Journal of Neuroscience最新文献

The medial orbitofrontal cortex (mOFC) has been implicated in shaping decisions involving reward uncertainty, in part by using memories to infer future outcomes. This region is interconnected with other key systems that mediated these decisions, including the basolateral amygdala (BLA) and prelimbic (PL) region of the medial prefrontal cortex, yet the functional importance of these circuits remains unclear. The present study used chemogenetic silencing to examine the contribution of different input and output pathways of the mOFC to risk/reward decision making. Male rats were well-trained on a probabilistic discounting task where they chose between a small/certain (1 pellet) and a large/uncertain 4 pellet option, the odds for which changed systematically across a session. Suppressing activity of descending mOFC terminals in the BLA impaired adjustment in choice biases as reward probabilities change, suggesting this circuit tracks changes in relative value to support flexible reward-seeking. Inhibiting bottom-up BLA→mOFC circuits had no effect on choice. With respect to cortico-cortical circuits, inhibiting mOFC inputs to PL led to more random choice patterns, indicating this circuit promotes advantageous choice by processing context-dependent information regarding wins and losses. In comparison, PL inputs to mOFC attenuates the allure of larger yet uncertain rewards and reduces loss sensitivity, particularly early in the choice sequence. The present findings provide novel insight into the functional contribution that mOFC/BLA and PL interactions make to distinct processes that shape decision making in situations of reward uncertainty.Significance Statement The medial orbitofrontal cortex supports the use of reward memories to guide efficient value-based decision-making, yet the functional circuits through which it mediates this form of cognition is unclear. The present study revealed that different mOFC interactions with the BLA and the PL facilitate dissociable component processes of decisions involving risks and rewards. These findings clarify the functions of cortico-cortical and cortico-amygdalar pathways and may have implications for understanding how dysfunction in these circuits relates to aberrant decision making seen in certain psychiatric disorders.

Modulation of neuronal oscillations holds promise for the treatment of neurological disorders. Nonetheless, conventional stimulation in a continuous open-loop manner can lead to side effects and suboptimal efficiency. Closed-loop strategies such as phase-locked stimulation aim to address these shortcomings by offering a more targeted modulation. While theories have been developed to understand the neural response to stimulation, their predictions have not been thoroughly tested using experimental data. Using a mechanistic coupled oscillator model, we elaborate on two key predictions describing the response to stimulation as a function of the phase and amplitude of ongoing neural activity. To investigate these predictions, we analyze electrocorticogram recordings from a previously conducted study in Parkinsonian rats, and extract the corresponding phase and response curves. We demonstrate that the amplitude response to stimulation is strongly correlated to the derivative of the phase response ([Formula: see text] > 0.8) in all animals except one, thereby validating a key model prediction. The second prediction postulates that the stimulation becomes ineffective when the network synchrony is high, a trend that appeared missing in the data. Our analysis explains this discrepancy by showing that the neural populations in Parkinsonian rats did not reach the level of synchrony for which the theory would predict ineffective stimulation. Our results highlight the potential of fine-tuning stimulation paradigms informed by mathematical models that consider both the ongoing phase and amplitude of the targeted neural oscillation.Significance Statement This study validates a mathematical model of coupled oscillators in predicting the response of neural activity to stimulation for the first time. Our findings also offer further insights beyond this validation. For instance, the demonstrated correlation between phase response and amplitude response is indeed a key theoretical concept within a subset of mathematical models. This prediction can bring about clinical implications in terms of predictive power for manipulation of neural activity. Additionally, while phase dependence in modulation has been previously studied, we propose a general framework for studying amplitude dependence as well. Lastly, our study reconciles the seemingly contradictory views of pathologic hypersynchrony and theoretical low synchrony in Parkinson's disease.

Seminal studies in animal neuroscience demonstrate that frontostriatal circuits exhibit a ventral-dorsal functional gradient to integrate neural functions related to reward processing and cognitive control. Prominent neurodevelopmental models posit that heightened reward-seeking and risk-taking during adolescence result from maturational imbalances between frontostriatal neural systems underlying reward processing and cognitive control. The present study investigated whether the development of ventral (VS) and dorsal (DS) striatal resting-state connectivity (rsFC) networks along this proposed functional gradient relates to putative imbalances between reward and executive systems posited by a dual neural systems theory of adolescent development. 163 participants aged 11-25 years (54% female, 90% white) underwent resting scans at baseline and biennially thereafter, yielding 339 scans across four assessment waves. We observed developmental increases in VS rsFC with brain areas implicated in reward processing (e.g., subgenual cingulate gyrus and medial orbitofrontal cortex) and concurrent decreases with areas implicated in executive function (e.g., ventrolateral and dorsolateral prefrontal cortices). DS rsFC exhibited the opposite pattern. More rapid developmental increases in VS rsFC with reward areas were associated with developmental improvements in reward-based decision making, whereas increases in DS rsFC with executive function areas were associated with improved executive function, though each network exhibited some crossover in function. Collectively, these findings suggest that typical adolescent neurodevelopment is characterized by a divergence in ventral and dorsal frontostriatal connectivity that may relate to developmental improvements in affective decision-making and executive function.Significance Statement Anatomical studies in nonhuman primates demonstrate that frontostriatal circuits are essential for integration of neural functions underlying reward processing and cognition, with human neuroimaging studies linking alterations in these circuits to psychopathology. The present study characterized the developmental trajectories of frontostriatal resting state networks from childhood to young adulthood. We demonstrate that ventral and dorsal aspects of the striatum exhibit distinct age-related changes that predicted developmental improvements in reward-related decision making and executive function. These results highlight that adolescence is characterized by distinct changes in frontostriatal networks that may relate to normative increases in risk-taking. Atypical developmental trajectories of frontostriatal networks may contribute to adolescent-onset psychopathology.

The brain builds hierarchical phrases during language comprehension; however, the representational details and dynamics of the phrase-building process remain underspecified. This study directly probes whether the neural code of verb phrases involves reactivating the syntactic property of a key subcomponent (the "head" verb). To this end, we train a part-of-speech sliding-window neural decoder (verb vs. adverb) on EEG signals recorded while 30 participants (17 females) read sentences in a controlled experiment. The decoder reaches above-chance performance that is spatiotemporally consistent and generalizes to unseen data across sentence positions. Appling the decoder to held-out data yields predicted activation levels for the verbal "head" of a verb phrase at a distant non-head word (adverb); the critical adverb appeared either at the end of a verb phrase or at a sequentially and lexically matched position with no verb phrase boundary. There is stronger verb activation beginning at ∼600 milliseconds at the critical adverb when it appears at a verb phrase boundary; this effect is not modulated by the strength of conceptual association between the two subcomponents in the verb phrase nor does it reflect word predictability. Time-locked analyses additionally reveal a negativity waveform component and increased beta-delta inter-trial phase coherence, both previously linked to linguistic composition, in a similar time window. With a novel application of neural decoding, our findings delineate the dynamics by which the brain encodes phrasal representations by, in part, reactivating the representation of key subcomponents. We thus establish a link between cognitive accounts of phrasal representations and electrophysiological dynamics.Significance Statement Neuroimaging studies suggest that the brain constructs hierarchical linguistic representations. However, current evidence does not specify the details of minimal hierarchical units, namely phrases. On the other hand, theoretical consensus postulates phrases represented with properties derived from a key subcomponent, so-called the "head". Here, we explore the neural code of headed phrases. Leveraging advances in neural decoding, this study introduces a training-prediction pipeline to probe the activation dynamics of the phrasal head in electrophysiological recordings. Our analysis provides novel evidence regarding the neural representation of phrases that, at phrasal boundaries, the head of a phrase is reactivated and integrated into the higher-level representation. This is a fundamental step to understanding the neural bases of language comprehension at the sentence level.

Temporal dynamics play a central role in models of emotion: "fear" is widely conceptualized as a phasic response to certain-and-imminent danger, whereas "anxiety" is a sustained response to uncertain-or-distal harm. Yet the underlying neurobiology remains contentious. Leveraging a translationally relevant fMRI paradigm and theory-driven modeling approach in 220 adult humans, we demonstrate that certain- and uncertain-threat anticipation recruit a shared circuit that encompasses the central extended amygdala (EAc), periaqueductal gray, midcingulate, and anterior insula. This circuit exhibits persistently elevated activation when threat is uncertain and distal, and transient bursts of activation just before certain encounters with threat. Although there is agreement that the EAc plays a critical role in orchestrating responses to threat, confusion persists about the respective contributions of its major subdivisions, the bed nucleus of the stria terminalis (BST) and central nucleus of the amygdala (Ce). Here we used anatomical regions-of-interest to demonstrate that the BST and Ce exhibit statistically indistinguishable threat dynamics. Both regions exhibited activation dynamics that run counter to popular models, with the Ce showing sustained responses to uncertain-and-distal threat and the BST showing phasic responses to certain-and-imminent threat. For many scientists, feelings are the hallmark of fear and anxiety. Here we used an independently validated multivoxel brain 'signature' to covertly probe the moment-by-moment dynamics of anticipatory distress for the first time. Results mirrored the dynamics of neural activation. These observations provide fresh insights into the neurobiology of threat-elicited emotions and set the stage for more ambitious clinical and mechanistic research.Significance statement "Fear" is widely viewed as a phasic response to certain-and-imminent danger, whereas "anxiety" is a sustained response to uncertain-or-distal harm. Prior work has begun to reveal the neural systems recruited by certain and uncertain anticipated threats, but has yet to rigorously plumb the moment-by-moment dynamics anticipated by theory. Here we used a novel combination of neuroimaging techniques to demonstrate that certain and uncertain threat recruit a common threat-anticipation circuit. Activity in this circuit and covert measures of distress showed similar patterns of context-dependent dynamics, exhibiting persistent increases when anticipating uncertain-threat encounters and transient surges just before certain encounters. These observations provide fresh insights into the neurobiology of fear and anxiety, laying the groundwork for more ambitious clinical and mechanistic research.

A promising therapeutic intervention for preventing the onset and progression of Alzheimer's Disease (AD) is to protect and improve synaptic resilience, a well-established early vulnerability associated with the toxic effects of oligomers of Aβ (AβO) and Tau (TauO). We have previously reported that exosomes from hippocampal neural stem cells (NSCs) protect synapses against AβO. Here, we demonstrate how exosomes can also shield against TauO toxicity in adult mice synapses, potentially benefiting primary and secondary tauopathies. Exosomes from hippocampal NSCs (NSCexo) or mature neurons (MNexo) were delivered intracerebroventricularly to adult wildtype male mice (C57Bl6/J). After 24 hours, TauO were administered to suppress long-term potentiation (LTP) and memory, measured by electrophysiology and contextual memory deficits measured using novel object recognition (NOR) test. We also assessed TauO binding to synapses using isolated synaptosomes and cultured hippocampal neurons. Furthermore, mimics of select miRNAs present in NSCexo, were delivered ICV to mice prior to assessment of TauO-induced suppression of hippocampal LTP. Our results showed that NSC-, not MN-, derived exosomes, prevented TauO-induced memory impairment, LTP suppression, and reduced Tau accumulation and TauO internalization in synaptosomes. These findings suggest that NSC-derived exosomes can protect against synaptic dysfunction and memory deficits induced by both AβO and TauO, offering a novel therapeutic strategy for multiple neurodegenerative states.Significance Statement NSCexo provide an unprecedented therapeutic strategy targeting an early vulnerability driven by amyloidogenic toxic oligomers associated with multiple neurodegenerative states.

Our brains are in a constant state of generating predictions, implicitly extracting environmental regularities to support later cognition and behavior, a process known as statistical learning (SL). While prior work investigating the neural basis of SL has focused on the activity of single brain regions in isolation, much less is known about how distributed brain areas coordinate their activity to support such learning. Using fMRI and a classic visual SL task, we investigated changes in whole-brain functional architecture as human female and male participants implicitly learned to associate pairs of images, and later, when predictions generated from learning were violated. By projecting individuals' patterns of cortical and subcortical functional connectivity onto a low-dimensional manifold space, we found that SL was associated with changes along a single neural dimension describing covariance across the visual-parietal and perirhinal cortex (PRC). During learning, we found regions within the visual cortex expanded along this dimension, reflecting their decreased communication with other networks, whereas regions within the dorsal attention network (DAN) contracted, reflecting their increased connectivity with higher-order cortex. Notably, when SL was interrupted, we found the PRC and entorhinal cortex, which did not initially show learning-related effects, now contracted along this dimension, reflecting their increased connectivity with the default mode and DAN, and decreased covariance with visual cortex. While prior research has linked SL to either broad cortical or medial temporal lobe changes, our findings suggest an integrative view, whereby cortical regions reorganize during association formation, while medial temporal lobe regions respond to their violation.Significance statement The current work is the first to investigate changes in whole-brain manifold architecture that underlie visual statistical learning (SL). We found that areas of the visual cortex and dorsal attention network showed significant connectivity changes during learning, reflecting their decreased, and increased covariance with other networks, respectively. Notably, when SL was later disrupted, regions within the medial temporal lobe, which had shown no evidence of initial learning, now began to increase connectivity with higher-order cortex. Together, these findings not only reveal the widespread neural interactions that underlie visual SL, but also extend prior work, suggesting separable cortical and medial temporal lobe contributions for the encoding versus violation of learned associations.

Human performance is endowed by neural representations of information that is relevant for behavior, some of which are also activated in a preparatory fashion to optimize later execution. Most studies to date have focused on highly practiced actions, leaving largely unaddressed the novel re-configuration of information to generate unique whole task-sets. Using electroencephalography (EEG), this study investigated the dynamics of the content and geometry reflected on the neural patterns of control representations during re-configuration of information. We designed a verbal instruction paradigm where each trial involved novel combinations of multi-component task information. By manipulating three task-relevant factors in a sample of 40 participants (26 females, 14 males), we observed complex coding schemes throughout the trial, during both preparation and implementation stages. The temporal profiles were consistent with a hierarchical structure: whereas task information was active in a sustained manner, the coding of more concrete stimulus features was more transient. Data showed both high dimensionality and abstraction, particularly during instruction encoding and target processing. Our results suggest that whenever task content could be recovered from neural patterns of activity, there was evidence of abstract coding, with an underlying geometry that favored generalization. During target processing, where potential interference across stimulus and response factors increased, orthogonal configurations also appeared. Overall, our findings uncover the dynamic manner with which control representations operate during novel recombination unique scenarios, with changes in dimensionality and abstraction adjusting along processing stages.Significance Statement The neural mechanisms that support task performance in novel contexts have been largely overlooked. Cognitive control is thought to enable complex behavior through the active maintenance of task sets, containing essential information for execution. However, how novel whole combinations of information are organized in neural patterns and their temporal dependencies remain unknown. Here, using a novel complex instruction paradigm, we observed that coding of informational content and its underlying geometry followed a dynamic temporal pattern. Our results reveal varying dimensionality and abstraction throughout the trial, with neural codes generally structured in a geometry favoring generalization of relevant information across task demands. These findings provide a first glimpse into the temporal computations engaged by the brain when encountering novel recombination settings.

Fetal alcohol spectrum disorders (FASDs) are characterized by a range of physical, cognitive, and behavioral impairments. Determining how temporally specific alcohol exposure (AE) affects neural circuits is crucial to understanding the FASD phenotype. Third trimester AE can be modeled in rats by administering alcohol during the first two postnatal weeks, which damages the medial prefrontal cortex (mPFC) and hippocampus (HPC), structures whose functional interactions are required for working memory and executive function. Therefore, we hypothesized that AE during this period would impair working memory, disrupt choice behaviors, and alter mPFC-HPC oscillatory synchrony. To test this hypothesis, we recorded local field potentials from the mPFC and dorsal HPC as male and female AE and sham-intubated (SI) rats performed a spatial working memory task in adulthood and implemented algorithms to detect vicarious trial and errors (VTEs), behaviors associated with deliberative decision-making. We found that, compared with the SI group, the AE group performed fewer VTEs and demonstrated a disturbed relationship between VTEs and choice outcomes, while spatial working memory was unimpaired. This behavioral disruption was accompanied by alterations to mPFC and HPC oscillatory activity in the theta and beta bands, respectively, and a reduced prevalence of mPFC-HPC synchronous events. When trained on multiple behavioral variables, a machine learning algorithm could accurately predict whether rats were in the AE or SI group, thus characterizing a potential phenotype following third trimester AE. Together, these findings indicate that third trimester AE disrupts mPFC-HPC oscillatory interactions and choice behaviors.

The brain's activity fluctuations have different temporal scales across the brain regions, with associative regions displaying slower timescales than sensory areas. This hierarchy of timescales has been shown to correlate with both structural brain connectivity and intrinsic regional properties. Here, using publicly available human resting-state fMRI and dMRI data, it was found that, while more structurally connected brain regions presented activity fluctuations with longer timescales, their activity fluctuations presented lower variance. The opposite relationships between the structural connectivity and the variance and temporal scales of resting-state fluctuations, respectively, were not trivially explained by simple network propagation principles. To understand these structure-function relationships, two commonly used whole-brain models were studied, namely, the Hopf and Wilson-Cowan models. These models use the brain's connectome to couple local nodes (representing brain regions) displaying noise-driven oscillations. The models show that the variance and temporal scales of activity fluctuations can oppositely relate to connectivity within specific parameter regions, even when all nodes have the same intrinsic dynamics-but also when intrinsic dynamics are constrained by the myelinization-related macroscopic gradient. These results show that, setting aside intrinsic regional differences, connectivity and network state are sufficient to explain the regional differences in fluctuations' scales. State dependence supports the vision that structure-function relationships can serve as biomarkers of altered brain states. Finally, the results indicate that the hierarchies of timescales and variances reflect a balance between stability and responsivity, with greater and faster responsiveness at the network periphery, while the network core ensures overall system robustness.