The Tolman-Eichenbaum Machine: Unifying Space and Relational Memory through Generalization in the Hippocampal Formation.

The hippocampal-entorhinal system is important for spatial and relational memory tasks. We formally link these domains, provide a mechanistic understanding of the hippocampal role in generalization, and offer unifying principles underlying many entorhinal and hippocampal cell types. We propose medial entorhinal cells form a basis describing structural knowledge, and hippocampal cells link this basis with sensory representations. Adopting these principles, we introduce the Tolman-Eichenbaum machine (TEM). After learning, TEM entorhinal cells display diverse properties resembling apparently bespoke spatial responses, such as grid, band, border, and object-vector cells. TEM hippocampal cells include place and landmark cells that remap between environments. Crucially, TEM also aligns with empirically recorded representations in complex non-spatial tasks. TEM also generates predictions that hippocampal remapping is not random as previously believed; rather, structural knowledge is preserved across environments. We confirm this structural transfer over remapping in simultaneously recorded place and grid cells.

Neuronal Computation Underlying Inferential Reasoning in Humans and Mice.

Every day we make decisions critical for adaptation and survival. We repeat actions with known consequences. But we also draw on loosely related events to infer and imagine the outcome of entirely novel choices. These inferential decisions are thought to engage a number of brain regions; however, the underlying neuronal computation remains unknown. Here, we use a multi-day cross-species approach in humans and mice to report the functional anatomy and neuronal computation underlying inferential decisions. We show that during successful inference, the mammalian brain uses a hippocampal prospective code to forecast temporally structured learned associations. Moreover, during resting behavior, coactivation of hippocampal cells in sharp-wave/ripples represent inferred relationships that include reward, thereby "joining-the-dots" between events that have not been observed together but lead to profitable outcomes. Computing mnemonic links in this manner may provide an important mechanism to build a cognitive map that stretches beyond direct experience, thus supporting flexible behavior.

Human Replay Spontaneously Reorganizes Experience.

Knowledge abstracted from previous experiences can be transferred to aid new learning. Here, we asked whether such abstract knowledge immediately guides the replay of new experiences. We first trained participants on a rule defining an ordering of objects and then presented a novel set of objects in a scrambled order. Across two studies, we observed that representations of these novel objects were reactivated during a subsequent rest. As in rodents, human "replay" events occurred in sequences accelerated in time, compared to actual experience, and reversed their direction after a reward. Notably, replay did not simply recapitulate visual experience, but followed instead a sequence implied by learned abstract knowledge. Furthermore, each replay contained more than sensory representations of the relevant objects. A sensory code of object representations was preceded 50 ms by a code factorized into sequence position and sequence identity. We argue that this factorized representation facilitates the generalization of a previously learned structure to new objects.

Decoding cognition from spontaneous neural activity.

In human neuroscience, studies of cognition are rarely grounded in non-task-evoked, 'spontaneous' neural activity. Indeed, studies of spontaneous activity tend to focus predominantly on intrinsic neural patterns (for example, resting-state networks). Taking a 'representation-rich' approach bridges the gap between cognition and resting-state communities: this approach relies on decoding task-related representations from spontaneous neural activity, allowing quantification of the representational content and rich dynamics of such activity. For example, if we know the neural representation of an episodic memory, we can decode its subsequent replay during rest. We argue that such an approach advances cognitive research beyond a focus on immediate task demand and provides insight into the functional relevance of the intrinsic neural pattern (for example, the default mode network). This in turn enables a greater integration between human and animal neuroscience, facilitating experimental testing of theoretical accounts of intrinsic activity, and opening new avenues of research in psychiatry.

Structural and functional brain rewiring clarifies preserved interhemispheric transfer in humans born without the corpus callosum.

Why do humans born without the corpus callosum, the major interhemispheric commissure, lack the disconnection syndrome classically described in callosotomized patients? This paradox was discovered by Nobel laureate Roger Sperry in 1968, and has remained unsolved since then. To tackle the hypothesis that alternative neural pathways could explain this puzzle, we investigated patients with callosal dysgenesis using structural and functional neuroimaging, as well as neuropsychological assessments. We identified two anomalous white-matter tracts by deterministic and probabilistic tractography, and provide supporting resting-state functional neuroimaging and neuropsychological evidence for their functional role in preserved interhemispheric transfer of complex tactile information, such as object recognition. These compensatory pathways connect the homotopic posterior parietal cortical areas (Brodmann areas 39 and surroundings) via the posterior and anterior commissures. We propose that anomalous brain circuitry of callosal dysgenesis is determined by long-distance plasticity, a set of hardware changes occurring in the developing brain after pathological interference. So far unknown, these pathological changes somehow divert growing axons away from the dorsal midline, creating alternative tracts through the ventral forebrain and the dorsal midbrain midline, with partial compensatory effects to the interhemispheric transfer of cortical function.

Fusion in diffusion MRI for improved fibre orientation estimation: An application to the 3T and 7T data of the Human Connectome Project.

Determining the acquisition parameters in diffusion magnetic resonance imaging (dMRI) is governed by a series of trade-offs. Images of lower resolution have less spatial specificity but higher signal to noise ratio (SNR). At the same time higher angular contrast, important for resolving complex fibre patterns, also yields lower SNR. Considering these trade-offs, the Human Connectome Project (HCP) acquires high quality dMRI data for the same subjects at different field strengths (3T and 7T), which are publically released. Due to differences in the signal behavior and in the underlying scanner hardware, the HCP 3T and 7T data have complementary features in k- and q-space. The 3T dMRI has higher angular contrast and resolution, while the 7T dMRI has higher spatial resolution. Given the availability of these datasets, we explore the idea of fusing them together with the aim of combining their benefits. We extend a previously proposed data-fusion framework and apply it to integrate both datasets from the same subject into a single joint analysis. We use a generative model for performing parametric spherical deconvolution and estimate fibre orientations by simultaneously using data acquired under different protocols. We illustrate unique features from each dataset and how they are retained after fusion. We further show that this allows us to complement benefits and improve brain connectivity analysis compared to analyzing each of the datasets individually.

Brain systems for probabilistic and dynamic prediction: computational specificity and integration.

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.

Dissociable effects of surprise and model update in parietal and anterior cingulate cortex.

Brains use predictive models to facilitate the processing of expected stimuli or planned actions. Under a predictive model, surprising (low probability) stimuli or actions necessitate the immediate reallocation of processing resources, but they can also signal the need to update the underlying predictive model to reflect changes in the environment. Surprise and updating are often correlated in experimental paradigms but are, in fact, distinct constructs that can be formally defined as the Shannon information (IS) and Kullback-Leibler divergence (DKL) associated with an observation. In a saccadic planning task, we observed that distinct behaviors and brain regions are associated with surprise/IS and updating/DKL. Although surprise/IS was associated with behavioral reprogramming as indexed by slower reaction times, as well as with activity in the posterior parietal cortex [human lateral intraparietal area (LIP)], the anterior cingulate cortex (ACC) was specifically activated during updating of the predictive model (DKL). A second saccade-sensitive region in the inferior posterior parietal cortex (human 7a), which has connections to both LIP and ACC, was activated by surprise and modulated by updating. Pupillometry revealed a further dissociation between surprise and updating with an early positive effect of surprise and late negative effect of updating on pupil area. These results give a computational account of the roles of the ACC and two parietal saccade regions, LIP and 7a, by which their involvement in diverse tasks can be understood mechanistically. The dissociation of functional roles between regions within the reorienting/reprogramming network may also inform models of neurological phenomena, such as extinction and Balint syndrome, and neglect.

Connectivity-based functional analysis of dopamine release in the striatum using diffusion-weighted MRI and positron emission tomography.

The striatum acts in conjunction with the cortex to control and execute functions that are impaired by abnormal dopamine neurotransmission in disorders such as Parkinson's and schizophrenia. To date, in vivo quantification of striatal dopamine has been restricted to structure-based striatal subdivisions. Here, we present a multimodal imaging approach that quantifies the endogenous dopamine release following the administration of d-amphetamine in the functional subdivisions of the striatum of healthy humans with [(11)C]PHNO and [(11)C]Raclopride positron emission tomography ligands. Using connectivity-based (CB) parcellation, we subdivided the striatum into functional subregions based on striato-cortical anatomical connectivity information derived from diffusion magnetic resonance imaging (MRI) and probabilistic tractography. Our parcellation showed that the functional organization of the striatum was spatially coherent across individuals, congruent with primate data and previous diffusion MRI studies, with distinctive and overlapping networks. d-amphetamine induced the highest dopamine release in the limbic followed by the sensory, motor, and executive areas. The data suggest that the relative regional proportions of D2-like receptors are unlikely to be responsible for this regional dopamine release pattern. Notably, the homogeneity of dopamine release was significantly higher within the CB functional subdivisions in comparison with the structural subdivisions. These results support an association between local levels of dopamine release and cortical connectivity fingerprints.

Trial-type dependent frames of reference for value comparison.

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.

Associative learning of social value.

Our decisions are guided by information learnt from our environment. This information may come via personal experiences of reward, but also from the behaviour of social partners. Social learning is widely held to be distinct from other forms of learning in its mechanism and neural implementation; it is often assumed to compete with simpler mechanisms, such as reward-based associative learning, to drive behaviour. Recently, neural signals have been observed during social exchange reminiscent of signals seen in studies of associative learning. Here we demonstrate that social information may be acquired using the same associative processes assumed to underlie reward-based learning. We find that key computational variables for learning in the social and reward domains are processed in a similar fashion, but in parallel neural processing streams. Two neighbouring divisions of the anterior cingulate cortex were central to learning about social and reward-based information, and for determining the extent to which each source of information guides behaviour. When making a decision, however, the information learnt using these parallel streams was combined within ventromedial prefrontal cortex. These findings suggest that human social valuation can be realized by means of the same associative processes previously established for learning other, simpler, features of the environment.

Distributional reinforcement learning in prefrontal cortex.

The prefrontal cortex is crucial for learning and decision-making. Classic reinforcement learning (RL) theories center on learning the expectation of potential rewarding outcomes and explain a wealth of neural data in the prefrontal cortex. Distributional RL, on the other hand, learns the full distribution of rewarding outcomes and better explains dopamine responses. In the present study, we show that distributional RL also better explains macaque anterior cingulate cortex neuronal responses, suggesting that it is a common mechanism for reward-guided learning.

Spatially constrained hierarchical parcellation of the brain with resting-state fMRI.

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.

The WU-Minn Human Connectome Project: an overview.

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.

Advances in diffusion MRI acquisition and processing in the Human Connectome Project.

The Human Connectome Project (HCP) is a collaborative 5-year effort to map human brain connections and their variability in healthy adults. A consortium of HCP investigators will study a population of 1200 healthy adults using multiple imaging modalities, along with extensive behavioral and genetic data. In this overview, we focus on diffusion MRI (dMRI) and the structural connectivity aspect of the project. We present recent advances in acquisition and processing that allow us to obtain very high-quality in-vivo MRI data, whilst enabling scanning of a very large number of subjects. These advances result from 2 years of intensive efforts in optimising many aspects of data acquisition and processing during the piloting phase of the project. The data quality and methods described here are representative of the datasets and processing pipelines that will be made freely available to the community at quarterly intervals, beginning in 2013.

Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project.

The Human Connectome Project (HCP) relies primarily on three complementary magnetic resonance (MR) methods. These are: 1) resting state functional MR imaging (rfMRI) which uses correlations in the temporal fluctuations in an fMRI time series to deduce 'functional connectivity'; 2) diffusion imaging (dMRI), which provides the input for tractography algorithms used for the reconstruction of the complex axonal fiber architecture; and 3) task based fMRI (tfMRI), which is employed to identify functional parcellation in the human brain in order to assist analyses of data obtained with the first two methods. We describe technical improvements and optimization of these methods as well as instrumental choices that impact speed of acquisition of fMRI and dMRI images at 3T, leading to whole brain coverage with 2 mm isotropic resolution in 0.7 s for fMRI, and 1.25 mm isotropic resolution dMRI data for tractography analysis with three-fold reduction in total dMRI data acquisition time. Ongoing technical developments and optimization for acquisition of similar data at 7 T magnetic field are also presented, targeting higher spatial resolution, enhanced specificity of functional imaging signals, mitigation of the inhomogeneous radio frequency (RF) fields, and reduced power deposition. Results demonstrate that overall, these approaches represent a significant advance in MR imaging of the human brain to investigate brain function and structure.

The human brain reactivates context-specific past information at event boundaries of naturalistic experiences.

Although we perceive the world in a continuous manner, our experience is partitioned into discrete events. However, to make sense of these events, they must be stitched together into an overarching narrative-a model of unfolding events. It has been proposed that such a stitching process happens in offline neural reactivations when rodents build models of spatial environments. Here we show that, while understanding a natural narrative, humans reactivate neural representations of past events. Similar to offline replay, these reactivations occur in the hippocampus and default mode network, where reactivations are selective to relevant past events. However, these reactivations occur, not during prolonged offline periods, but at the boundaries between ongoing narrative events. These results, replicated across two datasets, suggest reactivations as a candidate mechanism for binding temporally distant information into a coherent understanding of ongoing experience.

A cognitive map for value-guided choice in ventromedial prefrontal cortex.

The prefrontal cortex is crucial for economic decision-making and representing the value of options. However, how such representations facilitate flexible decisions remains unknown. We reframe economic decision-making in prefrontal cortex in line with representations of structure within the medial temporal lobe because such cognitive map representations are known to facilitate flexible behaviour. Specifically, we framed choice between different options as a navigation process in value space. Here we show that choices in a 2D value space defined by reward magnitude and probability were represented with a grid-like code, analogous to that found in spatial navigation. The grid-like code was present in ventromedial prefrontal cortex (vmPFC) local field potential theta frequency and the result replicated in an independent dataset. Neurons in vmPFC similarly contained a grid-like code, in addition to encoding the linear value of the chosen option. Importantly, both signals were modulated by theta frequency - occurring at theta troughs but on separate theta cycles. Furthermore, we found sharp-wave ripples - a key neural signature of planning and flexible behaviour - in vmPFC, which were modulated by accuracy and reward. These results demonstrate that multiple cognitive map-like computations are deployed in vmPFC during economic decision-making, suggesting a new framework for the implementation of choice in prefrontal cortex.

Diffusion-weighted imaging tractography-based parcellation of the human parietal cortex and comparison with human and macaque resting-state functional connectivity.

Despite the prominence of parietal activity in human neuroimaging 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 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 10 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.

Ball and rackets: Inferring fiber fanning from diffusion-weighted MRI.

A number of methods have been proposed for resolving crossing fibers from diffusion-weighted (DW) MRI. However, other complex fiber geometries have drawn minimal attention. In this study, we focus on fiber orientation dispersion induced by within-voxel fanning. We use a multi-compartment, model-based approach to estimate fiber dispersion. Bingham distributions are employed to represent continuous distributions of fiber orientations, centered around a main orientation, and capturing anisotropic dispersion. We evaluate the accuracy of the model for different simulated fanning geometries, under different acquisition protocols and we illustrate the high SNR and angular resolution needs. We also perform a qualitative comparison between our parametric approach and five popular non-parametric techniques that are based on orientation distribution functions (ODFs). This comparison illustrates how the same underlying geometry can be depicted by different methods. We apply the proposed model on high-quality, post-mortem macaque data and present whole-brain maps of fiber dispersion, as well as exquisite details on the local anatomy of fiber distributions in various white matter regions.