Annual review of neuroscience最新文献

Animals operate in complex environments, and salient social information is encoded in the nervous system and then processed to initiate adaptive behavior. This encoding involves biological embedding, the process by which social experience affects the brain to influence future behavior. Biological embedding is an important conceptual framework for understanding social decision-making in the brain, as it encompasses multiple levels of organization that regulate how information is encoded and used to modify behavior. The framework we emphasize here is that social stimuli provoke short-term changes in neural activity that lead to changes in gene expression on longer timescales. This process, simplified-neurons are for today and genes are for tomorrow-enables the assessment of the valence of a social interaction, an appropriate and rapid response, and subsequent modification of neural circuitry to change future behavioral inclinations in anticipation of environmental changes. We review recent research on the neural and molecular basis of biological embedding in the context of social interactions, with a special focus on the honeybee.

The discovery of neural signals that reflect the dynamics of perceptual decision formation has had a considerable impact. Not only do such signals enable detailed investigations of the neural implementation of the decision-making process but they also can expose key elements of the brain's decision algorithms. For a long time, such signals were only accessible through direct animal brain recordings, and progress in human neuroscience was hampered by the limitations of noninvasive recording techniques. However, recent methodological advances are increasingly enabling the study of human brain signals that finely trace the dynamics of the unfolding decision process. In this review, we highlight how human neurophysiological data are now being leveraged to furnish new insights into the multiple processing levels involved in forming decisions, to inform the construction and evaluation of mathematical models that can explain intra- and interindividual differences, and to examine how key ancillary processes interact with core decision circuits.

Adaptive behavior in a complex, dynamic, and multisensory world poses some of the most fundamental computational challenges for the brain, notably inference, decision-making, learning, binding, and attention. We first discuss how the brain integrates sensory signals from the same source to support perceptual inference and decision-making by weighting them according to their momentary sensory uncertainties. We then show how observers solve the binding or causal inference problem-deciding whether signals come from common causes and should hence be integrated or else be treated independently. Next, we describe the multifarious interplay between multisensory processing and attention. We argue that attentional mechanisms are crucial to compute approximate solutions to the binding problem in naturalistic environments when complex time-varying signals arise from myriad causes. Finally, we review how the brain dynamically adapts multisensory processing to a changing world across multiple timescales.

Pain is an immense clinical and societal challenge, and the key to understanding and treating it is variability. Robust interindividual differences are consistently observed in pain sensitivity, susceptibility to developing painful disorders, and response to analgesic manipulations. This review examines the causes of this variability, including both organismic and environmental sources. Chronic pain development is a textbook example of a gene-environment interaction, requiring both chance initiating events (e.g., trauma, infection) and more immutable risk factors. The focus is on genetic factors, since twin studies have determined that a plurality of the variance likely derives from inherited genetic variants, but sex, age, ethnicity, personality variables, and environmental factors are also considered.

During the approximately 5 days of Drosophila neurogenesis (late embryogenesis to the beginning of pupation), a limited number of neural stem cells produce approximately 200,000 neurons comprising hundreds of cell types. To build a functional nervous system, neuronal types need to be produced in the proper places, appropriate numbers, and correct times. We discuss how neural stem cells (neuroblasts) obtain so-called area codes for their positions in the nervous system (spatial patterning) and how they keep time to sequentially produce neurons with unique fates (temporal patterning). We focus on specific examples that demonstrate how a relatively simple patterning system (Notch) can be used reiteratively to generate different neuronal types. We also speculate on how different modes of temporal patterning that operate over short versus long time periods might be linked. We end by discussing how specification programs are integrated and lead to the terminal features of different neuronal types.

Animal behavior was classically considered to be determined exclusively by neuronal activity, whereas surrounding glial cells such as astrocytes played only supportive roles. However, astrocytes are as numerous as neurons in the mammalian brain, and current findings indicate a chemically based dialog between astrocytes and neurons. Activation of astrocytes by synaptically released neurotransmitters converges on regulating intracellular Ca²⁺ in astrocytes, which then can regulate the efficacy of near and distant tripartite synapses at diverse timescales through gliotransmitter release. Here, we discuss recent evidence on how diverse behaviors are impacted by this dialog. These recent findings support a paradigm shift in neuroscience, in which animal behavior does not result exclusively from neuronal activity but from the coordinated activity of both astrocytes and neurons. Decoding how astrocytes and neurons interact with each other in various brain circuits will be fundamental to fully understanding how behaviors originate and become dysregulated in disease.

Parkinson's disease (PD) is a common neurodegenerative disorder characterized by degeneration of the substantia nigra pars compacta and by accumulation of α-synuclein in Lewy bodies. PD is caused by a combination of environmental factors and genetic variants. These variants range from highly penetrant Mendelian alleles to alleles that only modestly increase disease risk. Here, we review what is known about the genetics of PD. We also describe how PD genetics have solidified the role of endosomal, lysosomal, and mitochondrial dysfunction in PD pathophysiology. Finally, we highlight how all three pathways are affected by α-synuclein and how this knowledge may be harnessed for the development of disease-modifying therapeutics.

Improvements in understanding the neurobiological basis of mental illness have unfortunately not translated into major advances in treatment. At this point, it is clear that psychiatric disorders are exceedingly complex and that, in order to account for and leverage this complexity, we need to collect longitudinal data sets from much larger and more diverse samples than is practical using traditional methods. We discuss how smartphone-based research methods have the potential to dramatically advance our understanding of the neuroscience of mental health. This, we expect, will take the form of complementing lab-based hard neuroscience research with dense sampling of cognitive tests, clinical questionnaires, passive data from smartphone sensors, and experience-sampling data as people go about their daily lives. Theory- and data-driven approaches can help make sense of these rich data sets, and the combination of computational tools and the big data that smartphones make possible has great potential value for researchers wishing to understand how aspects of brain function give rise to, or emerge from, states of mental health and illness.

Oxytocin regulates parturition, lactation, parental nurturing, and many other social behaviors in both sexes. The circuit mechanisms by which oxytocin modulates social behavior are receiving increasing attention. Here, we review recent studies on oxytocin modulation of neural circuit function and social behavior, largely enabled by new methods of monitoring and manipulating oxytocin or oxytocin receptor neurons in vivo. These studies indicate that oxytocin can enhance the salience of social stimuli and increase signal-to-noise ratios by modulating spiking and synaptic plasticity in the context of circuits and networks. We highlight oxytocin effects on social behavior in nontraditional organisms such as prairie voles and discuss opportunities to enhance the utility of these organisms for studying circuit-level modulation of social behaviors. We then discuss recent insights into oxytocin neuron activity during social interactions. We conclude by discussing some of the major questions and opportunities in the field ahead.