Search

During conversation, people often endeavor to convey information in an understandable way (finding common ground) while also sharing novel or surprising information (exploring new ground). Here, we test how friends and strangers balance these two strategies to connect with each other. Using fMRI hyperscanning, we measure a preference for common ground as convergence over time and exploring new ground as divergence over time by tracking dyads' neural and linguistic trajectories over the course of semi-structured intimacy-building conversations. In our study, 60 dyads (30 friend dyads) engaged in a real-time conversation with discrete prompts and demarcated turns. Our analyses reveal that friends diverge neurally and linguistically: their neural patterns become more dissimilar over time and they explore more diverse topics. In contrast, strangers converge: neural patterns and language become more similar over time. The more a conversation between strangers resembles the exploratory conversations of friends, the more they enjoy it. Our results highlight exploring new ground as a strategy for a successful conversation., (© 2024. The Author(s).)

Serial section transmission electron microscopy (TEM) has proven to be one of the leading methods for millimeter-scale 3D imaging of brain tissues at nanoscale resolution. It is important to further improve imaging efficiency to acquire larger and more brain volumes. We report here a threefold increase in the speed of TEM by using a beam deflecting mechanism to enable highly efficient acquisition of multiple image tiles (nine) for each motion of the mechanical stage. For millimeter-scale areas, the duty cycle of imaging doubles to more than 30%, yielding a net average imaging rate of 0.3 gigapixels per second. If fully utilized, an array of four beam deflection TEMs should be capable of imaging a dataset of cubic millimeter scale in five weeks., (© 2024. The Author(s).)

Reorientation, the process of regaining one's bearings after becoming lost, requires identification of a spatial context (context recognition) and recovery of facing direction within that context (heading retrieval). We previously showed that these processes rely on the use of features and geometry, respectively. Here, we examine reorientation behavior in a task that creates contextual ambiguity over a long timescale to demonstrate that male mice learn to combine both featural and geometric cues to recover heading. At the neural level, most CA1 neurons persistently align to geometry, and this alignment predicts heading behavior. However, a small subset of cells remaps coherently in a context-sensitive manner, which serves to predict context. Efficient heading retrieval and context recognition correlate with rate changes reflecting integration of featural and geometric information in the active ensemble. These data illustrate how context recognition and heading retrieval are coded in CA1 and how these processes change with experience., (© 2024. The Author(s).)

When processing language, the brain is thought to deploy specialized computations to construct meaning from complex linguistic structures. Recently, artificial neural networks based on the Transformer architecture have revolutionized the field of natural language processing. Transformers integrate contextual information across words via structured circuit computations. Prior work has focused on the internal representations ("embeddings") generated by these circuits. In this paper, we instead analyze the circuit computations directly: we deconstruct these computations into the functionally-specialized "transformations" that integrate contextual information across words. Using functional MRI data acquired while participants listened to naturalistic stories, we first verify that the transformations account for considerable variance in brain activity across the cortical language network. We then demonstrate that the emergent computations performed by individual, functionally-specialized "attention heads" differentially predict brain activity in specific cortical regions. These heads fall along gradients corresponding to different layers and context lengths in a low-dimensional cortical space., (© 2024. The Author(s).)

Staying engaged is necessary to maintain goal-directed behaviors. Despite this, engagement exhibits continuous, intrinsic fluctuations. Even in experimental settings, animals, unlike most humans, repeatedly and spontaneously move between periods of complete task engagement and disengagement. We, therefore, looked at behavior in male macaques (macaca mulatta) in four tasks while recording fMRI signals. We identified consistent autocorrelation in task disengagement. This made it possible to build models capturing task-independent engagement. We identified task general patterns of neural activity linked to impending sudden task disengagement in mid-cingulate gyrus. By contrast, activity centered in perigenual anterior cingulate cortex (pgACC) was associated with maintenance of performance across tasks. Importantly, we carefully controlled for task-specific factors such as the reward history and other motivational effects, such as response vigor, in our analyses. Moreover, we showed pgACC activity had a causal link to task engagement: transcranial ultrasound stimulation of pgACC changed task engagement patterns., (© 2024. The Author(s).)

The precise neural mechanisms within the brain that contribute to the remarkable lifetime persistence of memory are not fully understood. Two-photon calcium imaging allows the activity of individual cells to be followed across long periods, but conventional approaches require head-fixation, which limits the type of behavior that can be studied. We present a magnetic voluntary head-fixation system that provides stable optical access to the brain during complex behavior. Compared to previous systems that used mechanical restraint, there are no moving parts and animals can engage and disengage entirely at will. This system is failsafe, easy for animals to use and reliable enough to allow long-term experiments to be routinely performed. Animals completed hundreds of trials per session of an odor discrimination task that required 2-4 s fixations. Together with a reflectance fluorescence collection scheme that increases two-photon signal and a transgenic Thy1-GCaMP6f rat line, we are able to reliably image the cellular activity in the hippocampus during behavior over long periods (median 6 months), allowing us track the same neurons over a large fraction of animals' lives (up to 19 months)., (© 2024. The Author(s).)

Trial history biases and lapses are two of the most common suboptimalities observed during perceptual decision-making. These suboptimalities are routinely assumed to arise from distinct processes. However, previous work has suggested that they covary in their prevalence and that their proposed neural substrates overlap. Here we demonstrate that during decision-making, history biases and apparent lapses can both arise from a common cognitive process that is optimal under mistaken beliefs that the world is changing i.e. nonstationary. This corresponds to an accumulation-to-bound model with history-dependent updates to the initial state of the accumulator. We test our model's predictions about the relative prevalence of history biases and lapses, and show that they are robustly borne out in two distinct decision-making datasets of male rats, including data from a novel reaction time task. Our model improves the ability to precisely predict decision-making dynamics within and across trials, by positing a process through which agents can generate quasi-stochastic choices., (© 2024. The Author(s).)

Human behavior depends on both internal and external factors. Internally, people's mental states motivate and govern their behavior. Externally, one's situation constrains which actions are appropriate or possible. To predict others' behavior, one must understand the influences of mental states and situations on actions. On this basis, we hypothesize that people represent situations and states in terms of associated actions. To test this, we use functional neuroimaging to estimate neural activity patterns associated with situations, mental states, and actions. We compute sums of the action patterns, weighted by how often each action occurs in each situation and state. We find that these summed action patterns reconstructed the corresponding situation and state patterns. These results suggest that neural representations of situations and mental states are composed of sums of their action affordances. Summed action representations thus offer a biological mechanism by which people can predict actions given internal and external factors., (© 2024. The Author(s).)

The coding privilege of end-spectral hues (red and blue) in the early visual cortex has been reported in primates. However, the origin of such bias remains unclear. Here, we provide a complete picture of the end-spectral bias in visual system by measuring fMRI signals and spiking activities in macaques. The correlated end-spectral biases between the LGN and V1 suggest a subcortical source for asymmetric coding. Along the ventral pathway from V1 to V4, red bias against green peaked in V1 and then declined, whereas blue bias against yellow showed an increasing trend. The feedforward and recurrent modifications of end-spectral bias were further revealed by dynamic causal modeling analysis. Moreover, we found that the strongest end-spectral bias in V1 was in layer 4C[Formula: see text]. Our results suggest that end-spectral bias already exists in the LGN and is transmitted to V1 mainly through the parvocellular pathway, then embellished by cortical processing., (© 2024. The Author(s).)

The reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. Segmentation accuracy is often limited by the preceding step of aligning 2D section images to create a 3D image stack. Precise and robust alignment in the presence of image artifacts is challenging, especially as datasets are attaining the petascale. We present a computational pipeline for aligning ssEM images with several key elements. Self-supervised convolutional nets are trained via metric learning to encode and align image pairs, and they are used to initialize iterative fine-tuning of alignment. A procedure called vector voting increases robustness to image artifacts or missing image data. For speedup the series is divided into blocks that are distributed to computational workers for alignment. The blocks are aligned to each other by composing transformations with decay, which achieves a global alignment without resorting to a time-consuming global optimization. We apply our pipeline to a whole fly brain dataset, and show improved accuracy relative to prior state of the art. We also demonstrate that our pipeline scales to a cubic millimeter of mouse visual cortex. Our pipeline is publicly available through two open source Python packages., (© 2024. The Author(s).)

Regions in ventral temporal cortex that are involved in visual recognition of categories like words and faces undergo differential development during childhood. However, categories are also represented in distributed responses across high-level visual cortex. How distributed category representations develop and if this development relates to behavioral changes in recognition remains largely unknown. Here, we used functional magnetic resonance imaging to longitudinally measure the development of distributed responses across ventral temporal cortex to 10 categories in school-age children over several years. Our results reveal both strengthening and weakening of category representations with age, which was mainly driven by changes across category-selective voxels. Representations became particularly more distinct for words in the left hemisphere and for faces bilaterally. Critically, distinctiveness for words and faces across category-selective voxels in left and right lateral ventral temporal cortex, respectively, predicted individual children's word and face recognition performance. These results suggest that the development of distributed representations in ventral temporal cortex has behavioral ramifications and advance our understanding of prolonged cortical development during childhood., (© 2023. The Author(s).)

Maps of the nervous system that identify individual cells along with their type, subcellular components and connectivity have the potential to elucidate fundamental organizational principles of neural circuits. Nanometer-resolution imaging of brain tissue provides the necessary raw data, but inferring cellular and subcellular annotation layers is challenging. We present segmentation-guided contrastive learning of representations (SegCLR), a self-supervised machine learning technique that produces representations of cells directly from 3D imagery and segmentations. When applied to volumes of human and mouse cortex, SegCLR enables accurate classification of cellular subcompartments and achieves performance equivalent to a supervised approach while requiring 400-fold fewer labeled examples. SegCLR also enables inference of cell types from fragments as small as 10 μm, which enhances the utility of volumes in which many neurites are truncated at boundaries. Finally, SegCLR enables exploration of layer 5 pyramidal cell subtypes and automated large-scale analysis of synaptic partners in mouse visual cortex., (© 2023. The Author(s).)

The cortical population code is pervaded by activity patterns evoked by movement, but it remains largely unknown how such signals relate to natural behavior or how they might support processing in sensory cortices where they have been observed. To address this we compared high-density neural recordings across four cortical regions (visual, auditory, somatosensory, motor) in relation to sensory modulation, posture, movement, and ethograms of freely foraging male rats. Momentary actions, such as rearing or turning, were represented ubiquitously and could be decoded from all sampled structures. However, more elementary and continuous features, such as pose and movement, followed region-specific organization, with neurons in visual and auditory cortices preferentially encoding mutually distinct head-orienting features in world-referenced coordinates, and somatosensory and motor cortices principally encoding the trunk and head in egocentric coordinates. The tuning properties of synaptically coupled cells also exhibited connection patterns suggestive of area-specific uses of pose and movement signals, particularly in visual and auditory regions. Together, our results indicate that ongoing behavior is encoded at multiple levels throughout the dorsal cortex, and that low-level features are differentially utilized by different regions to serve locally relevant computations., (© 2023. The Author(s).)

Two different perspectives have informed efforts to explain the link between the brain and behaviour. One approach seeks to identify neural circuit elements that carry out specific functions, emphasizing connectivity between neurons as a substrate for neural computations. Another approach centres on neural manifolds - low-dimensional representations of behavioural signals in neural population activity - and suggests that neural computations are realized by emergent dynamics. Although manifolds reveal an interpretable structure in heterogeneous neuronal activity, finding the corresponding structure in connectivity remains a challenge. We highlight examples in which establishing the correspondence between low-dimensional activity and connectivity has been possible, unifying the neural manifold and circuit perspectives. This relationship is conspicuous in systems in which the geometry of neural responses mirrors their spatial layout in the brain, such as the fly navigational system. Furthermore, we describe evidence that, in systems in which neural responses are heterogeneous, the circuit comprises interactions between activity patterns on the manifold via low-rank connectivity. We suggest that unifying the manifold and circuit approaches is important if we are to be able to causally test theories about the neural computations that underlie behaviour., (© 2023. Springer Nature Limited.)

Intrinsic timescales characterize dynamics of endogenous fluctuations in neural activity. Variation of intrinsic timescales across the neocortex reflects functional specialization of cortical areas, but less is known about how intrinsic timescales change during cognitive tasks. We measured intrinsic timescales of local spiking activity within columns of area V4 in male monkeys performing spatial attention tasks. The ongoing spiking activity unfolded across at least two distinct timescales, fast and slow. The slow timescale increased when monkeys attended to the receptive fields location and correlated with reaction times. By evaluating predictions of several network models, we found that spatiotemporal correlations in V4 activity were best explained by the model in which multiple timescales arise from recurrent interactions shaped by spatially arranged connectivity, and attentional modulation of timescales results from an increase in the efficacy of recurrent interactions. Our results suggest that multiple timescales may arise from the spatial connectivity in the visual cortex and flexibly change with the cognitive state due to dynamic effective interactions between neurons., (© 2023. The Author(s).)

Mutation or deletion of the SHANK3 gene, which encodes a synaptic scaffolding protein, is linked to autism spectrum disorder and Phelan-McDermid syndrome, conditions associated with social memory impairments. Shank3B knockout mice also exhibit social memory deficits. The CA2 region of the hippocampus integrates numerous inputs and sends a major output to the ventral CA1 (vCA1). Despite finding few differences in excitatory afferents to the CA2 in Shank3B knockout mice, we found that activation of CA2 neurons as well as the CA2-vCA1 pathway restored social recognition function to wildtype levels. vCA1 neuronal oscillations have been linked to social memory, but we observed no differences in these measures between wildtype and Shank3B knockout mice. However, activation of the CA2 enhanced vCA1 theta power in Shank3B knockout mice, concurrent with behavioral improvements. These findings suggest that stimulating adult circuitry in a mouse model with neurodevelopmental impairments can invoke latent social memory function., (© 2023. The Author(s).)

Adult visual performance differs with angular location -it is better for stimuli along the horizontal than vertical, and lower than upper vertical meridian of the visual field. These perceptual asymmetries are paralleled by asymmetries in cortical surface area in primary visual cortex (V1). Children, unlike adults, have similar visual performance at the lower and upper vertical meridian. Do children have similar V1 surface area representing the upper and lower vertical meridian? Using MRI, we measure the surface area of retinotopic maps (V1-V3) in children and adults. Many features of the maps are similar between groups, including greater V1 surface area for the horizontal than vertical meridian. However, unlike adults, children have a similar amount of V1 surface area representing the lower and upper vertical meridian. These data reveal a late-stage change in V1 organization that may relate to the emergence of the visual performance asymmetry along the vertical meridian by adulthood., (© 2023. The Author(s).)

Working memory (WM) allows us to remember and selectively control a limited set of items. Neural evidence suggests it is achieved by interactions between bursts of beta and gamma oscillations. However, it is not clear how oscillations, reflecting coherent activity of millions of neurons, can selectively control individual WM items. Here we propose the novel concept of spatial computing where beta and gamma interactions cause item-specific activity to flow spatially across the network during a task. This way, control-related information such as item order is stored in the spatial activity independent of the detailed recurrent connectivity supporting the item-specific activity itself. The spatial flow is in turn reflected in low-dimensional activity shared by many neurons. We verify these predictions by analyzing local field potentials and neuronal spiking. We hypothesize that spatial computing can facilitate generalization and zero-shot learning by utilizing spatial component as an additional information encoding dimension., (© 2023. The Author(s).)

Early-life adversity (ELA) increases the likelihood of neuropsychiatric diagnoses, which are more prevalent in women than men. Since changes in reproductive hormone levels can also increase the probability of anxiety disorders in women, we examined the effects of ELA on adult female mice across the estrous cycle. We found that during diestrus, when progesterone levels are relatively high, ELA mice exhibit increased avoidance behavior and increased theta oscillation power in the ventral hippocampus (vHIP). We also found that diestrus ELA mice had higher levels of progesterone and lower levels of allopregnanolone, a neurosteroid metabolite of progesterone, in the vHIP compared with control-reared mice. Progesterone receptor antagonism normalized avoidance behavior in ELA mice, while treatment with a negative allosteric modulator of allopregnanolone promoted avoidance behavior in control mice. These results suggest that altered vHIP progesterone and allopregnanolone signaling during diestrus increases avoidance behavior in ELA mice., (© 2022. The Author(s).)

The ability to acquire ever larger datasets of brain tissue using volume electron microscopy leads to an increasing demand for the automated extraction of connectomic information. We introduce SyConn2, an open-source connectome analysis toolkit, which works with both on-site high-performance compute environments and rentable cloud computing clusters. SyConn2 was tested on connectomic datasets with more than 10 million synapses, provides a web-based visualization interface and makes these data amenable to complex anatomical and neuronal connectivity queries., (© 2022. The Author(s).)

Dense reconstruction of synaptic connectivity requires high-resolution electron microscopy images of entire brains and tools to efficiently trace neuronal wires across the volume. To generate such a resource, we sectioned and imaged a larval zebrafish brain by serial block-face electron microscopy at a voxel size of 14 × 14 × 25 nm 3 . We segmented the resulting dataset with the flood-filling network algorithm, automated the detection of chemical synapses and validated the results by comparisons to transmission electron microscopic images and light-microscopic reconstructions. Neurons and their connections are stored in the form of a queryable and expandable digital address book. We reconstructed a network of 208 neurons involved in visual motion processing, most of them located in the pretectum, which had been functionally characterized in the same specimen by two-photon calcium imaging. Moreover, we mapped all 407 presynaptic and postsynaptic partners of two superficial interneurons in the tectum. The resource developed here serves as a foundation for synaptic-resolution circuit analyses in the zebrafish nervous system., (© 2022. The Author(s).)

During decision making in a changing environment, evidence that may guide the decision accumulates until the point of action. In the rat, provisional choice is thought to be represented in frontal orienting fields (FOF), but this has only been tested in static environments where provisional and final decisions are not easily dissociated. Here, we characterize the representation of accumulated evidence in the FOF of rats performing a recently developed dynamic evidence accumulation task, which induces changes in the provisional decision, referred to as "changes of mind". We find that FOF encodes evidence throughout decision formation with a temporal gain modulation that rises until the period when the animal may need to act. Furthermore, reversals in FOF firing rates can be accounted for by changes of mind predicted using a model of the decision process fit only to behavioral data. Our results suggest that the FOF represents provisional decisions even in dynamic, uncertain environments, allowing for rapid motor execution when it is time to act., (© 2022. The Author(s).)

Population recordings of calcium activity are a major source of insight into neural function. Large datasets require automated processing, but this can introduce errors that are difficult to detect. Here we show that popular time course-estimation algorithms often contain substantial misattribution errors affecting 10-20% of transients. Misattribution, in which fluorescence is ascribed to the wrong cell, arises when overlapping cells and processes are imperfectly defined or not identified. To diagnose misattribution, we develop metrics and visualization tools for evaluating large datasets. To correct time courses, we introduce a robust estimator that explicitly accounts for contaminating signals. In one hippocampal dataset, removing contamination reduced the number of place cells by 15%, and 19% of place fields shifted by over 10 cm. Our methods are compatible with other cell-finding techniques, empowering users to diagnose and correct a potentially widespread problem that could alter scientific conclusions., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

The desire to understand how the brain generates and patterns behavior has driven rapid methodological innovation in tools to quantify natural animal behavior. While advances in deep learning and computer vision have enabled markerless pose estimation in individual animals, extending these to multiple animals presents unique challenges for studies of social behaviors or animals in their natural environments. Here we present Social LEAP Estimates Animal Poses (SLEAP), a machine learning system for multi-animal pose tracking. This system enables versatile workflows for data labeling, model training and inference on previously unseen data. SLEAP features an accessible graphical user interface, a standardized data model, a reproducible configuration system, over 30 model architectures, two approaches to part grouping and two approaches to identity tracking. We applied SLEAP to seven datasets across flies, bees, mice and gerbils to systematically evaluate each approach and architecture, and we compare it with other existing approaches. SLEAP achieves greater accuracy and speeds of more than 800 frames per second, with latencies of less than 3.5 ms at full 1,024 × 1,024 image resolution. This makes SLEAP usable for real-time applications, which we demonstrate by controlling the behavior of one animal on the basis of the tracking and detection of social interactions with another animal., (© 2022. The Author(s).)

Due to advances in automated image acquisition and analysis, whole-brain connectomes with 100,000 or more neurons are on the horizon. Proofreading of whole-brain automated reconstructions will require many person-years of effort, due to the huge volumes of data involved. Here we present FlyWire, an online community for proofreading neural circuits in a Drosophila melanogaster brain and explain how its computational and social structures are organized to scale up to whole-brain connectomics. Browser-based three-dimensional interactive segmentation by collaborative editing of a spatially chunked supervoxel graph makes it possible to distribute proofreading to individuals located virtually anywhere in the world. Information in the edit history is programmatically accessible for a variety of uses such as estimating proofreading accuracy or building incentive systems. An open community accelerates proofreading by recruiting more participants and accelerates scientific discovery by requiring information sharing. We demonstrate how FlyWire enables circuit analysis by reconstructing and analyzing the connectome of mechanosensory neurons., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Humans form lasting memories of stimuli that were only encountered once. This naturally occurs when listening to a story, however it remains unclear how and when memories are stored and retrieved during story-listening. Here, we first confirm in behavioral experiments that participants can learn about the structure of a story after a single exposure and are able to recall upcoming words when the story is presented again. We then track mnemonic information in high frequency activity (70-200 Hz) as patients undergoing electrocorticographic recordings listen twice to the same story. We demonstrate predictive recall of upcoming information through neural responses in auditory processing regions. This neural measure correlates with behavioral measures of event segmentation and learning. Event boundaries are linked to information flow from cortex to hippocampus. When listening for a second time, information flow from hippocampus to cortex precedes moments of predictive recall. These results provide insight on a fine-grained temporal scale into how episodic memory encoding and retrieval work under naturalistic conditions., (© 2021. The Author(s).)

The lateral prefrontal cortex (LPFC) is disproportionately expanded in humans compared to non-human primates, although the relationship between LPFC brain structures and uniquely human cognitive skills is largely unknown. Here, we test the relationship between variability in LPFC tertiary sulcal morphology and reasoning scores in a cohort of children and adolescents. Using a data-driven approach in independent discovery and replication samples, we show that the depth of specific LPFC tertiary sulci is associated with individual differences in reasoning scores beyond age. To expedite discoveries in future neuroanatomical-behavioral studies, we share tertiary sulcal definitions with the field. These findings support a classic but largely untested theory linking the protracted development of tertiary sulci to late-developing cognitive processes., (© 2021. The Author(s).)

It is thought that the brain's judicious reuse of previous computation underlies our ability to plan flexibly, but also that inappropriate reuse gives rise to inflexibilities like habits and compulsion. Yet we lack a complete, realistic account of either. Building on control engineering, here we introduce a model for decision making in the brain that reuses a temporally abstracted map of future events to enable biologically-realistic, flexible choice at the expense of specific, quantifiable biases. It replaces the classic nonlinear, model-based optimization with a linear approximation that softly maximizes around (and is weakly biased toward) a default policy. This solution demonstrates connections between seemingly disparate phenomena across behavioral neuroscience, notably flexible replanning with biases and cognitive control. It also provides insight into how the brain can represent maps of long-distance contingencies stably and componentially, as in entorhinal response fields, and exploit them to guide choice even under changing goals., (© 2021. The Author(s).)

Face-processing occurs across ventral and lateral visual streams, which are involved in static and dynamic face perception, respectively. However, the nature of spatial computations across streams is unknown. Using functional MRI and population receptive field (pRF) mapping, we measured pRFs in face-selective regions. Results reveal that spatial computations by pRFs in ventral face-selective regions are concentrated around the center of gaze (fovea), but spatial computations in lateral face-selective regions extend peripherally. Diffusion MRI reveals that these differences are mirrored by a preponderance of white matter connections between ventral face-selective regions and foveal early visual cortex (EVC), while connections with lateral regions are distributed more uniformly across EVC eccentricities. These findings suggest a rethinking of spatial computations in face-selective regions, showing that they vary across ventral and lateral streams, and further propose that spatial computations in high-level regions are scaffolded by the fine-grain pattern of white matter connections from EVC.

Despite major advances in measuring human brain activity during and after educational experiences, it is unclear how learners internalize new content, especially in real-life and online settings. In this work, we introduce a neural approach to predicting and assessing learning outcomes in a real-life setting. Our approach hinges on the idea that successful learning involves forming the right set of neural representations, which are captured in canonical activity patterns shared across individuals. Specifically, we hypothesized that learning is mirrored in neural alignment: the degree to which an individual learner's neural representations match those of experts, as well as those of other learners. We tested this hypothesis in a longitudinal functional MRI study that regularly scanned college students enrolled in an introduction to computer science course. We additionally scanned graduate student experts in computer science. We show that alignment among students successfully predicts overall performance in a final exam. Furthermore, within individual students, we find better learning outcomes for concepts that evoke better alignment with experts and with other students, revealing neural patterns associated with specific learned concepts in individuals.

The default mode network (DMN) is classically considered an 'intrinsic' system, specializing in internally oriented cognitive processes such as daydreaming, reminiscing and future planning. In this Perspective, we suggest that the DMN is an active and dynamic 'sense-making' network that integrates incoming extrinsic information with prior intrinsic information to form rich, context-dependent models of situations as they unfold over time. We review studies that relied on naturalistic stimuli, such as stories and movies, to demonstrate how an individual's DMN neural responses are influenced both by external information accumulated as events unfold over time and by the individual's idiosyncratic past memories and knowledge. The integration of extrinsic and intrinsic information over long timescales provides a space for negotiating a shared neural code, which is necessary for establishing shared meaning, shared communication tools, shared narratives and, above all, shared communities and social networks.

The vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18-45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86-0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86-0.91) and 0.90 (0.87-0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57-0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD.

The scale of human interaction is larger than ever before-people regularly interact with and learn from others around the world, and everyone impacts the global environment. We develop an evolutionary game theory model to ask how the scale of interaction affects the evolution of cognition. Our agents make decisions using automatic (e.g., reflexive) versus controlled (e.g., deliberative) cognition, interact with each other, and influence the environment (i.e., game payoffs). We find that globalized direct contact between agents can either favor or disfavor control, depending on whether controlled agents are harmed or helped by contact with automatic agents; globalized environment disfavors cognitive control, while also promoting strategic diversity and fostering mesoscale communities of more versus less controlled agents; and globalized learning destroys mesoscale communities and homogenizes the population. These results emphasize the importance of the scale of interaction for the evolution of cognition, and help shed light on modern challenges.

Connections between neuronal populations may be genetically hardwired or random. In the insect olfactory system, projection neurons of the antennal lobe connect randomly to Kenyon cells of the mushroom body. Consequently, while the odor responses of the projection neurons are stereotyped across individuals, the responses of the Kenyon cells are variable. Surprisingly, downstream of Kenyon cells, mushroom body output neurons show stereotypy in their responses. We found that the stereotypy is enabled by the convergence of inputs from many Kenyon cells onto an output neuron, and does not require learning. The stereotypy emerges in the total response of the Kenyon cell population using multiple odor-specific features of the projection neuron responses, benefits from the nonlinearity in the transfer function, depends on the convergence:randomness ratio, and is constrained by sparseness. Together, our results reveal the fundamental mechanisms and constraints with which convergence enables stereotypy in sensory responses despite random connectivity.

Dopamine neurons are proposed to signal the reward prediction error in model-free reinforcement learning algorithms. This term represents the unpredicted or 'excess' value of the rewarding event, value that is then added to the intrinsic value of any antecedent cues, contexts or events. To support this proposal, proponents cite evidence that artificially-induced dopamine transients cause lasting changes in behavior. Yet these studies do not generally assess learning under conditions where an endogenous prediction error would occur. Here, to address this, we conducted three experiments where we optogenetically activated dopamine neurons while rats were learning associative relationships, both with and without reward. In each experiment, the antecedent cues failed to acquire value and instead entered into associations with the later events, whether valueless cues or valued rewards. These results show that in learning situations appropriate for the appearance of a prediction error, dopamine transients support associative, rather than model-free, learning.

Abuse, neglect, and other forms of early life stress (ELS) significantly increase risk for psychiatric disorders including depression. In this study, we show that ELS in a postnatal sensitive period increases sensitivity to adult stress in female mice, consistent with our earlier findings in male mice. We used RNA-sequencing in the ventral tegmental area, nucleus accumbens, and prefrontal cortex of male and female mice to show that adult stress is distinctly represented in the brain's transcriptome depending on ELS history. We identify: 1) biological pathways disrupted after ELS and associated with increased behavioral stress sensitivity, 2) putative transcriptional regulators of the effect of ELS on adult stress response, and 3) subsets of primed genes specifically associated with latent behavioral changes. We also provide transcriptomic evidence that ELS increases sensitivity to future stress through enhancement of known programs of cortical plasticity.

Researchers and educators have long wrestled with the question of how best to teach their clients be they humans, non-human animals or machines. Here, we examine the role of a single variable, the difficulty of training, on the rate of learning. In many situations we find that there is a sweet spot in which training is neither too easy nor too hard, and where learning progresses most quickly. We derive conditions for this sweet spot for a broad class of learning algorithms in the context of binary classification tasks. For all of these stochastic gradient-descent based learning algorithms, we find that the optimal error rate for training is around 15.87% or, conversely, that the optimal training accuracy is about 85%. We demonstrate the efficacy of this 'Eighty Five Percent Rule' for artificial neural networks used in AI and biologically plausible neural networks thought to describe animal learning.

Across vertebrates, progressive changes in vocal behavior during postnatal development are typically attributed solely to developing neural circuits. How the changing body influences vocal development remains unknown. Here we show that state changes in the contact vocalizations of infant marmoset monkeys, which transition from noisy, low frequency cries to tonal, higher pitched vocalizations in adults, are caused partially by laryngeal development. Combining analyses of natural vocalizations, motorized excised larynx experiments, tensile material tests and high-speed imaging, we show that vocal state transition occurs via a sound source switch from vocal folds to apical vocal membranes, producing louder vocalizations with higher efficiency. We show with an empirically based model of descending motor control how neural circuits could interact with changing laryngeal dynamics, leading to adaptive vocal development. Our results emphasize the importance of embodied approaches to vocal development, where exploiting biomechanical consequences of changing material properties can simplify motor control, reducing the computational load on the developing brain.

The striatum is essential for learning which actions lead to reward and for implementing those actions. Decades of experimental and theoretical work have led to several influential theories and hypotheses about how the striatal circuit mediates these functions. However, owing to technical limitations, testing these hypotheses rigorously has been difficult. In this Review, we briefly describe some of the classic ideas of striatal function. We then review recent studies in rodents that take advantage of optical and genetic methods to test these classic ideas by recording and manipulating identified cell types within the circuit. This new body of work has provided experimental support of some longstanding ideas about the striatal circuit and has uncovered critical aspects of the classic view that are incorrect or incomplete.

Working memory is critical to cognition, decoupling behavior from the immediate world. Yet, it is imperfect; internal noise introduces errors into memory representations. Such errors have been shown to accumulate over time and increase with the number of items simultaneously held in working memory. Here, we show that discrete attractor dynamics mitigate the impact of noise on working memory. These dynamics pull memories towards a few stable representations in mnemonic space, inducing a bias in memory representations but reducing the effect of random diffusion. Model-based and model-free analyses of human and monkey behavior show that discrete attractor dynamics account for the distribution, bias, and precision of working memory reports. Furthermore, attractor dynamics are adaptive. They increase in strength as noise increases with memory load and experiments in humans show these dynamics adapt to the statistics of the environment, such that memories drift towards contextually-predicted values. Together, our results suggest attractor dynamics mitigate errors in working memory by counteracting noise and integrating contextual information into memories.

Social life requires us to treat each person according to their unique disposition. To tailor our behavior to specific individuals, we must represent their idiosyncrasies. Here, we advance the hypothesis that our representations of other people reflect the mental states we perceive those people to habitually experience. We tested this hypothesis by measuring whether neural representations of people could be accurately reconstructed by summing state representations. Separate participants underwent functional MRI while considering famous individuals and individual mental states. Online participants rated how often each famous person experiences each state. Results supported the summed state hypothesis: frequency-weighted sums of state-specific brain activity patterns accurately reconstructed person-specific patterns. Moreover, the summed state account outperformed the established alternative-that people represent others using trait dimensions-in explaining interpersonal similarity. These findings demonstrate that the brain represents people as the sums of the mental states they experience.

One can never know the internal workings of another person-one can only infer others' mental states based on external cues. In contrast, each person has direct access to the contents of their own mind. Here, we test the hypothesis that this privileged access shapes the way people represent internal mental experiences, such that they represent their own mental states more distinctly than the states of others. Across four studies, participants considered their own and others' mental states; analyses measured the distinctiveness of mental state representations. Two fMRI studies used representational similarity analyses to demonstrate that the social brain manifests more distinct activity patterns when thinking about one's own states vs. others'. Two behavioral studies complement these findings, and demonstrate that people differentiate between states less as social distance increases. Together, these results suggest that we represent our own mind with greater granularity than the minds of others.

From families to nations, what binds individuals in social groups is, to a large degree, their shared beliefs, norms, and memories. These emergent outcomes are thought to occur because communication among individuals results in community-wide synchronization. Here, we use experimental manipulations in lab-created networks to investigate how the temporal dynamics of conversations shape the formation of collective memories. We show that when individuals that bridge between clusters (i.e., bridge ties) communicate early on in a series of networked interactions, the network reaches higher mnemonic convergence compared to when individuals first interact within clusters (i.e., cluster ties). This effect, we show, is due to the tradeoffs between initial information diversity and accumulated overlap over time. Our approach provides a framework to analyze and design interventions in social networks that optimize information sharing and diminish the likelihood of information bubbles and polarization.

Deep brain stimulation (DBS) is a circuit-oriented treatment for mental disorders. Unfortunately, even well-conducted psychiatric DBS clinical trials have yielded inconsistent symptom relief, in part because DBS' mechanism(s) of action are unclear. One clue to those mechanisms may lie in the efficacy of ventral internal capsule/ventral striatum (VCVS) DBS in both major depression (MDD) and obsessive-compulsive disorder (OCD). MDD and OCD both involve deficits in cognitive control. Cognitive control depends on prefrontal cortex (PFC) regions that project into the VCVS. Here, we show that VCVS DBS' effect is explained in part by enhancement of PFC-driven cognitive control. DBS improves human subjects' performance on a cognitive control task and increases theta (5-8Hz) oscillations in both medial and lateral PFC. The theta increase predicts subjects' clinical outcomes. Our results suggest a possible mechanistic approach to DBS therapy, based on tuning stimulation to optimize these neurophysiologic phenomena.

The original version of this Article contained an error in the author affiliations. Affiliation 3 incorrectly read 'Department of Biological and Brain Sciences, Johns Hopkins University, Baltimore, MD 21218, USA'.