Biology subjects

Turk-Browne, N. B.

Publications and source records attributed to Turk-Browne, N. B..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Hippocampal mechanisms support cortisol-induced memory enhancements

Stress can powerfully influence episodic memory, often enhancing memory encoding for emotionally salient information. These stress-induced memory enhancements stand at odds with demonstrations that stress and the stress-related hormone cortisol can negatively affect the hippocampus, a brain region important for episodic memory encoding. To resolve this apparent conflict and determine whether and how the hippocampus supports memory encoding under cortisol, we combined behavioral assays of associative memory, high-resolution functional magnetic resonance imaging (fMRI), and pharmacological manipulation of cortisol in a within-participant, double-blinded procedure. Hydrocortisone led to enhanced functional connectivity between hippocampal subregions, which predicted subsequent memory enhancements for emotional information. Cortisol also modified the relationship between hippocampal representations and memory: whereas hippocampal signatures of distinctiveness predicted memory under placebo, relative integration predicted memory under cortisol. Together, these data provide novel evidence that the human hippocampus contains the necessary machinery to support emotional memory enhancements under stress.

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bioRxiv2023

Online abstraction during statistical learning revealed by neural entrainment from intracranial recordings

We encounter the same people, places, and objects in predictable sequences and configurations. These regularities are learned efficiently by humans via statistical learning. Importantly, statistical learning creates knowledge not only of specific regularities, but also of more abstract, generalizable regularities. However, prior evidence of such abstract learning comes from post-learning behavioral tests, leaving open the question of whether abstraction occurs online during initial exposure. We address this question by measuring neural entrainment during statistical learning with intracranial recordings. Neurosurgical patients viewed a stream of scene photographs with regularities at one of two levels: In the Exemplar-level Structured condition, the same photographs appeared repeatedly in pairs. In the Category-level Structured condition, the photographs were trial-unique but their categories were paired across repetitions. In a baseline Random condition, the same photographs repeated but in a scrambled order. We measured entrainment at the frequency of individual photographs, which was expected in all conditions, but critically also at half of that frequency -- the rate at which to-be-learned pairs appeared in the two structured conditions (but not the random condition). Neural entrainment to both exemplar and category pairs emerged within minutes throughout visual cortex and in frontal and temporal brain regions. Many electrode contacts were sensitive to only one level of structure, but a significant number encoded both exemplar and category regularities. These findings suggest that abstraction occurs spontaneously during statistical learning, providing insight into the brains unsupervised mechanisms for building flexible and robust knowledge that generalizes across input variation and conceptual hierarchies.

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bioRxiv2022

Neural signatures of second language proficiency in narrative processing.

Making sense of speech in a second language relies on multiple abilities. Differences in brain activity related to proficiency in language tasks have often been attributed to processing demands. However, during naturalistic narrative comprehension, listeners at different proficiency levels may form different representations of the same speech. We hypothesized that the synchronization of these representations across people could thus be used to measure second-language proficiency. Using a searchlight shared response model, we found that highly proficient participants showed synchronization in regions similar to those of native speakers, including in the default mode network and in the lateral prefrontal cortex. In contrast, participants with low proficiency showed more synchronization in auditory cortex and word-level semantic processing areas in the temporal lobe. Moderate proficiency showed the greatest neural diversity, suggesting lower consistency in the source of this partial proficiency. Based on these synchronization differences, we were able to reliably classify the proficiency level or predict behavioral performance on an independent English test in held-out participants, suggesting the identified neural systems represented proficiency-sensitive information that was generalizable to other individuals. These findings suggest higher second-language proficiency leads to a more native-like neural processing of naturalistic language, including in systems beyond the cognitive control network or the core language network. HighlightsO_LINeural synchronization in second-language speech processing reflects proficiency. C_LIO_LIHigh-proficiency individuals neurally resemble native speakers. C_LIO_LILow-proficiency individuals are synchronized in perceptual and word semantics areas. C_LIO_LIProficiency level can be predicted using neural synchronization signatures. C_LI

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bioRxiv2022

Perception and Memory Retrieval States are Reflected in Distributed Patterns of Background Functional Connectivity

The same visual input can serve as the target of perception or as a trigger for memory retrieval depending on whether cognitive processing is externally oriented (perception) or internally oriented (memory retrieval). While numerous human neuroimaging studies have characterized how visual stimuli are differentially processed during perception versus memory retrieval, perception and memory retrieval may also be associated with distinct neural states that are independent of stimulus-evoked neural activity. Here, we combined human fMRI with full correlation matrix analysis (FCMA) to reveal potential differences in "background" functional connectivity across perception and memory retrieval states. We found that perception and retrieval states could be discriminated with high accuracy based on patterns of connectivity across (1) the control network, (2) the default mode network (DMN), and (3) retrosplenial cortex (RSC). In particular, clusters in the control network increased connectivity with each other during the perception state, whereas clusters in the DMN were more strongly coupled during the retrieval state. Interestingly, RSC switched its coupling between networks as the cognitive state shifted from retrieval to perception. Finally, we show that background connectivity 1) was fully independent from stimulus-related variance in the signal and, further, 2) captured distinct aspects of cognitive states compared to traditional classification of stimulus-evoked responses. Together, our results reveal that perception and memory retrieval are associated with sustained cognitive states that manifest as distinct patterns of connectivity among large-scale brain networks.

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bioRxiv2022

Automated and manual segmentation of the hippocampus in human infants

The hippocampus, critical for learning and memory, undergoes substantial changes early in life. Investigating the developmental trajectory of hippocampal structure and function requires an accurate method for segmenting this region from anatomical MRI scans. Although manual segmentation is regarded as the "gold standard" approach, it is laborious and subjective. This has fueled the pursuit of automated segmentation methods in adults. However, little is known about the reliability of these protocols in human infants, particularly when anatomical scan quality is low from increased head motion or shorter sequences that minimize head motion. During a task-based fMRI protocol, we collected quiet T1-weighted anatomical scans from 42 sessions with awake infants aged 4-23 months. We first had two expert tracers manually segment the hippocampus bilaterally and assess inter-rater reliability. We then attempted to predict these manual segmentations using four protocols: average adult template, average infant template, FreeSurfer software, and Automated Segmentation of Hippocampal Subfields (ASHS) software. ASHS generated the most reliable hippocampal segmentations in infants, exceeding manual inter-rater reliability of the experts. Automated methods can thus provide robust hippocampal segmentations of noisy T1-weighted infant scans, opening new possibilities for interrogating early hippocampal development. HighlightsO_LIInter-rater reliability of manual segmentation of infant hippocampus is moderate. C_LIO_LITemplate-based methods and FreeSurfer provide reasonably accurate segmentations. C_LIO_LIASHS produces highly accurate segmentations, exceeding manual inter-rater reliability. C_LI

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bioRxiv2022

Temporal PHATE: A multi-view manifold learningmethod for brain state trajectories

1The complexity and intelligence of the brain give the illusion that measurements of brain activity will have intractably high dimensionality, rife with collection and biological noise. Nonlinear dimensionality reduction methods like UMAP and t-SNE have proven useful for high-throughput biomedical data. However, they have not been used extensively for brain imaging data such as from functional magnetic resonance imaging (fMRI), a noninvasive, secondary measure of neural activity over time containing redundancy and co-modulation from neural population activity. Here we introduce a nonlinear manifold learning algorithm for timeseries data like fMRI, called temporal potential of heat-diffusion for affinity-based transition embedding (T-PHATE). In addition to recovering a lower intrinsic dimensionality from timeseries data, T-PHATE exploits autocorrelative structure within the data to faithfully denoise dynamic signals and learn activation manifolds. We empirically validate T-PHATE on three human fMRI datasets, showing that T-PHATE significantly improves data visualization, classification, and segmentation of the data relative to several other state-of-the-art dimensionality reduction benchmarks.These notable improvements suggest many potential applications of T-PHATE to other high-dimensional datasets of temporally-diffuse processes.

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bioRxiv2022

Temporal dynamics of competition between statistical learning and episodic memory in intracranial recordings of human visual cortex

The function of long-term memory is not just to reminisce about the past, but also to make predictions that help us behave appropriately and efficiently in the future. This predictive function of memory provides a new perspective on the classic question from memory research of why we remember some things but not others. If prediction is a key outcome of memory, then the extent to which an item generates a prediction signifies that this information already exists in memory and need not be encoded. We tested this principle using human intracranial EEG as a time-resolved method to quantify prediction in visual cortex during a statistical learning task and link the strength of these predictions to subsequent episodic memory behavior. Epilepsy patients of both sexes viewed rapid streams of scenes, some of which contained regularities that allowed the category of the next scene to be predicted. We verified that statistical learning occurred using neural frequency tagging and measured category prediction with multivariate pattern analysis. Although neural prediction was robust overall, this was driven entirely by items that were subsequently forgotten. Such interference provides a mechanism by which prediction can regulate memory formation to prioritize encoding of information that could help learn new predictive relationships. Significance StatementWhen faced with a new experience, we are rarely at a loss for what to do. Rather, because many aspects of the world are stable over time, we rely upon past experiences to generate expectations that guide behavior. Here we show that these expectations during a new experience come at the expense of memory for that experience. From intracranial recordings of visual cortex, we decoded what humans expected to see next in a series of photographs based on patterns of neural activity. Photographs that generated strong neural expectations were more likely to be forgotten in a later behavioral memory test. Prioritizing the storage of experiences that currently lead to weak expectations could help improve these expectations in future encounters.

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bioRxiv2022

Face processing in the infant brain after pandemic lockdown

The role of visual experience in the development of face processing has long been debated. We present a new angle on this question through a serendipitous study that cannot easily be repeated. Infants viewed short blocks of faces during fMRI in a repetition suppression task. The same identity was presented multiple times in half of the blocks (Repeat condition) and different identities were presented once each in the other half (Novel condition). In adults, the fusiform face area (FFA) tends to show greater neural activity for Novel vs. Repeat blocks in such designs, suggesting that it can distinguish same vs. different face identities. As part of an ongoing study, we collected data before the COVID-19 pandemic and after an initial State lockdown was lifted. The resulting sample of 12 infants (9-24 months) divided equally into pre- and post-lockdown groups with matching ages and data quantity/quality. The groups had strikingly different FFA responses: pre-lockdown infants showed repetition suppression (Novel>Repeat), whereas post-lockdown infants showed the opposite (Repeat>Novel), often referred to as repetition enhancement. These findings provide speculative evidence that altered visual experience during the lockdown, or other correlated environmental changes, may have affected face processing in the infant brain.

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bioRxiv2021

Remembering the pattern: A longitudinal case study on statistical learning in spatial navigation and memory consolidation

Distinct brain systems are thought to support statistical learning over different timescales. Regularities encountered during online perceptual experience can be acquired rapidly by the hippocampus. Further processing during offline consolidation can establish these regularities gradually in cortical regions, including the medial prefrontal cortex (mPFC). These mechanisms of statistical learning may be critical during spatial navigation, for which knowledge of the structure of an environment can facilitate future behavior. Rapid acquisition and prolonged retention of regularities have been investigated in isolation, but how they interact in the context of spatial navigation is unknown. We had the rare opportunity to study the brain systems underlying both rapid and gradual timescales of statistical learning using intracranial electroencephalography (iEEG) longitudinally in the same patient over a period of three weeks. As hypothesized, spatial patterns were represented in the hippocampus but not mPFC for up to one week after statistical learning and then represented in the mPFC but not hippocampus two and three weeks after statistical learning. Taken together, these findings clarify that the hippocampus may do the initial work of extracting regularities and transfer these integrated memories to cortex, rather than only storing individual experiences and leaving it up to cortex to extract regularities. HighlightsO_LICase study of an epilepsy patient tested longitudinally over three weeks. C_LIO_LIWe tracked time-dependent changes in neural representations of spatial patterns. C_LIO_LIRepresentations reconstructed from hippocampal activity reflected patterns learned within a week. C_LIO_LIRepresentations reconstructed from activity in the mPFC reflected patterns learned 2-3 weeks ago. C_LI

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bioRxiv2021

Neural event segmentation of continuous experience in human infants

How infants experience the world is fundamental to understanding their cognition and development. A key principle of adult experience is that, despite receiving continuous sensory input, we perceive this input as discrete events. Here we investigate such event segmentation in infants and how it differs from adults. Research on event cognition in infants often uses simplified tasks in which (adult) experimenters help solve the segmentation problem for infants by defining event boundaries or presenting discrete actions/vignettes. This presupposes which events are experienced by infants and leaves open questions about the principles governing infant segmentation. We take a different, data-driven approach by studying infant event segmentation of continuous input. We collected whole-brain fMRI data from awake infants (and adults, for comparison) watching a cartoon and used a hidden Markov model to identify event states in the brain. We quantified the existence, timescale, and organization of multiple event representations across brain regions. The adult brain exhibited a known hierarchical gradient of event timescales, from shorter events in early visual regions to longer events in later visual and associative regions. In contrast, the infant brain only represented longer events, even in early visual regions, with no timescale hierarchy. The boundaries defining these infant events only partially overlapped with boundaries defined from adult brain activity and behavioral judgments. These findings suggest that events are organized differently in infants, with longer timescales and more stable neural patterns, even in sensory regions. This may indicate greater temporal integration and reduced temporal precision during dynamic, naturalistic perception. Significance StatementSensory input is continuous and yet humans perceive discrete events. This event segmentation has been studied in adults by asking them to indicate natural breaks in continuous input. This classic parsing task is impossible in infants who cannot understand or follow instructions. We circumvent this barrier by testing how the infant brain parses. We applied a computational model to rare awake fMRI data from infants to identify how their brains transitioned between stable states during a cartoon. Whereas adults showed a gradient in event timescales, from shorter events in sensory regions to longer events in associative regions, infants persistently segmented fewer, longer events across the cortical hierarchy. These findings provide new neuroscientific insight into how infants represent their environment.

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bioRxiv2021

Brain kernel: a new spatial covariance function for fMRI data

A key problem in functional magnetic resonance imaging (fMRI) is to estimate spatial activity patterns from noisy high-dimensional signals. Spatial smoothing provides one approach to regularizing such estimates. However, standard smoothing methods ignore the fact that correlations in neural activity may fall off at different rates in different brain areas, or exhibit discontinuities across anatomical or functional boundaries. Moreover, such methods do not exploit the fact that widely separated brain regions may exhibit strong correlations due to bilateral symmetry or the network organization of brain regions. To capture this non-stationary spatial correlation structure, we introduce the brain kernel, a continuous covariance function for whole-brain activity patterns. We define the brain kernel in terms of a continuous nonlinear mapping from 3D brain coordinates to a latent embedding space, parametrized with a Gaussian process (GP). The brain kernel specifies the prior covariance between voxels as a function of the distance between their locations in embedding space. The GP mapping warps the brain nonlinearly so that highly correlated voxels are close together in latent space, and uncorrelated voxels are far apart. We estimate the brain kernel using resting-state fMRI data, and we develop an exact, scalable inference method based on block coordinate descent to overcome the challenges of high dimensionality (10-100K voxels). Finally, we illustrate the brain kernels usefulness with applications to brain decoding and factor analysis with multiple task-based fMRI datasets.

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bioRxiv2021

Increasing stimulus similarity drives nonmonotonic representational change in hippocampus

Studies of hippocampal learning have obtained seemingly contradictory results, with manipulations that increase coactivation of memories sometimes leading to differentiation of these memories, but sometimes not. These results could potentially be reconciled using the nonmonotonic plasticity hypothesis, which posits that representational change (memories moving apart or together) is a U-shaped function of the coactivation of these memories during learning. Testing this hypothesis requires manipulating coactivation over a wide enough range to reveal the full U-shape. To accomplish this, we used a novel neural network image synthesis procedure to create pairs of stimuli that varied parametrically in their similarity in high-level visual regions that provide input to the hippocampus. Sequences of these pairs were shown to human participants during high-resolution fMRI. As predicted, learning changed the representations of paired images in the dentate gyrus as a U-shaped function of image similarity, with neural differentiation occurring only for moderately similar images.

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bioRxiv2020

Retinotopic organization of visual cortex in human infants

Vision develops rapidly during infancy, yet how visual cortex is organized during this period is unclear. One possibility is that the retinotopic organization of visual cortex emerges gradually as perceptual abilities improve. This may result in a hierarchical maturation of visual areas from striate to extrastriate cortex. Another possibility is that retinotopic organization is present from early infancy. This early maturation of area boundaries and tuning could scaffold further developmental changes. Here we test the functional maturity of infant visual cortex by performing retinotopic mapping with fMRI. Infants aged 5-23 months had retinotopic maps, with alternating preferences for vertical and horizontal meridians indicative of area boundaries from V1 to V4, and an orthogonal gradient of preferences from high to low spatial frequencies indicative of growing receptive field sizes. Although present in the youngest infants, these retinotopic maps showed subtle agerelated changes, suggesting that early maturation undergoes continued refinement.

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bioRxiv2020

Sculpting New Visual Concepts into the Human Brain

Learning requires changing the brain. This typically occurs through experience, study, or instruction. We report a proof-of-concept for a new way for humans to acquire visual knowledge by directly sculpting activity patterns in the human brain that mirror those expected to arise through learning. We used a non-invasive technique (closed-loop real-time functional magnetic resonance imaging neurofeedback) to create new categories of visual objects in the brain, without the participants explicit awareness. After neural sculpting, participants exhibited behavioral and neural biases for the sculpted, but not for the control categories. The ability to sculpt new perceptual distinctions in the human brain offers a new paradigm for human fMRI research that allows for non-invasive, causal testing of the link between neural representations and behavior. As such, beyond its current application to perception, our work potentially has broad relevance to other domains of cognition such as decision-making, memory, and motor control. Significance StatementObjects that belong to the same category tend to elicit similar patterns of brain activity. Here we reverse this mapping and ask whether neural similarity is sufficient to induce increased perceptual discrimination and categorical perception. We do this by using real-time fMRI to modify neural representations of objects in high-level visual cortex. Participants viewed an object and received closed-loop neurofeedback that pushed them to represent the object more similarly to a brain activity pattern we chose for that category. After successfully self-modulating their brain activity, participants began to perceive objects assigned to the same brain pattern as more categorically distinct from those assigned to a different brain pattern. These findings open a new avenue for understanding and accelerating human learning.

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bioRxiv2020

Attention recruits frontal cortex in human infants

Young infants learn about the world by overtly shifting their attention to perceptually salient events. In adults, attention recruits several brain regions spanning the frontal and parietal lobes. However, these regions are thought to have a protracted maturation and so it is unclear whether they are recruited in infancy and, more generally, how infant attention is supported by the brain. We used event-related fMRI with 24 awake behaving infants 3-12 months old while they performed a child-friendly attentional cuing task. A target was presented to either the left or right of the infants fixation and eye-tracking was used to measure the latency with which they saccaded to the target. To manipulate attention, a brief cue was presented before the target in three conditions: on the same side as the upcoming target (valid), on the other side (invalid), or on both sides (neutral). All infants were faster to look at the target on valid versus invalid trials, with valid faster than neutral and invalid slower than neutral, indicating that the cues effectively captured attention. We then compared the fMRI activity evoked by these trial types. Regions of adult attention networks activated more strongly for invalid than valid trials, particularly frontal regions such as anterior cingulate cortex. Neither behavioral nor neural effects varied by infant age within the first year, suggesting that these regions may function early in development to support the reorienting of attention. Together, this furthers our mechanistic understanding of how the infant brain controls the allocation of attention.

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bioRxiv2020

Evidence of hippocampal learning in human infants

The hippocampus is essential for human memory. Thus, memory deficiencies in infants are often attributed to hippocampal immaturity. However, the functionality of the infant hippocampus has never been tested directly. Here we report that the human hippocampus is indeed active in infancy. We recorded hippocampal activity using fMRI while awake infants aged 3-24 months viewed sequences of objects. Greater activity was observed when the order of the sequence contained regularities that could be learned compared to when the order was random. The involvement of the hippocampus in such statistical learning, with additional recruitment of the medial prefrontal cortex, is consistent with findings from adults. These results suggest that the hippocampus supports the important ability of infants to extract the structure of their environment through experience.

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bioRxiv2020

Behavioral and neural fusion of expectation with sensation

Humans perceive expected stimuli faster and more accurately. However, the mechanism behind the integration of expectations with sensory information during perception remains unclear. We investigated the hypothesis that such integration depends on fusion -- the weighted averaging of different cues informative about stimulus identity. We first trained participants to map a range of tones onto faces spanning a male-female continuum via associative learning. These two features served as expectation and sensory cues to sex, respectively. We then tested specific predictions about the consequences of fusion by manipulating the congruence of these cues in psychophysical and fMRI experiments. Behavioral judgments and patterns of neural activity in auditory association regions revealed fusion of sensory and expectation cues, providing evidence for a precise computational account of how expectations influence perception.

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bioRxiv2020

Closed-loop fMRI neurofeedback to reduce negative attentional bias in depression

Depressed individuals show an attentional bias toward negatively valenced stimuli and thoughts. Here we present a novel closed-loop neurofeedback procedure that seeks to remediate this bias. Internal attentional states were detected by applying machine learning techniques to fMRI data in real-time, and externalized using a visually presented stimulus that the participant could learn to control. We trained 15 depressed and 12 healthy control participants over three fMRI sessions, preceded and followed by behavioral and clinical assessments. Initially, depressed participants were more likely than non-depressed participants to get "stuck" in negative attentional states, but this diminished with neurofeedback training relative to controls. Depression severity also decreased from pre- to post-training. These results demonstrate that our method is sensitive to the negative attentional bias in depressed individuals, and its reduction after training showcases the potential of this method as a treatment in the future.

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