Henri Lottmann, MD, FEBU, FRCS (Eng), FEAPU

• Consultant in Pediatric Urology,
• H?pital Necker-Enfants Malades, Paris, France

In this case gastritis foods to eat list order renagel overnight delivery, the hand and arm map expanded its representation (Nudo gastritis diet fish order renagel toronto, Wise gastritis diet journal buy cheap renagel 400 mg, Sifuentes gastritis symptoms and treatment mayo clinic renagel 400 mg on line, Milliken gastritis unusual symptoms purchase renagel without prescription, & Millikent, 1996; figure 43. Thus, the opposite mapping effect was observed compared to the first study, but with the same behavioral outcome. Finally, a decade later the authors investigated the effects of delayed retraining (Barbay et al. Under this condition, the monkeys regained preoperative levels of behav ior but without the maintenance of the spared peri-infarct hand representation seen with early- onset training (figure 43. Thus, when considered as a whole, these studies suggest that the notion of cortical reorganization as causally supporting recovery is highly problematic, even though these studies are frequently cited as strong evidence for functional reorganization. Instead, the most parsimonious conclusion is that map expansions and contractions are epiphenomena, perhaps use- and learning-related, but not the causal factors for behavioral recovery. In other words, processes relating to learning and adaptive behav ior, likely necessary following injuries such as amputation and stroke, may be driving the map changes, but these changes are not driving behavioral changes. This conclusion is consistent with the above-mentioned studies of use- dependent plasticity in humans, showing that the relevance of cortical changes to voluntary motor control are minimal. Instead, the observed changes in the boundaries of cortical maps are likely to be markers for use and learning effects, with causal effects instead likely arising subcortically. Even in the extreme circumstance of amputation, representation of the hand is retained in the sensorimotor cortex for years and even decades following amputation. The effects of sensory deprivation and brain injury likely facilitate the appearance of training-induced cortical changes in two ways: First, they induce shifts in excitatory/inhibitory balance, potentially improving the neurophysiological profile for inducing long-term potentiation and depression (Polley, Chen-Bee, & Frostig, 1999). Second, the injury causing the amputation/stroke will also profoundly impair the motor abilities of the individual, promoting the learning of adaptive strategies and therefore changing input/output synchronization patterns. We therefore suggest that map changes should neither be attributed to categorical changes in cortical reorganization nor given causal behavioral relevance. Acknowledgments We thank Lisa Quarrell for artwork and Victoria Root and Andrew Pruszynski for helpful comments. Behavioral and neurophysiological effects of delayed training following a small ischemic infarct in primary motor cortex of squirrel monkeys. Phantom-limb pain as a perceptual correlate of cortical reorganization following arm amputation. The representation of the tail in the motor cortex of primates, with special reference to spider monkeys. Tactile perception in blind braille readers: A psychophysical study of acuity and hyperacuity using gratings and dot patterns. Intracortical connectivity of archtectonic fields in the somatic sensory, motor and parietal cortex of monkeys. Large- scale reorganization of the somatosensory cortex following spinal cord injuries is due to brainstem plasticity. Reorganization of the primary motor cortex of adult macaque monkeys after sensory loss resulting from partial spinal cord injuries. Contribution of the monkey corticomotoneuronal system to the control of force in precision grip. Reassessing cortical reorganization in the primary sensorimotor cortex following arm amputation. Topographic reorganization of somatosensory cortical areas 3b and 1 in adult monkeys following restricted deafferentation. Reorganization of movement representations in primary motor cortex following focal ischemic infarcts in adult squirrel monkeys. Makin, Diedrichsen, and Krakauer: Reorganization in Sensorimotor Cortex 525 Journal of Neurophysiology, 75(5), 2144­2149. Neural substrates for the effects of rehabilitative training on motor recovery after ischemic infarct. Cortical map plasticity improves learning but is not necessary for improved per for mance. Remodelling of hand representation in adult cortex determined by timing of tactile stimulation. Sensorimotor finger- specific information in the cortex of the congenitally blind. They integrate multimodal information from the entire neocortex, thalamus, limbic regions, and dopaminergic midbrain nuclei. The basal ganglia output nuclei can powerfully regulate behav ior either by modulating neuronal activity in frontal cortical regions indirectly through a thalamocortical pathway or directly by projections to midbrain/brain stem premotor regions. Dysfunction of the basal ganglia, occurring in diseases in humans or induced experimentally in animal models, leads to profound behavioral impairments, the most consistent being a reduction in speed and extent of movement. However, the specific computational aspect(s) of the basal ganglia that relate to the control of behav ior remains the subject of considerable debate. Here, informed by features of the evolutionary conserved anatomy of the basal ganglia and the analysis of the behavioral deficits in human disease, we propose that a key computational function of the basal ganglia is to control low-level parameters of movements. As a result, the basal ganglia provide a critical circuit in translating both implicit and explicit aspects of voluntary decisions into action. We also review a series of neurophysiological studies across multiple vertebrate species that begin to elucidate the circuit-level implementation mechanisms of basal ganglia function. Functional Anatomy of Basal Ganglia the basal ganglia are a collection of interconnected subcortical nuclei that are found essentially unchanged in their pattern of connectivity, identified cell types, and neurochemical markers in all vertebrate species, ranging from the lamprey to the primate (Grillner, Robertson, & Stephenson-Jones, 2013). This conservation across species covers approximately 500 million years of vertebrate evolution. Over the last several years, the intersection of cell type­ specific labeling, whole-brain anatomical-tracing techniques, and the development of refined molecular tools for anatomical tracing have led to substantial clarification of the cellular details of the inputs to the basal ganglia (Hintiryan et al. Our goal in this section is to provide a brief overview of the conserved functional architecture of the basal ganglia. The basal ganglia transform descending inputs from the telencephalon and then project them via a reentrant pathway through the thalamus back to the cortex and through convergent, feedforward pathways to subcortical premotor structures, such as the reticular areas of the midbrain, the deep layers of the superior colliculus, and the mesencephalic locomotor region (figure 44. There is a dramatic reduction in cell number as one progresses through the internal circuitry of the basal ganglia (figure 44. Although the reentrant pathway through the thalamus is often highlighted in descriptions that focus on the primate brain, the descending projection of the basal ganglia output is at least as prominent in all vertebrates so far studied (Cebrián, Parent, & Prensa, 2005; Deniau, Mailly, Maurice, & Charpier, 2007; Hikosaka, 2007) and appears to have arisen earlier in evolution (Robertson et al. In mammals, afferent inputs to the basal ganglia arise from excitatory projection neurons of the neocortex, thalamus, amygdala, and hippocampus. Three primary classes of excitatory projection neurons in cortex provide input to basal ganglia: superficial intratelencephalic neurons of layer 2/3, deep intratelencephalic neurons of layer 5a, and collaterals from corticofugal projection neurons of layer 5b (Dudman & Gerfen, 2015). In mammals, the caudate and putamen (collectively referred to as the striatum in rodents due to its perforation by massive tracts of the internal capsule) receives all three types of cortical input. Deeper structures in the basal ganglia, such as the subthalamic nucleus and substantia nigra, receive corticofugal (layer 5b) input via collaterals but do not receive direct input from either intratelencephalic population. Compared to the subthalamic nucleus and substantia nigra, the striatum contains the greatest number of neurons by a few orders of magnitude (Hardman et al. The organization of the major pathways of the dorsal basal ganglia is indicated by arrows for predominantly excitatory projections, lines for inhibitory projections, and an open circle for dopaminergic, modulatory projections. Detailed reviews of internal circuitry and topographical organization can be found in Dudman and Gerfen (2015). The striatum is composed of approximately 95% inhibitory projection neurons that fall into two distinct cell types distinguished by their neurochemical markers, dopamine receptor expression, neuropeptide expression, and efferent projections. In the dorsal striatum, these cell types and the functional pathways they constitute are generally referred to as the direct and indirect projection pathways to primary basal ganglia output nuclei (figure 44. However, the advent of methods that combine cell type identification with the recording of activity in behaving rodents (Cui et al. The apparent contradiction between these results might result from the inherent limitations of perturbation and correlative experiments. Second, the algorithmic meaning of patterns of activity that correlate with certain aspects of behav ior can remain obscure, even when complemented with perturbation experiments (Krakauer, Ghazanfar, GomezMarin, MacIver, & Poeppel, 2017). Insight into the 528 Intention, Action, Control function of the striatal circuits (and, more generally, of the basal ganglia) may be gained through a critical examination and comparison of the behavioral alterations associated with various forms of its dysfunction, in daily life activities or experimental setups. Individuals with bilateral lesions of the striatopallidal complex are characterized by an extreme lack of spontaneous mental activity (psychic akinesia) that can be reversed upon external stimulation (Laplane & Dubois, 2001). In addition, when tested with an incentivized handgrip squeezing task, these individuals display a complete incapacity to scale the vigor of their squeeze with the magnitude of a monetary reward (figure 44. Importantly, their capacity to squeeze with increasing strength was identical to control patients in an instructed version of the task. These findings, along with the fact that patients do not display tremor or rigidity, suggest that the basal ganglia are not required for the execution of movements per se but for their invigoration. Thus, dopamine likely provides the motivational signal that is translated into invigorated output from the basal ganglia. This discovery, largely overlooked at the time (Lees, Selikhova, Andrade, & Duyckaerts, 2008), was later explained by the fact that the aforementioned motor deficits are caused by dopamine depletion in the striatum following the progressive degeneration of dopaminergic nigrostriatal neurons (Hornykiewicz, 2006). If this was the case, further perturbation or lesioning of the basal ganglia should worsen movement deficits. This supports the view that damaged basal ganglia do not lead to a fundamental deficit in movement control but a modulatory deficit due to blunted motivation or perhaps an implicit overestimation of the energetical cost of moving (Baraduc, Thobois, Gan, Broussolle, & Desmurget, 2013) Reduced Vigor Is Also Present in Experimentally Induced Disorders of the Basal Ganglia the idea that an important function of the dopaminergic projection to the striatum is to scale movement extent and speed according to motivational factors is supported by pharmacological manipulations of the dopaminergic system in the ventral striatum that alter the amount of work animals are willing to exert to obtain rewards (Salamone, Correa, Farrar, & Mingote, 2007). Recordings of spiking activity in the dorsal striatum of control mice showed that such activity in a majority of neurons was modulated at an early movement phase and correlated with movement speed. Such modulations, compatible with a representation of movement vigor, were markedly altered in MitoPark mice. Importantly, both the behavioral and neuronal abnormalities were reversed by dopamine replacement therapy. A set of studies investigating reaching movements in nonhuman primates showed that inactivation/lesioning of the globus pallidus consistently slowed down movements and induced hypometria but preserved reaction time and movement accuracy (Desmurget & Turner, 2008; Horak & Anderson, 1984), consistent with a selective contribution of the basal ganglia to movement vigor. The vigor viewpoint is also consistent with results obtained in rats performing a completely dif ferent form of motor sequence using a motorized treadmill that required the animals to learn to modulate their running speed (Rueda- Orozco & Robbe, 2015). Inactivation of the sensorimotor striatum did not affect the overall structure of the motor sequence but increased variability in the speed of execution. Altogether, the studies reviewed in this section suggest that the basal ganglia do not generate motor commands to muscles but instead modulate the vigor of ongoing movements (figure 44. An important question is raised by the aforementioned studies: What are the respective contributions of dopamine acting on the dorsal and ventral striatum One possibility is that the modulation of neuronal activity in these regions contributes to two parameters that usually covary: response vigor (How many times do I knock on the door to be heard Due to space constraints, this chapter is largely focused on the dorsal striatum, but we refer interested readers to a recent review on the possible functional specificity of neuronal processes occurring in the dorsal and ventral striatum (Hart, Leung, & Balleine, 2014). A, Individuals with bilateral striatopallidal lesions lack the capacity to scale movement vigor with motivation (Schmidt et al. After a fixation cross, subjects were shown the monetary incentive as a coin image (0. After a fixation cross, subjects were shown two options side by side, each corresponding to a potential monetary reward (coin image) associated with a required force level (orange bar). Middle, Movement end points (small circle) relative to target center (big circle) for valid trials with slow (up) or fast (down) speed instruction. Robbe and Dudman: the Basal Ganglia Invigorate Actions and Decisions 531 depletion in mice is associated with the altered processing of sensory information in the striatum (Ketzef et al. In a constantly changing world and body, the role of the dorsal striatum in representing movements and their associated sensory consequences, coupled with dopaminergic teaching signals, could be important to maintain and update previously learned actions or to develop an adaptive repertoire of actions (Dudman & Krakauer, 2016; Robbe, 2018). We will see in this section that, surprisingly, most studies investigating the role of the basal ganglia in motor learning have not done this. In one study (Wilkinson, Khan, & Jahanshahi, 2009), subjects had to respond with four fingers of the right hand. Responding with independent movements of all four fingers (especially the ring and little finger) is aty pical and may require some motor dexterity when trying to respond as quickly as possible. Unfortunately, the quality of the finger movements was not quantified, leaving open the possibility that the longer reaction times were caused by vigor deficits (per for mance confound). Very few studies with human participants have directly examined how basal ganglia dysfunctions affect learning in tasks requiring motor acuity. To account for group differences in initial per for mance, the rotation speed of the target was adjusted. The problem here is that normalization procedures do not solve the issue of how to properly compare motor learning across groups with dif ferent baselines. Neurophysiological data that could provide a clue for how the basal ganglia might contribute to true motor learning are scarce. The processing of such signals by the striatum, which also integrates multimodal information, mainly from the cortex and the thalamus, is believed to be critical to motor learning. We will start this section by defining motor learning as the execution of movements with increased precision and accuracy (motor acuity) and at shorter latency (Krakauer, Hadjiosif, Xu, Wong, & Haith, 2018). It is important to distinguish motor learning from the process by which humans and animals learn to select the "right" actions in a given context. Motor sequence tasks are based on the execution of one or several ordered movements according to specific rules. In the field of animal operant conditioning, learning can take several weeks but, typically, this duration reflects the time it takes an animal to discover rules the experimenter could not verbally explain. At the end of this process, animals have learned to select movements (and sometimes their speed), but motor learning per se may not have occurred. Before conditioning, a rodent will not know how to press a lever or run in a particular manner in a maze. But it will often succeed in this type of task by assembling previously mastered elementary movements into an adaptive motor sequence. Importantly, a reduction in reaction and movement time in motor sequences following extensive practice has often been assumed to reflect implicit motor learning. However, it was recently shown in humans that such per formance improvements can be driven by a gain in explicit knowledge acting on vigor (Wong, Lindquist, Haith, & Krakauer, 2015). A more suitable strategy is to use tasks that require unnatural movements and/or focus on 532 Intention, Action, Control the dexterous four-fingers version of the serial reaction time (Jenkins, Brooks, Nixon, Frackowiak, & Passingham, 1994; Jueptner, Frith, Brooks, Frackowiak, & Passingham, 1997; Lehéricy et al.

It has been shown that the boundaries of the hand map can be blurred when inputs are synchronized across the digits gastritis doctor buy renagel 800 mg with amex, such as by stitching two of them together gastritis symptoms on dogs purchase renagel on line amex, stimulating them synchronously gastritis je cheapest generic renagel uk, or performing repetitive and highly stereotypic hand movements (Wang gastritis cystica profunda generic 800 mg renagel free shipping, Merzenich gastritis gastritis discount renagel american express, Sameshima, & Jenkins, 1995). Following prolonged periods of synchronized inputs, neurons previously showing greater selectivity to one digit expand their tuning to include the co- stimulated digit/s. Conversely, tactile training restricted to a single fingertip results in increased cortical representation of the stimulated digit. If Makin, Diedrichsen, and Krakauer: Reorganization in Sensorimotor Cortex 519 the neurons along the digit boundaries of the hand map were already natively tuned to receive inputs from the neighboring digit, then these findings better fit our above definition of gain modulation rather than strict reorganization. In summary, patterns of synchronized sensory input, due to daily hand usage and learning, dictate the tuning properties of neurons comprising the hand map, likely through well- established processes of Hebbian plasticity and gain modulation. It is worth noting that these map changes can also be induced by passively generated inputs, which suggests that it is not a unique consequence of voluntary movement. Indeed, there is no clear evidence that these fine- scale brain organization changes have any implications on sensorimotor processing, in terms of perceptual or motor skill. To address this potentially contentious issue more definitively, we next consider what happens when inputs are altered more dramatically- specifically, when the hand map is partially or completely deprived of synchronized inputs. Reorganization following amputation What happens to the highly organized hand area in S1 (figure 43. Seminal studies in monkeys have shown that the input- deprived brain territory becomes responsive to inputs from another body part. If, for example, digit 3 of the hand is amputated, over the course of weeks and months, the digit 3 part of the hand map becomes responsive to inputs from digits 2 and 4 (Merzenich et al. If input from the median nerve (innervating the glabrous skin of digits 1-3) is abolished due to nerve transection, then representation of the ulnar and radial nerves (innervating the dorsal skin of the hand) will seemingly expand into the deprived (median nerve) cortex (Merzenich et al. Since ulnar and radial responses were apparent in the deprived cortex immediately following deprivation, it is likely that these inputs were already native to this area but masked. While the newly observed representation in the deprived cortex of the dorsal skin is not initially topographically organized, over time it becomes so. This indicates that the synchronization of input, over the course of months, can promote topographic structure. Most strikingly, if input from the entire hand and arm is lost, due to deafferentation, neurons in the hand area become activated by inputs from the chin (Pons et al. Since the deprived hand area normally does not receive facial inputs, this is precisely the kind of result that had been interpreted as strong evidence for true reorganization. In all the cases of deafferentation discussed here, a new body-part representation, originally represented adjacently, is thought to "take over" the freed-up territory. Changes in M1 maps are most commonly studied using microstimulation to evoke muscle responses. Therefore, to study changes in M1 following input loss it is necessary to leave the efferent motor pathways preserved. When the dorsal column of the spinal cord is selectively injured (leaving the ventral column intact), S1 undergoes the above-mentioned facial remapping, yet movement representation in M1 is relatively unchanged (Kambi, Tandon, Mohammed, Lazar, & Jain, 2011). The preservation of M1 organization is probably due to the preservation of some motor function with the affected hand. Therefore, it seems that face-to-hand S1 reorganization does not necessitate a topographic change in M1. This unique dissociation between the S1 and M1 maps is intriguing, considering the strong bidirectional coupling between these two regions: If the former hand territory in S1 now supports facial processing, whereas its M1 counterpart still controls hand function, how would the functionality of the hand sensorimotor loop be maintained if true reorganization had occurred Persistent functional organization following amputation When aiming to characterize putative cortical reorganization in animals, scientists investigate the representation in the deprived sensory area of intact body parts-for example, facial responses following arm amputation. While this approach is suitable for documenting shifts in representational maps, it leaves unexplored the possibility that the original function of the deprived region may be preserved, although latent. Amputees experiencing phantom sensations provide a unique model to study what happens to the deprived hand area itself after sensory input loss. Many amputees report that they can elicit voluntary movements in their phantom hands. By utilizing reported phantom sensations, multiple studies have found that representations relating to the phantom hand persist in the peripheral and central ner vous systems (Raffin, Mattout, Reilly, & Giraux, 2012). Most strikingly, intracranial microstimulation in S1 of a tetraplegic patient elicited sensations of touch in specific hand locations (Flesher et al. Other studies show persistent communication between sensorimotor and higher- order motor areas relating to hand motor control. Collectively, these human studies clearly demonstrate that (1) the original functional layout of the sensory cortex is stable and does not disappear with afferent input loss; (2) representation in the deprived hand area is not altered- that is, the hand remains the target of S1/M1 processing; and (3) both upstream and downstream areas interpret input to or readout from the deprived hand area as relating to the missing hand. How can we resolve the evidence from monkeys, showing extensive face remapping in the hand area of S1, with the human evidence suggesting persistent representation of the missing hand The remapping observed in monkeys was originally thought to result from the widespread sprouting of intracortical connections, where new inputs from the face area travel to the hand area via lateral connections, formed following amputation (Pons et al. Alternatively, sparse widespread cortical connections that normally exist in the region may become unmasked following input loss, although anatomical evidence speaks against this possibility. Recent evidence suggests a third possibility: what appears to be cortical reorganization actually reflects subcortical reorganization, with subsequent changes in subcortical inputs to the cortex. For example, S1 facial remapping is abolished when the cuneate nucleus (normally receiving input from the hand and body) is inactivated (Kambi et al. This indicates that at the cortical level, the responses to subcortical inputs remain stable after amputation. The unchanged cortical hand area continues to receive input from the cuneate nucleus (via the thalamus) through the original pathways, but these are now being driven by Makin, Diedrichsen, and Krakauer: Reorganization in Sensorimotor Cortex 521 sprouting from the trigeminal nucleus to the cuneate. These findings can resolve the apparent conflict between face-to-hand remapping in monkeys and the preservation of cortical hand organization in S1 of humans after amputation: while facial inputs may activate the hand area, due to changes subcortically, cortical hand architecture remains invariant. In summary, classic studies in monkeys in which the main sensory input was removed from S1 were interpreted to mean that "freed-up" territory was invaded by new representations. Instead, they demonstrate the immutability of native functional organization in the deprived cortex, with map changes being attributable either to the facilitation of preexisting cortical architecture or subcortical reorganization. Therefore, maps changes are either due to gain modulation or altered subcortical inputs, rather than a categorical change in the identity of a cortical neuron or network. After these changes have occurred, the excitation/inhibition balance can return to normal and the map to its original shape, as demonstrated for the relationship between auditory perceptual learning and auditory cortex map changes (Reed et al. Input loss following amputation It has long been suggested that the expanded representation of the spared input into deprived cortex following amputation results in perceptual gains. For example, Merzenich and colleagues (1984) proposed that reorganization following digit amputation (figure 43. Underlying this idea is the assumption that the brain is able to correctly interpret signals arising from the deprived area (missing digit territory) as relating to its newly assigned function (neighboring digit representation), thereby providing greater (or better) information about the new representation. However, tactile acuity in human digit amputees is not affected by the amputation. Similarly, the popular notion that cross-modal reorganization in the visual cortex of people with congenital blindness contributes to heightened tactile abilities has also been questioned. For example, activity in visual cortex in response to touch does not contain information that pertains to somatotopic digit representation (Wesselink et al. Instead, the improved perceptual abilities in populations suffering from sensory deprivation could be attributed to altered experience and training, which does not necessitate reorganization (Grant, Thiagarajah, & Sathian, 2000; Polley, ChenBee, & Frostig, 1999). Referred sensations in amputees have famously also been regarded as the behavioral correlates of brain reorganization. Interestingly, some amputees report experiencing touch on their phantom hand when the skin of a dif ferent body part is touched. This "referred" sensation is most commonly experienced by touching the residual arm and thus it could be explained by aberrant reinnervation of the peripheral sensory nerves. In a few amputees, however, referred sensations have been reported to occur on the face, which have been suggested as even stronger evidence for reorganization-if touch on the face activates the hand area, the brain might interpret this touch as arising from the missing hand (Ramachandran, Stewart, & RogersRamachandran, 1992). However, facial referred sensations are not common, and referred sensations can be triggered by touch applied to multiple body parts. That is to say, even if the cortical architecture has not qualitatively changed, the changes in cortical maps could still be necessary for functional reorganization, which we define as a neural change of any kind that is causally related to a change in behavior. We next ask if changes in sensorimotor maps are necessary for functional reorganization. Map changes following training Motor training of a specific new movement-for example, learning how to rapidly extend the thumb-is associated with an apparent expansion of the motor map. This can manifest as an increase in activity (Shmuelof, Yang, Caffo, Mazzoni, & Krakauer, 2014), a larger cortical area devoted to that digit, or a lower threshold with which movement can be elicited by cortical stimulation (Classen, Liepert, Wise, Hallett, & Cohen, 1998). These map changes can be driven by gain changes, as defined above, but likely do not reflect true reorganization. This is because the map changes are only transient and disappear within hours or days after training stops, whereas the behavioral per for mance improvements persist (Molina-Luna, Hertler, Buitrago, & Luft, 2008). This dissociation clearly shows that increased map sizes are not a neural correlate of the new motor representation. Rather, it 522 Intention, Action, Control feet, chest, and neck), as well as to both sides of the body. Given that referred sensations to the phantom hand are triggered by body parts that do not neighbor the missing hand territory, these findings are inconsistent with the idea of cortical reorganization. Another highly influential model for the functional consequences of brain reorganization following amputation asserts that reorganization is harmful. While a phantom sensation per se is not necessarily bothersome, it can be highly debilitating when associated with phantom limb pain. Flor and colleagues (1995) explored the behavioral correlates of facial remapping in arm amputees and identified shifted cortical representation of the lower face. These shifts were greater in those amputees who reported experiencing the worst phantom limb pain. Based on these and similar findings, it was proposed that the mismatch between the invading facial inputs and the underlying infrastructure for the missing hand results in an "error" signal, consequentially interpreted by the brain as pain arising from the missing hand. This theory of maladaptive brain plasticity has been extremely influential, not only in the neuroscience community but also in the clinical literature: if pain is caused by maladaptive reorganization, then alleviating phantom pain could be achieved by reversing the reorganization. One common approach has been to "reinstate" the representation of the missing hand to its original territory, using illusory visual feedback of the missing hand (mirror box therapy). However, the evidence is, at best, mixed in terms of the success of these treatments (Richardson & Kulkarni, 2017). Furthermore, recent research has challenged the notion that the lower-face representation invades the missing hand cortex following amputation (Makin, Scholz, Henderson Slater, Johansen- Berg, & Tracey, 2015). Together with accumulating evidence emphasizing the role of persistent peripheral inputs after amputation in generating phantom pain, it appears that evidence for a relationship between brain reorganization and phantom limb pain is weak. In summary, dramatic changes in activity patterns following amputation, interpreted as reorganization, have long been considered to support behavioral change. Most famously, facial activity in the cortical hand areas was considered to be the mechanism underlying referred sensations and phantom limb pain. Yet, despite the appeal of this view, especially in the clinical community, the current evidence does not support the hypothesis that reorganization has a causal role, either adaptive or maladaptive, on either perception or action. Brain reorganization after stroke An applied area for which the idea of brain reorganization has had a lasting and problematic influence is the presumptive mechanism of motor recovery after stroke and other forms of focal brain injury. As in the case of S1, many studies of M1 have been interpreted as showing a takeover of motor function by undamaged areas of the motor cortex. To reiterate, reorganization in this context implies that following a lesion in region A of the motor cortex, region B (that moved effector b before the injury) can now move effector a. It is important to note that reorganization can be considered in both the negative and positive sense. In the positive, recoveryrelated sense, takeover pertains to when region A loses representation for effector a due to stroke, but region B for effector b reorganizes to move effector a. In the negative sense, to support effector a, region B loses some of its representation for effector b. We say this is negative because effector b loses representation due to the reorganization. This latter argument is directly analogous to the original claims made for S1 maladaptive reorganization after deafferentation that we reviewed above. It should be stated that sometimes these two forms of "reorganization" get conflated in the literature, confusing things even further. To make this more concrete, we briefly describe a series of studies that are considered the most compelling for motor cortical reorganization after a focal lesion, although careful consideration leads to more nuanced conclusions. In the original studies, squirrel monkeys were pretrained on a hand dexterity task and then small subtotal infarcts were made in the M1 hand representation. In the first study, which sought to investigate spontaneous recovery (Nudo & Milliken, 1996), the monkeys took about two months to return to preoperative levels of hand dexterity. Notably, however, cortical mapping with intracortical microstimulation revealed that the small infarcts resulted in a widespread reduction in the areal extent of digit representations adjacent to the stroke (figure 43. These results are not consistent with the idea that reorganization is behaviorally relevant: although there was a loss of digit representation from a map perspective, digit behav ior nevertheless returned to normal levels. Interestingly, the authors concluded that "substantial functional reorganization occurs in primary motor cortex of adult primates following a focal ischemic infarct, but at least in the absence of post-infarct training, the movements formerly represented in the infarcted zone do not reappear in adjacent cortical regions. The y- axis shows the change in motor map area devoted to the distal forelimb following the infarct and training. Remapping of hand representation after lesion to the digit area following (B) spontaneous recovery, (C) training-based recovery, and (D) delayed training. Despite the dif ferent remapping In a classic follow-up study, intense daily training identical to the pretraining was resumed at poststroke day 5 and continued until the monkeys regained their former dexterity around 1 month poststroke. However, here, too, movement quality was not directly measured, thus additional studies will be required to delineate the contribution of the basal ganglia to motor skill learning. Motor Learning and the Basal Ganglia: Insights from Studies in Animals Similar to human studies, most work in other mammals has examined the contribution of the basal ganglia in the operant- conditioning framework, leaving improvements in motor acuity largely unexplored.

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Several decades of cognitive neuroscience research suggest that semantic processing in the natural world is supported by three distinct subsystems: modality- specific semantic representations are located in sensory and motor areas; amodal semantic representations are located in association areas; and the prefrontal cortex exercises the cognitive control required to understand rich semantic content in context. In this article we briefly review the large body of work on semantic representation. We then examine current views of semantic representation in light of a recent series of studies in which brain activity was recorded while individuals performed naturalistic tasks, such as listening to stories or watching movies. These studies revealed that semantic information is represented in an intricate mosaic of semantically selective regions that are mapped continuously across much of the human cerebral cortex and are highly consistent across individuals. These data have two profound implications for current views of semantic representation. First, they indicate that modal sensory information likely enters the amodal semantic system through multiple routes. Second, they suggest that current views that the prefrontal cortex does not directly represent semantic information need to be revised. These data suggest that the semantic system is a hybrid network in which connections between modal sensory areas and amodal semantic representations bind information about current experience, in parallel with a separate system for semantic memory access mediated by the anterior temporal lobes. Natural human behav ior is based on a complex interaction between immediate sensory experience, stored knowledge about the natural world, and continuous evaluation of the world relative to our own plans and goals. Even seemingly simple tasks, such as watching a movie or listening to a story, likely involve a range of dif ferent perceptual and cognitive processes whose underlying circuitry is broadly distributed across the brain. When watching a movie, we integrate visual and auditory information into a perceptual whole; we recognize the objects and actions in the movie and the intentions of the actors; and we understand the narrative arc of the story as it develops over time. When reading a book, we can still comprehend the story and its narrative arc even though the perceptual information available to us is greatly reduced compared with a film of the same story. A large body of research indicates that these remarkable capacities are underpinned by a broadly distributed network of brain areas that represents and processes information relevant to different parts of these tasks (Binder, Desai, Graves, & Conant, 2009; Huth, de Heer, Griffiths, Theunissen, & Gallant, 2016; Huth, Nishimoto, Vu, & Gallant, 2012). In this review we focus on one specific aspect of this system, the representation of conceptual information about the world: semantics (Binder et al. The question of how the brain represents semantic information has been an intense topic of research in cognitive neuroscience for the past 40 years. Much of the early work on this topic involved neurological patients with temporal lobe degeneration, which causes a syndrome called semantic dementia (Hodges, Patterson, Oxbury, & Funnell, 1992; Snowden, 2015; Warrington, 1975; Wilkins & Moscovitch, 1978). These studies, and the subsequent research reviewed below, support the idea that semantic processing in the natural world is supported by three distinct subsystems. First, modalityspecific semantic representations are located in sensory and motor areas. Second, amodal semantic representations are located in association areas, though the precise location and nature of these representations are more controversial. Third, prefrontal cortex appears to be 469 involved in the cognitive control required to understand rich semantic content in context. In this article we will first review the existing literature on each of these three aspects of semantic representation. Then we will summarize findings on semantic representation that have grown out of recent naturalistic experiments and evaluate how these data fit into existing theories. Amodal Semantic Representations Other lesion and imaging data suggest that semantic information is also represented in an amodal form that is not closely tied to sensory or motor representations. Most importantly, some neurodegenerative diseases or brain lesions appear to affect semantic judgment regardless of modality. For example, a patient with anomia might identify a zebra as a horse and express confusion about the presence of stripes (Patterson, Nestor, & Rogers, 2007). However, other aspects of cognition (syntax, numerical abilities, executive function) appear to be relatively spared (Jefferies, Patterson, Jones, Bateman, & Lambon Ralph, 2004; Hodges et al. These profound semantic deficits are not observed in other neurodegenerative diseases that affect the hippocampus, parahippocampal cortex, and limbic structures, areas more closely involved with autobiographical memory than with semantic memory (Chan et al. First, there is some controversy about the organization of semantic representations along the temporal lobe. Some studies argue that degeneration of the most anterior regions of the temporal lobe produce the most profound deficits of semantic comprehension and that the degeneration of more posterior regions does not affect semantic judgment (Nestor, Fryer, & Hodges, 2006). Others have argued that the degradation of posterior regions is involved in semantic dementia (Galton et al. Another point of contention in studies of semantic dementia concerns whether this disease affects semantic Modality- Specific Semantic Representations Both lesion studies and neuroimaging experiments support the view that modality- specific semantic representations are distributed in a network of distinct sensory and motor areas. Lesion studies have shown that individuals who have suffered stroke often exhibit modalityspecific comprehension deficits, such as pure word deafness (Auerbach, Allard, Naeser, Alexander, & Albert, 1982; Kussmaul, 1877) or visual agnosia (Farah, 2004; Riddoch & Humphreys, 1987). For example, watching a close-up of a Western gunfighter pulling his weapon out of its holster would produce activity in visual areas that represent body parts (Nishimoto et al. Modality- specific representations have been identified in the visual and auditory systems, around the precentral and postcentral gyri, and across much of the ventral temporal cortex. These data have been used to support the view that semantic information is represented in a distributed form in the network of sensory and motor areas that serve as the source and sink for all human interactions with the world (Barsalou, 1999; Martin, 2007; Pulvermüller, 2013). According to this view, semantic concepts arise from connections between these distributed modality- specific representations (Meteyard, Cuadrado, Bahrami, & Vigliocco, 2012). While the theory of embodied cognition is broadly consistent with a large body of data, one area of contention concerns how such a system can represent abstract semantic concepts that have no direct sensory or motor correlates, such as truth, justice, and love (Meteyard et al. The answer to this question has profound implications for any theory of semantic representation. Some studies have argued that this disorder impairs representations of categories of concrete objects but that verbs and abstract concepts are relatively spared (Breedin, Saffran, & Branch Coslett, 1994; Silveri, Brita, Liperoti, Piludu, & Colosimo, 2018). Others argue that representations of concrete categories, verbs, and abstract concepts are all degraded equally in semantic dementia if the base-rate frequencies for the exemplars used in testing are all equated (Bird, Lambon Ralph, Patterson, & Hodges, 2000; Ralph, Graham, Ellis, & Hodges, 1998). However, whether this impairment occurs at the level of concepts or the linguistic representations of those concepts is still unclear (Caramazza & Mahon, 2003; Kiefer & Pulvermüller, 2012). Additionally, individuals with semantic dementia appear to lose finer categorical distinctions first and then coarser categorical distinctions at later stages of the disease (Ralph, Sage, Jones, & Mayberry, 2010; Lambon Ralph & Patterson, 2008). For example, someone with mild semantic dementia might be able to identify a picture of a robin as a bird but could be confused when presented with an ostrich (see Patterson, Nestor, & Rogers, 2007). Then, with further progression of the disease, the person would become unable to identify any bird. This pattern of deficits has been used to support the idea that semantic dementia impairs access to information about the hierarchical categorical structure of the world (Garrard, Ralph, Hodges, & Patterson, 2001; Laisney et al. This earlier idea proposes that dif ferent convergence zones mediate the interaction of dif ferent kinds of information, based on anatomical constraints and individual life experiences. A meta-analysis of over 120 studies of semantic representation in the brain identified a set of putative high-level convergence zones, including the angular gyrus; middle temporal gyrus; precuneus, fusiform, and parahippocampal gyri; and some portions of frontal cortex (Binder et al. When tested directly, the posterior middle temporal gyrus, angular gyrus, and precuneus were found to be responsive to both visual and linguistic stimuli of the same categories, lending support to the argument that they may function as high-level convergence zones (Fairhall & Caramazza, 2013). This theory was further supported by reports that neurodegenerative diseases and lesions that affect the prefrontal cortex but leave the temporal cortex intact sometimes cause semantic deficits (Jefferies & Lambon Ralph, 2006). Finally, it has been argued that the prefrontal cortex contains specific regions that mediate semantic judgments but remain completely separate from the regions involved in cognitive control (Fedorenko, Behr, & Kanwisher, 2011). In contrast, other studies of patients with lesions to the prefrontal cortex have reported that semantic deficits tend to be expressed only in tasks with relatively greater executive demands, such as comprehension of a complex narrative (Jefferies & Lambon Ralph, 2006). This suggests that prefrontal lesions do not affect semantic representations directly. In sum, a wide variety of lesion and neuroimaging studies suggest that prefrontal cortex is involved in cognitive- control and selection processes rather than semantic representation per se (Badre, Poldrack, ParéBlagoev, Insler, & Wagner, 2005; Gold et al. However, this interpretation has not received unanimous support (Nozari & Thompson- Schill, 2016). Recent Studies of Semantic Representation Until recently, much of the debate regarding semantic representation has focused on where semantic information is represented (Humphries, Binder, Medler, & Liebenthal, 2007; Patterson, Nestor, & Rogers, 2007; Visser, Jefferies, & Lambon Ralph, 2010), rather than precisely how semantic information is mapped across the cerebral cortex. Furthermore, the studies that have attempted to understand where some specific type of semantic information is represented have used classical experimental paradigms that manipulate a few semantic parameters under highly controlled and simplistic conditions (Binder, Westbury, McKiernan, Possing, & Medler, 2005; Epstein & Kanwisher, 1998; Kanwisher, McDermott, & Chun, 1997). While simple controlled studies have ample statistical power to identify specific semantic representations, they lack the power to support broad mapping of the semantic space. Our lab has taken a different approach to understanding semantic representations by using brain activity evoked by complex, naturalistic stimuli to create quantitative, highdimensional models of semantic selectivity (Naselaris, Kay, Nishimoto, & Gallant, 2011; Wu, David, & Gallant, 2006). This approach allows us to create rich, highdimensional maps of semantic selectivity across the entire cerebral cortex (Çukur, Nishimoto, Huth, & Gallant, 2013; Huth et al. Our experiments are based on a naturalistic, datadriven approach designed to reveal how semantic information is represented in individuals watching movies or listening to stories. Thus, our experiments are quite dif ferent from those usually used to study semantic representation, which often involve very reduced tasks such as naming pictures or defining words (Patterson, Nestor, & Rogers, 2007). We analyze these rich data by means of a power ful statistical approach called voxelwise modeling (Naselaris et al. First, semantic features- objects and actions in movies and stories- are extracted from the stimuli and encoded in an appropriate semantic feature space. Our methods allow us to model thousands of semantic features simultaneously, providing a means to answer many questions about semantic representations in parallel. Second, the output of this procedure produces a separate weight vector for every voxel that describes how each semantic feature contributes to measured brain activity within that voxel. Third, the semantic model of each voxel is tested using a separate data set reserved for this purpose. Prediction accuracy is quantified by the correlation between the prediction and the observed response, and statistical significance is assessed by permutation testing. The end result is a list of semantic features that significantly modulate activity in each cortical voxel, ordered by the influence of each feature on voxel responses. Finally, the fit voxelwise models are examined to understand how semantic features are represented across the cerebral cortex. The simplest method for this is to use principal component analysis to find a low- dimensional semantic space that best accounts for the data. An inspection of these principal components reveals the relative importance of each semantic feature within the semantic space. The principal components can also be visualized on the cortical surface to reveal how the dimensions of the semantic space are mapped across the surface of the cerebral cortex. These data are separated into two sets: a training set used to fit voxelwise models and a separate test set used to validate the fit models. Left, For each separate voxel, ridge regression is used to find a model that explains recorded brain activity as a weighted sum of the semantic features in the stories. Statistical significance of predictions and of specific model coefficients is assessed through permutation testing. We have used voxelwise modeling to recover semantic representations from brain activity recorded during several different naturalistic paradigms: while subjects were presented with a series of natural photographs (Naselaris et al. All these studies show that semantic information is represented in an intricate mosaic of semantically selective regions that are mapped continuously across much of the human cerebral cortex and which are highly consistent across individuals. Social concepts appear to be represented in a dif ferent collection of semantic regions distributed broadly across the cerebral cortex (bright red patches, figure 39. However, there is no obvious systematic relationship between the distribution of the semantic regions pertaining to one domain versus another. Furthermore, the semantic maps produced in these studies appear to be largely consistent regardless of whether they were acquired during listening to stories or during reading (Imamoglu, Huth, & Gallant, 2016). This consistency is found across a broadly distributed set of regions, including posterior cingulate cortex, parahippocampal cortex, the temporal lobes, posterior parietal cortex, the temporal-parietal junction, dorsolateral prefrontal cortex, ventromedial prefrontal cortex, and orbitofrontal cortex. The only regions that produce inconsistent maps across reading and listening are primary sensory and motor regions, an unsurprising result. Modal regions appear to be located in higher- order sensory areas in the occipital and temporal lobes and in motor areas between the motor strip and prefrontal cortex (see figure 39.

Jackson: the Neuroscience of Brain-Machine Interfaces 503 by the design of an interface gastritis symptoms for dogs purchase renagel canada. A nice example of this concept is provided by research into the bodymachine interfaces operated by partially paralyzed individuals using residual motion measured by wearable sensors gastritis juicing buy cheap renagel online. Simply by projecting the first two dimensions of this space onto a computer screen gastritis diet and yogurt purchase 400 mg renagel amex, the users rapidly learned to control cursors within a single session gastritis diet 6 days purchase renagel without prescription, even though the mapping from sensor space to cursor movements was abstract gastritis diet ������� purchase renagel 400 mg fast delivery. However, finding optimal strategies requires a time- consuming search of the high- dimensional neural space. Therefore, it is interest ing to ask whether, with sufficient training, local remapping could be achieved when solutions lie outside of the manifold associated with natural movements. However, it is not clear whether these reflect an entirely new manifold or simply different regions within a larger repertoire of naturalistic strategies. A related question is: What mechanisms guide this exploration for new neural strategies There remains the possibility of intermediate levels of reorganization between a single constrained manifold and entirely unstructured exploration. Fast reaiming/ reassociation effectively finds a new mapping at the highest level in figure 41. Exploration at intermediate levels-for example, preserving the dynamic structure of neuronal populations or the synchronous ensembles of neurons projecting to the same muscles (Jackson et al. The hierarchical remapping hypothesis (Jackson and Fetz, 2011) suggests that reorganization should proceed through these levels over time, as behav ior is optimized in progressively higherdimensional spaces. Partial tuning of motor cortex neurons to final posture in a free-moving paradigm. Proceedings of the National Academy of Sciences of the United States of Amer ica, 103(8), 2909­2914. Inference and decoding of motor cortex low- dimensional dynamics via latent state- space models. Restoration of reaching and grasping movements through brain- controlled muscle stimulation in a person with tetraplegia: A proof- of- concept demonstration. Cortical mechanisms for online control of hand movement trajectory: the role of the posterior parietal cortex. Emergence of coordinated neural dynamics underlies neuroprosthetic learning and skillful control. Corticomotoneuronal contribution to the fractionation of muscle activity during precision grip in the monkey. Differences in adaptation rates after virtual surgeries provide direct evidence for modularity. A brain- spine interface alleviating gait deficits after spinal cord injury in primates. Learning to control a brain-machine interface for reaching and grasping by primates. Restoration of grasp following paralysis through braincontrolled stimulation of muscles. Reversible large- scale modification of cortical networks during neuroprosthetic control. A common structure underlies low-frequency cortical dynamics in movement, sleep, and sedation. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Functional network reorga ni zation during learning in a brain- computer interface paradigm. Proceedings of the National Acad emy of Sciences of the United States of Amer ica, 105(49), 19486­19491. Virtual typing by people with tetraplegia using a self- calibrating intracortical brain- computer interface. Single- trial dynamics of motor cortex and their applications to brain- machine interfaces. Investigating the role of firingrate normalization and dimensionality reduction in brainmachine interface robustness. Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills. Dif ferent population dynamics in the supplementary motor area and motor cortex during reaching. Rapid acquisition of novel interface control by small ensembles of arbitrarily selected primary motor cortex neurons. A reward-modulated Hebbian learning rule can explain experimentally observed network reorganization in a brain control task. Volitional modulation of primary visual cortex activity requires the basal ganglia. Proceedings of the National Academy of Sciences of the United States of Amer ica, 110(45), 18279­18284. Utilizing movement synergies to improve decoding per formance for a brain machine interface. Closed-loop control of spinal cord stimulation to restore hand function after paralysis. We first review the peripheral neural basis of hand and arm control, specifically the signals arising from receptors embedded in muscles, tendons, and skin. Then, in the context of recent behavioral and neurophysiological studies, we describe several prominent factors that make dealing with feedback signals in the context of real-world hand control particularly interesting: the hierarchical organization of feedback control loops, the need to integrate sensory inputs from multiple modalities in real time, and the potential disconnect between perception and control. At the age of 19, Ian Waterman developed an autoimmune disorder that selectively destroyed the largediameter afferent neurons that convey sensory information from the muscles, tendons, and skin. Peripheral feedback is so essential for controlling the hand and arm that approximately 90% of the axons in the peripheral nerves of the upper limb transmit sensory information from the periphery into the central nervous system, while only 10% carry the motor commands from the central ner vous system to the muscles (Gesslbauer et al. The optimal reach, then, is the one that minimizes this cost function, which, in turn, is implemented by adjusting the gains of feedback control loops over time. With certain cost functions, the optimal feedback- control framework can account for many of the core features of reaching, as well as many other tasks. It also provides insight into how the system should best integrate sensory feedback in real time to achieve the task being performed. Because optimal feedback controllers only correct those errors that adversely affect task per for mance, observing how the ner vous system responds to sensory inputs can provide clues to the cost function that governs a particular action. Thus, in this framework, understanding how the ner vous system processes sensory feedback is key to understanding motor control. Optimal feedback control offers a useful normative framework to understand motor behav ior. Unfortunately, as it turns out, the mathematics of optimal feedback control are difficult even in simple scenarios, and establishing firm links between optimal feedback control and behav ior is hard because there exist many cost functions that can reproduce the same behavioral predictions. Moreover, a general scheme to make predictions about underlying neural circuits, say, at the level of single neurons, remains elusive. Luckily, there are heuristics, as others have laid out in detail (Scott, 2004, 2016; Shadmehr and Krakauer, 2008). In this article we stick to the level of heuristics and examine how sensory feedback rapidly contributes to reaching, grasping, and object manipulation. Although all sensory modalities can help support these actions, we primarily detail how the ner vous system uses somatosensory feedback provided by receptors embedded in muscles, tendons, and skin. We focus on real-world control in the sense that relatively naturalistic paradigms raise some specific issues, including (1) the hierarchical organization of feedback control loops and how this 507 organization trades computational flexibility for temporal delays, (2) how sensory feedback from multiple modalities is integrated in real time, and (3) the relationship between somatosensory feedback for perception versus motor control. The Peripheral Neural Basis of Somatosensory Inputs Understanding anatomy is always a good place to start. These receptors signal information to the processing centers of the spine, brain stem, and cortex, which all generate feedback responses. As we next describe, the signals arising from these receptors are more complex than typically appreciated, and understanding how these signals contribute to hand and arm function remains a substantial challenge. Proprioceptive Inputs from the Hand and Arm Muscle spindles are receptors embedded within, and running parallel to , the large extrafusal muscle fibers that generate muscle force. They are comprised of two types of intrafusal fibers: nuclear bag fibers and nuclear chain fibers. Large- diameter afferent nerve fibers (type Ia) wrap around the noncontractile central portions of both types of intrafusal fibers, forming the primary sensory ending traditionally thought to signal information about dynamic and static muscle length. The distal portions of the intrafusal fibers are able to contract and are innervated by gamma motor neurons. Understanding the precise role of gamma motor neurons remains elusive (Windhorst, 2007); the simplest idea is that their activation is yoked to the activation of alpha motor neurons (which innervate extrafusal muscle fibers) so that the muscle spindle remains in its working range when its parent muscle changes length. When a muscle contracts, these collagen fibers straighten and compress the afferent axon, generating action potentials in numbers roughly proportional to muscle tension. Understanding the static and dynamic configuration of the body in space-the sense of proprioception-is obviously important for planning and executing actions and is often linked to muscle spindle activity. The direct connection between muscle spindles and proprioception stems from studies in animals and ex vivo preparations, which showed that spindle activity is correlated with muscle length and velocity, features that are also correlated with the position and velocity of the joint(s) that the muscle spans. Indeed, muscle vibration, which stimulates the muscle spindles, can cause illusory joint rotations (Eklund, 1972; Goodwin, McCloskey, and Matthews, 1972). Recent work casts some doubt on a simple link between muscle spindle activity and proprioception because these receptors appear to encode kinematic and kinetic variables besides length information. For example, in the cat, muscle spindle activity can encode the muscle forces that arise because of the mechanical properties of muscle cross bridges (Blum et al. Human experiments have long described a more complex mapping between muscle length/velocity and spindle activity in the context of active movement. For example, Dimitriou and Edin (2010) recorded from muscle spindle afferents while people grasped objects and pressed keys, two relatively natural hand actions, and found that spindle activity best predicts the future state of the parent muscle and not its instantaneous joint position or muscle length. They suggested that muscle spindles operate as a forward sensory model that predicts the consequences of intended movement, acting as a peripheral mechanism to minimize the negative impact of sensory delays on controlling actions. Cutaneous Signals from the Glabrous Skin of the Hand Four distinct tactile afferent neurons innervate the glabrous. These afferents collectively encode most of the critical information about the mechanical events that occur when interacting with and manipulating objects (Johansson and Flanagan, 2009). Most work investigating the function of tactile afferent neurons has focused on perceptions that arise when tactile stimuli are passively applied to the skin (Goodwin and Wheat, 2004; Johnson, 2001). These studies typically associate the four classes of tactile afferents, and their distinct anatomy and physiology, with nonoverlapping functions based on correlations with perceptual phenomena. However, there is substantial evidence that almost all real-world functions attributed to specific tactile afferents actually involve integration across multiple types of tactile afferent neurons (Saal and Bensmaia, 2014). For example, some textures, often those used experimentally, preferentially excite a particular class of tactile afferent neurons. However, the textures encountered during real-world actions typically recruit all types of afferents and may even engage unique coding schemes-from spatial- coding schemes that operate statically at low spatial frequencies to temporalcoding schemes the require movement at high spatial frequencies. Determining how the ner vous system integrates inputs across these afferents at various levels of the neuraxis remains an underser viced area of study. Studies investigating shape perception typically assume that tactile afferent neurons are distributed in a pixel-like array across the skin and that the spatial aspects of a stimulus, like edge orientation or curvature, are represented in a veridical way-that is, the spatial layout of activated neurons provides a "neural image" of the stimulus being indented into the skin. However, this conceptualization ignores a basic organizational principle of the somatosensory system-namely, that tactile afferent neurons branch and innervate many mechanosensitive end organs. Important questions in the field include determining the evolutionary pressures that cause this peripheral complexity to arise, the limits of peripheral feature extraction, and how upstream neurons decode this information (Zhao et al. Muscle Feedback for Posture and Reaching: the Hierarchical Nature of Feedback Control Real-world hand actions can be thrown off course because of noise internal to the motor system or by external forces. One way the ner vous system overcomes these challenges is by processing sensory information arising from muscle receptors and generating a series of rapid feedback responses. The hierarchical organization of stretch reflexes and their underlying neural circuits have been extensively studied, with much of the effort focusing on documenting functional differences between evoked responses at dif ferent latencies (for reviews, see Pruszynski, 2014; Scott, 2016; Shemmell, Krutky, and Perreault, 2010). Although relatively large mechanical perturbations are used in these studies to evoke large reflex responses, the same set of responses are also evoked by perturbations on the much smaller scale of natural motor variability, suggesting that muscle feedback is continually processed in real time to control our actions (Crevecoeur, Kurtzer, and Scott, 2012). The standard view is that the hierarchical organization of fast feedback responses trades temporal delays for computational sophistication: spinal circuits are quick but "dumb," whereas cortical circuits are slow but "smart" (figure 42. Because of its latency, the short-latency stretch reflex almost certainly engages purely spinal circuits, most prominently a monosynaptic pathway linking primary muscle spindle afferents to motor neurons that project back to their parent muscle but also oligosynaptic pathways that can target other functionally related muscles (Pierrot-Deseilligny and Burke, 2005). In terms of function, the short-latency stretch reflex is typically thought to simply resist changes in muscle length. Consistent with this role, it is influenced by relatively low-level features of the stimulus or sensorimotor apparatus, such as the amount and speed of muscle stretch or the state of the motor neuron pool. Middle, the traditional view is that the distinction between spinal and cortical feedback circuits relates to their functional capacity. Right, An alternative view is that the distinction relates to the flexibility of these circuits, the spinal cord providing complex but relatively fixed functions driven by long-term evolutionary pressures and the cerebral cortex allowing for relatively arbitrary input- output relationships. We now know that the longlatency stretch reflex reflects processing in spinal and cortical circuits and that there are likely additional contributions via the brain stem (Shemmell, Krutky, and Perreault, 2010). The key piece of evidence for a spinal circuit is that spinalized cats and monkeys still exhibit a clear long-latency stretch reflex (Ghez and Shinoda, 1978; Tracey, Walmsley, and Brinkman, 1980). First, corticomotorneuronal cells in monkey primary motor cortex influence distal muscles of the limb and respond to mechanical perturbations at latencies early enough to contribute to the long-latency stretch reflex (Cheney and Fetz, 1984). In fact, cortical processing is not restricted to primary motor cortex, as mechanical perturbations quickly evoke vigorous, yet selective, responses in parietal area 5, dorsal premotor cortex, and the supplementary motor area (Hummelsheim et al. Second, patients with undesired bilateral movements because of bifurcating corticospinal projections from primary motor cortex show long-latency stretch reflexes in both limbs when the muscles of one limb are stretched (Capaday et al. A remarkable aspect of the long-latency stretch reflex is that, even though it occurs with latencies substantially shorter than typical mea sures of voluntary reaction time, it exhibits many sophisticated features typically attributed to voluntary control processes. Perhaps the best real-world example of such functionality was demonstrated by Cole, Gracco, and Abbs (1984), who mechanically extended the thumb while participants were performing a pinching action, as if to grasp an object. Unlike the short-latency stretch reflex, which was evoked only in the stretched thumb muscle, the long-latency stretch reflex was coordinated across thumb and finger muscles in a way that supported accurate grasping. Although the presence of such sophisticated functional attributes is often attributed to the cortical circuits that underlie the long-latency stretch reflex, definitive links between particular attributes and specific neural circuits are rare, and more work is definitely warranted.

The journey of a memory gastritis disease definition renagel 400 mg order with mastercard, such as the memory of a unique life event like reading this sentence gastritis erosive symptoms 400 mg renagel order with amex, begins with encoding and concurrent neural plasticity gastritis kronis pdf renagel 800 mg purchase free shipping. If so chronic gastritis bile reflux buy renagel 800 mg online, one might say that such a memory existed for the duration of that multiyear period gastritis diet ������� buy 800 mg renagel visa, like a file secured away in a file drawer. This commonplace notion-that "the memory" per se lasts from encoding until retrieval-reifies it as existing in a static manner, independently, set apart from other memories. Somehow, neural substrates of memory storage must traverse the entire storage interval for a memory to ultimately be retrieved. However, if memories are not static entities, how should we characterize memory storage during this interval Changes in storage are not a simple matter of the memory transitioning from a labile state to a stable one, such as when a newly created ceramic object is heated. A progression of neural restructuring seems more likely, particularly for an episode from long ago. Such progressive changes are widely acknowledged as fundamental to the neurobiology of consolidation, now being intensively investigated on many fronts. Through neural restructuring, the informational content of memories can also change. Whereas our thesis is that memory reactivation is a critical determinant of memory storage, one classic memory phenomenon-the flashbulb memory- seems in direct opposition. A classical flashbulb memory is found when a person can recount, in detail, learning of some momentous public event, such as an assassination. The metaphorical flashbulb would illuminate 263 every thing in view at that instant; that singular moment would be frozen in time, preserved in a permastore to remain forever available. Livingston (1967) proposed that the emotional impact engaged a "now-print" mechanism that permanently preserved the event and all concurrent details. However, flashbulb memories become distorted just like ordinary episodic memories (Schmolck, Buffalo, & Squire, 2000). So our view is that these momentous events are not immediately etched into memory. In place of the classic view of flashbulb memories, we attribute their dramatic persistence to repeated memory reactivation. Likewise, we may carry some memories with us throughout our lives, thanks to consolidation rather than to superior encoding. The most decisive memory process could be repeated reactivation, some of which occurs implicitly. Off-line reactivation and concomitant plasticity may even be a necessity for enduring memory storage, ultimately determining which memories we keep. In this account of memory preservation, how should we now conceptualize the "replay" of a memory Defining "Replay" in the Context of Memory Categories the prime directive of a Star Trek expedition to an alien planet is to avoid undue interference with another culture. The prime directive of an expedition in memory research is to acknowledge that different types of memory depend on distinct mechanisms. The former comprises the content of our moment-tomoment train of thought, whereas the latter concerns information brought back to mind after departing from awareness. Memory research typically emphasizes acquisition-toretrieval delays not longer than a few minutes. In contrast, here we strive to explain enduring memory storage-memories that somehow last days, weeks, even years in the face of the daily trudge of new learning, wherein forgetting seems to be the rule. Declarative memory is defined as the type of memory used in recalling and recognizing episodes and facts. Patients with circumscribed amnesia have difficulty with recent episodic and factual knowledge. Their capabilities on tests designed to assess other types of memory- such as skills, procedures, priming, conditioning, and habits- can be entirely preserved. These other types of memory have been categorized collectively as nondeclarative memory. Although replay is certainly relevant for nondeclarative memory, here we focus on declarative memory. The fundamental distinctiveness of declarative memory likely arises in relation to (1) storage across multiple neocortical regions and (2) the potential for conscious recollection. For example, the components of a specific event, including relevant causes and repercussions, are represented in multiple neocortical regions specialized for processing different informational features. Recollecting an enduring declarative memory relies on combining such assorted elements. Because the cortical fragments are spatially separated in the brain, they must be linked to form a cohesive unit, requiring what at a neural level can be called cross- cortical storage (Paller, 1997, 2002) or, at a cognitive level, relational representations (Eichenbaum & Cohen, 2001; Shimamura, 2002). Another fundamental characteristic of enduring declarative memories is that storage is altered gradually via consolidation (Squire, Cohen, & Nadel, 1984). Which pathway will a newly formed memory take- stabilization, integration, corruption, forgetting Synaptic consolidation involves molecular changes at individual synapses shortly after learning; systems consolidation concerns changes in storage that take place over a prolonged period of time and that involve multiple brain regions. Systems consolidation can include restructuring, and this restructuring may continue indef initely (Dudai, 2012). A pivotal physiological bond between consolidation and the hippocampus comes from reports of hippocampal replay in rodent place cells (reviewed by Foster, 2017). Firing patterns during sleep mirrored those previously exhibited during exploratory behav ior in a new environment (Pavlides & Winson, 1989; Wilson & McNaughton, 1994). Replay is also found during wake, in cortical regions, in the striatum, and in various forms in multiple species. Although the term replay is sometimes restricted to repeated firing sequences in hippocampal place cells, 264 Memory here we use the term replay to encompass the notion of any neural recapitulation of stored information and hippocampal replay to denote this specific example. If replay is at the heart of declarative memory consolidation, the opportunity may arise each and every time a memory is reactivated, online or off-line. Online reactivation would be when one knowingly recalls a memory, intentionally or other wise. Memory Processing during Sleep the notion that memories change during sleep has not always been on the radar of memory researchers. Our view is that declarative memories change both during waking and during sleep and that such changes contribute to the gradual process of consolidation (Paller, 1997; Paller & Voss, 2004). Substantial empirical support has accrued for sleep-based memory processing (Rasch & Born, 2013). According to this view, memories do not just lie dormant during sleep but instead receive regular exercise that changes what is stored. The classic staging of sleep into just four stages is deceptive in its apparent simplicity. In prior decades before the recent waves of empirical support, many theories on memory and sleep were entertained. An intuitively reasonable idea was that sleep supports adaptive mechanisms for evaluating recent experiences and relating them to current goals. Hippocampal replay connects with these ideas, although early studies of hippocampal replay lacked suitable behavioral measures that might show improved spatial memory following sleep, so hippocampal replay could not be directly linked with consolidation. More telling, hippocampal replay is specific to learning-related ensembles and correlates with retention (Dupret et al. In humans, ample results demonstrate superior memory after a period of sleep compared to a period of wake (Rasch & Born, 2013). In an extreme way, sleep deprivation can produce such a result, but this can be problematic because of memory difficulties arising from excessive sleepiness or nonspecific effects of deprivation, such as stress. In any such sleep/wake comparison, wakefulness can entail more memory interference than sleep, calling into question whether sleep necessarily made a specific contribution. Thus, this sort of evidence provides only tentative support for the notion that sleep after learning improves memory. One way to reach for this goal, while also avoiding the problem of differential memory interference that plagues sleep/wake comparisons, is to use subtle but systematic sensory stimulation during sleep. Manipulating memory during sleep the literature on presenting a sleeper with cues to information recently learned while awake has grown considerably in the last few years (Cellini & Capuozzo, 2018; Oudiette & Paller, 2013; Schouten, Pereira, Tops, & Louzada, 2017). Note that gaining new knowledge presented only during sleep was ostensibly ruled out by Emmons and Simon (1956), who investigated presenting spoken facts during sleep. Many studies on this topic up to that point did not include physiological verification of sleep state, which came to be deemed essential. The work of Emmons and Simon led to widespread skepticism in the scientific community about the validity of so- called sleep learning, impeding workers from pursuing many adjacent research directions (Paller & Oudiette, 2018). However, recent findings show that some implicit learning during sleep may indeed be possible (Arzi et al. Here we focus instead on the use of sensory stimulation to study brain mechanisms, whereby memories Paller et al. Among the early studies on this topic were classicalconditioning studies in rats trained to fear a tone repeatedly paired with a shock during wakefulness; conditioning was enhanced by a mild shock during sleep (Hars, Hennevin, & Pasques, 1985; Hennevin, Hars, Maho, & Bloch, 1995). Smith and Weeden (1990) trained people in a complex finger-tapping task while listening to a ticking sound, and per for mance was improved by playing the sound during sleep. In the landmark study of Rasch and colleagues (2007), a rose odor was presented while subjects learned spatial locations of objects. In 2009 we took the further step of showing that specific memories could be strengthened using sounds during sleep (Rudoy, Voss, Westerberg, & Paller, 2009; figure 23. Subjects were motivated to suppress auditory processing, given the exceedingly loud scanning noise. Supporting the idea of sensory gating operative at the level of the thalamus, the degree of memory benefit, which was not reliable overall, was correlated with brain activation in the thalamus across subjects. The degree of memory benefit was also correlated with activity in the medial temporal lobe and the cerebellum, as well as with parahippocampal-precuneus connectivity, thus identifying several measures of brain activity associated with sound- cued memory reactivation (see also Berkers, Ekman, van Dongen, Takashima, Paller, & Fernandez, 2018; Shanahan, Gjorgieva, Paller, Kahnt, & Gottfried, 2018). Tones previously associated with spatial learning were played during sleep, and a systematic bias in hippocampal place cell firing was found as a function of which tone was presented. Another way to manipulate sleep that can provide clues about the relevant physiology is to entrain brain oscillations. Slow waves and sleep spindles have been linked with memory consolidation on the basis of correlative findings, along with direct manipulations, that strongly suggest a causal link. Marshall and colleagues (2006) were the first to show that transcranial stimulation with slow oscillatory electrical currents can enhance slow waves and thereby benefit wordpair learning. Thus, there is convincing evidence that slow waves play a causal role in sleep-based memory consolidation. A pharmacological approach, using Ambien, produced both an increase in spindles and an improvement in memory (Mednick et al. Spindle timing relative to slow-wave phase may be critical (Helfrich, Mander, Jagust, Knight, & Walker, 2018; Niknazar, Krishnan, Bazhenov, & Mednick, 2015). Although the precise role of sleep spindles in memory consolidation remains to be elucidated, recent studies have made significant headway (Antony et al. Subjects moved each object from the center to where they thought it belonged (arrows). Slow waves may set the stage for the drama of intricate interactions manifested by neural oscillations and their cross-frequency coupling. Furthermore, spindles can be taken as a prime example of neural sleep signals that have a causal impact in enhancing specific memories due to replay-based consolidation. A neuropsychological perspective may have intriguing relevance, given the literature on diencephalic amnesia. That is, we speculate that the central role of the thalamus in generating spindles and corresponding replay events may be at the heart of both sleep-based consolidation and the classic symptoms of amnesia after diencephalic damage. Memory Processing during Wake Many electrophysiological and behavioral findings implicate memory reactivation during wake. Rodent hippocampal replay can be observed during or just after learning (Diba & Buzsáki, 2007), as well as more remotely during both wake and sleep (Karlsson & Frank, 2009). In addition, specific patterns of hippocampal activity associated with what was just learned can appear spontaneously shortly after learning and can correlate with retention (Gruber, Ritchey, Wang, Doss, & Ranganath, 2016; Schapiro, McDevitt, Rogers, Mednick, & Norman, 2018; Tambini & Davachi, 2013). Memory reactivation engaged when relevant information is encountered commonly leads to improved subsequent memory. This observation borders on the territory of standard methods to improve learning. Restudying material strengthens memories, but recall provides a superior benefit (Roediger & Karpicke, 2006). Likewise, cued recall in a spatial task one day after initial learning improves recall accuracy the following day (Bridge & Paller, 2012). Furthermore, reactivation of learning-related neural patterns occurs during restudy (Xue et al. Finally, both retrieval (relative to restudy) and sleep (relative to wake) were found to improve consolidation (Antony & Paller, 2018; Bäuml, Holterman, & Abel, 2014). These similar effects of retrieval during wake and sleep support a recent idea that retrieval may naturally engender online consolidation (Antony, Ferreira, Norman, & Wimber, 2017). In sum, consolidation may proceed during sleep and during wake, in conjunction with reactivation that can be intentional, unintentional, with awareness of retrieval, or without awareness of retrieval. Consolidation and Interference Whereas research on sleep and memory has largely focused on memory strengthening via replay, a limitation of this approach is that it typically neglects interactions between memories. Decades of memory research have established that interference from other similar memories can cause forgetting (Underwood, 1957). To predict whether memories will be retained in the long term, we need to understand both how reactivation can cause interference and how it might mitigate interference.

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