater variation in Cr chemistry within tissues compared with previous reports. Cr compounds including phosphate, hydroxide, oxide, metal and organic complexes were observed and correlated with Co and Mo distributions. This variability may help explain the lack of agreement between biological responses observed in experimental exposure models and clinical outcomes. The multi-scale 2D XAS mapping approach presents an essential tool in discriminating the chemistry in dilute biological systems where speciation heterogeneity is expected. Significance: Metal implants are routinely used in healthcare but may fail following degradation in the body. Although specific implants can be identified as ‘high-risk’, our analysis of failures is limited by a lack of understanding of the chemistry of implant metals within the peri-prosthetic milieu. A new approach to identify the speciation and variability in speciation at sub-micron resolution, of dilute exogenous metals within biological tissues is reported; applied to understanding the failure of metallic (CoCrMo) total-hip-replacements widely used in orthopedic surgery. Much greater variation in Cr chemistry was observed compared with previous reports and included phosphate, hydroxide, oxide, metal and organic complexes. This variability may explain lack of agreement between biological responses observed in experimental exposure models and clinical outcomes

White matter connectome correlates of auditory over-responsivity: edge density imaging and machine-learning classifiers

Sensory over-responsivity (SOR) commonly involves auditory and/or tactile domains, and can affect children with or without additional neurodevelopmental challenges. In this study, we examined white matter microstructural and connectome correlates of auditory over-responsivity (AOR), analyzing prospectively collected data from 39 boys, aged 8–12 years. In addition to conventional diffusion tensor imaging (DTI) maps – including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD); we used DTI and high-resolution T1 scans to develop connectome Edge Density (ED) maps. The tract-based spatial statistics was used for voxel-wise comparison of diffusion and ED maps. Then, stepwise penalized logistic regression was applied to identify independent variable (s) predicting AOR, as potential imaging biomarker (s) for AOR. Finally, we compared different combinations of machine learning algorithms (i.e., naïve Bayes, random forest, and support vector machine (SVM) and tract-based DTI/connectome metrics for classification of children with AOR. In direct sensory phenotype assessment, 15 (out of 39) boys exhibited AOR (with or without neurodevelopmental concerns). Voxel-wise analysis demonstrates extensive impairment of white matter microstructural integrity in children with AOR on DTI maps – evidenced by lower FA and higher MD and RD; moreover, there was lower connectome ED in anterior-superior corona radiata, genu and body of corpus callosum. In stepwise logistic regression, the average FA of left superior longitudinal fasciculus (SLF) was the single independent variable distinguishing children with AOR (p = 0.007). Subsequently, the left SLF average FA yielded an area under the curve of 0.756 in receiver operating characteristic analysis for prediction of AOR (p = 0.008) as a region-of-interest (ROI)-based imaging biomarker. In comparative study of different combinations of machine-learning models and DTI/ED metrics, random forest algorithms using ED had higher accuracy for AOR classification. Our results demonstrate extensive white matter microstructural impairment in children with AOR, with specifically lower connectomic ED in anterior-superior tracts and associated commissural pathways. Also, average FA of left SLF can be applied as ROI-based imaging biomarker for prediction of SOR. Finally, machine-learning models can provide accurate and objective image-based classifiers for identification of children with AOR based on white matter tracts connectome ED

Refocusing multiple stressor research around the targets and scales of ecological impacts

Ecological communities face a variety of environmental and anthropogenic stressors acting simultaneously. Stressor impacts can combine additively or can interact, causing synergistic or antagonistic effects. Our knowledge of when and how interactions arise is limited, as most models and experiments only consider the effect of a small number of non-interacting stressors at one or few scales of ecological organization. This is concerning because it could lead to significant underestimations or overestimations of threats to biodiversity. Furthermore, stressors have been largely classified by their source rather than by the mechanisms and ecological scales at which they act (the target). Here, we argue, first, that a more nuanced classification of stressors by target and ecological scale can generate valuable new insights and hypotheses about stressor interactions. Second, that the predictability of multiple stressor effects, and consistent patterns in their impacts, can be evaluated by examining the distribution of stressor effects across targets and ecological scales. Third, that a variety of existing mechanistic and statistical modelling tools can play an important role in our framework and advance multiple stressor research

Suppression of mutant Kirsten-RAS (KRASG12D)-driven pancreatic carcinogenesis by dual-specificity MAP kinase phosphatases 5 and 6

The cytoplasmic phosphatase DUSP6 and its nuclear counterpart DUSP5 are negative regulators of RAS/ERK signalling. Here we use deletion of either Dusp5 or Dusp6 to explore the roles of these phosphatases in a murine model of KRASG12D-driven pancreatic cancer. By 56-days, loss of either DUSP5 or DUSP6 causes a significant increase in KRASG12D-driven pancreatic hyperplasia. This is accompanied by increased pancreatic acinar to ductal metaplasia (ADM) and the development of pre-neoplastic pancreatic intraepithelial neoplasia (PanINs). In contrast, by 100-days, pancreatic hyperplasia is reversed with significant atrophy of pancreatic tissue and weight loss observed in animals lacking either DUSP5 or DUSP6. On further ageing, Dusp6−/− mice display accelerated development of metastatic pancreatic ductal adenocarcinoma (PDAC), while in Dusp5−/− animals, although PDAC development is increased this process is attenuated by atrophy of pancreatic acinar tissue and severe weight loss in some animals before cancer could progress. Our data suggest that despite a common target in the ERK MAP kinase, DUSP5 and DUSP6 play partially non-redundant roles in suppressing oncogenic KRASG12D signalling, thus retarding both tumour initiation and progression. Our data suggest that loss of either DUSP5 or DUSP6, as observed in certain human tumours, including the pancreas, could promote carcinogenesis

Ibrutinib Unmasks Critical Role of Bruton Tyrosine Kinase in Primary CNS Lymphoma.

Bruton tyrosine kinase (BTK) links the B-cell antigen receptor (BCR) and Toll-like receptors with NF-κB. The role of BTK in primary central nervous system (CNS) lymphoma (PCNSL) is unknown. We performed a phase I clinical trial with ibrutinib, the first-in-class BTK inhibitor, for patients with relapsed or refractory CNS lymphoma. Clinical responses to ibrutinib occurred in 10 of 13 (77%) patients with PCNSL, including five complete responses. The only PCNSL with complete ibrutinib resistance harbored a mutation within the coiled-coil domain of CARD11, a known ibrutinib resistance mechanism. Incomplete tumor responses were associated with mutations in the B-cell antigen receptor-associated protein CD79B

White Matter Microstructure Associations of Cognitive and Visuomotor Control in Children: A Sensory Processing Perspective

Objective: Recent evidence suggests that co-occurring deficits in cognitive control and visuomotor control are common to many neurodevelopmental disorders. Specifically, children with sensory processing dysfunction (SPD), a condition characterized by sensory hyper/hypo-sensitivity, show varying degrees of overlapping attention and visuomotor challenges. In this study, we assess associations between cognitive and visuomotor control abilities among children with and without SPD. In this same context, we also examined the common and unique diffusion tensor imaging (DTI) tracts that may support the overlap of cognitive control and visuomotor control.Method: We collected cognitive control and visuomotor control behavioral measures as well as DTI data in 37 children with SPD and 25 typically developing controls (TDCs). We constructed regressions to assess for associations between behavioral performance and mean fractional anisotropy (FA) in selected regions of interest (ROIs).Results: We observed an association between behavioral performance on cognitive control and visuomotor control. Further, our findings indicated that FA in the anterior limb of the internal capsule (ALIC), the anterior thalamic radiation (ATR), and the superior longitudinal fasciculus (SLF) are associated with both cognitive control and visuomotor control, while FA in the superior corona radiata (SCR) uniquely correlate with cognitive control performance and FA in the posterior limb of the internal capsule (PLIC) and the cerebral peduncle (CP) tract uniquely correlate with visuomotor control performance.Conclusions: These findings suggest that children who demonstrate lower cognitive control are also more likely to demonstrate lower visuomotor control, and vice-versa, regardless of clinical cohort assignment. The overlapping neural tracts, which correlate with both cognitive and visuomotor control suggest a possible common neural mechanism supporting both control-based processes