A Standard Test Case Suite for Two-Dimensional Linear Transport on the Sphere: Results from a Collection of State-of-the-Art Schemes

Recently, a standard test case suite for 2-D linear transport on the sphere was proposed to assess important aspects of accuracy in geophysical fluid dynamics with a minimal set of idealized model configurations/runs/diagnostics. Here we present results from 19 state-of-the-art transport scheme formulations based on finite-difference/finite-volume methods as well as emerging (in the context of atmospheric/oceanographic sciences) Galerkin methods. Discretization grids range from traditional regular latitude–longitude grids to more isotropic domain discretizations such as icosahedral and cubed-sphere tessellations of the sphere. The schemes are evaluated using a wide range of diagnostics in idealized flow environments. Accuracy is assessed in single- and two-tracer configurations using conventional error norms as well as novel diagnostics designed for climate and climate–chemistry applications. In addition, algorithmic considerations that may be important for computational efficiency are reported on. The latter is inevitably computing platform dependent. The ensemble of results from a wide variety of schemes presented here helps shed light on the ability of the test case suite diagnostics and flow settings to discriminate between algorithms and provide insights into accuracy in the context of global atmospheric/ocean modeling. A library of benchmark results is provided to facilitate scheme intercomparison and model development. Simple software and data sets are made available to facilitate the process of model evaluation and scheme intercomparison

Altered Small-World Brain Networks in Temporal Lobe in Patients with Schizophrenia Performing an Auditory Oddball Task

The functional architecture of the human brain has been extensively described in terms of complex networks characterized by efficient small-world features. Recent functional magnetic resonance imaging (fMRI) studies have found altered small-world topological properties of brain functional networks in patients with schizophrenia (SZ) during the resting state. However, little is known about the small-world properties of brain networks in the context of a task. In this study, we investigated the topological properties of human brain functional networks derived from fMRI during an auditory oddball (AOD) task. Data were obtained from 20 healthy controls and 20 SZ; A left and a right task-related network which consisted of the top activated voxels in temporal lobe of each hemisphere were analyzed separately. All voxels were detected by group independent component analysis. Connectivity of the left and right task-related networks were estimated by partial correlation analysis and thresholded to construct a set of undirected graphs. The small-worldness values were decreased in both hemispheres in SZ. In addition, SZ showed longer shortest path length and lower global efficiency only in the left task-related networks. These results suggested small-world attributes are altered during the AOD task-related networks in SZ which provided further evidences for brain dysfunction of connectivity in SZ

Disruption to control network function correlates with altered dynamic connectivity in the wider autism spectrum.

Autism is a common developmental condition with a wide, variable range of co-occurring neuropsychiatric symptoms. Contrasting with most extant studies, we explored whole-brain functional organization at multiple levels simultaneously in a large subject group reflecting autism's clinical diversity, and present the first network-based analysis of transient brain states, or dynamic connectivity, in autism. Disruption to inter-network and inter-system connectivity, rather than within individual networks, predominated. We identified coupling disruption in the anterior-posterior default mode axis, and among specific control networks specialized for task start cues and the maintenance of domain-independent task positive status, specifically between the right fronto-parietal and cingulo-opercular networks and default mode network subsystems. These appear to propagate downstream in autism, with significantly dampened subject oscillations between brain states, and dynamic connectivity configuration differences. Our account proposes specific motifs that may provide candidates for neuroimaging biomarkers within heterogeneous clinical populations in this diverse condition

Components of Cross-Frequency Modulation in Health and Disease

The cognitive deficits associated with schizophrenia are commonly believed to arise from the abnormal temporal integration of information, however a quantitative approach to assess network coordination is lacking. Here, we propose to use cross-frequency modulation (cfM), the dependence of local high-frequency activity on the phase of widespread low-frequency oscillations, as an indicator of network coordination and functional integration. In an exploratory analysis based on pre-existing data, we measured cfM from multi-channel EEG recordings acquired while schizophrenia patients (n = 47) and healthy controls (n = 130) performed an auditory oddball task. Novel application of independent component analysis (ICA) to modulation data delineated components with specific spatial and spectral profiles, the weights of which showed covariation with diagnosis. Global cfM was significantly greater in healthy controls (F1,175 = 9.25, P < 0.005), while modulation at fronto-temporal electrodes was greater in patients (F1,175 = 17.5, P < 0.0001). We further found that the weights of schizophrenia-relevant components were associated with genetic polymorphisms at previously identified risk loci. Global cfM decreased with copies of 957C allele in the gene for the dopamine D2 receptor (r = −0.20, P < 0.01) across all subjects. Additionally, greater “aberrant” fronto-temporal modulation in schizophrenia patients was correlated with several polymorphisms in the gene for the α2-subunit of the GABAA receptor (GABRA2) as well as the total number of risk alleles in GABRA2 (r = 0.45, P < 0.01). Overall, our results indicate great promise for this approach in establishing patterns of cfM in health and disease and elucidating the roles of oscillatory interactions in functional connectivity

Convergent Approaches for Defining Functional Imaging Endophenotypes in Schizophrenia

In complex genetic disorders such as schizophrenia, endophenotypes have potential utility both in identifying risk genes and in illuminating pathophysiology. This is due to their presumed status as closer in the etiopathological pathway to the causative genes than is the currently defining clinical phenomenology of the illness and thus their simpler genetic architecture than that of the full syndrome. There, many genes conferring slight individual risk are additive or epistatic (interactive) with regard to cumulative schizophrenia risk. In addition the use of endophenotypes has encouraged a conceptual shift away from the exclusive study of categorical diagnoses in manifestly ill patients, towards the study of quantitative traits in patients, unaffected relatives and healthy controls. A more recently employed strategy is thus to study unaffected first-degree relatives of schizophrenia patients, who share some of the genetic diathesis without illness-related confounds that may themselves impact fMRI task performance. Consistent with the multiple biological abnormalities associated with the disorder, many candidate endophenotypes have been advanced for schizophrenia, including measures derived from structural brain imaging, EEG, sensorimotor integration, eye movements and cognitive performance (Allen et al., 2009), but recent data derived from quantitative functional brain imaging measures present additional attractive putative endophenotypes. We will review two major, conceptually different approaches that use fMRI in this context. One, the dominant paradigm, employs defined cognitive tasks on which schizophrenia patients perform poorly as “cognitive stress tests”. The second uses very simple probes or “task-free” approaches where performance in patients and controls is equal. We explore the potential advantages and disadvantages of each method, the associated data analytic approaches and recent studies exploring their interface with the genetic risk architecture of schizophrenia