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.
Recent advances in non-invasive neuroimaging have enabled the measurement of connections between distant regions in the living human brain, thus opening up a new field of research: Human connectomics. Different imaging modalities allow the mapping of structural connections (axonal fiber tracts) as well as functional connections (correlations in time series), and individual variations in these connections may be related to individual variations in behaviour and cognition. Connectivity analysis has already led to several important advances. Segregated brain regions may be identified by their unique patterns of connectivity, structural and functional connectivity may be compared to elucidate how dynamic interactions arise from the anatomical substrate, and the architecture of large-scale networks connecting sets of brain regions may be analyzed in detail. The combination of structural and functional connectivity has begun to reveal key patterns of human brain organization, such as the existence of distinct modules or sub-networks that become engaged in different cognitive tasks. Collectively, advances in human connectomics open up the possibility of studying how brain connections mediate regional brain function and thence behaviour.
Neuroimaging; Network; Neuroanatomy; Connectome; Diffusion Imaging; fMRI; Resting-State
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.
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
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.
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
Psychophysiological interactions (PPIs) analysis is a method for investigating task-specific changes in the relationship between activity in different brain areas, using functional magnetic resonance imaging (fMRI) data. Specifically, PPI analyses identify voxels in which activity is more related to activity in a seed region of interest (seed ROI) in a given psychological context, such as during attention or in the presence of emotive stimuli. In this tutorial, we aim to give a simple conceptual explanation of how PPI analysis works, in order to assist readers in planning and interpreting their own PPI experiments.
psychophysiological interactions; PPI; functional connectivity; resting state
Adaptive success in social animals depends on an ability to infer the likely actions of others. Little is known about the neural computations that underlie this capacity. Here, we show that the brain models the values and choices of others even when these values are currently irrelevant. These modeled choices use the same computations that underlie our own choices, but are resolved in a distinct neighboring medial prefrontal brain region. Crucially, however, when subjects choose on behalf of a partner instead of themselves, these regions exchange their functional roles. Hence, regions that represented values of the subject’s executed choices now represent the values of choices executed on behalf of the partner, and those that previously modeled the partner now model the subject. These data tie together neural computations underlying self-referential and social inference, and in so doing establish a new functional axis characterizing the medial wall of prefrontal cortex.
► Valuation and choice for self and other exhibit parallel computations in PFC ► vmPFC computes choices that will be executed, whether for self or other ► Rostral dmPFC computes choices that are modeled, whether for self or other ► A similar gradient is present in temporoparietal cortex
Nicolle et al. show that valuation and choice for self and other exhibit parallel computations, where gradients exist within both medial prefrontal and temporoparietal cortices. Ventral regions compute choices that will be executed, while dorsal regions compute choices that are merely modeled.
Damage to prefrontal cortex (PFC) impairs decision-making, but the underlying value computations that might cause such impairments remain unclear. Here we report that value computations are doubly dissociable within PFC neurons. While many PFC neurons encoded chosen value, they used opponent encoding schemes such that averaging the neuronal population eliminated value coding. However, a special population of neurons in anterior cingulate cortex (ACC) - but not orbitofrontal cortex (OFC) - multiplex chosen value across decision parameters using a unified encoding scheme, and encoded reward prediction errors. In contrast, neurons in OFC - but not ACC - encoded chosen value relative to the recent history of choice values. Together, these results suggest complementary valuation processes across PFC areas: OFC neurons dynamically evaluate current choices relative to recent choice values, while ACC neurons encode choice predictions and prediction errors using a common valuation currency reflecting the integration of multiple decision parameters.
Reward prediction error (RPE) signals are central to current models of reward-learning. Temporal difference (TD) learning models posit that these signals should be modulated by predictions, not only of magnitude but also timing of reward. Here we show that BOLD activity in the VTA conforms to such TD predictions: responses to unexpected rewards are modulated by a temporal hazard function and activity between a predictive stimulus and reward is depressed in proportion to predicted reward. By contrast, BOLD activity in ventral striatum (VS) does not reflect a TD RPE, but instead encodes a signal on the variable relevant for behavior, here timing but not magnitude of reward. The results have important implications for dopaminergic models of cortico-striatal learning and suggest a modification of the conventional view that VS BOLD necessarily reflects inputs from dopaminergic VTA neurons signaling an RPE.
► Novel closed-loop (based on neuronal activity) DBS (CL-DBS) compared to standard DBS ► Cortico-pallidal CL-DBS yields greater alleviation of Parkinsonian akinesia ► Cortico-pallidal CL-DBS yields greater reduction of oscillatory neuronal discharge ► Pallido-pallidal CL-DBS leads to dissociation between discharge rate and patterns
Despite the prominence of parietal activity in human neuromaging investigations of sensorimotor and cognitive processes there remains uncertainty about basic aspects of parietal cortical anatomical organization. Descriptions of human parietal cortex draw heavily on anatomical schemes developed in other primate species but the validity of such comparisons has been questioned by claims that there are fundamental differences between the parietal cortex in humans and other primates. A scheme is presented for parcellation of human lateral parietal cortex into component regions on the basis of anatomical connectivity and the functional interactions of the resulting clusters with other brain regions. Anatomical connectivity was estimated using diffusion-weighted magnetic resonance image (MRI) based tractography and functional interactions were assessed by correlations in activity measured with functional MRI (fMRI) at rest. Resting state functional connectivity was also assessed directly in the rhesus macaque lateral parietal cortex in an additional experiment and the patterns found reflected known neuroanatomical connections. Cross-correlation in the tractography-based connectivity patterns of parietal voxels reliably parcellated human lateral parietal cortex into ten component clusters. The resting state functional connectivity of human superior parietal and intraparietal clusters with frontal and extrastriate cortex suggested correspondences with areas in macaque superior and intraparietal sulcus. Functional connectivity patterns with parahippocampal cortex and premotor cortex again suggested fundamental correspondences between inferior parietal cortex in humans and macaques. In contrast, the human parietal cortex differs in the strength of its interactions between the central inferior parietal lobule region and the anterior prefrontal cortex.
AIP; MIP; LIP; VIP; IPL; SPL
Diffusion imaging of post mortem brains has great potential both as a reference for brain specimens that undergo sectioning, and as a link between in vivo diffusion studies and “gold standard” histology/dissection. While there is a relatively mature literature on post mortem diffusion imaging of animals, human brains have proven more challenging due to their incompatibility with high-performance scanners. This study presents a method for post mortem diffusion imaging of whole, human brains using a clinical 3-Tesla scanner with a 3D segmented EPI spin-echo sequence. Results in eleven brains at 0.94 × 0.94 × 0.94 mm resolution are presented, and in a single brain at 0.73 × 0.73 × 0.73 mm resolution. Region-of-interest analysis of diffusion tensor parameters indicate that these properties are altered compared to in vivo (reduced diffusivity and anisotropy), with significant dependence on post mortem interval (time from death to fixation). Despite these alterations, diffusion tractography of several major tracts is successfully demonstrated at both resolutions. We also report novel findings of cortical anisotropy and partial volume effects.
► Acquisition and processing protocols for diffusion MRI of post-mortem human brains. ► Effect of post-mortem and scan intervals on diffusion indices. ► Tractography in post-mortem human brains. ► Radial diffusion anisotropy in cortical gray matter.
Diffusion tensor imaging; Tractography; Post mortem; Human; Brain
Purpose of review
Diffusion tractography uses non-invasive brain imaging data to trace fibre bundles in the human brain in vivo. This raises immediate possibilities for clinical application but responsible use of this approach requires careful consideration of the scope and limitations of the technique.
To illustrate the potential for tractography to provide new information in clinical neuroscience we review recent studies in three broad areas: First, use of tractography for quantitative comparisons of specific white matter pathways in disease; second, evidence from tractography for the presence of qualitatively different pathways in congenital disorders or following recovery; third, use of tractography to gain insights into normal brain anatomy that can aid our understanding of the consequences of localised pathology, or guide interventions.
Diffusion tractography opens exciting new possibilities for exploring features of brain anatomy that previously were not visible to us in vivo.
Diffusion imaging; tractography; white matter
Choosing an appropriate response in an uncertain and varying world is central to adaptive behaviour. The frequent activation of the anterior cingulate cortex (ACC) in a diverse range of tasks has lead to intense interest in and debate over its role in the guidance and control of performance. Here, we consider how this issue can be informed by a series of studies considering the ACC's role in more naturalistic situations where there is no single certain correct response and the relationships between choices and their consequences vary. A neuroimaging study of response switching demonstrates that dorsal ACC is not simply concerned with self-generated responses or error monitoring in isolation, but is instead involved in evaluating the outcome of choices, positive or negative, that have been voluntarily chosen. By contrast, an interconnected part of the orbitofrontal cortex is shown to be more active when attending to consequences of actions instructed by the experimenter. This dissociation is explained with reference to the anatomy of these regions in humans as demonstrated by diffusion weighted imaging. Lesions to a corresponding ACC region in monkeys has no effect on animals' ability to detect or immediately correct errors when response contingencies reverse, but renders them unable to sustain appropriate behaviour due to an impairment in the ability to integrate over time their recent history of choices and outcomes. Taken together, this implies a prominent role for the ACC within a distributed network of regions that determine the dynamic value of actions and guide decision making appropriately.
Studies in monkeys show clear anatomical and functional distinctions among networks connecting with subregions within the prefrontal cortex. Three such networks are centered on lateral orbitofrontal cortex, medial frontal and cingulate cortex, and lateral prefrontal cortex and all have been identified with distinct cognitive roles. Although these areas differ in a number of their cortical connections, some of the first anatomical evidence for these networks came from tracer studies demonstrating their distinct patterns of connectivity with the mediodorsal (MD) nucleus of the thalamus. Here, we present evidence for a similar topography of MD thalamus prefrontal connections, using non-invasive imaging and diffusion tractography (DWI–DT) in human and macaque. DWI–DT suggested that there was a high probability of interconnection between medial MD and lateral orbitofrontal cortex, between caudodorsal MD and medial frontal/cingulate cortex, and between lateral MD and lateral prefrontal cortex, in both species. Within the lateral prefrontal cortex a dorsolateral region (the principal sulcus in the macaque and middle frontal gyrus in the human) was found to have a high probability of interconnection with the MD region between the regions with a high probability of interconnection with other parts of the lateral prefrontal cortex and with the lateral orbitofrontal cortex. In addition to suggesting that the thalamic connectivity in the macaque is a good guide to human prefrontal cortex, and therefore that there are likely to be similarities in the cognitive roles played by the prefrontal areas in both species, the present results are also the first to provide insight into the topography of projections of an individual thalamic nucleus in the human brain.
Anatomy; DTI; Human; Macaque; Thalamus
Although experience-dependent structural changes have been demonstrated in adult gray matter, there is little evidence for such changes in white matter. Using diffusion imaging, we detected a localised increase in fractional anisotropy, a measure of microstructure, in white matter underlying the intraparietal sulcus, following training of a complex visuo-motor skill. This provides the first evidence for training related changes in white matter structure in the healthy human adult brain.
Orbitofrontal cortex (OFC) is widely held to be critical for flexibility in decision-making when established choice values change. OFC's role in such decision making was investigated in macaques performing dynamically changing three-armed bandit tasks. After selective OFC lesions, animals were impaired at discovering the identity of the highest value stimulus following reversals. However, this was not caused either by diminished behavioral flexibility or by insensitivity to reinforcement changes, but instead by paradoxical increases in switching between all stimuli. This pattern of choice behavior could be explained by a causal role for OFC in appropriate contingent learning, the process by which causal responsibility for a particular reward is assigned to a particular choice. After OFC lesions, animals' choice behavior no longer reflected the history of precise conjoint relationships between particular choices and particular rewards. Nonetheless, OFC-lesioned animals could still approximate choice-outcome associations using a recency-weighted history of choices and rewards.
► OFC lesions can impair decision making in both changeable and fixed environments ► OFC is critical for assigning credit for a particular reward to a particular choice ► OFC lesions spare system to approximate learning based on choice and reward history ► Role in contingent learning underlies impairments in flexible decision making
This article presents results obtained from applying various tools from FSL (FMRIB Software Library) to data from the repetition priming experiment used for the HBM’05 Functional Image Analysis Contest. We present analyses from the model-based General Linear Model (GLM) tool (FEAT) and from the model-free independent component analysis tool (MELODIC). We also discuss the application of tools for the correction of image distortions prior to the statistical analysis and the utility of recent advances in functional magnetic resonance imaging (FMRI) time series modeling and inference such as the use of optimal constrained HRF basis function modeling and mixture modeling inference. The combination of hemodynamic response function (HRF) and mixture modeling, in particular, revealed that both sentence content and speaker voice priming effects occurred bilaterally along the length of the superior temporal sulcus (STS). These results suggest that both are processed in a single underlying system without any significant asymmetries for content vs. voice processing.
functional magnetic resonance imaging (FMRI); independent component analysis (ICA); linear modeling; Functional Image Analysis Contest (FIAC)