Annual Review of Neuroscience - Volume 39, 2016

The Cannabis sativa plant has been used to treat various physiological and psychiatric conditions for millennia. Current research is focused on isolating potentially therapeutic chemical constituents from the plant for use in the treatment of many central nervous system disorders. Of particular interest is the primary nonpsychoactive constituent cannabidiol (CBD). Unlike Δ⁹-tetrahydrocannabinol (THC), CBD does not act through the cannabinoid type 1 (CB1) receptor but has many other receptor targets that may play a role in psychiatric disorders. Here we review preclinical and clinical data outlining the therapeutic efficacy of CBD for the treatment of motivational disorders such as drug addiction, anxiety, and depression. Across studies, findings suggest promising treatment effects and potentially overlapping mechanisms of action for CBD in these disorders and indicate the need for further systematic investigation of the viability of CBD as a psychiatric pharmacotherapy.

The medial entorhinal cortex (MEC) creates a neural representation of space through a set of functionally dedicated cell types: grid cells, border cells, head direction cells, and speed cells. Grid cells, the most abundant functional cell type in the MEC, have hexagonally arranged firing fields that tile the surface of the environment. These cells were discovered only in 2005, but after 10 years of investigation, we are beginning to understand how they are organized in the MEC network, how their periodic firing fields might be generated, how they are shaped by properties of the environment, and how they interact with the rest of the MEC network. The aim of this review is to summarize what we know about grid cells and point out where our knowledge is still incomplete.

Many exciting studies have begun to elucidate the genetics of the morphological and physiological diversity of ants, but as yet few studies have investigated the genetics of ant behavior directly. Ant genomes are marked by extreme rates of gene turnover, especially in gene families related to olfactory communication, such as the synthesis of cuticular hydrocarbons and the perception of environmental semiochemicals. Transcriptomic and epigenetic differences are apparent between reproductive and sterile females, males and females, and workers that differ in body size. Quantitative genetic approaches suggest heritability of task performance, and population genetic studies indicate a genetic association with reproductive status in some species. Gene expression is associated with behavior including foraging, response to queens attempting to join a colony, circadian patterns of task performance, and age-related changes of task. Ant behavioral genetics needs further investigation of the feedback between individual-level physiological changes and socially mediated responses to environmental conditions.

Ten years of remarkable progress in understanding the fundamental biochemistry of Alzheimer's disease have been followed by ten years of remarkable and increasing clinical insight into the natural progression of the disorder. The concept of a long, intermediary, prodromal phase between the first appearance of amyloid plaques and tangles and the manifestation of dementia is now well established. The major challenge for the next decade is to chart the many cellular processes that underlie this phase and link the biochemical alterations to the clinical manifestation of Alzheimer's disease. We discuss here how genetics, new cell culture systems, and improved animal models will fuel this work. We anticipate that the resulting novel insights will provide a basis for further drug development for this terrible disease.

Human studies in the past three decades have provided us with an emerging understanding of how cortical and spinal networks collaborate to ensure the vast repertoire of human behaviors. Humans have direct cortical connections to spinal motoneurons, which bypass spinal interneurons and exert a direct (willful) muscle control with the aid of a context-dependent integration of somatosensory and visual information at cortical level. However, spinal networks also play an important role. Sensory feedback through spinal circuitries is integrated with central motor commands and contributes importantly to the muscle activity underlying voluntary movements. Regulation of spinal interneurons is used to switch between motor states such as locomotion (reciprocal innervation) and stance (coactivation pattern). Cortical regulation of presynaptic inhibition of sensory afferents may focus the central motor command by opening or closing sensory feedback pathways. In the future, human studies of spinal motor control, in close collaboration with animal studies on the molecular biology of the spinal cord, will continue to document the neural basis for human behavior.

Progress in magnetic resonance imaging (MRI) now makes it possible to identify the major white matter tracts in the living human brain. These tracts are important because they carry many of the signals communicated between different brain regions. MRI methods coupled with biophysical modeling can measure the tissue properties and structural features of the tracts that impact our ability to think, feel, and perceive. This review describes the fundamental ideas of the MRI methods used to identify the major white matter tracts in the living human brain.

Categorization is our ability to flexibly assign sensory stimuli into discrete, behaviorally relevant groupings. Categorical decisions can be used to study decision making more generally by dissociating category identity of stimuli from the actions subjects use to signal their decisions. Here we discuss the evidence for such abstract categorical encoding in the primate brain and consider the relationship with other perceptual decision paradigms. Recent work on visual categorization has examined neuronal activity across a hierarchically organized network of cortical areas in monkeys trained to group visual stimuli into arbitrary categories. This has revealed a transformation of visual-feature encoding in early visual cortical areas into more flexible categorical representations in downstream parietal and prefrontal areas. These neuronal category representations are encoded as abstract internal cognitive states because they are not rigidly linked with either specific sensory stimuli or the actions that the monkeys use to signal their categorical choices.

The dorsal anterior cingulate cortex (dACC) has attracted great interest from neuroscientists because it is associated with so many important cognitive functions. Despite, or perhaps because of, its rich functional repertoire, we lack a single comprehensive view of its function. Most research has approached this puzzle from the top down, using aggregate measures such as neuroimaging. We provide a view from the bottom up, with a focus on single-unit responses and anatomy. We summarize the strengths and weaknesses of the three major approaches to characterizing the dACC: as a monitor, as a controller, and as an economic structure. We argue that neurons in the dACC are specialized for representing contexts, or task-state variables relevant for behavior, and strategies, or aspects of future plans. We propose that dACC neurons link contexts with strategies by integrating diverse task-relevant information to create a rich representation of task space and exert high-level and abstract control over decision and action.

The world has a complex, three-dimensional (3-D) spatial structure, but until recently the neural representation of space was studied primarily in planar horizontal environments. Here we review the emerging literature on allocentric spatial representations in 3-D and discuss the relations between 3-D spatial perception and the underlying neural codes. We suggest that the statistics of movements through space determine the topology and the dimensionality of the neural representation, across species and different behavioral modes. We argue that hippocampal place-cell maps are metric in all three dimensions, and might be composed of 2-D and 3-D fragments that are stitched together into a global 3-D metric representation via the 3-D head-direction cells. Finally, we propose that the hippocampal formation might implement a neural analogue of a Kalman filter, a standard engineering algorithm used for 3-D navigation.

One goal of systems neuroscience is a structure-function model of nervous system organization that would allow mechanistic linking of mind, brain, and behavior. A necessary but not sufficient foundation is a connectome, a complete matrix of structural connections between the nodes of a nervous system. Connections between two nodes can be described at four nested levels of analysis: macroconnections between gray matter regions, mesoconnections between neuron types, microconnections between individual neurons, and nanoconnections at synapses. A long history of attempts to understand how the brain operates as a system began at the macrolevel in the fifth century, was revolutionized at the meso- and microlevels by Cajal and others in the late nineteenth century, and reached the nanolevel in the mid-twentieth century with the advent of electron microscopy. The greatest challenge today is extracting knowledge and understanding of nervous system structure-function architecture from vast amounts of data.

In this review, we discuss the emerging field of computational behavioral analysis—the use of modern methods from computer science and engineering to quantitatively measure animal behavior. We discuss aspects of experiment design important to both obtaining biologically relevant behavioral data and enabling the use of machine vision and learning techniques for automation. These two goals are often in conflict. Restraining or restricting the environment of the animal can simplify automatic behavior quantification, but it can also degrade the quality or alter important aspects of behavior. To enable biologists to design experiments to obtain better behavioral measurements, and computer scientists to pinpoint fruitful directions for algorithm improvement, we review known effects of artificial manipulation of the animal on behavior. We also review machine vision and learning techniques for tracking, feature extraction, automated behavior classification, and automated behavior discovery, the assumptions they make, and the types of data they work best with.

Brain function involves the activity of neuronal populations. Much recent effort has been devoted to measuring the activity of neuronal populations in different parts of the brain under various experimental conditions. Population activity patterns contain rich structure, yet many studies have focused on measuring pairwise relationships between members of a larger population—termed noise correlations. Here we review recent progress in understanding how these correlations affect population information, how information should be quantified, and what mechanisms may give rise to correlations. As population coding theory has improved, it has made clear that some forms of correlation are more important for information than others. We argue that this is a critical lesson for those interested in neuronal population responses more generally: Descriptions of population responses should be motivated by and linked to well-specified function. Within this context, we offer suggestions of where current theoretical frameworks fall short.

Addiction is a disease of altered behavior. Addicts use drugs compulsively and will continue to do so despite negative consequences. Even after prolonged periods of abstinence, addicts are at risk of relapse, particularly when cues evoke memories that are associated with drug use. Rodent models mimic many of the core components of addiction, from the initial drug reinforcement to cue-associated relapse and continued drug intake despite negative consequences. Rodent models have also enabled unprecedented mechanistic insight into addiction, revealing plasticity of glutamatergic synaptic transmission evoked by the strong activation of mesolimbic dopamine—a defining feature of all addictive drugs—as a neural substrate for these drug-adaptive behaviors. Cell type–specific optogenetic manipulations have allowed both identification of the relevant circuits and design of protocols to reverse drug-evoked plasticity and to establish links of causality with drug-adaptive behaviors. The emergence of a circuit model for addiction will open the door for novel therapies, such as deep brain stimulation.

Recent studies of autophagic and lysosomal pathways have significantly changed our understanding of lysosomes; once thought to be simple degradative and recycling centers, lysosomes are now known to be organelles capable of influencing signal transduction, via the mammalian target of rapamycin complex 1 (mTORC1), and regulating gene expression, via transcription factor EB (TFEB) and other transcription factors. These pathways are particularly relevant to maintaining brain homeostasis, as dysfunction of the endolysosomal and autophagic pathways has been associated with common neurodegenerative diseases, such as Alzheimer's, Parkinson's, and Huntington's, and lysosomal storage disorders, a group of inherited disorders characterized by the intralysosomal buildup of partially degraded metabolites. This review focuses on the cellular biology of lysosomes and discusses the possible mechanisms by which disruption of their function contributes to neurodegeneration. We also review and discuss how targeting TFEB and lysosomes may offer innovative therapeutic approaches for treating a wide range of neurological conditions.

To benefit from opportunities and cope with challenges in the environment, animals must adapt their behavior to acquire rewards and to avoid punishments. Maladaptive changes in the neuromodulatory systems and neural circuits for reward and aversion can lead to manifestation of several prominent psychiatric disorders including addiction and depression. Recent progress is pushing the boundaries of knowledge on two major fronts in research on reward and aversion: First, new layers of complexity have been reported on the functions of dopamine (DA) and serotonin (5-HT) neuromodulatory systems in reward and aversion. Second, specific circuit components in the neural pathways that encode reward and aversion have begun to be identified. This review aims to outline historic perspectives and new insights into the functions of DA and 5-HT systems in coding the distinct components of rewards. It also highlights recent advances in neural circuit studies enabled by new technologies, such as cell-type-specific electrophysiology and tracing, and optogenetics-based behavioral manipulation. This knowledge may provide guidance for developing novel treatment strategies for neuropsychiatric diseases related to the malfunction of the reward system.

Primate face processing depends on a distributed network of interlinked face-selective areas composed of face-selective neurons. In both humans and macaques, the network is divided into a ventral stream and a dorsal stream, and the functional similarities of the areas in humans and macaques indicate they are homologous. Neural correlates for face detection, holistic processing, face space, and other key properties of human face processing have been identified at the single neuron level, and studies providing causal evidence have established firmly that face-selective brain areas are central to face processing. These mechanisms give rise to our highly accurate familiar face recognition but also to our error-prone performance with unfamiliar faces. This limitation of the face system has important implications for consequential situations such as eyewitness identification and policing.

Mammalian evolution entailed multiple innovations in gene regulation, including the emergence of genomic imprinting, an epigenetic regulation leading to the preferential expression of a gene from its maternal or paternal allele. Genomic imprinting is highly prevalent in the brain, yet, until recently, its central roles in neural processes have not been fully appreciated. Here, we provide a comprehensive survey of adult and developmental brain functions influenced by imprinted genes, from neural development and wiring to synaptic function and plasticity, energy balance, social behaviors, emotions, and cognition. We further review the widespread identification of parental biases alongside monoallelic expression in brain tissues, discuss their potential roles in dosage regulation of key neural pathways, and suggest possible mechanisms underlying the dynamic regulation of imprinting in the brain. This review should help provide a better understanding of the significance of genomic imprinting in the normal and pathological brain of mammals including humans.

One of the fundamental properties of the mammalian brain is that sensory regions of cortex are formed of multiple, functionally specialized cortical field maps (CFMs). Each CFM comprises two orthogonal topographical representations, reflecting two essential aspects of sensory space. In auditory cortex, auditory field maps (AFMs) are defined by the combination of tonotopic gradients, representing the spectral aspects of sound (i.e., tones), with orthogonal periodotopic gradients, representing the temporal aspects of sound (i.e., period or temporal envelope). Converging evidence from cytoarchitectural and neuroimaging measurements underlies the definition of 11 AFMs across core and belt regions of human auditory cortex, with likely homology to those of macaque. On a macrostructural level, AFMs are grouped into cloverleaf clusters, an organizational structure also seen in visual cortex. Future research can now use these AFMs to investigate specific stages of auditory processing, key for understanding behaviors such as speech perception and multimodal sensory integration.

Studies of syndromic hydrocephalus have led to the identification of >100 causative genes. Even though this work has illuminated numerous pathways associated with hydrocephalus, it has also highlighted the fact that the genetics underlying this phenotype are more complex than anticipated originally. Mendelian forms of hydrocephalus account for a small fraction of the genetic burden, with clear evidence of background-dependent effects of alleles on penetrance and expressivity of driver mutations in key developmental and homeostatic pathways. Here, we synthesize the currently implicated genes and inheritance paradigms underlying hydrocephalus, grouping causal loci into functional modules that affect discrete, albeit partially overlapping, cellular processes. These in turn have the potential to both inform pathomechanism and assist in the rational molecular classification of a clinically heterogeneous phenotype. Finally, we discuss conceptual methods that can lead to enhanced gene identification and dissection of disease basis, knowledge that will potentially form a foundation for the design of future therapeutics.