Annual Review of Neuroscience - Volume 41, 2018

The mechanistic target of rapamycin (mTOR) is an important signaling hub that integrates environmental information regarding energy availability and stimulates anabolic molecular processes and cell growth. Abnormalities in this pathway have been identified in several syndromes in which autism spectrum disorder (ASD) is highly prevalent. Several studies have investigated mTOR signaling in developmental and neuronal processes that, when dysregulated, could contribute to the development of ASD. Although many potential mechanisms still remain to be fully understood, these associations are of great interest because of the clinical availability of mTOR inhibitors. Clinical trials evaluating the efficacy of mTOR inhibitors to improve neurodevelopmental outcomes have been initiated.

The development of advanced noninvasive techniques to image the human brain has enabled the demonstration of structural plasticity during adulthood in response to motor learning. Understanding the basic mechanisms of structural plasticity in the context of motor learning is essential to improve motor rehabilitation in stroke patients. Here, we review and discuss the emerging evidence for motor-learning-related structural plasticity and the implications for stroke rehabilitation. In the clinical context, a few studies have started to assess the effects of rehabilitation on structural measures to understand recovery poststroke and additionally to predict intervention outcomes. Structural imaging will likely have a role in the future in providing measures that inform patient stratification for optimal outcomes.

Dystonia is a collection of symptoms with involuntary muscle activation causing hypertonia, hyperkinetic movements, and overflow. In children, dystonia can have numerous etiologies with varying neuroanatomic distribution. The semiology of dystonia can be explained by gain-of-function failure of a feedback controller that is responsible for stabilizing posture and movement. Because postural control is maintained by a widely distributed network, many different anatomic regions may be responsible for symptoms of dystonia, although all features of dystonia can be explained by uncontrolled activation or hypersensitivity of motor cortical regions that can cause increased reflex gain, inserted postures, or sensitivity to irrelevant sensory variables. Effective treatment of dystonia in children requires an understanding of the relationship between etiology, anatomy, and the specific mechanism of failure of postural stabilization.

Structural plasticity in the myelinated infrastructure of the nervous system has come to light. Although an innate program of myelin development proceeds independent of nervous system activity, a second mode of myelination exists in which activity-dependent, plastic changes in myelin-forming cells influence myelin structure and neurological function. These complementary and possibly temporally overlapping activity-independent and activity-dependent modes of myelination crystallize in a model of experience-modulated myelin development and plasticity with broad implications for neurological function. In this article, I consider the contributions of myelin to neural circuit function, the dynamic influences of experience on myelin microstructure, and the role that plasticity of myelin may play in cognition.

Understanding how cognitive processes affect the responses of sensory neurons may clarify the relationship between neuronal population activity and behavior. However, tools for analyzing neuronal activity have not kept up with technological advances in recording from large neuronal populations. Here, we describe prevalent hypotheses of how cognitive processes affect sensory neurons, driven largely by a model based on the activity of single neurons or pools of neurons as the units of computation. We then use simple simulations to expand this model to a new conceptual framework that focuses on subspaces of population activity as the relevant units of computation, uses comparisons between brain areas or to behavior to guide analyses of these subspaces, and suggests that population activity is optimized to decode the large variety of stimuli and tasks that animals encounter in natural behavior. This framework provides new ways of understanding the ever-growing quantity of recorded population activity data.

Activity in a network of areas spanning the superior temporal sulcus, dorsomedial frontal cortex, and anterior cingulate cortex is concerned with how nonhuman primates negotiate the social worlds in which they live. Central aspects of these circuits are retained in humans. Activity in these areas codes for primates’ interactions with one another, their attempts to find out about one another, and their attempts to prevent others from finding out too much about themselves. Moreover, important features of the social world, such as dominance status, cooperation, and competition, modulate activity in these areas. We consider the degree to which activity in these regions is simply encoding an individual's own actions and choices or whether this activity is especially and specifically concerned with social cognition. Recent advances in comparative anatomy and computational modeling may help us to gain deeper insights into the nature and boundaries of primate social cognition.

The noncoding portion of the genome, including microRNAs, has been fertile evolutionary soil for cortical development in primates. A major contribution to cortical expansion in primates is the generation of novel precursor cell populations. Because miRNA expression profiles track closely with cell identity, it is likely that numerous novel microRNAs have contributed to cellular diversity in the brain. The tools to determine the genomic context within which novel microRNAs emerge and how they become integrated into molecular circuitry are now in hand.

The addition of new neurons and oligodendroglia in the postnatal and adult mammalian brain presents distinct forms of gray and white matter plasticity. Substantial effort has been devoted to understanding the cellular and molecular mechanisms controlling postnatal neurogenesis and gliogenesis, revealing important parallels to principles governing the embryonic stages. While during central nervous system development, scripted temporal and spatial patterns of neural and glial progenitor proliferation and differentiation are necessary to create the nervous system architecture, it remains unclear what driving forces maintain and sustain postnatal neural stem cell (NSC) and oligodendrocyte progenitor cell (OPC) production of new neurons and glia. In recent years, neuronal activity has been identified as an important modulator of these processes. Using the distinct properties of neurotransmitter ionotropic and metabotropic channels to signal downstream cellular events, NSCs and OPCs share common features in their readout of neuronal activity patterns. Here we review the current evidence for neuronal activity-dependent control of NSC/OPC proliferation and differentiation in the postnatal brain, highlight some potential mechanisms used by the two progenitor populations, and discuss future studies that might advance these research areas further.

The thalamus has long been suspected to have an important role in cognition, yet recent theories have favored a more corticocentric view. According to this view, the thalamus is an excitatory feedforward relay to or between cortical regions, and cognitively relevant computations are exclusively cortical. Here, we review anatomical, physiological, and behavioral studies along evolutionary and theoretical dimensions, arguing for essential and unique thalamic computations in cognition. Considering their architectural features as well as their ability to initiate, sustain, and switch cortical activity, thalamic circuits appear uniquely suited for computing contextual signals that rapidly reconfigure task-relevant cortical representations. We introduce a framework that formalizes this notion, show its consistency with several findings, and discuss its prediction of thalamic roles in perceptual inference and behavioral flexibility. Overall, our framework emphasizes an expanded view of the thalamus in cognitive computations and provides a roadmap to test several of its theoretical and experimental predictions.

Understanding the biological basis for human-specific cognitive traits presents both immense challenges and unique opportunities. Although the question of what makes us human has been investigated with several different methods, the rise of comparative genomics, epigenomics, and medical genetics has provided tools to help narrow down and functionally assess the regions of the genome that seem evolutionarily relevant along the human lineage. In this review, we focus on how medical genetic cases have provided compelling functional evidence for genes and loci that appear to have interesting evolutionary signatures in humans. Furthermore, we examine a special class of noncoding regions, human accelerated regions (HARs), that have been suggested to show human-lineage-specific divergence, and how the use of clinical and population data has started to provide functional information to examine these regions. Finally, we outline methods that provide new insights into functional noncoding sequences in evolution.

Brain tumors are the leading cause of cancer-related death in children, and medulloblastoma (MB) is the most common malignant pediatric brain tumor. Advances in surgery, radiation, and chemotherapy have improved the survival of MB patients. But despite these advances, 25–30% of patients still die from the disease, and survivors suffer severe long-term side effects from the aggressive therapies they receive. Although MB is often considered a single disease, molecular profiling has revealed a significant degree of heterogeneity, and there is a growing consensus that MB consists of multiple subgroups with distinct driver mutations, cells of origin, and prognosis. Here, we review recent progress in MB research, with a focus on the genes and pathways that drive tumorigenesis, the animal models that have been developed to study tumor biology, and the advances in conventional and targeted therapy.

Supervised learning plays a key role in the operation of many biological and artificial neural networks. Analysis of the computations underlying supervised learning is facilitated by the relatively simple and uniform architecture of the cerebellum, a brain area that supports numerous motor, sensory, and cognitive functions. We highlight recent discoveries indicating that the cerebellum implements supervised learning using the following organizational principles: (a) extensive preprocessing of input representations (i.e., feature engineering), (b) massively recurrent circuit architecture, (c) linear input–output computations, (d) sophisticated instructive signals that can be regulated and are predictive, (e) adaptive mechanisms of plasticity with multiple timescales, and (f) task-specific hardware specializations. The principles emerging from studies of the cerebellum have striking parallels with those in other brain areas and in artificial neural networks, as well as some notable differences, which can inform future research on supervised learning and inspire next-generation machine-based algorithms.

One of the fundamental properties of the cell is the capability to digest and remodel its own components according to metabolic and developmental needs. This is accomplished via the autophagy-lysosome system, a pathway of critical importance in the brain, where it contributes to neuronal plasticity and must protect nonreplaceable neurons from the potentially harmful accumulation of cellular waste. The study of lysosomal biogenesis and function in the context of common and rare neurodegenerative diseases has revealed that a dysfunctional autophagy-lysosome system is the shared nexus where multiple, interconnected pathogenic events take place. The characterization of pathways and mechanisms regulating the lysosomal system and autophagic clearance offers unprecedented opportunities for the development of polyvalent therapeutic strategies based on the enhancement of the autophagy-lysosome pathway to maintain cellular homeostasis and achieve neuroprotection.

A major mystery of many types of neurological and psychiatric disorders, such as Alzheimer's disease (AD), remains the underlying, disease-specific neuronal damage. Because of the strong interconnectivity of neurons in the brain, neuronal dysfunction necessarily disrupts neuronal circuits. In this article, we review evidence for the disruption of large-scale networks from imaging studies of humans and relate it to studies of cellular dysfunction in mouse models of AD. The emerging picture is that some forms of early network dysfunctions can be explained by excessively increased levels of neuronal activity. The notion of such neuronal hyperactivity receives strong support from in vivo and in vitro cellular imaging and electrophysiological recordings in the mouse, which provide mechanistic insights underlying the change in neuronal excitability. Overall, some key aspects of AD-related neuronal dysfunctions in humans and mice are strikingly similar and support the continuation of such a translational strategy.

Long-lasting changes of brain function in response to experience rely on diverse forms of activity-dependent synaptic plasticity. Chief among them are long-term potentiation and long-term depression of neurotransmitter release, which are widely expressed by excitatory and inhibitory synapses throughout the central nervous system and can dynamically regulate information flow in neural circuits. This review article explores recent advances in presynaptic long-term plasticity mechanisms and contributions to circuit function. Growing evidence indicates that presynaptic plasticity may involve structural changes, presynaptic protein synthesis, and transsynaptic signaling. Presynaptic long-term plasticity can alter the short-term dynamics of neurotransmitter release, thereby contributing to circuit computations such as novelty detection, modifications of the excitatory/inhibitory balance, and sensory adaptation. In addition, presynaptic long-term plasticity underlies forms of learning and its dysregulation participates in several neuropsychiatric conditions, including schizophrenia, autism, intellectual disabilities, neurodegenerative diseases, and drug abuse.

Recombinant viruses allow for targeted transgene expression in specific cell populations throughout the nervous system. The adeno-associated virus (AAV) is among the most commonly used viruses for neuroscience research. Recombinant AAVs (rAAVs) are highly versatile and can package most cargo composed of desired genes within the capsid's ∼5-kb carrying capacity. Numerous regulatory elements and intersectional strategies have been validated in rAAVs to enable cell type–specific expression. rAAVs can be delivered to specific neuronal populations or globally throughout the animal. The AAV capsids have natural cell type or tissue tropism and trafficking that can be modified for increased specificity. Here, we describe recently engineered AAV capsids and associated cargo that have extended the utility of AAVs in targeting molecularly defined neurons throughout the nervous system, which will further facilitate neuronal circuit interrogation and discovery.

The recently determined connectome of the Caenorhabditis elegans adult male, together with the known connectome of the hermaphrodite, opens up the possibility for a comprehensive description of sexual dimorphism in this species and the identification and study of the neural circuits underlying sexual behaviors. The C. elegans nervous system consists of 294 neurons shared by both sexes plus neurons unique to each sex, 8 in the hermaphrodite and 91 in the male. The sex-specific neurons are well integrated within the remainder of the nervous system; in the male, 16% of the input to the shared component comes from male-specific neurons. Although sex-specific neurons are involved primarily, but not exclusively, in controlling sex-unique behavior—egg-laying in the hermaphrodite and copulation in the male—these neurons act together with shared neurons to make navigational choices that optimize reproductive success. Sex differences in general behaviors are underlain by considerable dimorphism within the shared component of the nervous system itself, including dimorphism in synaptic connectivity.

In everyday life, the outcomes of our actions are rarely certain. Further, we often lack the information needed to precisely estimate the probability and value of potential outcomes as well as how much effort will be required by the courses of action under consideration. Under such conditions of uncertainty, individual differences in the estimation and weighting of these variables, and in reliance on model-free versus model-based decision making, have the potential to strongly influence our behavior. Both anxiety and depression are associated with difficulties in decision making. Further, anxiety is linked to increased engagement in threat-avoidance behaviors and depression is linked to reduced engagement in reward-seeking behaviors. The precise deficits, or biases, in decision making associated with these common forms of psychopathology remain to be fully specified. In this article, we review evidence for which of the computations supporting decision making are altered in anxiety and depression and consider the potential consequences for action selection. In addition, we provide a schematic framework that integrates the findings reviewed and will hopefully be of value to future studies.

Memories for events are thought to be represented in sparse, distributed neuronal ensembles (or engrams). In this article, we review how neurons are chosen to become part of a particular engram, via a process of neuronal allocation. Experiments in rodents indicate that eligible neurons compete for allocation to a given engram, with more excitable neurons winning this competition. Moreover, fluctuations in neuronal excitability determine how engrams interact, promoting either memory integration (via coallocation to overlapping engrams) or separation (via disallocation to nonoverlapping engrams). In parallel with rodent studies, recent findings in humans verify the importance of this memory integration process for linking memories that occur close in time or share related content. A deeper understanding of allocation promises to provide insights into the logic underlying how knowledge is normally organized in the brain and the disorders in which this process has gone awry.

The fields of human motor control, motor learning, and neurorehabilitation have long been linked by the intuition that understanding how we move (and learn to move) leads to better rehabilitation. In reality, these fields have remained largely separate. Our knowledge of the neural control of movement has expanded, but principles that can directly impact rehabilitation efficacy remain somewhat sparse. This raises two important questions: What can basic studies of motor learning really tell us about rehabilitation, and are we asking the right questions to improve the lives of patients? This review aims to contextualize recent advances in computational and behavioral studies of human motor learning within the framework of neurorehabilitation. We also discuss our views of the current challenges facing rehabilitation and outline potential clinical applications from recent theoretical and basic studies of motor learning and control.