Annual review of neuroscience最新文献

The field of stereotactic neurosurgery developed more than 70 years ago to address a therapy gap for patients with severe psychiatric disorders. In the decades since, it has matured tremendously, benefiting from advances in clinical and basic sciences. Deep brain stimulation (DBS) for severe, treatment-resistant psychiatric disorders is currently poised to transition from a stage of empiricism to one increasingly rooted in scientific discovery. Current drivers of this transition are advances in neuroimaging, but rapidly emerging ones are neurophysiological-as we understand more about the neural basis of these disorders, we will more successfully be able to use interventions such as invasive stimulation to restore dysfunctional circuits to health. Paralleling this transition is a steady increase in the consistency and quality of outcome data. Here, we focus on obsessive-compulsive disorder and depression, two topics that have received the most attention in terms of trial volume and scientific effort.

Many animals can navigate toward a goal they cannot see based on an internal representation of that goal in the brain's spatial maps. These maps are organized around networks with stable fixed-point dynamics (attractors), anchored to landmarks, and reciprocally connected to motor control. This review summarizes recent progress in understanding these networks, focusing on studies in arthropods. One factor driving recent progress is the availability of the Drosophila connectome; however, it is increasingly clear that navigation depends on ongoing synaptic plasticity in these networks. Functional synapses appear to be continually reselected from the set of anatomical potential synapses based on the interaction of Hebbian learning rules, sensory feedback, attractor dynamics, and neuromodulation. This can explain how the brain's maps of space are rapidly updated; it may also explain how the brain can initialize goals as stable fixed points for navigation.

Flexible behavior requires the creation, updating, and expression of memories to depend on context. While the neural underpinnings of each of these processes have been intensively studied, recent advances in computational modeling revealed a key challenge in context-dependent learning that had been largely ignored previously: Under naturalistic conditions, context is typically uncertain, necessitating contextual inference. We review a theoretical approach to formalizing context-dependent learning in the face of contextual uncertainty and the core computations it requires. We show how this approach begins to organize a large body of disparate experimental observations, from multiple levels of brain organization (including circuits, systems, and behavior) and multiple brain regions (most prominently the prefrontal cortex, the hippocampus, and motor cortices), into a coherent framework. We argue that contextual inference may also be key to understanding continual learning in the brain. This theory-driven perspective places contextual inference as a core component of learning.

Despite increasing evidence of its involvement in several key functions of the cerebral cortex, the vestibular sense rarely enters our consciousness. Indeed, the extent to which these internal signals are incorporated within cortical sensory representation and how they might be relied upon for sensory-driven decision-making, during, for example, spatial navigation, is yet to be understood. Recent novel experimental approaches in rodents have probed both the physiological and behavioral significance of vestibular signals and indicate that their widespread integration with vision improves both the cortical representation and perceptual accuracy of self-motion and orientation. Here, we summarize these recent findings with a focus on cortical circuits involved in visual perception and spatial navigation and highlight the major remaining knowledge gaps. We suggest that vestibulo-visual integration reflects a process of constant updating regarding the status of self-motion, and access to such information by the cortex is used for sensory perception and predictions that may be implemented for rapid, navigation-related decision-making.

Striosomes form neurochemically specialized compartments of the striatum embedded in a large matrix made up of modules called matrisomes. Striosome-matrix architecture is multiplexed with the canonical direct-indirect organization of the striatum. Striosomal functions remain to be fully clarified, but key information is emerging. First, striosomes powerfully innervate nigral dopamine-containing neurons and can completely shut down their activity, with a following rebound excitation. Second, striosomes receive limbic and cognition-related corticostriatal afferents and are dynamically modulated in relation to value-based actions. Third, striosomes are spatially interspersed among matrisomes and interneurons and are influenced by local and global neuromodulatory and oscillatory activities. Fourth, striosomes tune engagement and the motivation to perform reinforcement learning, to manifest stereotypical behaviors, and to navigate valence conflicts and valence discriminations. We suggest that, at an algorithmic level, striosomes could serve as distributed scaffolds to provide formats of the striatal computations generated through development and refined through learning. We propose that striosomes affect subjective states. By transforming corticothalamic and other inputs to the functional formats of the striatum, they could implement state transitions in nigro-striato-nigral circuits to affect bodily and cognitive actions according to internal motives whose functions are compromised in neuropsychiatric conditions.

Cell replacement therapy represents a promising approach for treating neurodegenerative diseases. Contrary to the common addition strategy to generate new neurons from glia by overexpressing a lineage-specific transcription factor(s), a recent study introduced a subtraction strategy by depleting a single RNA-binding protein, Ptbp1, to convert astroglia to neurons not only in vitro but also in the brain. Given its simplicity, multiple groups have attempted to validate and extend this attractive approach but have met with difficulty in lineage tracing newly induced neurons from mature astrocytes, raising the possibility of neuronal leakage as an alternative explanation for apparent astrocyte-to-neuron conversion. This review focuses on the debate over this critical issue. Importantly, multiple lines of evidence suggest that Ptbp1 depletion can convert a selective subpopulation of glial cells into neurons and, via this and other mechanisms, reverse deficits in a Parkinson's disease model, emphasizing the importance of future efforts in exploring this therapeutic strategy.

In mammals, the activity of neurons in the entorhinal-hippocampal network is modulated by the animal's position and its movement through space. At multiple stages of this distributed circuit, distinct populations of neurons can represent a rich repertoire of navigation-related variables like the animal's location, the speed and direction of its movements, or the presence of borders and objects. Working together, spatially tuned neurons give rise to an internal representation of space, a cognitive map that supports an animal's ability to navigate the world and to encode and consolidate memories from experience. The mechanisms by which, during development, the brain acquires the ability to create an internal representation of space are just beginning to be elucidated. In this review, we examine recent work that has begun to investigate the ontogeny of circuitry, firing patterns, and computations underpinning the representation of space in the mammalian brain.