Bayesian log-Gaussian Cox process regression: applications to meta-analysis of neuroimaging working memory studies

Working memory (WM) was one of the first cognitive processes studied with functional magnetic resonance imaging. With now over 20 years of studies on WM, each study with tiny sample sizes, there is a need for meta-analysis to identify the brain regions that are consistently activated by WM tasks, and to understand the interstudy variation in those activations. However, current methods in the field cannot fully account for the spatial nature of neuroimaging meta-analysis data or the heterogeneity observed among WM studies. In this work, we propose a fully Bayesian random-effects metaregression model based on log-Gaussian Cox processes, which can be used for meta-analysis of neuroimaging studies. An efficient Markov chain Monte Carlo scheme for posterior simulations is presented which makes use of some recent advances in parallel computing using graphics processing units. Application of the proposed model to a real data set provides valuable insights regarding the function of the WM

Behavior, sensitivity, and power of activation likelihood estimation characterized by massive empirical simulation

Given the increasing number of neuroimaging publications, the automated knowledge extraction on brain-behavior associations by quantitative meta-analyses has become a highly important and rapidly growing field of research. Among several methods to perform coordinate-based neuroimaging meta-analyses, Activation Likelihood Estimation (ALE) has been widely adopted. In this paper, we addressed two pressing questions related to ALE meta-analysis: i) Which thresholding method is most appropriate to perform statistical inference? ii) Which sample size, i.e., number of experiments, is needed to perform robust meta-analyses? We provided quantitative answers to these questions by simulating more than 120,000 meta-analysis datasets using empirical parameters (i.e., number of subjects, number of reported foci, distribution of activation foci) derived from the BrainMap database. This allowed to characterize the behavior of ALE analyses, to derive first power estimates for neuroimaging meta-analyses, and to thus formulate recommendations for future ALE studies. We could show as a first consequence that cluster-level family-wise error (FWE) correction represents the most appropriate method for statistical inference, while voxel-level FWE correction is valid but more conservative. In contrast, uncorrected inference and false-discovery rate correction should be avoided. As a second consequence, researchers should aim to include at least 20 experiments into an ALE meta-analysis to achieve sufficient power for moderate effects. We would like to note, though, that these calculations and recommendations are specific to ALE and may not be extrapolated to other approaches for (neuroimaging) meta-analysis

Influence of Polymorphism on the Electronic Structure of Ga2O3

The search for new wide band gap materials is intensifying to satisfy the need for more advanced and energy efficient power electronic devices. Ga$_2$O$_3$ has emerged as an alternative to SiC and GaN, sparking a renewed interest in its fundamental properties beyond the main $\beta$-phase. Here, three polymorphs of Ga$_2$O$_3$, $\alpha$, $\beta$ and $\varepsilon$, are investigated using X-ray diffraction, X-ray photoelectron and absorption spectroscopy, and ab initio theoretical approaches to gain insights into their structure - electronic structure relationships. Valence and conduction electronic structure as well as semi-core and core states are probed, providing a complete picture of the influence of local coordination environments on the electronic structure. State-of-the-art electronic structure theory, including all-electron density functional theory and many-body perturbation theory, provide detailed understanding of the spectroscopic results. The calculated spectra provide very accurate descriptions of all experimental spectra and additionally illuminate the origin of observed spectral features. This work provides a strong basis for the exploration of the Ga$_2$O$_3$ polymorphs as materials at the heart of future electronic device generations.Comment: Updated manuscript version after peer revie

ENIGMA-Sleep:Challenges, opportunities, and the road map

Neuroimaging and genetics studies have advanced our understanding of the neurobiology of sleep and its disorders. However, individual studies usually have limitations to identifying consistent and reproducible effects, including modest sample sizes, heterogeneous clinical characteristics and varied methodologies. These issues call for a large-scale multi-centre effort in sleep research, in order to increase the number of samples, and harmonize the methods of data collection, preprocessing and analysis using pre-registered well-established protocols. The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium provides a powerful collaborative framework for combining datasets across individual sites. Recently, we have launched the ENIGMA-Sleep working group with the collaboration of several institutes from 15 countries to perform large-scale worldwide neuroimaging and genetics studies for better understanding the neurobiology of impaired sleep quality in population-based healthy individuals, the neural consequences of sleep deprivation, pathophysiology of sleep disorders, as well as neural correlates of sleep disturbances across various neuropsychiatric disorders. In this introductory review, we describe the details of our currently available datasets and our ongoing projects in the ENIGMA-Sleep group, and discuss both the potential challenges and opportunities of a collaborative initiative in sleep medicine

Characterizing Acupuncture Stimuli Using Brain Imaging with fMRI - A Systematic Review and Meta-Analysis of the Literature

Background The mechanisms of action underlying acupuncture, including acupuncture point specificity, are not well understood. In the previous decade, an increasing number of studies have applied fMRI to investigate brain response to acupuncture stimulation. Our aim was to provide a systematic overview of acupuncture fMRI research considering the following aspects: 1) differences between verum and sham acupuncture, 2) differences due to various methods of acupuncture manipulation, 3) differences between patients and healthy volunteers, 4) differences between different acupuncture points. Methodology/Principal Findings We systematically searched English, Chinese, Korean and Japanese databases for literature published from the earliest available up until September 2009, without any language restrictions. We included all studies using fMRI to investigate the effect of acupuncture on the human brain (at least one group that received needle-based acupuncture). 779 papers were identified, 149 met the inclusion criteria for the descriptive analysis, and 34 were eligible for the meta-analyses. From a descriptive perspective, multiple studies reported that acupuncture modulates activity within specific brain areas, including somatosensory cortices, limbic system, basal ganglia, brain stem, and cerebellum. Meta-analyses for verum acupuncture stimuli confirmed brain activity within many of the regions mentioned above. Differences between verum and sham acupuncture were noted in brain response in middle cingulate, while some heterogeneity was noted for other regions depending on how such meta-analyses were performed, such as sensorimotor cortices, limbic regions, and cerebellum. Conclusions Brain response to acupuncture stimuli encompasses a broad network of regions consistent with not just somatosensory, but also affective and cognitive processing. While the results were heterogeneous, from a descriptive perspective most studies suggest that acupuncture can modulate the activity within specific brain areas, and the evidence based on meta-analyses confirmed some of these results. More high quality studies with more transparent methodology are needed to improve the consistency amongst different studies

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Functional connectivity modeling of consistent cortico-striatal degeneration in Huntington's disease

Huntington's disease (HD) is a progressive neurodegenerative disorder characterized by a complex neuropsychiatric phenotype. In a recent meta-analysis we identified core regions of consistent neurodegeneration in premanifest HD in the striatum and middle occipital gyrus (MOG). For early manifest HD convergent evidence of atrophy was most prominent in the striatum, motor cortex (M1) and inferior frontal junction (IFJ). The aim of the present study was to functionally characterize this topography of brain atrophy and to investigate differential connectivity patterns formed by consistent cortico-striatal atrophy regions in HD. Using areas of striatal and cortical atrophy at different disease stages as seeds, we performed task-free resting-state and task-based meta-analytic connectivity modeling (MACM). MACM utilizes the large data source of the BrainMap database and identifies significant areas of above-chance co-activation with the seed-region via the activation-likelihood-estimation approach. In order to delineate functional networks formed by cortical as well as striatal atrophy regions we computed the conjunction between the co-activation profiles of striatal and cortical seeds in the premanifest and manifest stages of HD, respectively. Functional characterization of the seeds was obtained using the behavioral meta-data of BrainMap. Cortico-striatal atrophy seeds of the premanifest stage of HD showed common co-activation with a rather cognitive network including the striatum, anterior insula, lateral prefrontal, premotor, supplementary motor and parietal regions. A similar but more pronounced co-activation pattern, additionally including the medial prefrontal cortex and thalamic nuclei was found with striatal and IFJ seeds at the manifest HD stage. The striatum and M1 were functionally connected mainly to premotor and sensorimotor areas, posterior insula, putamen and thalamus. Behavioral characterization of the seeds confirmed that experiments activating the MOG or IFJ in conjunction with the striatum were associated with cognitive functions, while the network formed by M1 and the striatum was driven by motor-related tasks. Thus, based on morphological changes in HD, we identified functionally distinct cortico-striatal networks resembling a cognitive and motor loop, which may be prone to early disruptions in different stages of the disease and underlie HD-related cognitive and motor symptom profiles. Our findings provide an important link between morphometrically defined seed-regions and corresponding functional circuits highlighting the functional and ensuing clinical relevance of structural damage in HD

Lack of Meta-Analytic Evidence for an Impact of COMT Val158Met Genotype on Brain Activation During Working Memory Tasks

Imaging genetics, that is, the investigation of neuroimaging correlates for genetic variation, have been highly popular over the last decade. Within this framework, schizophrenia risk genes and, in particular, the effect of the Val158Met (rs4680) single nucleotide polymorphism in the catechol-O-methyltransferase (COMT) gene on activations related to working memory have been studied extensively. COMT mediates the degradation of dopamine in the synaptic cleft, and its Val allele is associated with lower working memory performance ( 1). It is assumed that these effects are due to increased enzymatic activity in Val allele carriers leading to decreased prefrontal dopamine concentration, causing changes in regional activity during working memory tasks. However, is there reliable evidence to support this conclusion?To investigate whether there is converging evidence from imaging genetics studies for COMT genotype effects on working memory-related activation, we performed a quantitative coordinate-based meta-analysis using the activation likelihood estimation (ALE) approach ( 2). Relevant studies were retrieved through PubMed and Google Scholar, review articles, and reference tracing. Inclusion criteria are reported in the legend to Table 1. In total, 14 studies published between 2004 and 2014 met inclusion criteria ( Table 1). Together, these studies enrolled a total of 995 subjects (920 healthy subjects and 75 schizophrenia patients). Although most studies tested for linear effects of increasing or decreasing Val alleles, not all publications used this contrast (cf. Table 1). To establish a standardized classification for assessing the convergence of results despite methodical heterogeneity of the original studies, we summarized different contrasts by focusing on the number of Val alleles. In the following, we thus refer to hyper- or hypoactivation associated with a higher number of Val alleles in the original contrast (e.g., Met/Met > Val/Met > Val/Val, but also, e.g., Met allele carriers > Val/Val). Among the included studies, eight studies reported only hyperactivations, whereas three studies reported only hypoactivation, and four studies reported both hyper- and hypoactivation with higher numbers of Val alleles. To determine whether genotype effects showed convergent results regardless of diagnosis, both the healthy controls and the schizophrenia patients were included in a first analysis. We also conducted a subanalysis that included only the healthy controls

Co-activation Probability Estimation (CoPE): An approach for modeling functional co-activation architecture based on neuroimaging coordinates

Recent progress in functional neuroimaging has prompted studies of brain activation during various cognitive tasks. Coordinate-based meta-analysis has been utilized to discover the brain regions that are consistently activated across experiments. However, within-experiment co-activation relationships, which can reflect the underlying functional relationships between different brain regions, have not been widely studied. In particular, voxel-wise co-activation, which may be able to provide a detailed configuration of the co-activation network, still needs to be modeled. To estimate the voxel-wise co-activation pattern and deduce the co-activation network, a Co-activation Probability Estimation (CoPE) method was proposed to model within-experiment activations for the purpose of defining the co-activations. A permutation test was adopted as a significance test. Moreover, the co-activations were automatically separated into local and long-range ones, based on distance. The two types of co-activations describe distinct features: the first reflects convergent activations; the second represents co-activations between different brain regions. The validation of CoPE was based on five simulation tests and one real dataset derived from studies of working memory. Both the simulated and the real data demonstrated that CoPE was not only able to find local convergence but also significant long-range co-activation. In particular, CoPE was able to identify a ‘core’ co-activation network in the working memory dataset. As a data-driven method, the CoPE method can be used to mine underlying co-activation relationships across experiments in future studies