Functional Interaction Patterns Research Articles

Functional organization of the human corpus callosum unveiled with BOLD-fMRI gradients

Abstract Gradients capture the underlying functional organization of the brain. Cortical gradients have been well characterized, however very little is known about the underlying gradient of the white matter. Here, we proposed a functionally gradient mapping of the corpus callosum by using blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD-fMRI), which for the first time uncovered three distinct but stable spatial axes: posterior-anterior, dorsal-ventral, and left-right. The three spatial patterns were replicated in another independent cohort and robust across scanning conditions. We further associated the three gradient maps with brain anatomy, connectome, and task-related brain functions, by using structural magnetic resonance imaging, both resting-state and task fMRI, and diffusion tensor imaging data. The posterior-anterior gradient distribution of the corpus callosum showed a similar pattern with the cerebral cortex, gradually extending from the primary cortex to the transmodal cortex. The dorsal-ventral gradient distribution revealed an N-shaped pattern from the primary cortex to the higher-order cognitive cortex. The posterior-anterior and dorsal-ventral gradient maps were also associated with white-matter microstructures, such as fractional anisotropy and myelin water fraction. The left-right gradient showed an inverted V-shaped pattern, which delineated the inter-hemisphere separation. These findings provide fundamental insight into the functional organization of the human corpus callosum, unveiling potential patterns of functional interaction with the cerebral cortex and their associations with cognitive behaviors.

Dysregulated Salience Network Control over Default-Mode and Central-Executive Networks in Schizophrenia Revealed Using Stochastic Dynamical Causal Modeling.

Introduction: Neuroimaging studies suggest that the human brain consists of intrinsically organized, large-scale neural networks. Among these networks, the interplay among the default-mode network (DMN), salience network (SN), and central-executive network (CEN) has been widely used to understand the functional interaction patterns in health and disease. This triple network model suggests that the SN causally controls over the DMN and CEN in healthy individuals. This interaction is often referred to as SN's dynamic regulating mechanism. However, such interactions are not well understood in individuals with schizophrenia. Methods: In this study, we leveraged resting-state functional magnetic resonance imaging data from schizophrenia (n = 67) and healthy controls (n = 81) and evaluated the directional functional interactions among DMN, SN, and CEN using stochastic dynamical causal modeling methodology. Results: In healthy controls, our analyses replicated previous findings that SN regulates DMN and CEN activities (Mann-Whitney U test; p < 10-8). In schizophrenia, however, our analyses revealed a disrupted SN-based controlling mechanism over the DMN and CEN (Mann-Whitney U test; p < 10-16). Conclusions: These results indicate that the disrupted controlling mechanism of SN over the other two neural networks may be a candidate neuroimaging phenotype in schizophrenia.

The dynamic organicity theory of consciousness: how consciousness arises from the functionality of multiscale complexity in the material brain

The fine structure of consciousness is temporally experienced. This makes possible a dynamic organicity theory of consciousness through disunified order in the pre-, sub- and noncognitive levels of causal processes and dynamics. This multilevel approach is based on functional systems where space is implicitly grounded as changeable boundary conditions (due to organicity), and energy capture and storage under energy flow entail structuring intrinsic information (as both hidden thermodynamic energy and hidden thermodynamic information). The use of anaesthesia indicates consciousness disappears when there is an obliteration of intrinsic information within membrane protein amino acids, suggesting that quantum-thermal fluctuations of the electromagnetic field can be functional and non-vacuous. The origin of thermo-qubit syn-tax is derived from the principle of self-reference as the syntax of consciousness. In addition to the classical Brownian motion of macroscopically observable mean values, the negentropically derived quantum potential is an additional degree of freedom where the structuring of intrinsic information takes place by negentropic action from the microscopically random quantum-thermal fluctuations. Such functional fluctuations provide a way for intentionality to come in from spontaneous ordering, leading to path selection as an instruction to act. The resultant intentionality becomes part of information-based action when functional interactions are selected and boundary conditions change, causing interference pattern matching through temporal re-organization of informational redundancy structures (not used in functional interactions). The self-referential dynamic structures (of evolving informational holons) transform syntac-tic structures into experienceable forms. In comparing the interference patterns of functional interactions during restructuring informational redundancy structures, the functionality of multiscale complexity as functional systems patterns gives experienceable forms the potential to understand “meaning” by reducing uncertainty in the process of structuring intrinsic information. The self-reference principle establishes dynamical pathways from the microscale to the macroscale (this includes nonlocal pathways), in which diachronic causation and how the disunity of causal order in the redundancy creates a weak unity of consciousness through its temporal structure, which has an inferred purpose that gives rise to a sense of self.

Identification of spatial and functional interactions in Beijing based on trajectory data

The spatial interactions in a city help us understand how the city spaces are structured. Human needs change constantly throughout the day, leading to changing functions of urban space and spatial interactions. Most existing studies on spatial interaction are confined to static interaction, which does not reflect the dynamic spatial interactions and urban space functions. Taxies are an important means of urban transportation, and their trajectories are time sensitive. The clusters of pick-up and drop-off points of taxies directly reflect the spatiotemporal human mobility pattern in a city, and their implicit semantic information can be used to infer the passengers’ activities. Using Beijing as a case study, this paper employs taxi origin and destination trajectory data to explore the dynamic spatial and functional interaction patterns. First, we apply tensor decomposition to obtain the spatial and temporal patterns of human mobility. Second, geotagged Weibo texts are incorporated to extract the dynamic urban functional areas. Then, we obtain the spatial and functional interactions by trajectory clustering and visualization. The results reveal two daily patterns and four hourly patterns of human mobility. The spatial interactions and the functional interaction reveal the spatial patterns and semantics of human taxi travel behaviors in different time periods.

Understanding multi-modal brain network data: An immersive 3D visualization approach

Understanding the human brain requires the incorporation of functional interaction patterns that depend on a variety of features like experimental setup, strength of directed connectedness or variability between several individuals or groups. In addition to these external factors, there are internal properties of the brain network as for example temporal propagation of connections, or connectivity patterns that only occur in a distinct frequency range of the signal. The visualization of detected networks covering all necessary information poses a substantial problem which is mainly due to the high number of features that have to be integrated within the same view in a natural spatial context.To address this problem, we propose a new tool that transfers the network into an anatomically arranged origin–destination view in a virtual visual analysis lab. This offers the user an opportunity to assess the temporal evolution of connectivity patterns and provides an intuitive and motivating way of exploring the corresponding features via navigation and interaction in virtual reality (VR). The approach was evaluated in a user study including participants with neuroscientific background as well as people working in the field of computer science. As a first proof of concept trial we used functional brain networks derived from time series of electroencephalography recordings evoked by visual stimuli. All participants gave a positive general feedback, notably they saw a benefit in using the VR view instead of the compared 2D desktop variant. This suggests that our application successfully fills a gap in the visualization of high-dimensional brain networks and that it is worthwhile to further follow and enhance the proposed representation method.

Antidepressant Treatment-Induced State-Dependent Reconfiguration of Emotion Regulation Networks in Major Depressive Disorder.

Deficits in emotion regulation are the main clinical features, common risk factors, and treatment-related targets for major depressive disorder (MDD). The neural bases of emotion regulation are moving beyond specific functions and emphasizing instead the integrative functions of spatially distributed brain areas that work together as large-scale brain networks, but it is still unclear whether the dynamic interactions among these emotion networks would be the target of clinical intervention for MDD. Data were collected from 70 MDD patients and 43 sex- and age-matched healthy controls. The dynamic functional connectivity (dFC) between emotion regions was estimated via a sliding-window method based on resting-state functional magnetic resonance imaging (R-fMRI). A k-means clustering method was applied to classify all time windows across all participants into several dFC states reflecting recurring functional interaction patterns among emotion regions over time. The results showed that four dFC states were identified in the emotion networks. Their alterations of state-related occurrence proportion were found in MDD and subsequently normalized following 12-week antidepressant treatment. Baseline strong dFC could predict the reduction rate of Hamilton Depression Rating Scale (HAMD) scores. These findings highlighted the state-dependent reconfiguration of emotion regulation networks in MDD patients owing to antidepressant treatment.

Future of processing and facilitating change and learning

The field of the sciences of the mind is evolving fast. With the diversification of knowledge and accumulation of data, often lacking integration and reproducibility, questions arise. The role of critical thinking and research is evident. As the science of the unconscious, psychoanalysis provides a method and theory to understand human minds and mentalities, helping the patient know his mind and transform action into reflection. Mental activities, including social skills, develop in the social context, depending on the social environment’s demands and resources put onto the individual. Encoding emotional signals, markers of meaning for the individual, is ontogenetically necessary and has influences on memory encoding. Beyond theoretical understanding, implicit relational knowledge is actualized in the therapeutic setting. With a strong focus on experiencing emotional reconsolidation of memories, previous relationships’ repercussions are enriched with broadening viewpoints in the analytic environment. The long-term effects of psychotherapeutic treatments have been examined. A sufficient explanation of the specific factors contributing to success or an answer when an impact is lacking is still under investigation. When investigating subliminal and implicit mechanisms leading to memory reconsolidation and the formation of functional object relations and interaction patterns, the focus is set on affective interplay and processing prior/during and after social interactions. The present paper discusses which parameters might contribute to the reshaping of memories and the linkage of memory with the emotional load of experience. Providing insights into such dynamic mental phenomena could enhance process research by investigating moment by moment interactions in psychoanalysis, treatment, and learning processes. Due to the research subject’s complexity, different research methods and integration of associated research fields are required.

Dynamic Functional Connectivity Change-Point Detection With Random Matrix Theory Inference.

To study the dynamic nature of brain activity, functional magnetic resonance imaging (fMRI) data is useful including some temporal dependencies between the corresponding neural activity estimates. Recent studies have shown that the functional connectivity (FC) varies according to time and location which should be incorporated into the model. Modeling this dynamic FC (DFC) requires time-varying measures of spatial region of interest (ROI) sets. To know about the DFC, change-point detection in FC is of particular interest. In this paper, we propose a method of detecting a change-point based on the maximum of eigenvalues via random matrix theory (RMT). From covariance matrices for FC of all ROI's, the temporal change-point of FC is decided by an RMT approach. Simulation results show that our proposed method can detect meaningful FC change-points. We also illustrate the effectiveness of our FC detection approach by applying our method to epilepsy data where change-points detected are explained by the changes in memory capacity. Our study shows the possibility of RMT based approach in DFC change-point problem and in studying the complex dynamic pattern of functional brain interactions.

Reorganisation of Brain Hubs across Altered States of Consciousness

Patterns of functional interactions across distributed brain regions are suggested to provide a scaffold for the conscious processing of information, with marked topological alterations observed in loss of consciousness. However, establishing a firm link between macro-scale brain network organisation and conscious cognition requires direct investigations into neuropsychologically-relevant architectural modifications across systematic reductions in consciousness. Here we assessed both global and regional disturbances to brain graphs in a group of healthy participants across baseline resting state fMRI as well as two distinct levels of propofol-induced sedation. We found a persistent modular architecture, yet significant reorganisation of brain hubs that formed parts of a wider rich-club collective. Furthermore, the reduction in the strength of rich-club connectivity was significantly associated with the participants’ performance in a semantic judgment task, indicating the importance of this higher-order topological feature for conscious cognition. These results highlight a remarkable interplay between global and regional properties of brain functional interactions in supporting conscious cognition that is relevant to our understanding of clinical disorders of consciousness.

Impact of electronic polarizability on protein-functional group interactions.

Interactions of proteins with functional groups are key to their biological functions, making it essential that they be accurately modeled. To investigate the impact of the inclusion of explicit treatment of electronic polarizability in force fields on protein-functional group interactions, the additive CHARMM and Drude polarizable force field are compared in the context of the Site-Identification by Ligand Competitive Saturation (SILCS) simulation methodology from which functional group interaction patterns with five proteins for which experimental binding affinities of multiple ligands are available, were obtained. The explicit treatment of polarizability produces significant differences in the functional group interactions in the ligand binding sites including overall enhanced binding of functional groups to the proteins. This is associated with variations of the dipole moments of solutes representative of functional groups in the binding sites relative to aqueous solution with higher dipole moments systematically occurring in the latter, though exceptions occur with positively charged methylammonium. Such variation indicates the complex, heterogeneous nature of the electronic environments of ligand binding sites and emphasizes the inherent limitation of fixed charged, additive force fields for modeling ligand-protein interactions. These effects yield more defined orientation of the functional groups in the binding pockets and a small, but systematic improvement in the ability of the SILCS method to predict the binding orientation and relative affinities of ligands to their target proteins. Overall, these results indicate that the physical model associated with the explicit treatment of polarizability along with the presence of lone pairs in a force field leads to changes in the nature of the interactions of functional groups with proteins versus that occurring with additive force fields, suggesting the utility of polarizable force fields in obtaining a more realistic understanding of protein-ligand interactions.

Altered functional interactions between neurons in primary visual cortex of macaque monkeys with experimental amblyopia.

Amblyopia, a disorder in which vision through one of the eyes is degraded, arises because of defective processing of information by the visual system. Amblyopia often develops in humans after early misalignment of the eyes (strabismus) and can be simulated in macaque monkeys by artificially inducing strabismus. In such amblyopic animals, single-unit responses in primary visual cortex (V1) are appreciably reduced when evoked by the amblyopic eye compared with the other (fellow) eye. However, this degradation in single V1 neuron responsivity is not commensurate with the marked losses in visual sensitivity and resolution measured behaviorally. Here we explored the idea that changes in patterns of coordinated activity across populations of V1 neurons may contribute to degraded visual representations in amblyopia, potentially making it more difficult to read out evoked activity to support perceptual decisions. We studied the visually evoked activity of V1 neuronal populations in three macaques (Macaca nemestrina) with strabismic amblyopia and in one control animal. Activity driven through the amblyopic eye was diminished, and these responses also showed more interneuronal correlation at all stimulus contrasts than responses driven through the fellow eye or responses in the control animal. A decoding analysis showed that responses driven through the amblyopic eye carried less visual information than other responses. Our results suggest that part of the reduced visual capacity of amblyopes may be due to changes in the patterns of functional interaction among neurons in V1.NEW & NOTEWORTHY Previous work on the neurophysiological basis of amblyopia has largely focused on relating behavioral deficits to changes in visual processing by single neurons in visual cortex. In this study, we recorded simultaneously from populations of primary visual cortical (V1) neurons in macaques with amblyopia. We found changes in the strength and pattern of shared response variability between neurons. These changes in neuronal interactions could impair the visual representations of V1 populations driven by the amblyopic eye.

A Comparative Study of Cluster Detection Algorithms in Protein-Protein Interaction for Drug Target Discovery and Drug Repurposing.

The interactions between drugs and their target proteins induce altered expression of genes involved in complex intracellular networks. The properties of these functional network modules are critical for the identification of drug targets, for drug repurposing, and for understanding the underlying mode of action of the drug. The topological modules generated by a computational approach are defined as functional clusters. However, the functions inferred for these topological modules extracted from a large-scale molecular interaction network, such as a protein–protein interaction (PPI) network, could differ depending on different cluster detection algorithms. Moreover, the dynamic gene expression profiles among tissues or cell types causes differential functional interaction patterns between the molecular components. Thus, the connections in the PPI network should be modified by the transcriptomic landscape of specific cell lines before producing topological clusters. Here, we systematically investigated the clusters of a cell-based PPI network by using four cluster detection algorithms. We subsequently compared the performance of these algorithms for target gene prediction, which integrates gene perturbation data with the cell-based PPI network using two drug target prioritization methods, shortest path and diffusion correlation. In addition, we validated the proportion of perturbed genes in clusters by finding candidate anti-breast cancer drugs and confirming our predictions using literature evidence and cases in the ClinicalTrials.gov. Our results indicate that the Walktrap (CW) clustering algorithm achieved the best performance overall in our comparative study.

Large-scale network interactions supporting item-context memory formation.

Episodic memory is thought to involve functional interactions of large-scale brain networks that dynamically reconfigure depending on task demands. Although the hippocampus and closely related structures have been implicated, little is known regarding how large-scale and distributed networks support different memory formation demands. We investigated patterns of interactions among distributed networks while human individuals formed item-context memories for two stimulus categories. Subjects studied object-scene and object-location associations in different fMRI sessions. Stimulus-responsive brain regions were organized based on their fMRI interconnectivity into networks and modules using probabilistic module-detection algorithms to maximize measurement of individual differences in modular structure. Although there was a great deal of consistency in the modular structure between object-scene and object-location memory formation, there were also significant differences. Interactions among functional modules predicted later memory accuracy, explaining substantial portions of variability in memory formation success. Increased interactivity of modules associated with internal thought and anti-correlation of these modules with those related to stimulus-evoked processing robustly predicted object-scene memory, whereas decreased interactivity of stimulus-evoked processing modules predicted object-location memory. Assessment of individual differences in network organization therefore allowed identification of distinct patterns of functional interactions that robustly predicted memory formation. This highlights large-scale brain network interactions for memory formation and indicates that although networks are largely robust to task demands, reconfiguration nonetheless occurs to support distinct memory formation demands.

Arousal dependent modulation of thalamo-cortical functional interaction

Ongoing changes in arousal influence sensory processing and behavioral performance. Yet the circuit-level correlates for this influence remain poorly understood. Here, we investigate how functional interaction between posterior parietal cortex (PPC) and lateral posterior (LP)/Pulvinar is influenced by ongoing fluctuations in pupil-linked arousal, which is a non-invasive measure of neuromodulatory tone in the brain. We find that fluctuations in pupil-linked arousal correlate with changes to PPC to LP/Pulvinar oscillatory interaction, with cortical alpha oscillations driving activity during low arousal states, and LP/Pulvinar driving PPC in the theta frequency band during higher arousal states. Active visual exploration by saccadic eye movements elicits similar transitions in thalamo-cortical interaction. Furthermore, the presentation of naturalistic video stimuli induces thalamo-cortical network states closely resembling epochs of high arousal in the absence of visual input. Thus, neuromodulators may play a role in dynamically sculpting the patterns of thalamo-cortical functional interaction that underlie visual processing.

Tracking the Reorganization of Module Structure in Time-Varying Weighted Brain Functional Connectivity Networks.

Identification of module structure in brain functional networks is a promising way to obtain novel insights into neural information processing, as modules correspond to delineated brain regions in which interactions are strongly increased. Tracking of network modules in time-varying brain functional networks is not yet commonly considered in neuroscience despite its potential for gaining an understanding of the time evolution of functional interaction patterns and associated changing degrees of functional segregation and integration. We introduce a general computational framework for extracting consensus partitions from defined time windows in sequences of weighted directed edge-complete networks and show how the temporal reorganization of the module structure can be tracked and visualized. Part of the framework is a new approach for computing edge weight thresholds for individual networks based on multiobjective optimization of module structure quality criteria as well as an approach for matching modules across time steps. By testing our framework using synthetic network sequences and applying it to brain functional networks computed from electroencephalographic recordings of healthy subjects that were exposed to a major balance perturbation, we demonstrate the framework's potential for gaining meaningful insights into dynamic brain function in the form of evolving network modules. The precise chronology of the neural processing inferred with our framework and its interpretation helps to improve the currently incomplete understanding of the cortical contribution for the compensation of such balance perturbations.

Contrasting patterns of freshwater microbial metabolic potentials and functional gene interactions between an acidic mining lake and a weakly alkaline lake

AbstractAcidic coal mining lakes (AMLs) are characterized by low pH, low biodiversity, and high concentrations of sulfate and metals. However, to date, knowledge is inadequate on the involvement of the aquatic microbial functions in biogeochemical processes of an AML. Here, we use a natural weakly alkaline lake (WAL) as a reference to investigate the functional gene structure of microbial communities in the aerobic surface waters of an AML by using a metagenomic approach with GeoChip‐based functional gene arrays (GeoChip 5.0). We found significant differences in the compositions of the microbial functional genes between the two lake types. The AML had significantly lower gene diversity than the WAL, and the functional genes detected in the AML were almost a subset of those in the WAL. We also found significant differences in the microbial metabolic potentials between the two lake types. In the AML, the microbial communities had higher functional capacities in phosphorus‐utilization, sulfur oxidation, and metal‐detoxification and had lower metabolic potentials of carbon and nitrogen fixation, and the degradation of starch, hemicellulose, cellulose, chitin, and lignin than those in the WAL. By performing null model and network analyses, we observed stronger deterministic effects of the functional gene assemblages and greater clustering among the co‐occurring functional genes in the AML than those in the WAL. Altogether, we demonstrated contrasting patterns of freshwater microbial metabolic potentials and functional gene interactions between an AML and a WAL, which may advance the understanding of microbial functional response to acidification in freshwater lakes.

Recurring Functional Interactions Predict Network Architecture of Interictal and Ictal States in Neocortical Epilepsy.

Human epilepsy patients suffer from spontaneous seizures, which originate in brain regions that also subserve normal function. Prior studies demonstrate focal, neocortical epilepsy is associated with dysfunction, several hours before seizures. How does the epileptic network perpetuate dysfunction during baseline periods? To address this question, we developed an unsupervised machine learning technique to disentangle patterns of functional interactions between brain regions, or subgraphs, from dynamic functional networks constructed from approximately 100 h of intracranial recordings in each of 22 neocortical epilepsy patients. Using this approach, we found: (1) subgraphs from ictal (seizure) and interictal (baseline) epochs are topologically similar, (2) interictal subgraph topology and dynamics can predict brain regions that generate seizures, and (3) subgraphs undergo slower and more coordinated fluctuations during ictal epochs compared to interictal epochs. Our observations suggest that seizures mark a critical shift away from interictal states that is driven by changes in the dynamical expression of strongly interacting components of the epileptic network.

Vikodak - A Modular Framework for Inferring Functional Potential of Microbial Communities from 16S Metagenomic Datasets

BackgroundThe overall metabolic/functional potential of any given environmental niche is a function of the sum total of genes/proteins/enzymes that are encoded and expressed by various interacting microbes residing in that niche. Consequently, prior (collated) information pertaining to genes, enzymes encoded by the resident microbes can aid in indirectly (re)constructing/ inferring the metabolic/ functional potential of a given microbial community (given its taxonomic abundance profile). In this study, we present Vikodak—a multi-modular package that is based on the above assumption and automates inferring and/ or comparing the functional characteristics of an environment using taxonomic abundance generated from one or more environmental sample datasets. With the underlying assumptions of co-metabolism and independent contributions of different microbes in a community, a concerted effort has been made to accommodate microbial co-existence patterns in various modules incorporated in Vikodak.ResultsValidation experiments on over 1400 metagenomic samples have confirmed the utility of Vikodak in (a) deciphering enzyme abundance profiles of any KEGG metabolic pathway, (b) functional resolution of distinct metagenomic environments, (c) inferring patterns of functional interaction between resident microbes, and (d) automating statistical comparison of functional features of studied microbiomes. Novel features incorporated in Vikodak also facilitate automatic removal of false positives and spurious functional predictions.ConclusionsWith novel provisions for comprehensive functional analysis, inclusion of microbial co-existence pattern based algorithms, automated inter-environment comparisons; in-depth analysis of individual metabolic pathways and greater flexibilities at the user end, Vikodak is expected to be an important value addition to the family of existing tools for 16S based function prediction.Availability and ImplementationA web implementation of Vikodak can be publicly accessed at: http://metagenomics.atc.tcs.com/vikodak. This web service is freely available for all categories of users (academic as well as commercial).

Bayesian Inference for Functional Dynamics Exploring in fMRI Data.

This paper aims to review state-of-the-art Bayesian-inference-based methods applied to functional magnetic resonance imaging (fMRI) data. Particularly, we focus on one specific long-standing challenge in the computational modeling of fMRI datasets: how to effectively explore typical functional interactions from fMRI time series and the corresponding boundaries of temporal segments. Bayesian inference is a method of statistical inference which has been shown to be a powerful tool to encode dependence relationships among the variables with uncertainty. Here we provide an introduction to a group of Bayesian-inference-based methods for fMRI data analysis, which were designed to detect magnitude or functional connectivity change points and to infer their functional interaction patterns based on corresponding temporal boundaries. We also provide a comparison of three popular Bayesian models, that is, Bayesian Magnitude Change Point Model (BMCPM), Bayesian Connectivity Change Point Model (BCCPM), and Dynamic Bayesian Variable Partition Model (DBVPM), and give a summary of their applications. We envision that more delicate Bayesian inference models will be emerging and play increasingly important roles in modeling brain functions in the years to come.

Emotional and Utilitarian Appraisals of Moral Dilemmas Are Encoded in Separate Areas and Integrated in Ventromedial Prefrontal Cortex.

Moral judgment often requires making difficult tradeoffs (e.g., is it appropriate to torture to save the lives of innocents at risk?). Previous research suggests that both emotional appraisals and more deliberative utilitarian appraisals influence such judgments and that these appraisals often conflict. However, it is unclear how these different types of appraisals are represented in the brain, or how they are integrated into an overall moral judgment. We addressed these questions using an fMRI paradigm in which human subjects provide separate emotional and utilitarian appraisals for different potential actions, and then make difficult moral judgments constructed from combinations of these actions. We found that anterior cingulate, insula, and superior temporal gyrus correlated with emotional appraisals, whereas temporoparietal junction and dorsomedial prefrontal cortex correlated with utilitarian appraisals. Overall moral value judgments were represented in an anterior portion of the ventromedial prefrontal cortex. Critically, the pattern of responses and functional interactions between these three sets of regions are consistent with a model in which emotional and utilitarian appraisals are computed independently and in parallel, and passed to the ventromedial prefrontal cortex where they are integrated into an overall moral value judgment. Significance statement: Popular accounts of moral judgment often describe it as a battle for control between two systems, one intuitive and emotional, the other rational and utilitarian, engaged in winner-take-all inhibitory competition. Using a novel fMRI paradigm, we identified distinct neural signatures of emotional and utilitarian appraisals and used them to test different models of how they compete for the control of moral behavior. Importantly, we find little support for competitive inhibition accounts. Instead, moral judgments resembled the architecture of simple economic choices: distinct regions represented emotional and utilitarian appraisals independently and passed this information to the ventromedial prefrontal cortex for integration into an overall moral value signal.