Neural underpinnings of atypical wanting and liking
Related Articles
Innovating beyond electrophysiology through multimodal neural interfaces
Neural circuits distributed across different brain regions mediate how neural information is processed and integrated, resulting in complex cognitive capabilities and behaviour. To understand dynamics and interactions of neural circuits, it is crucial to capture the complete spectrum of neural activity, ranging from the fast action potentials of individual neurons to the population dynamics driven by slow brain-wide oscillations. In this Review, we discuss how advances in electrical and optical recording technologies, coupled with the emergence of machine learning methodologies, present a unique opportunity to unravel the complex dynamics of the brain. Although great progress has been made in both electrical and optical neural recording technologies, these alone fail to provide a comprehensive picture of the neuronal activity with high spatiotemporal resolution. To address this challenge, multimodal experiments integrating the complementary advantages of different techniques hold great promise. However, they are still hindered by the absence of multimodal data analysis methods capable of providing unified and interpretable explanations of the complex neural dynamics distinctly encoded in these modalities. Combining multimodal studies with advanced data analysis methods will offer novel perspectives to address unresolved questions in basic neuroscience and to develop treatments for various neurological disorders.
Moderate-to-vigorous and light-intensity aerobic exercise yield similar effects on food reward, appetitive responses, and energy intake in physically inactive adults
To examine the effect of acute aerobic exercise at moderate-to-vigorous and light intensity on food reward, appetite sensation, and energy intake (EI) in physically inactive adults.
An integrative data-driven model simulating C. elegans brain, body and environment interactions
The behavior of an organism is influenced by the complex interplay between its brain, body and environment. Existing data-driven models focus on either the brain or the body–environment. Here we present BAAIWorm, an integrative data-driven model of Caenorhabditis elegans, which consists of two submodels: the brain model and the body–environment model. The brain model was built by multicompartment models with realistic morphology, connectome and neural population dynamics based on experimental data. Simultaneously, the body–environment model used a lifelike body and a three-dimensional physical environment. Through the closed-loop interaction between the two submodels, BAAIWorm reproduced the realistic zigzag movement toward attractors observed in C. elegans. Leveraging this model, we investigated the impact of neural system structure on both neural activities and behaviors. Consequently, BAAIWorm can enhance our understanding of how the brain controls the body to interact with its surrounding environment.
MARBLE: interpretable representations of neural population dynamics using geometric deep learning
The dynamics of neuron populations commonly evolve on low-dimensional manifolds. Thus, we need methods that learn the dynamical processes over neural manifolds to infer interpretable and consistent latent representations. We introduce a representation learning method, MARBLE, which decomposes on-manifold dynamics into local flow fields and maps them into a common latent space using unsupervised geometric deep learning. In simulated nonlinear dynamical systems, recurrent neural networks and experimental single-neuron recordings from primates and rodents, we discover emergent low-dimensional latent representations that parametrize high-dimensional neural dynamics during gain modulation, decision-making and changes in the internal state. These representations are consistent across neural networks and animals, enabling the robust comparison of cognitive computations. Extensive benchmarking demonstrates state-of-the-art within- and across-animal decoding accuracy of MARBLE compared to current representation learning approaches, with minimal user input. Our results suggest that a manifold structure provides a powerful inductive bias to develop decoding algorithms and assimilate data across experiments.
ZBTB16/PLZF regulates juvenile spermatogonial stem cell development through an extensive transcription factor poising network
Spermatogonial stem cells balance self-renewal with differentiation and spermatogenesis to ensure continuous sperm production. Here, we identify roles for the transcription factor zinc finger and BTB domain-containing protein 16 (ZBTB16; also known as promyelocytic leukemia zinc finger (PLZF)) in juvenile mouse undifferentiated spermatogonia (uSPG) in promoting self-renewal and cell-cycle progression to maintain uSPG and transit-amplifying states. Notably, ZBTB16, Spalt-like transcription factor 4 (SALL4) and SRY-box transcription factor 3 (SOX3) colocalize at over 12,000 promoters regulating uSPG and meiosis. These regions largely share broad histone 3 methylation and acetylation (H3K4me3 and H3K27ac), DNA hypomethylation, RNA polymerase II (RNAPol2) and often CCCTC-binding factor (CTCF). Hi-C analyses show robust three-dimensional physical interactions among these cobound promoters, suggesting the existence of a transcription factor and higher-order active chromatin interaction network within uSPG that poises meiotic promoters for subsequent activation. Conversely, these factors do not notably occupy germline-specific promoters driving spermiogenesis, which instead lack promoter–promoter physical interactions and bear DNA hypermethylation, even when active. Overall, ZBTB16 promotes uSPG cell-cycle progression and colocalizes with SALL4, SOX3, CTCF and RNAPol2 to help establish an extensive and interactive chromatin poising network.
Responses