Adolescent social anxiety is associated with diminished discrimination of anticipated threat and safety in the bed nucleus of the stria terminalis.

Social anxiety-which typically emerges in adolescence-lies on a continuum and, when extreme, can be devastating. Socially anxious individuals are prone to heightened fear, anxiety, and the avoidance of contexts associated with potential social scrutiny. Yet most neuroimaging research has focused on acute social threat. Much less attention has been devoted to understanding the neural systems recruited during the uncertain anticipation of potential encounters with social threat. Here we used a novel fMRI paradigm to probe the neural circuitry engaged during the anticipation and acute presentation of threatening faces and voices in a racially diverse sample of 66 adolescents selectively recruited to encompass a range of social anxiety and enriched for clinically significant levels of distress and impairment. Results demonstrated that adolescents with more severe social anxiety symptoms experience heightened distress when anticipating encounters with social threat, and reduced discrimination of uncertain social threat and safety in the bed nucleus of the stria terminalis (BST), a key division of the central extended amygdala (EAc). Although the EAc-including the BST and central nucleus of the amygdala-was robustly engaged by the acute presentation of threatening faces and voices, the degree of EAc engagement was unrelated to the severity of social anxiety. Together, these observations provide a neurobiologically grounded framework for conceptualizing adolescent social anxiety and set the stage for the kinds of prospective-longitudinal and mechanistic research that will be necessary to determine causation and, ultimately, to develop improved interventions for this often-debilitating illness.

Neuroticism/negative emotionality is associated with increased reactivity to uncertain threat in the bed nucleus of the stria terminalis, not the amygdala.

Neuroticism/Negative Emotionality (N/NE)-the tendency to experience anxiety, fear, and other negative emotions-is a fundamental dimension of temperament with profound consequences for health, wealth, and wellbeing. Elevated N/NE is associated with a panoply of adverse outcomes, from reduced socioeconomic attainment and divorce to mental illness and premature death. Work in animals suggests that N/NE reflects heightened reactivity to uncertain threat in the bed nucleus of the stria terminalis (BST) and central nucleus of the amygdala (Ce), but the relevance of these discoveries to the human brain and temperament have remained unclear. Here we used a combination of psychometric, psychophysiological, and neuroimaging approaches to rigorously test this hypothesis in an ethnoracially diverse sample of 220 emerging adults selectively recruited to encompass a broad spectrum of N/NE. Cross-validated robust-regression analyses demonstrated that N/NE is selectively associated with heightened BST activation during the uncertain anticipation of a genuinely distressing threat. In contrast, N/NE was unrelated to BST activation during certain-threat anticipation, Ce activation during either type of threat anticipation, or BST/Ce reactivity to 'threat-related' faces. Implicit in much of the neuroimaging literature is the assumption that different threat paradigms are statistically interchangeable probes of individual differences in neural function, yet our results revealed negligible evidence of convergence between popular threat-anticipation and emotional-face tasks. These observations provide a framework for conceptualizing emotional traits and disorders; for guiding the design and interpretation of biobank and other neuroimaging studies of psychiatric risk, disease, and treatment; and for informing the next generation of mechanistic research.

Acute nicotine abstinence amplifies subjective withdrawal symptoms and threat-evoked fear and anxiety, but not extended amygdala reactivity.

Tobacco smoking imposes a staggering burden on public health, underscoring the urgency of developing a deeper understanding of the processes that maintain addiction. Clinical and experience-sampling data highlight the importance of anxious withdrawal symptoms, but the underlying neurobiology has remained elusive. Mechanistic work in animals implicates the central extended amygdala (EAc)-including the central nucleus of the amygdala and the neighboring bed nucleus of the stria terminalis-but the translational relevance of these discoveries remains unexplored. Here we leveraged a randomized trial design, well-established threat-anticipation paradigm, and multidimensional battery of assessments to understand the consequences of 24-hour nicotine abstinence. The threat-anticipation paradigm had the expected consequences, amplifying subjective distress and arousal, and recruiting the canonical threat-anticipation network. Abstinence increased smoking urges and withdrawal symptoms, and potentiated threat-evoked distress, but had negligible consequences for EAc threat reactivity, raising questions about the translational relevance of prominent animal and human models of addiction. These observations provide a framework for conceptualizing nicotine abstinence and withdrawal, with implications for basic, translational, and clinical science.

Structural Brain Correlates of Childhood Inhibited Temperament: An ENIGMA-Anxiety Mega-analysis.

Temperament involves stable behavioral and emotional tendencies that differ between individuals, which can be first observed in infancy or early childhood and relate to behavior in many contexts and over many years.1 One of the most rigorously characterized temperament classifications relates to the tendency of individuals to avoid the unfamiliar and to withdraw from unfamiliar people, objects, and unexpected events. This temperament is referred to as behavioral inhibition or inhibited temperament (IT).2 IT is a moderately heritable trait1 that can be measured in multiple species.3 In humans, levels of IT can be quantified from the first year of life through direct behavioral observations or reports by caregivers or teachers. Similar approaches as well as self-report questionnaires on current and/or retrospective levels of IT1 can be used later in life.

Anxiety-Related Frontocortical Activity Is Associated With Dampened Stressor Reactivity in the Real World.

Negative affect is a fundamental dimension of human emotion. When extreme, it contributes to a variety of adverse outcomes, from physical and mental illness to divorce and premature death. Mechanistic work in animals and neuroimaging research in humans and monkeys have begun to reveal the broad contours of the neural circuits governing negative affect, but the relevance of these discoveries to everyday distress remains incompletely understood. Here, we used a combination of approaches-including neuroimaging assays of threat anticipation and emotional-face perception and more than 10,000 momentary assessments of emotional experience-to demonstrate that individuals who showed greater activation in a cingulo-opercular circuit during an anxiety-eliciting laboratory paradigm experienced lower levels of stressor-dependent distress in their daily lives (ns = 202-208 university students). Extended amygdala activation was not significantly related to momentary negative affect. These observations provide a framework for understanding the neurobiology of negative affect in the laboratory and in the real world.

Anxiety and the Neurobiology of Temporally Uncertain Threat Anticipation.

When extreme, anxiety-a state of distress and arousal prototypically evoked by uncertain danger-can be debilitating. Uncertain anticipation is a shared feature of situations that elicit signs and symptoms of anxiety across psychiatric disorders, species, and assays. Despite the profound significance of anxiety for human health and wellbeing, the neurobiology of uncertain-threat anticipation remains unsettled. Leveraging a paradigm adapted from animal research and optimized for fMRI signal decomposition, we examined the neural circuits engaged during the anticipation of temporally uncertain and certain threat in 99 men and women. Results revealed that the neural systems recruited by uncertain and certain threat anticipation are anatomically colocalized in frontocortical regions, extended amygdala, and periaqueductal gray. Comparison of the threat conditions demonstrated that this circuitry can be fractionated, with frontocortical regions showing relatively stronger engagement during the anticipation of uncertain threat, and the extended amygdala showing the reverse pattern. Although there is widespread agreement that the bed nucleus of the stria terminalis and dorsal amygdala-the two major subdivisions of the extended amygdala-play a critical role in orchestrating adaptive responses to potential danger, their precise contributions to human anxiety have remained contentious. Follow-up analyses demonstrated that these regions show statistically indistinguishable responses to temporally uncertain and certain threat anticipation. These observations provide a framework for conceptualizing anxiety and fear, for understanding the functional neuroanatomy of threat anticipation in humans, and for accelerating the development of more effective intervention strategies for pathological anxiety.SIGNIFICANCE STATEMENT Anxiety-an emotion prototypically associated with the anticipation of uncertain harm-has profound significance for public health, yet the underlying neurobiology remains unclear. Leveraging a novel neuroimaging paradigm in a relatively large sample, we identify a core circuit responsive to both uncertain and certain threat anticipation, and show that this circuitry can be fractionated into subdivisions with a bias for one kind of threat or the other. The extended amygdala occupies center stage in neuropsychiatric models of anxiety, but its functional architecture has remained contentious. Here we demonstrate that its major subdivisions show statistically indistinguishable responses to temporally uncertain and certain threat. Collectively, these observations indicate the need to revise how we think about the neurobiology of anxiety and fear.

Striatal bases of temporal discounting in early adolescents.

Steeper rates of temporal discounting-the degree to which smaller-sooner (SS) rewards are preferred over larger-later (LL) ones-have been associated with impulsive and ill-advised behaviors in adolescence. Yet, the underlying neural systems remain poorly understood. Here we used a well-established temporal discounting paradigm and functional MRI (fMRI) to examine engagement of the striatum-including the caudate, putamen, and ventral striatum (VS)-in early adolescence (13-15 years; N = 27). Analyses provided evidence of enhanced activity in the caudate and VS during impulsive choice. Exploratory analyses revealed that trait impulsivity was associated with heightened putamen activity during impulsive choices. A more nuanced pattern was evident in the cortex, with the dorsolateral prefrontal cortex mirroring the putamen and posterior parietal cortex showing the reverse association. Taken together, these observations provide an important first glimpse at the distributed neural systems underlying economic choice and trait-like individual differences in impulsivity in the early years of adolescence, setting the stage for prospective-longitudinal and intervention research.

Acute alcohol administration dampens central extended amygdala reactivity.

Alcohol use is common, imposes a staggering burden on public health, and often resists treatment. The central extended amygdala (EAc)-including the bed nucleus of the stria terminalis (BST) and the central nucleus of the amygdala (Ce)-plays a key role in prominent neuroscientific models of alcohol drinking, but the relevance of these regions to acute alcohol consumption in humans remains poorly understood. Using a single-blind, randomized-groups design, multiband fMRI data were acquired from 49 social drinkers while they performed a well-established emotional faces paradigm after consuming either alcohol or placebo. Relative to placebo, alcohol significantly dampened reactivity to emotional faces in the BST. To rigorously assess potential regional differences in activation, data were extracted from unbiased, anatomically predefined regions of interest. Analyses revealed similar levels of dampening in the BST and Ce. In short, alcohol transiently reduces reactivity to emotional faces and it does so similarly across the two major divisions of the human EAc. These observations reinforce the translational relevance of addiction models derived from preclinical work in rodents and provide new insights into the neural systems most relevant to the consumption of alcohol and to the initial development of alcohol abuse in humans.

Intrinsic functional connectivity of the central extended amygdala.

The central extended amygdala (EAc)-including the bed nucleus of the stria terminalis (BST) and central nucleus of the amygdala (Ce)-plays a critical role in triggering fear and anxiety and is implicated in the development of a range of debilitating neuropsychiatric disorders. Although it is widely believed that these disorders reflect the coordinated activity of distributed neural circuits, the functional architecture of the EAc network and the degree to which the BST and the Ce show distinct patterns of functional connectivity is unclear. Here, we used a novel combination of imaging approaches to trace the connectivity of the BST and the Ce in 130 healthy, racially diverse, community-dwelling adults. Multiband imaging, high-precision registration techniques, and spatially unsmoothed data maximized anatomical specificity. Using newly developed seed regions, whole-brain regression analyses revealed robust functional connectivity between the BST and Ce via the sublenticular extended amygdala, the ribbon of subcortical gray matter encompassing the ventral amygdalofugal pathway. Both regions displayed coupling with the ventromedial prefrontal cortex (vmPFC), midcingulate cortex (MCC), insula, and anterior hippocampus. The BST showed stronger connectivity with the thalamus, striatum, periaqueductal gray, and several prefrontal territories. The only regions showing stronger functional connectivity with the Ce were neighboring regions of the dorsal amygdala, amygdalohippocampal area, and anterior hippocampus. These observations provide a baseline against which to compare a range of special populations, inform our understanding of the role of the EAc in normal and pathological fear and anxiety, and showcase image registration techniques that are likely to be useful for researchers working with "deidentified" neuroimaging data.

The Functional Overlap of Executive Control and Language Processing in Bilinguals.

The need to control multiple languages is thought to require domain-general executive control (EC) in bilinguals such that the EC and language systems become interdependent. However, there has been no systematic investigation into how and where EC and language processes overlap in the bilingual brain. If the concurrent recruitment of EC during bilingual language processing is domain-general and extends to non-linguistic EC, we hypothesize that regions commonly involvement in language processing, linguistic EC, and non-linguistic EC may be selectively altered in bilinguals compared to monolinguals. A conjunction of functional magnetic resonance imaging (fMRI) data from a flanker task with linguistic and nonlinguistic distractors and a semantic categorization task showed functional overlap in the left inferior frontal gyrus (LIFG) in bilinguals, whereas no overlap occurred in monolinguals. This research therefore identifies a neural locus of functional overlap of language and EC in the bilingual brain.

PreSMA stimulation changes task-free functional connectivity in the fronto-basal-ganglia that correlates with response inhibition efficiency.

Previous work using transcranial magnetic stimulation (TMS) demonstrated that the right presupplementary motor area (preSMA), a node in the fronto-basal-ganglia network, is critical for response inhibition. However, TMS influences interconnected regions, raising the possibility of a link between the preSMA activity and the functional connectivity within the network. To understand this relationship, we applied single-pulse TMS to the right preSMA during functional magnetic resonance imaging when the subjects were at rest to examine changes in neural activity and functional connectivity within the network in relation to the efficiency of response inhibition evaluated with a stop-signal task. The results showed that preSMA-TMS increased activation in the right inferior-frontal cortex (rIFC) and basal ganglia and modulated their task-free functional connectivity. Both the TMS-induced changes in the basal-ganglia activation and the functional connectivity between rIFC and left striatum, and of the overall network correlated with the efficiency of response inhibition and with the white-matter microstructure along the preSMA-rIFC pathway. These results suggest that the task-free functional and structural connectivity between the rIFCop and basal ganglia are critical to the efficiency of response inhibition. Hum Brain Mapp 37:3236-3249, 2016. © 2016 Wiley Periodicals, Inc.

Separating lexical-semantic access from other mnemonic processes in picture-name verification.

We present a novel paradigm to identify shared and unique brain regions underlying non-semantic, non-phonological, abstract, audio-visual (AV) memory vs. naming using a longitudinal functional magnetic resonance imaging experiment. Participants were trained to associate novel AV stimulus pairs containing hidden linguistic content. Half of the stimulus pairs were distorted images of animals and sine-wave speech versions of the animal's name. Images and sounds were distorted in such a way as to make their linguistic content easily recognizable only after being made aware of its existence. Memory for the pairings was tested by presenting an AV pair and asking participants to verify if the two stimuli formed a learned pairing. After memory testing, the hidden linguistic content was revealed and participants were tested again on their recollection of the pairings in this linguistically informed state. Once informed, the AV verification task could be performed by naming the picture. There was substantial overlap between the regions involved in recognition of non-linguistic sensory memory and naming, suggesting a strong relation between them. Contrasts between sessions identified left angular gyrus and middle temporal gyrus as key additional players in the naming network. Left inferior frontal regions participated in both naming and non-linguistic AV memory suggesting the region is responsible for AV memory independent of phonological content contrary to previous proposals. Functional connectivity between angular gyrus and left inferior frontal gyrus and left middle temporal gyrus increased when performing the AV task as naming. The results are consistent with the hypothesis that, at the spatial resolution of fMRI, the regions that facilitate non-linguistic AV associations are a subset of those that facilitate naming though reorganized into distinct networks.

Identifying effective connectivity parameters in simulated fMRI: a direct comparison of switching linear dynamic system, stochastic dynamic causal, and multivariate autoregressive models.

The number and variety of connectivity estimation methods is likely to continue to grow over the coming decade. Comparisons between methods are necessary to prune this growth to only the most accurate and robust methods. However, the nature of connectivity is elusive with different methods potentially attempting to identify different aspects of connectivity. Commonalities of connectivity definitions across methods upon which base direct comparisons can be difficult to derive. Here, we explicitly define "effective connectivity" using a common set of observation and state equations that are appropriate for three connectivity methods: dynamic causal modeling (DCM), multivariate autoregressive modeling (MAR), and switching linear dynamic systems for fMRI (sLDSf). In addition while deriving this set, we show how many other popular functional and effective connectivity methods are actually simplifications of these equations. We discuss implications of these connections for the practice of using one method to simulate data for another method. After mathematically connecting the three effective connectivity methods, simulated fMRI data with varying numbers of regions and task conditions is generated from the common equation. This simulated data explicitly contains the type of the connectivity that the three models were intended to identify. Each method is applied to the simulated data sets and the accuracy of parameter identification is analyzed. All methods perform above chance levels at identifying correct connectivity parameters. The sLDSf method was superior in parameter estimation accuracy to both DCM and MAR for all types of comparisons.

Temporal microstructure of cortical networks (TMCN) underlying task-related differences.

Neuro-electromagnetic recording techniques (EEG, MEG, iEEG) provide high temporal resolution data to study the dynamics of neurocognitive networks: large scale neural assemblies involved in task-specific information processing. How does a neurocognitive network reorganize spatiotemporally on the order of a few milliseconds to process specific aspects of the task? At what times do networks segregate for task processing, and at what time scales does integration of information occur via changes in functional connectivity? Here, we propose a data analysis framework-Temporal microstructure of cortical networks (TMCN)-that answers these questions for EEG/MEG recordings in the signal space. Method validation is established on simulated MEG data from a delayed-match to-sample (DMS) task. We then provide an example application on MEG recordings during a paired associate task (modified from the simpler DMS paradigm) designed to study modality specific long term memory recall. Our analysis identified the times at which network segregation occurs for processing the memory recall of an auditory object paired to a visual stimulus (visual-auditory) in comparison to an analogous visual-visual pair. Across all subjects, onset times for first network divergence appeared within a range of 0.08-0.47 s after initial visual stimulus onset. This indicates that visual-visual and visual auditory memory recollection involves equivalent network components without any additional recruitment during an initial period of the sensory processing stage which is then followed by recruitment of additional network components for modality specific memory recollection. Therefore, we propose TMCN as a viable computational tool for extracting network timing in various cognitive tasks.

Discrimination task reveals differences in neural bases of tinnitus and hearing impairment.

We investigated auditory perception and cognitive processing in individuals with chronic tinnitus or hearing loss using functional magnetic resonance imaging (fMRI). Our participants belonged to one of three groups: bilateral hearing loss and tinnitus (TIN), bilateral hearing loss without tinnitus (HL), and normal hearing without tinnitus (NH). We employed pure tones and frequency-modulated sweeps as stimuli in two tasks: passive listening and active discrimination. All subjects had normal hearing through 2 kHz and all stimuli were low-pass filtered at 2 kHz so that all participants could hear them equally well. Performance was similar among all three groups for the discrimination task. In all participants, a distributed set of brain regions including the primary and non-primary auditory cortices showed greater response for both tasks compared to rest. Comparing the groups directly, we found decreased activation in the parietal and frontal lobes in the participants with tinnitus compared to the HL group and decreased response in the frontal lobes relative to the NH group. Additionally, the HL subjects exhibited increased response in the anterior cingulate relative to the NH group. Our results suggest that a differential engagement of a putative auditory attention and short-term memory network, comprising regions in the frontal, parietal and temporal cortices and the anterior cingulate, may represent a key difference in the neural bases of chronic tinnitus accompanied by hearing loss relative to hearing loss alone.

Effective Connectivity Modeling for fMRI: Six Issues and Possible Solutions Using Linear Dynamic Systems.

Analysis of directionally specific or causal interactions between regions in functional magnetic resonance imaging (fMRI) data has proliferated. Here we identify six issues with existing effective connectivity methods that need to be addressed. The issues are discussed within the framework of linear dynamic systems for fMRI (LDSf). The first concerns the use of deterministic models to identify inter-regional effective connectivity. We show that deterministic dynamics are incapable of identifying the trial-to-trial variability typically investigated as the marker of connectivity while stochastic models can capture this variability. The second concerns the simplistic (constant) connectivity modeled by most methods. Connectivity parameters of the LDSf model can vary at the same timescale as the input data. Further, extending LDSf to mixtures of multiple models provides more robust connectivity variation. The third concerns the correct identification of the network itself including the number and anatomical origin of the network nodes. Augmentation of the LDSf state space can identify additional nodes of a network. The fourth concerns the locus of the signal used as a "node" in a network. A novel extension LDSf incorporating sparse canonical correlations can select most relevant voxels from an anatomically defined region based on connectivity. The fifth concerns connection interpretation. Individual parameter differences have received most attention. We present alternative network descriptors of connectivity changes which consider the whole network. The sixth concerns the temporal resolution of fMRI data relative to the timescale of the inter-regional interactions in the brain. LDSf includes an "instantaneous" connection term to capture connectivity occurring at timescales faster than the data resolution. The LDS framework can also be extended to statistically combine fMRI and EEG data. The LDSf framework is a promising foundation for effective connectivity analysis.

Imaging systems level consolidation of novel associate memories: a longitudinal neuroimaging study.

Previously, a standard theory of systems level memory consolidation was developed to describe how memory recall becomes independent of the medial temporal memory system. More recently, an extended consolidation theory was proposed that predicts seven changes in regional neural activity and inter-regional functional connectivity. Using longitudinal event-related functional magnetic resonance imaging of an associate memory task, we simultaneously tested all predictions and additionally tested for consolidation-related changes in recall of associate memories at a sub-trial temporal resolution, analyzing cue, delay and target periods of each trial separately. Results consistent with the theoretical predictions were observed though two inconsistent results were also obtained. In particular, while medial temporal recall related delay period activity decreased with consolidation as predicted, visual cue activity increased for consolidated memories. Though the extended theory of memory consolidation is largely supported by our study, these results suggest that the extended theory needs further refinement and the medial temporal memory system has multiple, temporally distinct roles in associate memory recall. Neuroimaging analysis at a sub-trial temporal resolution, as used here, may further clarify the role of the hippocampal complex in memory consolidation.

Identification and validation of effective connectivity networks in functional magnetic resonance imaging using switching linear dynamic systems.

Dynamic connectivity networks identify directed interregional interactions between modeled brain regions in neuroimaging. However, problems arise when the regions involved in a task and their interconnections are not fully known a priori. Objective measures of model adequacy are necessary to validate such models. We present a connectivity formalism, the Switching Linear Dynamic System (SLDS), that is capable of identifying both Granger-Geweke and instantaneous connectivity that vary according to experimental conditions. SLDS explicitly models the task condition as a Markov random variable. The series of task conditions can be estimated from new data given an identified model providing a means to validate connectivity patterns. We use SLDS to model functional magnetic resonance imaging data from five regions during a finger alternation task. Using interregional connectivity alone, the identified model predicted the task condition vector from a different subject with a different task ordering with high accuracy. In addition, important regions excluded from a model can be identified by augmenting the model state space. A motor task model excluding primary motor cortices was augmented with a new neural state constrained by its connectivity with the included regions. The augmented variable time series, convolved with a hemodynamic kernel, was compared to all brain voxels. The right primary motor cortex was identified as the best region to add to the model. Our results suggest that the SLDS model framework is an effective means to address several problems with modeling connectivity including measuring overall model adequacy and identifying important regions missing from models.

Symbolic gestures and spoken language are processed by a common neural system.

Symbolic gestures, such as pantomimes that signify actions (e.g., threading a needle) or emblems that facilitate social transactions (e.g., finger to lips indicating "be quiet"), play an important role in human communication. They are autonomous, can fully take the place of words, and function as complete utterances in their own right. The relationship between these gestures and spoken language remains unclear. We used functional MRI to investigate whether these two forms of communication are processed by the same system in the human brain. Responses to symbolic gestures, to their spoken glosses (expressing the gestures' meaning in English), and to visually and acoustically matched control stimuli were compared in a randomized block design. General Linear Models (GLM) contrasts identified shared and unique activations and functional connectivity analyses delineated regional interactions associated with each condition. Results support a model in which bilateral modality-specific areas in superior and inferior temporal cortices extract salient features from vocal-auditory and gestural-visual stimuli respectively. However, both classes of stimuli activate a common, left-lateralized network of inferior frontal and posterior temporal regions in which symbolic gestures and spoken words may be mapped onto common, corresponding conceptual representations. We suggest that these anterior and posterior perisylvian areas, identified since the mid-19th century as the core of the brain's language system, are not in fact committed to language processing, but may function as a modality-independent semiotic system that plays a broader role in human communication, linking meaning with symbols whether these are words, gestures, images, sounds, or objects.

A link between neuroscience and informatics: large-scale modeling of memory processes.

Utilizing advances in functional neuroimaging and computational neural modeling, neuroscientists have increasingly sought to investigate how distributed networks, composed of functionally defined subregions, combine to produce cognition. Large-scale, biologically realistic neural models, which integrate data from cellular, regional, whole brain, and behavioral sources, delineate specific hypotheses about how these interacting neural populations might carry out high-level cognitive tasks. In this review, we discuss neuroimaging, neural modeling, and the utility of large-scale biologically realistic models using modeling of short-term memory as an example. We present a sketch of the data regarding the neural basis of short-term memory from non-human electrophysiological, computational and neuroimaging perspectives, highlighting the multiple interacting brain regions believed to be involved. Through a review of several efforts, including our own, to combine neural modeling and neuroimaging data, we argue that large scale neural models provide specific advantages in understanding the distributed networks underlying cognition and behavior.