The transition from quiet wakeful rest to sleep represents a period over which attention to the external environment fades. Neuroimaging methodologies have provided much information on the shift in neural activity patterns in sleep, but the dynamic restructuring of human brain networks in the transitional period from wake to sleep remains poorly understood. Analysis of electrophysiological measures and functional network connectivity of these early transitional states shows subtle shifts in network architecture that are consistent with reduced external attentiveness and increased internal and self-referential processing. Further, descent to sleep is accompanied by the loss of connectivity in anterior and posterior portions of the default-mode network and more locally organized global network architecture. These data clarify the complex and dynamic nature of the transitional period between wake and sleep and suggest the need for more studies investigating the dynamics of these processes.
sleep; functional connectivity; graph theory; brain networks; alpha EEG; fMRI; EEG/fMRI
Motor learning is an essential part of human behavior, but poorly understood in the context of walking control. Here, we discuss our recent work on locomotor adaptation, which is an error driven motor learning process used to alter spatiotemporal elements of walking. Locomotor adaptation can be induced using a split-belt treadmill that controls the speed of each leg independently. Practicing split-belt walking changes the coordination between the legs, resulting in storage of a new walking pattern. Here, we review findings from this experimental paradigm regarding the learning and generalization of locomotor adaptation. First, we discuss how split-belt walking adaptation develops slowly throughout childhood and adolescence. Second, we demonstrate that conscious effort to change the walking pattern during split-belt training can speed up adaptation but worsens retention. In contrast, distraction (i.e., performing a dual task) during training slows adaptation but improves retention. Finally, we show the walking pattern acquired on the split-belt treadmill generalizes to natural walking when vision is removed. This suggests that treadmill learning can be generalized to different contexts if visual cues specific to the treadmill are removed. These findings allow us to highlight the many future questions that will need to be answered in order to develop more rational methods of rehabilitation for walking deficits.
locomotion; motor learning; adaptation; generalization of learning; rehabilitation
The cochlear implant (CI) is one of the great success stories of modern medicine. A high level of function is provided for most patients. However, some patients still do not achieve excellent or even good results using the present-day devices. Accumulating evidence is pointing to differences in the processing abilities of the “auditory brain” among patients as a principal contributor to this remaining and still large variability in outcomes. In this chapter, we describe a new approach to the design of CIs that takes these differences into account and thereby may improve outcomes for patients with compromised auditory brains.
cochlear implant; cochlear prosthesis; auditory prosthesis; brain–machine interface; brain plasticity; neural prostheses; hearing; deafness; central auditory processing; auditory cortex
After birth, there is striking biological and functional development of the brain’s fiber tracts as well as remodeling of cortical and subcortical structures. Behavioral development in children involves a complex and dynamic set of genetically guided processes by which neural structures interact constantly with the environment. This is a protracted process, beginning in the third week of gestation and continuing into early adulthood. Reviewed here are studies using structural imaging techniques, with a special focus on diffusion weighted imaging, describing age-related brain maturational changes in children and adolescents, as well as studies that link these changes to behavioral differences. Finally, we discuss evidence for effects on the brain of several factors that may play a role in mediating these brain–behavior associations in children, including genetic variation, behavioral interventions, and hormonal variation associated with puberty. At present longitudinal studies are few, and we do not yet know how variability in individual trajectories of biological development in specific neural systems map onto similar variability in behavioral trajectories.
MRI; DTI; brain development; cognitive development; individual differences; fiber tracts
Neural prosthetic systems aim to help disabled patients suffering from a range of neurological injuries and disease by using neural activity from the brain to directly control assistive devices. This approach in effect bypasses the dysfunctional neural circuitry, such as an injured spinal cord. To do so, neural prostheses depend critically on a scientific understanding of the neural activity that drives them. We review here several recent studies aimed at understanding the neural processes in premotor cortex that precede arm movements and lead to the initiation of movement. These studies were motivated by hypotheses and predictions conceived of within a dynamical systems perspective. This perspective concentrates on describing the neural state using as few degrees of freedom as possible and on inferring the rules that govern the motion of that neural state. Although quite general, this perspective has led to a number of specific predictions that have been addressed experimentally. It is hoped that the resulting picture of the dynamical role of preparatory and movement-related neural activity will be particularly helpful to the development of neural prostheses, which can themselves be viewed as dynamical systems under the control of the larger dynamical system to which they are attached.
premotor cortex; motor cortex; motor preparation; state space; dynamical systems; singletrial analysis; neural prostheses; brain machine interface; brain computer interface
Breathing emerges through complex network interactions involving neurons distributed throughout the nervous system. The respiratory rhythm generating network is composed of micro networks functioning within larger networks to generate distinct rhythms and patterns that characterize breathing. The pre-Bötzinger complex, a rhythm generating network located within the ventrolateral medulla assumes a core function without which respiratory rhythm generation and breathing cease altogether. It contains subnetworks with distinct synaptic and intrinsic membrane properties that give rise to different types of respiratory rhythmic activities including eupneic, sigh, and gasping activities. While critical aspects of these rhythmic activities are preserved when isolated in in vitro preparations, the pre-Bötzinger complex functions in the behaving animal as part of a larger network that receives important inputs from areas such as the pons and parafacial nucleus. The respiratory network is also an integrator of modulatory and sensory inputs that imbue the network with the important ability to adapt to changes in the behavioral, metabolic, and developmental conditions of the organism. This review summarizes our current understanding of these interactions and relates the emerging concepts to insights gained in other rhythm generating networks.
Breathing; Respiratory rhythm generation; Pre-Botzinger complex and interactions
Functional MRI (fMRI) studies performed during both waking rest and sleep show that the brain is continually active in distinct patterns that appear to reflect its underlying functional connectivity. In this review, potential sources that contribute to spontaneous fMRI activity will be discussed.
Studies of adaptation to patterns of deterministic forces have revealed the ability of the motor control system to form and use predictive representations of the environment. These studies have also pointed out that adaptation to novel dynamics is aimed at preserving the trajectories of a controlled endpoint, either the hand of a subject or a transported object. We review some of these experiments and present more recent studies aimed at understanding how the motor system forms representations of the physical space in which actions take place. An extensive line of investigations in visual information processing has dealt with the issue of how the Euclidean properties of space are recovered from visual signals that do not appear to possess these properties. The same question is addressed here in the context of motor behavior and motor learning by observing how people remap hand gestures and body motions that control the state of an external device. We present some theoretical considerations and experimental evidence about the ability of the nervous system to create novel patterns of coordination that are consistent with the representation of extrapersonal space. We also discuss the perspective of endowing human–machine interfaces with learning algorithms that, combined with human learning, may facilitate the control of powered wheelchairs and other assistive devices.
motor learning; space; dimensionality reduction; human-machine interface; brain-computer interface.
Large amplitude slow waves are characteristic for the summary brain activity, recorded as electroencephalogram (EEG) or local field potentials (LFP), during deep stages of sleep and some types of anesthesia. Slow rhythm of the synchronized EEG reflects an alternation of active (depolarized, UP) and silent (hyperpolarized, DOWN) states of neocortical neurons. In neurons, involvement in the generalized slow oscillation results in a long-range synchronization of changes of their membrane potential as well as their firing. Here, we aimed at intracellular analysis of details of this synchronization. We asked which components of neuronal activity exhibit long-range correlations during the synchronized EEG? To answer this question, we made simultaneous intracellular recordings from two to four neocortical neurons in cat neocortex. We studied how correlated is the occurrence of active and silent states, and how correlated are fluctuations of the membrane potential in pairs of neurons located close one to the other or separated by up to 13 mm. We show that strong long-range correlation of the membrane potential was observed only (i) during the slow oscillation but not during periods without the oscillation, (ii) during periods which included transitions between the states but not during within-the-state periods, and (iii) for the low-frequency (<5 Hz) components of membrane potential fluctuations but not for the higher-frequency components (>10 Hz). In contrast to the neurons located several millimeters one from the other, membrane potential fluctuations in neighboring neurons remain strongly correlated during periods without slow oscillation. We conclude that membrane potential correlation in distant neurons is brought about by synchronous transitions between the states, while activity within the states is largely uncorrelated. The lack of the generalized fine-scale synchronization of membrane potential changes in neurons during the active states of slow oscillation may allow individual neurons to selectively engage in short living episodes of correlated activity—a process that may be similar to dynamical formation of neuronal ensembles during activated brain states.
intracellular recording; cat; sleep; synchrony
Although multisensory integration has been well modeled at the behavioral level, the link between these behavioral models and the underlying neural circuits is still not clear. This gap is even greater for the problem of sensory integration during movement planning and execution. The difficulty lies in applying simple models of sensory integration to the complex computations that are required for movement control and to the large networks of brain areas that perform these computations. Here I review psychophysical, computational, and physiological work on multisensory integration during movement planning, with an emphasis on goal-directed reaching. I argue that sensory transformations must play a central role in any modeling effort. In particular the statistical properties of these transformations factor heavily into the way in which downstream signals are combined. As a result, our models of optimal integration are only expected to apply “locally”, i.e. independently for each brain area. I suggest that local optimality can be reconciled with globally optimal behavior if one views the collection of parietal sensorimotor areas not as a set of task-specific domains, but rather as a palette of complex, sensorimotor representations that are flexibly combined to drive downstream activity and behavior.
Sensory integration; reaching; neurophysiology; parietal cortex; computational models; vision; proprioception
Once the topic of folklore and science fiction, the notion of restoring vision to the blind is now approaching a tractable reality. Technological advances have inspired numerous multidisciplinary groups worldwide to develop visual neuroprosthetic devices that could potentially provide useful vision and improve the quality of life of profoundly blind individuals. While a variety of approaches and designs are being pursued, they all share a common principle of creating visual percepts through the stimulation of visual neural elements using appropriate patterns of electrical stimulation. Human clinical trials are now well underway and initial results have been met with a balance of excitement and cautious optimism. As remaining technical and surgical challenges continue to be solved and clinical trials move forward, we now enter a phase of development that requires careful consideration of a new set of issues. Establishing appropriate patient selection criteria, methods of evaluating long-term performance and effectiveness, and strategies to rehabilitate implanted patients will all need to be considered in order to achieve optimal outcomes and establish these devices as viable therapeutic options.
Throughout life, thalamocortical (TC) network alternates between activated states (wake or rapid eye movement sleep) and slow oscillatory state dominating slow-wave sleep. The patterns of neuronal firing are different during these distinct states. I propose that due to relatively regular firing, the activated states preset some steady state synaptic plasticity and that the silent periods of slow-wave sleep contribute to a release from this steady state synaptic plasticity. In this respect, I discuss how states of vigilance affect short-, mid-, and long-term synaptic plasticity, intrinsic neuronal plasticity, as well as homeostatic plasticity. Finally, I suggest that slow oscillation is intrinsic property of cortical network and brain homeostatic mechanisms are tuned to use all forms of plasticity to bring cortical network to the state of slow oscillation. However, prolonged and profound shift from this homeostatic balance could lead to development of paroxysmal hyperexcitability and seizures as in the case of brain trauma.
sleep; wake; oscillations; synaptic transmission; synaptic plasticity; intrinsic plasticity
The electrical activity of the brain does not only reflect the current level of arousal, ongoing behavior or involvement in a specific task, but is also influenced by what kind of activity, and how much sleep and waking occurred before. The best marker of sleep-wake history is the electroencephalogram (EEG) spectral power in slow frequencies (slow-wave activity, 0.5–4 Hz, SWA) during sleep, which is high after extended wakefulness and low after consolidated sleep. While sleep homeostasis has been well characterized in various species and experimental paradigms, the specific mechanisms underlying homeostatic changes in brain activity or their functional significance remain poorly understood. However, several recent studies in humans, rats and computer simulations shed light on the cortical mechanisms underlying sleep regulation. First, it was found that the homeostatic changes in SWA can be fully accounted for by the variations in amplitude and slope of EEG slow waves, which are in turn determined by the efficacy of cortico-cortical connectivity. Specifically, the slopes of sleep slow waves were steeper in early sleep compared to late sleep. Second, the slope of cortical evoked potentials, which is an established marker of synaptic strength, was steeper after waking and decreased after sleep. Furthermore, cortical long-term potentiation (LTP) was partially occluded if it was induced after a period of waking, but it could again be fully expressed after sleep. Finally, multiunit activity recordings during sleep revealed that cortical neurons fired more synchronously after waking, and less so after a period of consolidated sleep. The decline of all these electrophysiological measures - the slopes of slow waves and evoked potentials and neuronal synchrony – during sleep correlated with the decline of the traditional marker of sleep homeostasis, EEG SWA. Taken together, these data suggest that homeostatic changes in sleep EEG are the result of altered neuronal firing and synchrony, which in turn arise from changes in functional neuronal connectivity.
sleep homeostasis; synaptic homeostasis; multiunit activity; neurons; cortex
Slow waves are the most prominent electroencephalographic (EEG) feature of non-rapid eye movement (NREM) sleep. During NREM sleep, cortical neurons oscillate approximately once every second between a depolarized upstate, when cortical neurons are actively firing, and a hyperpolarized downstate, when cortical neurons are virtually silent (Steriade et al., 1993a; Destexhe et al., 1999; Steriade et al., 2001). Intracellular recordings indicate that the origins of the slow oscillation are cortical and that cortico-cortical connections are necessary for their synchronization (Steriade et al. 1993b; Amzica and Steriade, 1995; Timofeev and Steriade, 1996; Timofeev et al., 2000). The currents produced by the near-synchronous slow oscillation of large populations of neurons appear on the scalp as EEG slow waves (Amzica and Steriade, 1997).
Despite this cellular understanding, questions remain about the role of specific cortical structures in individual slow waves. Early EEG studies of slow waves in humans were limited by the small number of derivations employed and by the difficulty of relating scalp potentials to underlying brain activity (Brazier 1949; Roth et al 1956). Functional neuroimaging methods offer exceptional spatial resolution but lack the temporal resolution to track individual slow waves (Maquet, 2000; Dang-Vu et al., 2008). Intracranial recordings in patient populations are limited by the availability of medically necessary electrode placements and can be confounded by pathology and medications (Nir et al., 2010; Cash et al., 2009; Wenneberg 2010).
Source modeling of high-density EEG recordings offers a unique opportunity for neuroimaging sleep slow waves. So far, the results have challenged several of the influential topographic observations about slow waves that had persisted since the original EEG recordings of sleep. These recent analyses revealed that individual slow waves are idiosyncratic cortical events and that the negative peak of the EEG slow wave often involves cortical structures not necessarily apparent from the scalp, like the inferior frontal gyrus, anterior cingulate, posterior cingulate and precuneus (Murphy et al., 2009). In addition, not only do slow waves travel (Massimini et al., 2004), but they often do so preferentially through the areas comprising the major connectional backbone of the human cortex (Hagmann et al., 2008). In this chapter we will review the cellular, intracranial recording and neuroimaging results concerning EEG slow waves. We will also confront a long held belief about peripherally evoked slow waves, also known as K-complexes, namely that they are modality-independent and do not involve cortical sensory pathways. The analysis included here is the first to directly compare K-complexes evoked with three different stimulation modalities within the same subject on the same night using high-density EEG.
slow oscillation; source modeling; K-complex; neuroimaging; electroencephalography
The past decade of neuroscience research has provided considerable evidence that the adult brain can undergo substantial reorganization following injury. For example, following an ischemic lesion, such as occurs following a stroke, there is a cascade of molecular, genetic, physiological and anatomical events that allows the remaining structures in the brain to reorganize. Often, these events are associated with recovery, suggesting that they contribute to it. Indeed, the term plasticity in stroke research has had a positive connotation historically. But more recently, efforts have been made to differentiate beneficial from detrimental changes. These notions are timely now that neurorehabilitative research is developing novel treatments to modulate, increase, or inhibit plasticity in targeted brain regions. We will review basic principles of plasticity and some of the new and exciting approaches that are currently being investigated to shape plasticity following injury in the central nervous system.
Cortex; Stimulation; Plasticity; Recovery; Rehabilitation; Stroke
Spinal cord injury is a devastating neurological trauma, often resulting in the impairment of bladder, bowel, and sexual function as well as the loss of voluntary control of muscles innervated by spinal cord segments below the lesion site. Research is ongoing into several classes of therapies to restore lost function. These include the encouragement of neural sparing and regeneration of the affected tissue, and the intervention with pharmacological and rehabilitative means to improve function. This review will focus on the application of electrical current in the spinal cord in order to reactivate extant circuitry which coordinates and controls smooth and skeletal muscle below the injury. We first present a brief historical review of intraspinal microstimulation (ISMS) focusing on its use for restoring bladder function after spinal cord injury as well as its utilization as a research tool for mapping spinal cord circuits that coordinate movements. We then present a review of our own results related to the use of ISMS for restoring standing and walking movements after spinal cord injury. We discuss the mechanisms of action of ISMS and how they relate to observed functional outcomes in animal models. These include the activation of fibers-in-passage which lead to the transsynaptic spread of activation through the spinal cord and the ability of ISMS to produce fatigue-resistant, weight-bearing movements. We present our thoughts on the clinical potential for ISMS with regard to implantation techniques, stability, and damage induced by mechanical and electrical factors. We conclude by suggesting improvements in materials and techniques that are needed in preparation for a clinical proof-of-principle and review our current attempts to achieve these.
Electrical stimulation; lumbosacral enlargement; locomotor networks; standing; walking; muscle fatigue
In the absence of external stimuli the mammalian brain continues to display a rich variety of spontaneous activity. Such activity is often highly stereotypical, invariably rhythmic and can occur with periodicities ranging from a few milliseconds to several minutes. Recently there has been a particular resurgence of interest in fluctuations in brain activity occurring at <0.1 Hz, commonly referred to as very slow or infra-slow oscillations (ISOs). Whilst this is primarily due to the emergence of functional magnetic resonance imaging (fMRI) as a technique which has revolutionised the study of human brain dynamics it is also a consequence of the application of full band electroencephalography (fbEEG). Despite these technical advances the precise mechanisms which lead to ISOs in the brain remain unclear. In a host of animal studies, one brain region that consistently shows oscillations at <0.1 Hz is the thalamus. Importantly, similar oscillations can also be observed in slices of isolated thalamic relay nuclei maintained in vitro. Here, we discuss the nature and mechanisms of these oscillations, paying particular attention to a potential role for astrocytes in their genesis. We also highlight the relationship between this activity and ongoing local network oscillations in the alpha (α) (~8-13 Hz) band, drawing clear parallels with observations made in vivo. Lastly, we consider the relevance of these thalamic ISOs to the pathological activity that occurs in certain types of epilepsy.
acetylcholine; metabotropic glutamate receptor; EEG; gap junctions; alpha rhythm; epilepsy; adenosine; astrocytes; GIRK channels
Neuronal activity elicits vascular dilation, delivering additional blood and metabolites to the activated region. With increasing neural activity, vessels stretch and may become less compliant. Most functional imaging studies assume that limits to vascular expansion are not normally reached except under pathological conditions, with the possibility that metabolism could outpace supply. However, we previously demonstrated that evoked hemodynamic responses were larger during quiet sleep when compared to both waking and REM sleep, suggesting that high basal activity during wake may elicit blunted evoked hemodynamic responses due to vascular expansion limits. We hypothesized that extended brain activity through sleep deprivation will further dilate blood vessels, and exacerbate the blunted evoked hemodynamic responses observed during wake, and dampen responses in subsequent sleep. We measured evoked electrical and hemodynamic responses from rats using auditory clicks (0.5 s, 10 Hz, 2–13 s random ISIs) for one hour following 2, 4, or 6 hours of sleep deprivation. Time-of-day matched controls were recorded continuously for 7 hours. Within quiet sleep periods following deprivation, ERP amplitude did not differ; however, the evoked vascular response was smaller with longer sleep deprivation periods. These results suggest that prolonged neural activity periods through sleep deprivation may diminish vascular compliance as indicated by the blunted vascular response. Subsequent sleep may allow vessels to relax, restoring their ability to deliver blood. These results also suggest that severe sleep deprivation or chronic sleep disturbances could push the vasculature to critical limits, leading to metabolic deficit and the potential for tissue trauma.
auditory cortex; blood volume; evoked response potential (ERP); hemoglobin; optical; NIRS; quiet sleep