Learning induces plasticity in neuronal networks. As neuronal populations contribute to multiple representations, we reasoned plasticity in one representation might influence others. We used human fMRI repetition suppression to show that plasticity induced by learning another individual’s values impacts upon a value representation for oneself in medial prefrontal cortex (mPFC), a plasticity also evident behaviorally in a preference shift. We show this plasticity is driven by a striatal “prediction error,” signaling the discrepancy between the other’s choice and a subject’s own preferences. Thus, our data highlight that mPFC encodes agent-independent representations of subjective value, such that prediction errors simultaneously update multiple agents’ value representations. As the resulting change in representational similarity predicts interindividual differences in the malleability of subjective preferences, our findings shed mechanistic light on complex human processes such as the powerful influence of social interaction on beliefs and preferences.
•Learning the values of another causes plasticity in a mPFC value representation•This plasticity predicts how much subjects’ own preferences change•Plasticity is explained by a striatal surprise signal•Value coding in mPFC occurs independently of the agent for whom a decision is made
Garvert et al. demonstrate that learning the preferences of another person increases the similarity between neural value representations for self and other. This plasticity in medial prefrontal cortex predicts how much one’s own preferences shift toward those of the other.
This paper considers goal-directed decision-making in terms of embodied or active inference. We associate bounded rationality with approximate Bayesian inference that optimizes a free energy bound on model evidence. Several constructs such as expected utility, exploration or novelty bonuses, softmax choice rules and optimism bias emerge as natural consequences of free energy minimization. Previous accounts of active inference have focused on predictive coding. In this paper, we consider variational Bayes as a scheme that the brain might use for approximate Bayesian inference. This scheme provides formal constraints on the computational anatomy of inference and action, which appear to be remarkably consistent with neuroanatomy. Active inference contextualizes optimal decision theory within embodied inference, where goals become prior beliefs. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (associated with softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution. Crucially, this sensitivity corresponds to the precision of beliefs about behaviour. The changes in precision during variational updates are remarkably reminiscent of empirical dopaminergic responses—and they may provide a new perspective on the role of dopamine in assimilating reward prediction errors to optimize decision-making.
active inference; agency; Bayesian inference; bounded rationality; free energy; utility theory
The Human Connectome Project (HCP) is a collaborative 5-year effort to map human brain connections and their variability in healthy adults. A consortium of HCP investigators will study a population of 1200 healthy adults using multiple imaging modalities, along with extensive behavioral and genetic data. In this overview, we focus on diffusion MRI (dMRI) and the structural connectivity aspect of the project. We present recent advances in acquisition and processing that allow us to obtain very high-quality in-vivo MRI data, while enabling scanning of a very large number of subjects. These advances result from 2 years of intensive efforts in optimising many aspects of data acquisition and processing during the piloting phase of the project. The data quality and methods described here are representative of the datasets and processing pipelines that will be made freely available to the community at quarterly intervals, beginning in 2013.
The human connectome project (HCP) relies primarily on three complementary magnetic resonance (MR) methods. These are: 1) resting state functional MR imaging (rfMRI) which uses correlations in the temporal fluctuations in an fMRI time series to deduce ‘functional connectivity’; 2) diffusion imaging (dMRI), which provides the input for tractography algorithms used for the reconstruction of the complex axonal fiber architecture; and 3) task based fMRI (tfMRI), which is employed to identify functional parcellation in the human brain in order to assist analyses of data obtained with the first two methods. We describe technical improvements and optimization of these methods as well as instrumental choices that impact speed of acquisition of fMRI and dMRI images at 3 Tesla, leading to whole brain coverage with 2 mm isotropic resolution in 0.7 second for fMRI, and 1.25 mm isotropic resolution dMRI data for tractography analysis with three-fold reduction in total data acquisition time. Ongoing technical developments and optimization for acquisition of similar data at 7 Tesla magnetic field are also presented, targeting higher resolution, specificity of functional imaging signals, mitigation of the inhomogeneous radio frequency (RF) fields and power deposition. Results demonstrate that overall, these approaches represent a significant advance in MR imaging of the human brain to investigate brain function and structure.
The Human Connectome Project consortium led by Washington University, University of Minnesota, and Oxford University is undertaking a systematic effort to map macroscopic human brain circuits and their relationship to behavior in a large population of healthy adults. This overview article focuses on progress made during the first half of the 5-year project in refining the methods for data acquisition and analysis. Preliminary analyses based on a finalized set of acquisition and preprocessing protocols demonstrate the exceptionally high quality of the data from each modality. The first quarterly release of imaging and behavioral data via the ConnectomeDB database demonstrates the commitment to making HCP datasets freely accessible. Altogether, the progress to date provides grounds for optimism that the HCP datasets and associated methods and software will become increasingly valuable resources for characterizing human brain connectivity and function, their relationship to behavior, and their heritability and genetic underpinnings.
A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological datasets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA)1. Here, we performed a genome-wide association study (GWAS) meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating ~10 million single nucleotide polymorphisms (SNPs). We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 1012–4. We devised an in-silico pipeline using established bioinformatics methods based on functional annotation5, cis-acting expression quantitative trait loci (cis-eQTL)6, and pathway analyses7–9 – as well as novel methods based on genetic overlap with human primary immunodeficiency (PID), hematological cancer somatic mutations and knock-out mouse phenotypes – to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery.
We propose a novel computational strategy to partition the cerebral cortex into disjoint, spatially contiguous and functionally homogeneous parcels. The approach exploits spatial dependency in the fluctuations observed with functional Magnetic Resonance Imaging (fMRI) during rest. Single subject parcellations are derived in a two stage procedure in which a set of (~1000 to 5000) stable seeds is grown into an initial detailed parcellation. This parcellation is then further clustered using a hierarchical approach that enforces spatial contiguity of the parcels.
A major challenge is the objective evaluation and comparison of different parcellation strategies; here, we use a range of different measures. Our single subject approach allows a subject-specific parcellation of the cortex, which shows high scan-to-scan reproducibility and whose borders delineate clear changes in functional connectivity. Another important measure, on which our approach performs well, is the overlap of parcels with task fMRI derived clusters. Connectivity-derived parcellation borders are less well matched to borders derived from cortical myelination and from cytoarchitectonic atlases, but this may reflect inherent differences in the data.
Resting state fMRI; Cortical parcellation; Connectomics
Although the ventromedial prefrontal cortex (vmPFC) has long been implicated in reward-guided decision making, its exact role in this process has remained an unresolved issue. Here, we show that vmPFC levels of GABA and glutamate in human volunteers are predictive of both behavioural performance and the dynamics of a neural value comparison signal in a manner as predicted by models of decision-making. These data provide evidence for a neural competition mechanism in vmPFC supporting value-guided choice.
Numerous observations implicate interferon-α (IFNα) in the pathophysiology of systemic lupus erythematosus (SLE); however, the potential impact of endogenous anti-IFNα autoantibodies (AIAAs) on IFN-pathway and disease activity is unclear. The aim of this study was to characterize IFN-pathway activity and the serologic and clinical profiles of AIAA-positive patients with SLE.
Sera obtained from patients with SLE (n = 49), patients with rheumatoid arthritis (n = 25), and healthy control subjects (n = 25) were examined for the presence of AIAAs, using a biosensor immunoassay. Serum type I IFN bioactivity and the ability of AIAA-positive sera to neutralize IFNα activity were determined using U937 cells. Levels of IFN-regulated gene expression in peripheral blood were determined by microarray, and serum levels of BAFF, IFN-inducible chemokines, and other autoantibodies were measured using immunoassays.
AIAAs were detected in 27% of the serum samples from patients with SLE, using a biosensor immunoassay. Unsupervised hierarchical clustering analysis identified 2 subgroups of patients, IFNlow and IFNhigh, that differed in the levels of serum type I IFN bioactivity, IFN-regulated gene expression, BAFF, anti-ribosomal P, and anti-chromatin autoantibodies, and in AIAA status. The majority of AIAA-positive patients had significantly lower levels of serum type I IFN bioactivity, reduced downstream IFN-pathway activity, and lower disease activity compared with the IFNhigh patients. AIAA-positive sera were able to effectively neutralize type I IFN activity in vitro.
Patients with SLE commonly harbor AIAAs. AIAA-positive patients have lower levels of serum type I IFN bioactivity and evidence for reduced downstream IFN-pathway and disease activity. AIAAs may influence the clinical course in SLE by blunting the effects produced by IFNα.
Prior experience plays a critical role in decision making. It enables explicit representation of potential outcomes and provides training to valuation mechanisms. However, we can also make choices in the absence of prior experience, by merely imagining the consequences of a new experience. Here, using fMRI repetition suppression in humans, we show how neuronal representations of novel rewards can be constructed and evaluated. A likely novel experience is constructed by invoking multiple independent memories within hippocampus and medial prefrontal cortex. This construction persists for only a short time period, during which new associations are observed between the memories for component items. Together these findings suggest that in the absence of direct experience, co-activation of multiple relevant memories can provide a training signal to the valuation system which allows the consequences of new experiences to be imagined and acted upon.
Humans and monkeys can learn to classify perceptual information in a statistically optimal fashion if the functional groupings remain stable over many hundreds of trials, but little is known about categorisation when the environment changes rapidly. Here, we used a combination of computational modelling and functional neuroimaging to understand how humans classify visual stimuli drawn from categories whose mean and variance jumped unpredictably. Models based on optimal learning (Bayesian model) and a cognitive strategy (working memory model) both explained unique variance in choice, reaction time and brain activity. However, the working memory model was the best predictor of performance in volatile environments, whereas statistically optimal performance emerged in periods of relative stability. Bayesian and working memory models predicted decision-related activity in distinct regions of the prefrontal cortex and midbrain. These findings suggest that perceptual category judgments, like value-guided choices, may be guided by multiple controllers.
Decision-making; Categorization; fMRI; Computational modelling
Central to macro-connectomics and much of systems neuroscience is the idea that we can summarise macroscopic brain connectivity using a network of “nodes” and “edges” – functionally distinct brain regions and the connections between them. This is an approach that allows a deep understanding of brain dynamics and how they relate to brain circuitry. This approach, however, ignores key features of anatomical connections, such as spatial arrangement and topographic mappings. In this article, we suggest an alternative to this paradigm. We propose that connection topographies can inform us about brain networks in ways that are complementary to the concepts of “nodes” and “edges”. We also show that current neuroimaging technology is capable of revealing details of connection topographies in vivo. These advances, we hope, will allow us to explore brain connectivity in novel ways in the immediate future.
Marginal Zone (MZ) B cells play an important role in the clearance of blood-borne bacterial infections via rapid T-independent IgM responses. We have previously demonstrated that MZ B cells respond rapidly and robustly to bacterial particulates. To determine the MZ-specific genes that are expressed to allow for this response, MZ and Follicular (FO) B cells were sort-purified and analyzed via DNA microarray analysis. We identified 181 genes that were significantly different between the two B cell populations. 99 genes were more highly expressed in MZ B cells while 82 genes were more highly expressed in FO B cells. To further understand the molecular mechanisms by which MZ B cells respond so rapidly to bacterial challenge, idiotype positive and negative MZ B cells were sort-purified before (0 hour) or after (1 hour) i.v. immunization with heat killed Streptococcus pneumoniae, R36A, and analyzed via DNA microarray analysis. We identified genes specifically up regulated or down regulated at 1 hour following immunization in the idiotype positive MZ B cells. These results give insight into the gene expression pattern in resting MZ vs. FO B cells and the specific regulation of gene expression in antigen-specific MZ B cells following interaction with antigen.
MZ B cell; FO B cell; microarray; cytokine; idiotype
To provide an effective substrate for cognitive processes, functional brain networks should be able to reorganize and coordinate on a sub-second temporal scale. We used magnetoencephalography recordings of spontaneous activity to characterize whole-brain functional connectivity dynamics at high temporal resolution. Using a novel approach that identifies the points in time at which unique patterns of activity recur, we reveal transient (100–200 ms) brain states with spatial topographies similar to those of well-known resting state networks. By assessing temporal changes in the occurrence of these states, we demonstrate that within-network functional connectivity is underpinned by coordinated neuronal dynamics that fluctuate much more rapidly than has previously been shown. We further evaluate cross-network interactions, and show that anticorrelation between the default mode network and parietal regions of the dorsal attention network is consistent with an inability of the system to transition directly between two transient brain states.
When subjects lie motionless inside scanners without any particular task to perform, their brains show stereotyped patterns of activity across regions known as resting state networks. Each network consists of areas with a common function, such as the ‘motor’ network or the ‘visual’ network. The role of resting state networks is unclear, but these spontaneous activity patterns are altered in disorders including autism, schizophrenia, and Alzheimer’s disease.
One puzzling feature of resting state networks is that they seem to last for relatively long times. However, the majority of studies into resting state networks have used fMRI brain scans, in which changes in the level of oxygen in the blood are used as a proxy for the activity of a given brain region. Since changes in blood oxygen occur relatively slowly, the ability of fMRI to detect rapid changes in activity is limited: it is thus possible that the long-lived nature of resting state networks is an artefact of the use of fMRI.
Now, Baker et al. have used a different type of brain scan known as an MEG scan to show that the activity of resting state networks is shorter lived than previously thought. MEG scanners measure changes in the magnetic fields generated by electrical currents in the brain, which means that they can detect alterations in brain activity much more rapidly than fMRI.
MEG recordings from the brains of nine healthy subjects revealed that individual resting state networks were typically stable for only 100 ms to 200 ms. Moreover, transitions between different networks did not occur randomly; instead, certain networks were much more likely to become active after others. The work of Baker et al. suggests that the resting brain is constantly changing between different patterns of activity, which enables it to respond quickly to any given situation.
magnetoencephalography; resting state; connectivity; non-stationary; hidden Markov model; microstates; human
Previous work has demonstrated that northern and southern European ancestries are associated with specific systemic lupus erythematosus (SLE) manifestations. Here, 1855 SLE cases of European descent were genotyped for 4965 single nucleotide polymorphisms and principal components analysis of genotype information was used to define population substructure. The first principal component (PC1) distinguished northern from southern European ancestry, PC2 differentiated eastern from western European ancestry, and PC3 delineated Ashkenazi Jewish ancestry. Compared to northern European ancestry, southern European ancestry was associated with autoantibody production (OR=1.40, 95% CI 1.07-1.83) and renal involvement (OR 1.41, 95% CI 1.06-1.87), and was protective for discoid rash (OR=0.51, 95% CI 0.32-0.82) and photosensitivity (OR=0.74, 95% CI 0.56-0.97). Both serositis (OR=1.46, 95% CI 1.12-1.89) and autoantibody production (OR=1.38, 95% CI 1.06-1.80) were associated with Western compared to Eastern European ancestry. Ashkenazi Jewish ancestry was protective against neurologic manifestations of SLE (OR=0.62, 95% CI 0.40-0.94). Homogeneous clusters of cases defined by multiple PCs demonstrated stronger phenotypic associations. Genetic ancestry may contribute to the development of SLE endophenotypes and should be accounted for in genetic studies of disease characteristics.
Systemic lupus erythematosus; epidemiology; population substructure; genetics
To investigate whether the two subtypes of advanced age-related macular degeneration (AMD), choroidal neovascularization (CNV) and geographic atrophy (GA), segregate separately in families and to identify which genetic variants are associated with these two subtypes.
Sibling correlation study and genome-wide association study (GWAS)
For the sibling correlation study, we included 209 sibling pairs with advanced AMD. For the GWAS, we included 2594 participants with advanced AMD subtypes and 4134 controls. Replication cohorts included 5383 advanced AMD participants and 15,240 controls.
Participants had AMD grade assigned based on fundus photography and/or examination. To determine heritability of advanced AMD subtypes, we performed a sibling correlation study. For the GWAS, we conducted genome-wide genotyping and imputed 6,036,699 single nucleotide polymorphism (SNPs). We then analyzed SNPs with a generalized linear model controlling for genotyping platform and genetic ancestry. The most significant associations were evaluated in independent cohorts.
Main Outcome Measures
Concordance of advanced AMD subtypes in sibling pairs and associations between SNPs with GA and CNV advanced AMD subtypes.
The difference between the observed and expected proportion of siblings concordant for the same subtype of advanced AMD was different to a statistically significant degree (P=4.2 x 10−5) meaning that siblings of probands with CNV or GA are more likely to develop CNV or GA, respectively. In the analysis comparing participants with CNV to those with GA, we observed a statistically significant association at the ARMS2/HTRA1 locus [rs10490924, odds ratio (OR)=1.47, P=4.3 ×10−9] which was confirmed in the replication samples (OR=1.38, P=7.4 x 10−14 for combined discovery and replication analysis).
Whether a patient with AMD develops CNV vs. GA is determined in part by genetic variation. In this large GWAS meta-analysis and replication analysis, the ARMS2/HTRA1 locus confers increased risk for both advanced AMD subtypes but imparts greater risk for CNV than for GA. This locus explains a small proportion of the excess sibling correlation for advanced AMD subtype. Other loci were detected with suggestive associations which differ for advanced AMD subtypes and deserve follow-up in additional studies.
The TNFAIP3 (tumor necrosis factor alpha–induced protein 3) gene encodes a ubiquitin editing enzyme, A20, that restricts NF-κB–dependent signaling and prevents inflammation. We show that three independent SNPs in the TNFAIP3 region (rs13192841, rs2230926 and rs6922466) are associated with systemic lupus erythematosus (SLE) among individuals of European ancestry. These findings provide critical links between A20 and the etiology of SLE.
African Americans, East Asians, and Hispanics with systemic lupus erythematosus (SLE) are more likely to develop renal disease than SLE patients of European descent. We investigated whether European genetic ancestry protects against the development of lupus nephritis and explored genetic and socioeconomic factors that might explain this effect.
This was a cross-sectional study of 1906 adults with SLE. Participants were genotyped for 126 single nucleotide polymorphisms (SNPs) informative for ancestry. A subset of participants was also genotyped for 80 SNPs in 14 candidate genes for renal disease in SLE. We used logistic regression to test the association between European ancestry and renal disease. Analyses adjusted for continental ancestries, socioeconomic status, and candidate genes.
Participants (n=1906) had on average 62.4% European, 15.8% African, 11.5% East Asian, 6.5% Amerindian, and 3.8% South Asian ancestry. Among participants, 34% (n=656) had renal disease. A 10% increase in European ancestry was associated with a 15% reduction in the odds of having renal disease after adjustment for disease duration and sex (OR 0.85, 95% CI 0.82-0.87, p=1.9 × 10−30). Adjusting for other genetic ancestries, measures of socioeconomic status, or SNPs in genes most associated with renal disease (IRF5 (rs4728142), BLK (rs2736340), STAT4 (rs3024912), ITGAM (rs9937837) and HLA-DRB1*0301 and DRB1*1501, p<0.05) did not substantively alter this relationship.
European ancestry is protective against the development of renal disease in SLE, an effect independent of other genetic ancestries, common risk alleles, and socioeconomic status.
In this article, we highlight an issue that arises when using multiple b-values in a model-based analysis of diffusion MR data for tractography. The non-mono-exponential decay, commonly observed in experimental data, is shown to induce over-fitting in the distribution of fibre orientations when not considered in the model. Extra fibre orientations perpendicular to the main orientation arise to compensate for the slower apparent signal decay at higher b-values. We propose a simple extension to the ball and stick model based on a continuous Gamma distribution of diffusivities, which significantly improves the fitting and reduces the over-fitting. Using in-vivo experimental data, we show that this model outperforms a simpler, noise floor model, especially at the interfaces between brain tissues, suggesting that partial volume effects are a major cause of the observed non-mono-exponential decay. This model may be helpful for future data acquisition strategies that may attempt to combine multiple shells to improve estimates of fibre orientations in white matter and near the cortex.
Using computational modelling and neuroimaging, two distinct brain systems are shown to use distinct algorithms to make parallel predictions about the environment. The predictions are then optimally combined to control behavior.
A computational approach to functional specialization suggests that brain systems can be characterized in terms of the types of computations they perform, rather than their sensory or behavioral domains. We contrasted the neural systems associated with two computationally distinct forms of predictive model: a reinforcement-learning model of the environment obtained through experience with discrete events, and continuous dynamic forward modeling. By manipulating the precision with which each type of prediction could be used, we caused participants to shift computational strategies within a single spatial prediction task. Hence (using fMRI) we showed that activity in two brain systems (typically associated with reward learning and motor control) could be dissociated in terms of the forms of computations that were performed there, even when both systems were used to make parallel predictions of the same event. A region in parietal cortex, which was sensitive to the divergence between the predictions of the models and anatomically connected to both computational networks, is proposed to mediate integration of the two predictive modes to produce a single behavioral output.
To interact effectively with the environment, brains must predict future events based on past and current experience. Predictions associated with different behavioural domains of the brain are often associated with different algorithmic forms. For example, whereas the motor system makes dynamic moment-by-moment predictions based on physical world models, the reward system is more typically associated with statistical predictions learned over discrete events. However, in perceptually rich natural environments, behaviour is not neatly segmented into tasks like “reward learning” and “motor control.” Instead, many different types of information are available in parallel. The brain must both select behaviourally relevant information and arbitrate between conflicting predictions. To investigate how the brain balances and integrates different types of predictive information, we set up a task in which humans predicted an object's flight trajectory by using one of two strategies: either a statistical model (based on where objects had often landed in the past) or dynamic calculation of the current flight trajectory. Using fMRI, we show that brain activity switches between different regions of the brain, depending on which predictive strategy was used, even though behavioural output remained the same. Furthermore, we found that brain regions involved in selecting actions took into account the predictions from both competing algorithms, weighting each algorithm optimally in terms of the precision with which it could predict the event of interest. Thus, these distinct brain systems compete to control predictive behaviour.
A central question in cognitive neuroscience regards the means by which options are compared and decisions are resolved during value-guided choice. It is clear that several component processes are needed; these include identifying options, a value-based comparison, and implementation of actions to execute the decision. What is less clear is the temporal precedence and functional organisation of these component processes in the brain. Competing models of decision making have proposed that value comparison may occur in the space of alternative actions, or in the space of abstract goods. We hypothesized that the signals observed might in fact depend upon the framing of the decision. We recorded magnetoencephalographic data from humans performing value-guided choices in which two closely related trial types were interleaved. In the first trial type, each option was revealed separately, potentially causing subjects to estimate each action's value as it was revealed and perform comparison in action-space. In the second trial type, both options were presented simultaneously, potentially leading to comparison in abstract goods-space prior to commitment to a specific action. Distinct activity patterns (in distinct brain regions) on the two trial types demonstrated that the observed frame of reference used for decision making indeed differed, despite the information presented being formally identical, between the two trial types. This provides a potential reconciliation of conflicting accounts of value-guided choice.
There are several competing theories of how the primate brain supports the ability to choose between different opportunities to obtain rewards – such as food, shelter, or more abstract goods (e.g. money). These theories suggest that the comparison of different options is either fundamentally dependent upon regions in prefrontal cortex (in which representations of abstract goods are often found), or upon motoric areas such as pre-motor and motor cortices (in which representations of specific actions are found). Evidence has been provided in support of both theories, derived largely from studies using different behavioural tasks. In this study, we show that a subtle manipulation in the behavioural task can have profound consequences for which brain regions appear to support value comparison. We recorded whole-brain magnetoencephalography data whilst subjects performed a decision task. Value comparison-related 13–30 Hz oscillations were found in ‘goods space’ in ventromedial prefrontal cortex in one trial type, but in ‘action space’ in pre-motor and primary motor cortices in another trial type - despite information presented being identical across trial types. This suggests both decision mechanisms are available in the brain, and that the brain adopts the most appropriate mechanism depending upon the current context.
The trade-off between signal-to-noise ratio (SNR) and spatial specificity governs the choice of spatial resolution in magnetic resonance imaging (MRI); diffusion-weighted (DW) MRI is no exception. Images of lower resolution have higher signal to noise ratio, but also more partial volume artifacts. We present a data-fusion approach for tackling this trade-off by combining DW MRI data acquired both at high and low spatial resolution. We combine all data into a single Bayesian model to estimate the underlying fiber patterns and diffusion parameters. The proposed model, therefore, combines the benefits of each acquisition. We show that fiber crossings at the highest spatial resolution can be inferred more robustly and accurately using such a model compared to a simpler model that operates only on high-resolution data, when both approaches are matched for acquisition time.
Brain; diffusion-weighted imaging; inverse methods; magnetic resonance imaging (MRI); tractography
Recent genome-wide association studies (GWASs) conducted in Asian populations have identified novel risk loci for systemic lupus erythematosus (SLE). Here, we genotyped 10 single-nucleotide polymorphisms (SNPs) in eight such loci and investigated their disease associations in three independent Caucasian SLE case–control cohorts recruited from Sweden, Finland and the United States. The disease associations of the SNPs in ETS1, IKZF1, LRRC18-WDFY4, RASGRP3, SLC15A4, TNIP1 and 16p11.2 were replicated, whereas no solid evidence of association was observed for the 7q11.23 locus in the Caucasian cohorts. SLC15A4 was significantly associated with renal involvement in SLE. The association of TNIP1 was more pronounced in SLE patients with renal and immunological disorder, which is corroborated by two previous studies in Asian cohorts. The effects of all the associated SNPs, either conferring risk for or being protective against SLE, were in the same direction in Caucasians and Asians. The magnitudes of the allelic effects for most of the SNPs were also comparable across different ethnic groups. On the contrary, remarkable differences in allele frequencies between Caucasian and Asian populations were observed for all associated SNPs. In conclusion, most of the novel SLE risk loci identified by GWASs in Asian populations were also associated with SLE in Caucasian populations. We observed both similarities and differences with respect to the effect sizes and risk allele frequencies across ethnicities.
systemic lupus erythematosus; genetic-association study; Asian; Caucasian
A dominant focus in studies of learning and decision-making is the neural coding of scalar reward value. This emphasis ignores the fact that choices are strongly shaped by a rich representation of potential rewards. Here, using fMRI adaptation we demonstrate that responses in the human orbitofrontal cortex (OFC) encode a representation of the specific type of food reward predicted by a visual cue. By controlling for value across rewards, and by linking each reward with two distinct stimuli, we could test for representations of reward-identity that were independent of associative information. Our results show reward-identity representations in a medial-caudal region of OFC, independent of the associated predictive stimulus. This contrasts with a more rostro-lateral OFC region encoding reward-identity representations tied to the predicate stimulus. This demonstration of adaptation in OFC to reward specific representations opens an avenue for investigation of more complex decision mechanisms that are not immediately accessible in standard analyses which focus on correlates of average activity.
This article is a comparative study of white matter projections from ventral prefrontal cortex (vPFC) between human and macaque brains. We test whether the organizational rules that vPFC connections follow in macaques are preserved in humans. These rules concern the trajectories of some of the white matter projections from vPFC, and how the position of regions in the vPFC dictate the trajectories of their projections in the white matter. In order to address this question, we present a novel approach that combines direct tracer measurements of entire white matter trajectories in macaque monkeys with diffusion MRI tractography of both macaques and humans. The approach allows us to provide explicit validation of diffusion tractography and transfer tractography strategies across species to test the extent to which inferences from macaques can be applied to human neuroanatomy. Apart from one exception, we found a remarkable overlap between the two techniques in the macaque. Furthermore the organizational principles followed by vPFC tracts in macaques are preserved in humans.