Recurrent inhibition refines mental templates to optimize perceptual decisions Translating sensory inputs to perceptual decisions relies on building internal representations of features critical for solving complex tasks. Yet, we still lack a mechanistic account of how the brain forms these mental templates of task-relevant features to optimize decision-making. Here, we provide evidence for recurrent inhibition: an experience-dependent plasticity mechanism that refines mental templates by enhancing γ-aminobutyric acid (GABA)–mediated (GABAergic) inhibition and recurrent processing in superficial visual cortex layers. We combine ultrahigh-field (7 T) functional magnetic resonance imaging at submillimeter resolution with magnetic resonance spectroscopy to investigate the fine-scale functional and neurochemical plasticity mechanisms for optimized perceptual decisions. We demonstrate that GABAergic inhibition increases following training on a visual (i.e., fine orientation) discrimination task, enhancing the discriminability of orientation representations in superficial visual cortex layers that are known to support recurrent processing. Modeling functional and neurochemical plasticity interactions reveals that recurrent inhibitory processing optimizes brain computations for perpetual decisions and adaptive behavior.
Cortical–subcortical interactions underlie processing of auditory predictions measured with 7T fMRI Perception integrates both sensory inputs and internal models of the environment. In the auditory domain, predictions play a critical role because of the temporal nature of sounds. However, the precise contribution of cortical and subcortical structures in these processes and their interaction remain unclear. It is also unclear whether these brain interactions are specific to abstract rules or if they also underlie the predictive coding of local features. We used high-field 7T functional magnetic resonance imaging to investigate interactions between cortical and subcortical areas during auditory predictive processing. Volunteers listened to tone sequences in an oddball paradigm where the predictability of the deviant was manipulated. Perturbations in periodicity were also introduced to test the specificity of the response. Results indicate that both cortical and subcortical auditory structures encode high-order predictive dynamics, with the effect of predictability being strongest in the auditory cortex. These predictive dynamics were best explained by modeling a top–down information flow, in contrast to unpredicted responses. No error signals were observed to deviations of periodicity, suggesting that these responses are specific to abstract rule violations. Our results support the idea that the high-order predictive dynamics observed in subcortical areas propagate from the auditory cortex.
A shared model-based linguistic space for transmitting our thoughts from brain to brain in natural conversations Effective communication hinges on a mutual understanding of word meaning in different contexts. We recorded brain activity using electrocorticography during spontaneous, face-to-face conversations in five pairs of epilepsy patients. We developed a model-based coupling framework that aligns brain activity in both speaker and listener to a shared embedding space from a large language model (LLM). The context-sensitive LLM embeddings allow us to track the exchange of linguistic information, word by word, from one brain to another in natural conversations. Linguistic content emerges in the speaker’s brain before word articulation and rapidly re-emerges in the listener’s brain after word articulation. The contextual embeddings better capture word-by-word neural alignment between speaker and listener than syntactic and articulatory models. Our findings indicate that the contextual embeddings learned by LLMs can serve as an explicit numerical model of the shared, context-rich meaning space humans use to communicate their thoughts to one another.
Future movement plans interact in sequential arm movements Real world actions often comprise of a series of movements that cannot be entirely planned before initiation. When these actions are executed rapidly, the planning of multiple future movements needs to occur simultaneously with the ongoing action. How the brain solves this task remains unknown. Here we address this question with a new sequential arm reaching paradigm that manipulates how many future reaches are available for planning while controlling execution of the ongoing reach. We show that participants plan at least two future reaches simultaneously with an ongoing reach. Further, the planning processes of the two future reaches are not independent of one another. Evidence that the planning processes interact is two-fold. First, correcting for a visual perturbation of the ongoing reach target is slower when more future reaches are planned. Second, the curvature of the current reach is modified based on the next reach only when their planning processes temporally overlap. These interactions between future planning processes may enable smooth production of sequential actions by linking individual segments of a long sequence at the level of motor planning.
Motor cortex latent dynamics encode spatial and temporal arm movement parameters independently The fluid movement of an arm requires multiple spatiotemporal parameters to be set independently. Recent studies have argued that arm movements are generated by the collective dynamics of neurons in motor cortex. An untested prediction of this hypothesis is that independent parameters of movement must map to independent components of the neural dynamics. Using a task where three male monkeys made a sequence of reaching movements to randomly placed targets, we show that the spatial and temporal parameters of arm movements are independently encoded in the lowdimensional trajectories of population activity in motor cortex: Each movement’s direction corresponds to a fixed neural trajectory through neural state space and its speed to how quickly that trajectory is traversed. Recurrent neural network models show this coding allows independent control over the spatial and temporal parameters of movement by separate network parameters. Our results support a key prediction of the dynamical systems view of motor cortex, but also argue that not all parameters of movement are defined by different trajectories of population activity
Targeting the muscarinic M1 receptor with a selective, brain-penetrant antagonist to promote remyelination in multiple sclerosis Multiple sclerosis (MS) is a chronic and debilitating neurological disease that results in inflammatory demyelination. While endogenous remyelination helps to recover function, this restorative process tends to become less efficient over time. Currently, intense efforts aimed at the mechanisms that promote remyelination are being considered promising therapeutic approaches. The M1 muscarinic acetylcholine receptor (M1R) was previously identified as a negative regulator of oligodendrocyte differentiation and myelination. Here, we validate M1R as a target for remyelination by characterizing expression in human and rodent oligodendroglial cells (including those in human MS tissue) using a highly selective M1R probe. As a breakthrough to conventional methodology, we conjugated a fluorophore to a highly M1R selective peptide (MT7) which targets the M1R in the subnanomolar range. This allows for exceptional detection of M1R protein expression in the human CNS. More importantly, we introduce PIPE-307, a brain-penetrant, small-molecule antagonist with favorable drug-like properties that selectively targets M1R. We evaluate PIPE-307 in a series of in vitro and in vivo studies to characterize potency and selectivity for M1R over M2-5R and confirm the sufficiency of blocking this receptor to promote differentiation and remyelination. Further, PIPE-307 displays significant efficacy in the mouse experimental autoimmune encephalomyelitis model of MS as evaluated by quantifying disability, histology, electron microscopy, and visual evoked potentials. Together, these findings support targeting M1R for remyelination and support further development of PIPE-307 for clinical studies.
Nogo-A-mediated constraints on activity-dependent synaptic plasticity and associativity in rat hippocampal CA2 synapses Hippocampal area CA2 has garnered attention in recent times owing to its significant involvement in social memory and distinctive plasticity characteristics. Research has revealed that the CA2 region demonstrates a remarkable resistance to plasticity, particularly in the Schaffer Collateral (SC)-CA2 pathway. In this study we investigated the role of Nogo-A, a well-known axon growth inhibitor and more recently discovered plasticity regulator, in modulating plasticity within the CA2 region. The findings demonstrate that blocking Nogo-A in male rat hippocampal slices facilitates the establishment of both short-term and long-term plasticity in the SC-CA2 pathway, while having no impact on the Entorhinal Cortical (EC)-CA2 pathway. Additionally, the study reveals that inhibiting Nogo-A enables association between the SC and EC pathways. Mechanistically, we confirm that Nogo-A operates through its well-known co-receptor, p75 neurotrophin receptor (p75NTR), and its downstream signaling factor such as Rho-associated protein kinase (ROCK), as their inhibition also allows plasticity induction in the SC-CA2 pathway. Additionally, the induction of long-term depression (LTD) in both the EC and SC-CA2 pathways led to persistent LTD, which was not affected by Nogo-A inhibition. Our study demonstrates the involvement of Nogo-A mediated signaling mechanisms in limiting synaptic plasticity within the CA2 region.
Modulation of Comorbid Chronic Neuropathic Pain and Anxiety-Like Behaviors by Glutamatergic Neurons in the vlPAG and the Analgesic and Anxiolytic Effects of EA Comorbid chronic neuropathic pain and anxiety is a common disease that represents a major clinical challenge. The underlying mechanisms of chronic neuropathic pain and anxiety are not entirely understood, which limits the exploration of effective treatment methods. Glutamatergic neurons in the ventrolateral periaqueductal gray (vlPAG) have been implicated in regulating pain, but the potential roles of the vlPAG in neuropathic pain-induced anxiety have not been investigated. Herein, whole-cell recording and immunofluorescence showed that the excitability of CamkIIα neurons in the vlPAG (vlPAGCamkIIα+ neurons) was decreased in mice with spared nerve injury (SNI), while electroacupuncture (EA) activated these neurons. We also showed that chemogenetic inhibition of vlPAGCamkIIα+ neurons resulted in allodynia and anxiety-like behaviors in naive mice. Furthermore, chemogenetic activation of vlPAGCamkIIα+ neurons reduced anxiety-like behaviors and allodynia in mice with SNI, and EA had a similar effect in alleviating these symptoms. Nevertheless, EA combined with chemogenetic activation failed to further relieve allodynia and anxiety-like behaviors. Artificial inhibition of vlPAGCamkIIα+ neurons abolished the analgesic and anxiolytic effects of EA. Overall, our study reveals a novel mechanism of neuropathic pain-induced anxiety and shows that EA may relieve comorbid chronic neuropathic pain and anxiety by activating vlPAGCamkIIα+ neurons.