Annual Review of Neuroscience - Current Issue

A holy grail of regenerative medicine is to replenish the cells that are lost due to disease. The adult mammalian central nervous system (CNS) has, however, largely lost such a regenerative ability. An emerging strategy for the generation of new neurons is through glia-to-neuron (GtN) conversion in vivo, mainly accomplished by the regulation of fate-determining factors. When inhibited, PTBP1, a factor involved in RNA biology, was reported to induce rapid and efficient GtN conversion in multiple regions of the adult CNS. Remarkably, PTBP1 inhibition was also claimed to greatly improve behaviors of mice with neurological diseases or aging. These phenomenal claims, if confirmed, would constitute a significant advancement in regenerative medicine. Unfortunately, neither GtN conversion nor therapeutic potential via PTBP1 inhibition was validated by the results of multiple subsequent replication studies with stringent methods. Here we review these controversial studies and conclude with recommendations for examining GtN conversion in vivo and future investigations of PTBP1.

How neurons detect the direction of motion is a prime example of neural computation: Motion vision is found in the visual systems of virtually all sighted animals, it is important for survival, and it requires interesting computations with well-defined linear and nonlinear processing steps—yet the whole process is of moderate complexity. The genetic methods available in the fruit fly Drosophila and the charting of a connectome of its visual system have led to rapid progress and unprecedented detail in our understanding of how neurons compute the direction of motion in this organism. The picture that emerged incorporates not only the identity, morphology, and synaptic connectivity of each neuron involved but also its neurotransmitters, its receptors, and their subcellular localization. Together with the neurons’ membrane potential responses to visual stimulation, this information provides the basis for a biophysically realistic model of the circuit that computes the direction of visual motion.

Migraine is a complex neurovascular pain disorder linked to the meninges, a border tissue innervated by neuropeptide-containing primary afferent fibers chiefly from the trigeminal nerve. Electrical or mechanical stimulation of this nerve surrounding large blood vessels evokes headache patterns as in migraine, and the brain, blood, and meninges are likely sources of headache triggers. Cerebrospinal fluid may play a significant role in migraine by transferring signals released from the brain to overlying pain-sensitive meningeal tissues, including dura mater. Interactions between trigeminal afferents, neuropeptides, and adjacent meningeal cells and tissues cause neurogenic inflammation, a critical target for current prophylactic and abortive migraine therapies. Here we review the importance of the cranial meninges to migraine headaches, explore the properties of trigeminal meningeal afferents, and briefly review emerging concepts, such as meningeal neuroimmune interactions, that may one day prove therapeutically relevant.

All mammalian cell membranes contain cholesterol to maintain membrane integrity. The transport of this hydrophobic lipid is mediated by lipoproteins. Cholesterol is especially enriched in the brain, particularly in synaptic and myelin membranes. Aging involves changes in sterol metabolism in peripheral organs and also in the brain. Some of those alterations have the potential to promote or to counteract the development of neurodegenerative diseases during aging. Here, we summarize the current knowledge of general principles of sterol metabolism in humans and mice, the most widely used model organism in biomedical research. We discuss changes in sterol metabolism that occur in the aged brain and highlight recent developments in cell type–specific cholesterol metabolism in the fast-growing research field of aging and age-related diseases, focusing on Alzheimer's disease. We propose that cell type–specific cholesterol handling and the interplay between cell types critically influence age-related disease processes.

The spinal cord is home to the intrinsic networks for locomotion. An animal in which the spinal cord has been fully severed from the brain can still produce rhythmic, patterned locomotor movements as long as some excitatory drive is provided, such as physical, pharmacological, or electrical stimuli. Yet it remains a challenge to define the underlying circuitry that produces these movements because the spinal cord contains a wide variety of neuron classes whose patterns of interconnectivity are still poorly understood. Computational models of locomotion accordingly rely on untested assumptions about spinal neuron network element identity and connectivity. In this review, we consider the classes of spinal neurons, their interconnectivity, and the significance of their circuit connections along the long axis of the spinal cord. We suggest several lines of analysis to move toward a definitive understanding of the spinal network.

Astrocyte endfeet enwrap the entire vascular tree within the central nervous system, where they perform important functions in regulating the blood-brain barrier (BBB), cerebral blood flow, nutrient uptake, and waste clearance. Accordingly, astrocyte endfeet contain specialized organelles and proteins, including local protein translation machinery and highly organized scaffold proteins, which anchor channels, transporters, receptors, and enzymes critical for astrocyte-vascular interactions. Many neurological diseases are characterized by the loss of polarization of specific endfoot proteins, vascular dysregulation, BBB disruption, altered waste clearance, or, in extreme cases, loss of endfoot coverage. A role for astrocyte endfeet has been demonstrated or postulated in many of these conditions. This review provides an overview of the development, composition, function, and pathological changes of astrocyte endfeet and highlights the gaps in our knowledge that future research should address.

This review explores the interface between circadian timekeeping and the regulation of brain function by astrocytes. Although astrocytes regulate neuronal activity across many time domains, their cell-autonomous circadian clocks exert a particular role in controlling longer-term oscillations of brain function: the maintenance of sleep states and the circadian ordering of sleep and wakefulness. This is most evident in the central circadian pacemaker, the suprachiasmatic nucleus, where the molecular clock of astrocytes suffices to drive daily cycles of neuronal activity and behavior. In Alzheimer's disease, sleep impairments accompany cognitive decline. In mouse models of the disease, circadian disturbances accelerate astroglial activation and other brain pathologies, suggesting that daily functions in astrocytes protect neuronal homeostasis. In brain cancer, treatment in the morning has been associated with prolonged survival, and gliomas have daily rhythms in gene expression and drug sensitivity. Thus, circadian time is fast becoming critical to elucidating reciprocal astrocytic-neuronal interactions in health and disease.

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.

Treatment outcomes are strongly influenced by expectations, as evidenced by the placebo effect. Meta-analyses of clinical trials reveal that placebo effects are strongest in pain, indicating that psychosocial factors directly influence pain. In this review, I focus on the neural and psychological mechanisms by which instructions, learning, and expectations shape subjective pain. I address new experimental designs that help researchers tease apart the impact of these distinct processes and evaluate the evidence regarding the neural mechanisms by which these cognitive factors shape subjective pain. Studies reveal that expectations modulate pain through parallel circuits that include both pain-specific and domain-general circuits such as those involved in affect and learning. I then review how expectations, learning, and verbal instructions impact clinical outcomes, including placebo analgesia and responses to pharmacological treatments, and discuss implications for future work.

Examination of cognition has historically been approached from language and introspection. However, human language–dependent definitions ignore the evolutionary roots of brain mechanisms and constrain their study in experimental animals. We promote an alternative view, namely that cognition, including memory, can be explained by exaptation and expansion of the circuits and algorithms serving bodily functions. Regulation and protection of metabolic and energetic processes require time-evolving brain computations enabling the organism to prepare for altered future states. Exaptation of such circuits was likely exploited for exploration of the organism's niche. We illustrate that exploration gives rise to a cognitive map, and in turn, environment-disengaged computation allows for mental travel into the past (memory) and the future (planning). Such brain-body interactions not only occur during waking but also persist during sleep. These exaptation steps are illustrated by the dual, endocrine-homeostatic and memory, contributions of the hippocampal system, particularly during hippocampal sharp-wave ripples.

Emotions are fundamental to our experience and behavior, affecting and motivating all aspects of our lives. Scientists of various disciplines have been fascinated by emotions for centuries, yet even today vigorous debates abound about how to define emotions and how to best study their neural underpinnings. Defining emotions from an evolutionary perspective and acknowledging their important functional roles in supporting survival allows the study of emotion states in diverse species. This approach enables taking advantage of modern tools in behavioral, systems, and circuit neurosciences, allowing the precise dissection of neural mechanisms and behavior underlying emotion processes in model organisms. Here we review findings about the neural circuit mechanisms underlying emotion processing across species and try to identify points of convergence as well as important next steps in the pursuit of understanding how emotions emerge from neural activity.

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.

Radial cell columns are a hallmark feature of cortical architecture in many mammalian species. It has long been held, based on the lack of orientation columns, that such functional units are absent in rodent primary visual cortex (V1). These observations led to the view that rodent visual cortex has a fundamentally different network architecture than that of carnivores and primates. While columns may be lacking in rodent V1, we describe in this review that modular clusters of inputs to layer 1 and projection neurons in the layers below are prominent features of the mouse visual cortex. We propose that modules organize thalamocortical inputs, intracortical processing streams, and transthalamic communications that underlie distinct sensory and sensorimotor functions.

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.

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.

Rapid advances in the neural control of social behavior highlight the role of interconnected nodes engaged in differential information processing to generate behavior. Many innate social behaviors are essential to reproductive fitness and therefore fundamentally different in males and females. Programming these differences occurs early in development in mammals, following gonadal differentiation and copious androgen production by the fetal testis during a critical period. Early-life programming of social behavior and its adult manifestation are separate but yoked processes, yet how they are linked is unknown. This review seeks to highlight that gap by identifying four core mechanisms (epigenetics, cell death, circuit formation, and adult hormonal modulation) that could connect developmental changes to the adult behaviors of mating and aggression. We further propose that a unique social behavior, adolescent play, bridges the preweaning to the postpubertal brain by engaging the same neural networks underpinning adult reproductive and aggressive behaviors.

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.

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.

Primates have evolved diverse cognitive capabilities to navigate their complex social world. To understand how the brain implements critical social cognitive abilities, we describe functional specialization in the domains of face processing, social interaction understanding, and mental state attribution. Systems for face processing are specialized from the level of single cells to populations of neurons within brain regions to hierarchically organized networks that extract and represent abstract social information. Such functional specialization is not confined to the sensorimotor periphery but appears to be a pervasive theme of primate brain organization all the way to the apex regions of cortical hierarchies. Circuits processing social information are juxtaposed with parallel systems involved in processing nonsocial information, suggesting common computations applied to different domains. The emerging picture of the neural basis of social cognition is a set of distinct but interacting subnetworks involved in component processes such as face perception and social reasoning, traversing large parts of the primate brain.

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.