Heritability of head motion during resting state functional MRI in 462 healthy twins

Head motion (HM) is a critical confounding factor in functional MRI. Here we investigate whether HM during resting state functional MRI (RS-fMRI) is influenced by genetic factors in a sample of 462 twins (65% fema≤ 101 MZ (monozygotic) and 130 DZ (dizygotic) twin pairs; mean age: 21 (SD=3.16), range 16-29). Heritability estimates for three HM components-mean translation (MT), maximum translation (MAXT) and mean rotation (MR)-ranged from 37 to 51%. We detected a significant common genetic influence on HM variability, with about two-thirds (genetic correlations range 0.76-1.00) of the variance shared between MR, MT and MAXT. A composite metric (HM-PC1), which aggregated these three, was also moderately heritable (h2=42%). Using a sub-sample (N=35) of the twins we confirmed that mean and maximum translational and rotational motions were consistent "traits" over repeated scans (r=0.53-0.59); reliability was even higher for the composite metric (r=0.66). In addition, phenotypic and cross-trait cross-twin correlations between HM and resting state functional connectivities (RS-FCs) with Brodmann areas (BA) 44 and 45, in which RS-FCs were found to be moderately heritable (BA44: h2-=0.23 (sd=0.041), BA45: h2-=0.26 (sd=0.061)), indicated that HM might not represent a major bias in genetic studies using FCs. Even so, the HM effect on FC was not completely eliminated after regression. HM may be a valuable endophenotype whose relationship with brain disorders remains to be elucidated.

Strong inference in functional neuroimaging

A recurring question for cognitive science is whether functional neuroimaging data can provide evidence for or against psychological theories. As posed, the question reflects an adherence to a popular scientific method known as 'strong inference'. The method entails constructing multiple hypotheses (Hs) and designing experiments so that alternative possible outcomes will refute at least one (i.e., 'falsify' it). In this article, after first delineating some well-documented limitations of strong inference, I provide examples of functional neuroimaging data being used to test Hs from rival modular information-processing models of spoken word production. 'Strong inference' for neuroimaging involves first establishing a systematic mapping of 'processes to processors' for a common modular architecture. Alternate Hs are then constructed from psychological theories that attribute the outcome of manipulating an experimental factor to two or more distinct processing stages within this architecture. Hs are then refutable by a finding of activity differentiated spatially and chronometrically by experimental condition. When employed in this manner, the data offered by functional neuroimaging may be more useful for adjudicating between accounts of processing loci than behavioural measures.

Prefrontal cortex involvement in selective letter generation: A functional magnetic resonance imaging study

Cerebral responses to alternating periods of a control task and a selective letter generation paradigm were investigated with functional Magnetic Resonance Imaging (fMRI). Subjects selectively generated letters from four designated sets of six letters from the English language alphabet, with the instruction that they were not to produce letters in alphabetical order either forward or backward, repeat or alternate letters. Performance during this condition was compared with that of a control condition in which subjects recited the same letters in alphabetical order. Analyses revealed significant and extensive foci of activation in a number of cerebral regions including mid-dorsolateral frontal cortex, inferior frontal gyrus, precuneus, supramarginal gyrus, and cerebellum during the selective letter generation condition. These findings are discussed with respect to recent positron emission tomography (PET) and fMRI studies of verbal working memory and encoding/retrieval in episodic memory.

L-dopa modulates functional connectivity in striatal cognitive and motor networks: A double-blind placebo-controlled study

Functional connectivity (FC) analyses of resting-state fMRI data allow for the mapping of large-scale functional networks, and provide a novel means of examining the impact of dopaminergic challenge. Here, using a double-blind, placebo-controlled design, we examined the effect of L-dopa, a dopamine precursor, on striatal resting-state FC in 19 healthy young adults.Weexamined the FC of 6 striatal regions of interest (ROIs) previously shown to elicit networks known to be associated with motivational, cognitive and motor subdivisions of the caudate and putamen (Di Martino et al., 2008). In addition to replicating the previously demonstrated patterns of striatal FC, we observed robust effects of L-dopa. Specifically, L-dopa increased FC in motor pathways connecting the putamen ROIs with the cerebellum and brainstem. Although L-dopa also increased FC between the inferior ventral striatum and ventrolateral prefrontal cortex, it disrupted ventral striatal and dorsal caudate FC with the default mode network. These alterations in FC are consistent with studies that have demonstrated dopaminergic modulation of cognitive and motor striatal networks in healthy participants. Recent studies have demonstrated altered resting state FC in several conditions believed to be characterized by abnormal dopaminergic neurotransmission. Our findings suggest that the application of similar experimental pharmacological manipulations in such populations may further our understanding of the role of dopaminergic neurotransmission in those conditions.

A new combined surface and volume registration

3D registration of brain MRI data is vital for many medical imaging applications. However, purely intensitybased approaches for inter-subject matching of brain structure are generally inaccurate in cortical regions, due to the highly complex network of sulci and gyri, which vary widely across subjects. Here we combine a surfacebased cortical registration with a 3D fluid one for the first time, enabling precise matching of cortical folds, but allowing large deformations in the enclosed brain volume, which guarantee diffeomorphisms. This greatly improves the matching of anatomy in cortical areas. The cortices are segmented and registered with the software Freesurfer. The deformation field is initially extended to the full 3D brain volume using a 3D harmonic mapping that preserves the matching between cortical surfaces. Finally, these deformation fields are used to initialize a 3D Riemannian fluid registration algorithm, that improves the alignment of subcortical brain regions. We validate this method on an MRI dataset from 92 healthy adult twins. Results are compared to those based on volumetric registration without surface constraints; the resulting mean templates resolve consistent anatomical features both subcortically and at the cortex, suggesting that the approach is well-suited for cross-subject integration of functional and anatomic data.

A functional MRI study of the relationship between naming treatment outcomes and resting state functional connectivity in post-stroke aphasia

Background: The majority of studies investigating the neural mechanisms underlying treatment in people with aphasia have examined task-based brain activity. However, the use of resting-state fMRI may provide another method of examining the brain mechanisms responsible for treatment-induced recovery, and allows for investigation into connectivity within complex functional networks Methods: Eight people with aphasia underwent 12 treatment sessions that aimed to improve object naming. Half the sessions employed a phonologically-based task, and half the sessions employed a semantic-based task, with resting-state fMRI conducted pre- and post-treatment. Brain regions in which the amplitude of low frequency fluctuations (ALFF) correlated with treatment outcomes were used as seeds for functional connectivity (FC) analysis. FC maps were compared from pre- to post-treatment, as well as with a group of 12 healthy older controls Results: Pre-treatment ALFF in the right middle temporal gyrus (MTG) correlated with greater outcomes for the phonological treatment, with a shift to the left MTG and supramarginal gyrus, as well as the right inferior frontal gyrus, post-treatment. When compared to controls, participants with aphasia showed both normalization and up-regulation of connectivity within language networks post-treatment, predominantly in the left hemisphere Conclusions: The results provide preliminary evidence that treatments for naming impairments affect the FC of language networks, and may aid in understanding the neural mechanisms underlying the rehabilitation of language post-stroke.

Towards adaptive management of QoS-aware service compositions - Functional architecture

Service compositions enable users to realize their complex needs as a single request. Despite intensive research, especially in the area of business processes, web services and grids, an open and valid question is still how to manage service compositions in order to satisfy both functional and non-functional requirements as well as adapt to dynamic changes. In this paper we propose an (functional) architecture for adaptive management of QoS-aware service compositions. Comparing to the other existing architectures this one offers two major advantages. Firstly, this architecture supports various execution strategies based on dynamic selection and negotiation of services included in a service composition, contracting based on service level agreements, service enactment with flexible support for exception handling, monitoring of service level objectives, and profiling of execution data. Secondly, the architecture is built on the basis of well know existing standards to communicate and exchange data, which significantly reduces effort to integrate existing solutions and tools from different vendors. A first prototype of this architecture has been implemented within an EU-funded Adaptive Service Grid project. © 2006 Springer-Verlag.

A Web-based system enabling the integration, analysis, and 3D sub-surface visualization of groundwater monitoring data and geological models

The 3D Water Chemistry Atlas is an intuitive, open source, Web-based system that enables the three-dimensional (3D) sub-surface visualization of ground water monitoring data, overlaid on the local geological model (formation and aquifer strata). This paper firstly describes the results of evaluating existing virtual globe technologies, which led to the decision to use the Cesium open source WebGL Virtual Globe and Map Engine as the underlying platform. Next it describes the backend database and search, filtering, browse and analysis tools that were developed to enable users to interactively explore the groundwater monitoring data and interpret it spatially and temporally relative to the local geological formations and aquifers via the Cesium interface. The result is an integrated 3D visualization system that enables environmental managers and regulators to assess groundwater conditions, identify inconsistencies in the data, manage impacts and risks and make more informed decisions about coal seam gas extraction, waste water extraction, and water reuse.

Integration over song classification replicates: song variant analysis in the hihi

Human expert analyses are commonly used in bioacoustic studies and can potentially limit the reproducibility of these results. In this paper, a machine learning method is presented to statistically classify avian vocalizations. Automated approaches were applied to isolate bird songs from long field recordings, assess song similarities, and classify songs into distinct variants. Because no positive controls were available to assess the true classification of variants, multiple replicates of automatic classification of song variants were analyzed to investigate clustering uncertainty. The automatic classifications were more similar to the expert classifications than expected by chance. Application of these methods demonstrated the presence of discrete song variants in an island population of the New Zealand hihi (Notiomystis cincta). The geographic patterns of song variation were then revealed by integrating over classification replicates. Because this automated approach considers variation in song variant classification, it reduces potential human bias and facilitates the reproducibility of the results.

Global business services: Increasing specialization and integration of the world economy as drivers of economic growth

Purpose The purpose of this paper is to explore the contribution of global business services to improved productivity and economic growth of the world economy, which has gone largely unnoticed in service research. Design/methodology/approach The authors draw on macroeconomic data and industry reports, and link them to the non-ownership-concept in service research and theories of the firm. Findings Business services explain a large share of the growth of the global service economy. The fast growth of business services coincides with shifts from domestic production towards global outsourcing of services. A new wave of global business services are traded across borders and have emerged as important drivers of growth in the world’s service sector. Research limitations/implications This paper advances the understanding of non-ownership services in an increasingly global and specialized post-industrial economy. The paper makes a conceptual contribution supported by descriptive data, but without empirical testing. Originality/value The authors integrate the non-ownership concept and three related economic theories of the firm to explain the role of global business services in driving business performance and the international transformation of service economies.

The role of knowledge integration in innovation and capability development

Purpose This paper investigates the interrelationships between knowledge integration (KI), product innovation and capability development to enhance our understanding of how firms can develop capability at the firm level, which in turn enhances their performance. One of the critical underlying mechanisms for capability building identified in the literature is the role of knowledge integration, which operates within product innovation projects and contributes to dynamic capability development. Therefore, the main research question is “how does the integration of knowledge across product innovation projects lead to the development of capability?” Design/methodology/approach We adopted a case-based approach and investigated the case of a successful firm that was able to sustain its performance through a series of product innovation projects. In particular this research focused on the role of KI and firm-level capability development over the course of four projects, during which the firm successfully managed the transformation of its product base and renewal of its competitive advantage. For this purpose an in-depth case study of capability development was undertaken at the Iran Khodro Company (IKCO), the key player in the Iranian auto industry transformation. Originality/value This research revealed that along with changes at each level of product architecture “design knowledge” and “design capability” have been developed at the same level of product architecture, leading to capability development at that level. It can be argued that along the step by step maturation of radical innovation across the four case projects, architectural knowledge and capability have been developed at the case company, resulting in the gradual emergence of a modular product and capability architecture across different levels of product architecture. Such findings basically add to extensive emphasis in the literature on the interrelationship of the concept of modularity with knowledge management and capability development. Practical implications Findings of this study indicate that firms manage their knowledge in accordance with the level of specialization in knowledge and capability. Furthermore, firms design appropriate knowledge integration mechanisms within and among functions in order dynamically align knowledge processes at different levels of the product architecture. Accordingly, the outcomes of this study may guide practitioners in managing their knowledge processes, through dynamically employing knowledge integration modes step-by-step and from the part level to the architectural level of product architecture across a sequence of product innovation projects to encourage learning and radical innovation.

Lateralization of temporal lobe epilepsy based on resting-state functional magnetic resonance imaging and machine learning

Lateralization of temporal lobe epilepsy (TLE) is critical for successful outcome of surgery to relieve seizures. TLE affects brain regions beyond the temporal lobes and has been associated with aberrant brain networks, based on evidence from functional magnetic resonance imaging. We present here a machine learning-based method for determining the laterality of TLE, using features extracted from resting-state functional connectivity of the brain. A comprehensive feature space was constructed to include network properties within local brain regions, between brain regions, and across the whole network. Feature selection was performed based on random forest and a support vector machine was employed to train a linear model to predict the laterality of TLE on unseen patients. A leave-one-patient-out cross validation was carried out on 12 patients and a prediction accuracy of 83% was achieved. The importance of selected features was analyzed to demonstrate the contribution of resting-state connectivity attributes at voxel, region, and network levels to TLE lateralization.

Molecular and functional characterizations of a Kunitz-type serine protease inhibitor FcKuSPI of the shrimp Fenneropenaeus chinensis

Serine proteinase inhibitors play important and diverse roles in biological processes such as coagulation, defense mechanisms, and immune responses. Here, we identified and characterized a Kunitz-type proteinase inhibitor, designated FcKuSPI, of the BPTI/Kunitz family of serine proteinase inhibitors from the hemocyte cDNA library of the shrimp Fenneropenaeus chinensis. The deduced amino acid sequence of FcKuSPI comprises 80 residues with a putative signal peptide of 15 amino acids. The predicted molecular weight of the mature peptide is 7.66 kDa and its predicted isoelectric point is 8.84. FcKuSPI includes a Kunitz domain containing six conserved cysteine residues that are predicted to form three disulfide bonds. FcKuSPI shares 44e53% homology with BPTI/Kunitz family members from other species. FcKuSPI mRNAwas expressed highly in the hemocytes and moderately in muscle in healthy shrimp. Recombinant FcKuSPI protein demonstrated anti-protease activity against trypsin and anticoagulant activity against citrated human plasma in a dose-dependent manner in in vitro assays.

Crowning of dibenzosilole with a naphthalenediimide functional group to prepare an electron acceptor for organic solar cells

A novel, solution-processable non-fullerene electron acceptor 9,9′-(5,5-dioctyl-5H-dibenzo [b,d]silole-3,7-diyl)bis(2,7-dioctyl-4-(octylamino)benzo[lmn][3,8]phenanthroline-1,3,6,8(2H,7H)-tetraone) (B3) based on dibenzosilole and naphthalenediimide building blocks was designed, synthesized, characterized and successfully used in a bulk-heterojunction organic solar cell. B3 displayed excellent solubility, thermal stability and acquired electron energy levels matching with those of archetypal donor polymer poly(3-hexylthiophene). Solution-processable bulk-heterojunction devices afforded 1.16% power conversion efficiency with a high fill factor of 53%. B3 is the first example in the literature using this design principle, where mild donor units at the peripheries of end-capped naphthalenediimide units tune solubility and optical energy levels simultaneously.

Functional immunoglobulin E cross-reactivity between Pas n 1 of Bahia grass pollen and other group 1 grass pollen allergens

Background Grass pollens are major triggers of allergic rhinitis and asthma, but the immunological relationships between pollen allergens of the subtropical Bahia grass, Paspalum notatum, and temperate grasses are unresolved. Objective To assess serum IgE cross-reactivity between subtropical P. notatum and temperate Lolium perenne (Ryegrass) pollen allergens. Methods Serum IgE reactivities of grass pollen-allergic patients with P. notatum, L. perenne and Cynodon dactylon (Bermuda grass) pollen extracts and their respective purified group 1 allergens, Pas n 1, Lol p 1 and Cyn d 1, were compared by immunoblotting, ELISA and basophil activation. Results In a cohort of 51 patients from a temperate region, a high frequency of IgE reactivity with each grass pollen was detected, but reactivity with L. perenne pollen was substantially greater than with P. notatum and C. dactylon pollen. Similarly, serum IgE reactivity with Lol p 1 was greater than with Pas n 1 or Cyn d 1. For seven of eight sera studied in detail, asymmetric serum IgE cross-reactivity was observed; L. perenne pollen inhibited IgE reactivity with P. notatum pollen but not the converse, and IgE reactivity with Pas n 1 was inhibited by Lol p 1 but IgE reactivity with Lol p 1 was not inhibited by Pas n 1 or Cyn d 1. Importantly, P. notatum pollen and Pas n 1 activated basophils in grass pollen-allergic patients from a temperate region, although stimulation was greater by pollen of L. perenne than P. notatum or C. dactylon, and by Lol p 1 than Pas n 1 or Cyn d 1. In contrast, a cohort of 47 patients from a subtropical region showed similar IgE reactivity with P. notatum and L. perenne pollen, and reciprocal cross-inhibition of IgE reactivity between L. perenne and P. notatum. Conclusions Pollen allergens of the subtropical P. notatum, including Pas n 1, show clinically relevant IgE cross-reactivity with pollen allergens of L. perenne but also species-specific IgE reactivity.