Abstract and Keywords
The cerebral cortex is a learning engine. The ability to encode information about sensory experience or practiced movements is a universal property of all cortical areas. This capacity, known as cortical plasticity, is seen in experience dependent changes in the functional properties of cortical neurons and in the alteration of cortical circuits. Certain properties are mutable only during a short period in postnatal life, which is known as the critical period, while others retain the ability to change throughout life. The same changes associated with assimilating normal experiences can be implemented for functional recovery following lesions of the central nervous system.
Plasticity is a universal property of nearly all regions of the brain. It reflects the capacity to encode and retrieve information as we assimilate new experiences and is based on the ability of brain circuits to form new connections or to change the strength of preexisting connections. The term was originally used by William James in the context of our ability to develop new habits.
Plasticity, then, in the wide sense of the word, means the possession of a structure weak enough to yield to an influence, but strong enough not to yield all at once. Each relatively stable phase of equilibrium in such a structure is marked by what we may call a new set of habits. Organic matter, especially nervous tissue, seems endowed with a very extraordinary degree of plasticity of this sort, so that we may without hesitation lay down as our first proposition the following, that the phenomena of habit in living beings are due to the plasticity of the organic materials of which their bodies are composed.
William James, 1890/1950, The Principles of Psychology
A more modern application of the term was posed by the physiologist Jerzy Konorski, who put the idea of plasticity in a neuronal context.
The plastic changes would be related to the formation and multiplication of new synaptic junctions between the axon terminals of one nerve cell and the soma (i.e. the body and the dendrites) of the other.
Jerzy Konorski, 1948, Conditioned reflexes and neuron organization
The rule by which neurons can change the strength of connections between them was proposed by Donald Hebb.
When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A’s efficiency, as one of the cells firing B, is increased.
Donald O. Hebb, 1949, The Organization of Behavior
This rule has been expressed more concisely as “neurons that fire together wire together.” We continually encode learned information in various brain regions throughout life, though there are important differences in the kinds of information, and the brain circuits involved, between the plasticity that is exerted early in postnatal life and that occurring into adulthood. Certain forms of plasticity are limited to a critical period early in postnatal life. The critical period governing the balance of input from the two eyes was discovered by Torsten Wiesel and David Hubel, based on experiments on visual deprivation in the cat:
The susceptibility of very young kittens to a few months of visual deprivation apparently does not extend to older animals, since there is a detectable lessening of effects when deprivation is begun at 2 months, and an absence of behavioral or physiological effects in adults.
Torsten Wiesel and David Hubel, 1963, Single-cell responses in striate cortex of kittens deprived of vision in one eye
Although one might have assumed from the existence of the critical period that, at least in early visual cortical areas such as primary visual cortex (V1), the circuits and function would be fixed in the adult. Yet one knows that there are many forms of learning that continue throughout life, which begs the question as to what brain regions represent which aspect of memory. A useful categorization of the different forms of memory and the associated brain regions representing these forms was devised by Larry Squire in the following diagram (Figure 1):
In the visual modality, one normally associates memory with declarative or explicit memory—such as that involved with object recognition. This information is represented by the medial temporal lobe, as well as the inferotemporal cortex. But there are other forms of memory that involve improvement in ability to detect or discriminate features in visual scenes with repetitive practice. This form of learning, known as perceptual learning, is a component of non-declarative or implicit (unconscious) memory. Hermann von Helmholtz characterized perceptual learning as follows:
… the judgment of the senses may be modified by experience and by training derived under various circumstances, and may be adapted to the new conditions. Thus persons may learn in some measure to utilize details of the sensation which otherwise would escape notice and not contribute to obtaining any idea of the object.
Helmholtz, 1866, Treatise on Physiological Optics
In effect, our brain develops strategies for analyzing what we see based on our integrated experience of the structural regularities of form. By learning the statistical regularities of our environment we can employ this information in the ongoing interaction between expectation and sensory input.
In this chapter, we will first consider the properties and mechanisms of critical-period plasticity. We will then describe the functional and circuit changes associated with perceptual learning and the further implementation of adult cortical plasticity for functional recovery after central nervous system damage.
Cortical plasticity in the early postnatal period was explored in the pioneering studies of Torsten Wiesel and David Hubel. Their work was based on the way by which the primary visual cortex (V1) integrates input from the two eyes. Each eye sends a projection to the cortex by way of a thalamic nucleus, the lateral geniculate nucleus (LGN). The pathways coming from the two eyes are kept separate through the thalamus and into layer 4 of V1, as seen in the cortical arborization patterns of the axons of LGN principal neurons. Neurons in the cortex, depending on the balance of input they receive from each eye, can be more strongly activated by presenting images to either the left or right eye, a property known as ocular dominance. Neurons with similar ocular dominance are grouped in columns extending from the cortical surface to the white matter, the ocular dominance columns, and when projected on to the cortical surface the columns form alternating left and right eye bands that look something like a fingerprint. Whereas in a normal animal the right and left eye columns occupy more or less the same territory, if one alters visual experience by suturing shut the eyelid over one eye, known as monocular deprivation, the ocular dominance profile shifts toward the open, non-deprived eye, with wider non-deprived eye columns and thinned deprived eye columns (Wiesel & Hubel, 1963). The substrate for this change is an expansion of the territory occupied by the geniculate afferents receiving input from the non-deprived eye and shrinkage in the territory occupied by the deprived eye afferents (Figure 2, Hubel, Wiesel, & Levay, 1977; LeVay, Wiesel, & Hubel, 1980). An important discovery from these experiments is that this plasticity is limited to a short period in the first few months of postnatal life, known as the critical period, which, in the cat, includes the first 3 postnatal months (Hubel & Wiesel, 1970). During the critical period the effect of eye closure can be reversed by opening the deprived eye and allowing for normal visual experience. But if the eye is opened after the end of the critical period the input from that eye is no longer capable of effectively driving activity in the visual cortex, and the geniculate afferents coming from that eye remain permanently shrunken. These findings have had a profound effect on clinical practice: Children born with a cataract, a clouding of the lens in one eye, have to have the cataract removed before the end of the critical period. If the procedure is done too late, the input from the eye will no longer be effective, even when the optical media are surgically corrected.
More recently, key players involved in the neural circuitry and the underlying molecular mechanisms that mediate critical-period plasticity and its close have been explored. Much has been written about critical-period plasticity; therefore, we will only highlight areas of research that are relevant to cortical plasticity and, more specifically, ocular dominance plasticity. It should be noted that all sensory areas undergo critical-period plasticity and are extensively modified by changes in sensory experience early in life (Chang & Merzenich, 2003; Mower, 1991; Nottebohm & Nottebohm, 1978). The onset of the critical period is regulated by the presence of GABAergic circuits (Hensch et al., 1998), expression of brain derived neurotropic factor (BDNF; Hanover, Huang, Tonegawa, & Stryker, 1999; Huang et al., 1999), and the uptake of OTX2 by parvalbumin expressing GABAergic neurons (Sugiyama, Prochiantz, & Hensch, 2009). Furthermore, CaMKII, PKA, ERK and TNFalpha (Di Cristo et al., 2001; Fischer et al., 2004; Kaneko, Stellwagen, Malenka, & Stryker, 2008; Taha, Hanover, Silva, & Stryker, 2002) all play important roles. The maturation of dendritic spines during critical-period plasticity is mediated in part by miR-132. Not only does miR-132 expression correlate with the critical period window, its removal obstructs critical-period plasticity (Mellios et al., 2011; Tognini, Putignano, Coatti, & Pizzorusso, 2011). All of this suggests that miR-132 is a contributing factor in the strengthening of neural circuits during critical-period plasticity.
The opening and closing of this period of extensive morphological change in response to environmental factors and changes in sensory inputs is tightly regulated. Critical-period plasticity opening can be modified or even rescued when GABAergic circuits are depleted by altering GABAergic responses with benzodiazepines, a GABA-A receptor agonist (Fagiolini & Hensch, 2000) or artificially maturing the neural nets of GABAergic neurons (Di Cristo et al., 2007). In addition, experiences that alter BDNF expression such as dark rearing delay or prolong critical-period plasticity. While the closing of critical plasticity marks a period when lability of neural circuits in response to environmental change is diminished, structural plasticity is not terminated. As we detail in this chapter, large-scale reorganization is not completely outside thereach of the adult brain. Multiple factors play a role in regulating the duration of the critical period, and considerable effort has gone into identifying the “brakes” that end the critical period (Bavelier, Levi, Li, Dan, & Hensch, 2010). The key feature of the close of the critical period is the maturation of the GABAergic neurons (Hensch, 2005), though this in itself is insufficient for bring the critical period to a close. The development of perineuronal nets (PNN) correlates with the close of critical-period plasticity (Pizzorusso et al., 2002; Sur, Frost, & Hockfield, 1988). PNN, which encapsulate GABAergic neurons, have been proposed to structurally inhibit GABAergic plasticity, as well as acting as a physical barrier to the extracellular environment (Hensch, 2005). Another molecular brake is Lynx1, which is expressed by GABAergic neurons (Morishita, Miwa, Heintz, & Hensch, 2010) and binds the nicotinic acetylcholine receptor (Miwa et al., 1999). Its expression correlates with the end of the critical period, and its occlusion prevents critical period closure (Morishita et al., 2010). Other players are Nogo-66 receptor (McGee, Yang, Fischer, Daw, & Strittmatter, 2005), which limits axonal growth, and paired immunoglobulin-like receptor B (PirB; Atwal et al., 2008; Syken, Grandpre, Kanold, & Shatz, 2006), which blocks axon regeneration and restricts spine density. PirB limits ocular dominance plasticity during the critical period and may stabilize synapses throughout life. Eliminating synapses based on sensory experience and circuit activity is an important step of critical-period plasticity. One way synapses are removed and neuronal circuits are modified is by microglia that identify dendritic spines to be pruned by utilizing complement receptor 3 (Schafer et al., 2012; Tremblay, Lowery, & Majewska, 2010). Therefore one sees a multiplicity of factors responsible for regulating plasticity, either via inducing experience-dependent synapse elimination and formation or by locking the system in place, thus making the system immune to experience.
The discovery of a limited period of cortical plasticity might lead one to assume that all cortical connections and functional properties would be fixed in the adult brain. Of course, that can’t be true, given our continuing ability to learn many recognition and discrimination tasks throughout life (Figure 1). But it might have been presumed that this ability is based in higher-order cortical areas, with mature primary sensory cortex in particular having fixed functional properties. In fact, however, even the adult primary sensory cortex, as we shall discuss, retains the ability to undergo experience-dependent changes. The difference between critical-period plasticity and that seen in the adult is at least a matter of the circuits involved, with thalamocortical connections becoming fixed and intrinsic horizontal cortical connections being mutable throughout life.
Adult Cortical Plasticity
The mechanism of cortical plasticity has to allow for the nature of the information that is encoded and retrieved. As noted by Edward Thorndike, a pioneer in educational research, perceptual learning is specific to the trained task and does not transfer to other tasks. At the time he did his studies, and even in the current era when training programs have gained popularity, it was assumed that the brain is like a muscle, and training on one demanding task improves one’s cognitive abilities in general. But the specificity of learning shows that this is not the case, and if one wants to improve on a given task, one has to train on that task. This is exemplified in Figure 3, where subjects were trained first on a 3-line bisection task, having to discriminate the position of the central of 3 parallel lines as being either closer to the one on the left or the one on the right. A minimum offset from the central position, the threshold in task performance, is required for one to reliably judge the proximity of the central line to either of the two flanking lines. With practice, doing hundreds of trials over many days, the threshold can improve by a factor of 2 or 3. Training on this task does not transfer to a vernier discrimination task—whether the target line is positioned to the left or right of a collinear line, even when the target line is in the same visual field position and orientation in the second task as in the first, but the context is different. Yet you can train specifically on the vernier task and obtain a 3-fold improvement in the task threshold (Crist, Kapadia, Westheimer, & Gilbert, 1997, Figure 3). Therefore, the underlying cortical mechanism has to be consistent with this kind of specificity.
The cortical underpinnings of perceptual learning have been studied at the levels of neuronal function, cortical functional architecture, and changes in neuronal morphology and cortical circuits. It has been studied in the visual, somatosensory, and auditory modalities. The functional basis of motor learning has been investigated in motor cortex. One observed effect of learning in the somatosensory modality, involving discriminating the frequency of a vibrating stimulus by a finger, is an increased size in the cortical representation of the trained digit in primary somatosensory cortex (S1, Recanzone, Merzenich, Jenkins, Grajski, & Dinse, 1992). But here the performance is not well correlated with the size of the cortical area involved. In learning to discriminate the pitch of a tone, changes in the size of the cortical representation of the trained frequency has been observed in primary auditory cortex (A1; Recanzone, Schreiner, & Merzenich, 1993). A fixed change in the size of cortical representation could in theory lead to an improvement by having more similarly tuned neurons activated by the stimulus, with the resultant increase in signal to noise. One would also expect that such changes would lead to an improvement in any task involving that part of the sensory map, which would violate the idea of specificity of perceptual learning. Moreover, an increase in the size of representation of one part of the sensory map would have to lead to a decrease in the size of adjoining parts of the map and a decrease in performance for discrimination ability involving those parts. Yet one sees little evidence for such “robbing” of performance. In experiments on the cortical changes with visual perceptual learning, on the other hand, one sees a very different associated functional change. For animals trained on the bisection and vernier tasks described earlier, there is no change in the size of the representation of the trained part of the visual field. Instead, there is a task-dependent change in the tuning to the task-relevant stimulus attributes. This is also seen in animals trained on a contour detection task, which involves detecting a contour composed of a series of collinear line segments embedded in a background of randomly oriented and positioned line segments. Although the detectability of the contour depends on its length, with training subjects can detect shorter contours. For animals trained on this task, recordings in V1, and measures of the animals’ performance on the task, there is a parallel upward shift of the neurometric and psychometric curves reflecting their improved performance. And these changes are only present when animals perform the trained task (Crist, Li, & Gilbert, 2001; Li, Piech, & Gilbert, 2004, 2008).
Experience alters the representation of the encoded stimuli. For two stimuli that are similar and share common neural circuits, perceptual learning disengages the overlapping neural circuits. The representation of the two stimuli become more distinct with fewer shared neurons following perceptual learning (Chu, Li, & Komiyama, 2016). A shift from bottom-up processing to top-down processing involves a decrease in neural activity (Makino & Komiyama, 2015) of somatostatin neurons within the primary sensory map. Somatostatin neurons are a subtype of inhibitory neurons that make synapses to distal branches of excitatory neurons within the cortex. Redistribution of inhibitory drive occurs with perceptual learning with pruning of somatostatin connections on distal dendrites while simultaneously increasing inhibitory drive from horizontally projecting parvalbumin positive neurons (Chen, Kim, Peters, & Komiyama, 2015). Parvalbumin neurons also target the soma, suggesting a switch in the location of inhibitory input on excitatory neurons. Interestingly, somatostatin neuronal firing rate may act as a gate to control the major excitatory drive on the primary sensory map.
Structural Changes Associated with Learning and Recovery of Function after CNS Lesions
The mechanism underlying the functional changes we have described involves alterations in cortical circuits, and therefore certain forms of plasticity are not restricted to the critical period but are retained throughout life. The advent of high-resolution in vivo imaging, exemplified by the two-photon microscope (Denk, Strickler, & Webb, 1990), has allowed us to peer into the brain of mammals and observe the anatomical correlates of adult plasticity. This can be observed by in vivo synaptic turnover under normal conditions and anatomical rearrangement of circuits that occur during learning and in functional recovery following CNS damage. With technical advances in microscopy and genetically encoded fluorescent proteins, it is now possible to see that even under normal conditions, a percentage of presynaptic and postsynaptic components of synapses undergo turnover.
Dendritic spines, the post-synaptic component of synapses on excitatory neurons, are labile throughout life. For example, while dendritic spine density is constant on apical dendrites of layer V neurons, approximately 40% of spines are actively being added and removed on a weekly basis (Trachtenberg et al., 2002) in primary sensory maps. The overall spine density remains constant even though subpopulations of spines are being added and removed from dendritic arbors. Several labs report spine turnover of lower percentages in primary sensory cortex depending on the age of the mouse, frequency of imaging, and sensory map of interest (Grutzendler, Kasthuri, & Gan, 2002; Majewska & Sur, 2003; Zuo, Yang, Kwon, & Gan, 2005). Interestingly, in aged mice dendritic spines become more labile (Grillo et al., 2013). The purpose of this is unknown. Changes in the turnover of dendritic spines have been seen in the cortex of animals undergoing various forms of learning. Increased dendritic spine density in mouse barrel cortex accompanies learning on a whisker- based object localization task (Kuhlman, O’Connor, Fox, & Svoboda, 2014). Spine turnover and stabilization, as well as changes in the axonal boutons of inhibitory neurons in motor cortex, are seen with motor skill learning (Xu et al., 2009). A fear conditioning model, with association to auditory cues, there is a transient increase in spine formation in auditory cortex (Moczulska et al., 2013).
The presynaptic component of synapses, the axonal bouton, is also dynamic. It has been determined that under normal conditions the extent and branching patterns of excitatory axons are stable, yet their boutons have a turnover rate of 6% per week (De Paola et al., 2006; Stettler, Yamahachi, Li, Denk, & Gilbert, 2006). Inhibitory neurons experience slightly more plasticity than excitatory neurons under normal conditions with a turnover rate of 8–10%/week of inhibitory boutons (Marik, Yamahachi, McManus, Szabo, & Gilbert, 2010) in normal conditions. The turnover of boutons and spines suggests an extraordinary degree of dynamic change in the adult brain and begs a number of questions. Do all of our synapses undergo replacement in time, or are there different populations of mutable and stable synapses? This is suggested by observations on spine turnover, but axonal bouton turnover does not seem to be restricted to a constantly changing subgroup of boutons with a privileged set of stable boutons. Given the rate of synaptic turnover observed in many studies, one wonders how the network can retain the amount of information that must be stable over extended periods. Certainly, it is known that perceptual learning is remarkably constant, with improvement on a task being retained years after the initial period of training. Alternatively, one may speculate that there is a mechanism whereby the network of connections that is stable during a period of turnover of other connections can inform the process of synaptogenesis to maintain information content (Fauth, Worgotter, & Tetzlaff, 2015).
The baseline turnover of synapses in the absence of manipulation of experience may represent the background upon which experience-dependent modifications are imposed. Experience-dependent plasticity is associated with a range of forms of learning, as described earlier, environmental enrichment, or functional adaptation to central or peripheral lesions, such as those associated with stroke or neurodegenerative disease. There have been numerous examples where alterations in experience lead to changes in spines. In the mouse, removal of whiskers increases the rate of turnover of spines of layer V excitatory neurons of somatosensory cortex by 30% (Trachtenberg et al., 2002). Initially, most two-photon studies focused on Layer V dendrites due to genetically encoded transgenic mice that had sparse labeling of layer V neurons that were available early on (Feng et al., 2000). Now more detailed information about the plasticity of neural circuits can be ascertained with the availability of new mouse lines and the ability to quickly generate mouse lines with specific subsets of neurons of interest fluorescently labeled (Heintz, 2001; Orban, Chui, & Marth, 1992; Tsien, 2016; Tsien et al., 1996; Yang, Model, & Heintz, 1997) and genetically engineered AAVs that allow for control of fluorescent probes under the guidance of cell-type specific promoters that permit control of cell type and functional location with map topography. With the ability to know the location within a sensory map where functional changes occur, and to interrogate the changes associated with specific cell types, one can paint a fuller picture of the circuit mechanisms underlying the functional change.
It also allows one to distinguish between the connections whose plasticity is limited to the critical period and those that retain that capacity into adulthood. As described above, the thalamocortical input to visual cortex carrying information from the two eyes shows the critical period limitation. On the other hand, intrinsic cortical circuits, including long range horizontal connections formed by cortical pyramidal neurons and inhibitory interneurons have been shown to exhibit plasticity in the adult. Several experimental models have been developed to explore the circuit mechanisms of cortical plasticity. Many have stemmed from inducing local deprivation: looking at the cortical effects of retinal lesions, whisker removal and forelimb denervation.
Modification of sensory experience can result in remapping of cortical functional topography by rearrangement of the horizontal circuits. These horizontal connections that are normally important for modulating activity within a sensory map may take on the role as the major excitatory driver in areas of the map following alterations in experience. Following sensory loss, e.g. removal of somatosensory input or by retinal lesion, there is an area within the map that no longer receives its original input from the sensory periphery. This is referred to as the lesion projection zone (LPZ). The neurons located within the LPZ are reassigned functional roles and become excited by stimulation that drives adjacent topographic sites within the map. Following the sensory loss, competitive interactions in the horizontal connections within the cortical circuit take place. The two major theories put forth to describe the underlying mechanism of remapping of sensory maps in response to changes in experience either involve (1) change in the “weight” of existing synapses by long-term potentiation (LTP) and long-term depression (LTD) or (2) the growth of new axonal collaterals (Buonomano & Merzenich, 1998). Since the topographic remapping occurs in the horizontal dimension, horizontal connections that integrate information across cortical columns in superficial and lower cortical layers are likely involved. Horizontal connections in the adult are capable of undergoing LTP and LTD-like phenomena (Hirsch & Gilbert, 1993; Marik & Hickmott, 2009), supporting the first theory. However, changes in cortical circuits go beyond the level of changing the strength of existing synapses with post-mortem data demonstrating significant reorganization of dendrites (Hickmott & Steen, 2005) and large-scale axonal sprouting in response to deprivation (Darian-Smith & Gilbert, 1994), which suggests that large-scale physical rearrangement of cortical circuits occur following changes in experience.
Postmortem data was able to document outgrowth of new collaterals into the deprived cortex (Darian-Smith & Gilbert, 1994) but a fuller picture of axonal dynamics was made possible by injections of genetically modified adeno-associated virus (AAV) to label neurons, and longitudinal in vivo two-photon mi croscopy. With this approach one could track the growth and pruning of axonal arbors and turnover of axonal boutons. Most importantly, when these techniques are combined with electrophysiological recordings to determine the topographic location within a sensory map, the function and morphology of the neurons examined are known. Neural circuits can be followed over time, extending from the period before manipulation of sensory experience and for periods up to months following the manipulation. In a set of experiments using this combination of techniques, excitatory neurons just adjacent to what would become the LPZ were labelled and imaged, since the postmortem data suggested that it was axons from these neurons that underwent axonal growth to alter the cortical topography of the LPZ following the change in experience (Marik, Olsen, Tessier-Lavigne, & Gilbert, 2013, 2016; Marik et al., 2010; Marik et al., 2014; Yamahachi et al., 2009). In two model systems where a LPZ was created in the visual cortex of non-human primates following retinal lesions and in the somatosensory cortex of mice following whisker plucking, functional remapping of the primary sensory map occurred and the morphology of horizontal circuits were examined in detail (Yamahachi et al., 2009). Following the sensory loss in both systems, massive axonal growth of the horizontal axons projecting into the LPZ occurs, which is consistent with the increase in axonal density for axons projecting into the LPZ observed in the postmortem data. However, the in vivo longitudinal imaging led to the novel finding of axonal pruning of excitatory axons that occurred in parallel with axonal outgrowth and to a continuing process of exuberant outgrowth and pruning leading to a net increase in the plexus of horizontal connections in the LPZ (Figure 4; Marik et al., 2010; Yamahachi et al., 2009). This represents a recapitulation of the process seen in early development. Axonal pruning plays an integral role in the formation of new cortical circuits in the developing organism. During development, new neural circuits are formed via axonal growth and axonal pruning. This flexibility works well to alter circuits depending on local cues, activity, and environment. When making retinal lesions there is a remarkable rapidity of the onset of remapping of visual topography, with shifts in receptive field position for neurons lying just inside the LPZ boundary occurring within hours, and recovery of visually driven activity extending into the center of the LPZ occurring over a period of weeks and months, with neurons showing much larger shifts in RF position. While one may have supposed that the initial rapid phase of recovery would be mediated by LTP/LTD-like phenomena with the longer-term changes accomplished by axonal growth, the imaging experiments showed that massive axonal changes could occur at the same time scale as the functional changes. Thus, significant morphological changes may represent the anatomical signature of the functional changes in cortical maps observed following sensory loss.
Neurons within the LPZ also undergo structural rearrangement. Dendritic spines of layer 5 neurons in the LPZ undergo increased turn-over rate so that within two months almost all spines on apical branches had been replaced following retinal lesions in mice (Keck et al., 2008). Interestingly, competitive interaction of adjacent non-LPZ regions is imperative for structural plasticity, since the increased synaptic plasticity is not seen when the entire input of the retina is eliminated. This suggests that the axonal growth of the adjacent horizontal connections is the crucial component of remapping of cortical topography following sensory loss.
In addition to the rearrangement of the axonal arbors of excitatory neurons, inhibitory neurons also play an important role in experience-dependent plasticity in the cortex. Several labs report a decrease in inhibitory synapses within the LPZ (Akhtar & Land, 1991; Keck et al., 2011; Kossut, Stewart, Siucinska, Bourne, & Gabbott, 1991; Welker, Soriano, & Van der Loos, 1989). Longitudinal two-photon imaging of horizontally projecting axons of inhibitory neurons inside the LPZ reveals massive axonal pruning within the LPZ yet also axonal sprouting across the LPZ border to target neurons in the adjacent non-LPZ areas (Marik et al., 2010; 2014). The reciprocal relationship between the sprouting of excitatory and inhibitory axons may enable the circuit to maintain a balance between excitation and inhibition, so that the increased excitatory input from the horizontally projecting excitatory neurons from adjacent regions into the LPZ does not cause the local circuit to undergo runaway excitation. Reciprocal axonal growth and pruning of excitatory and inhibitory neurons have been observed in both visual (Marik et al., 2014; Yamahachi et al., 2009) and somatosensory cortex (Marik et al., 2010) and in non-human primates (Figure 5; Marik et al., 2014; Yamahachi et al., 2009) and mice (Marik et al., 2010), thus suggesting that this is a ubiquitous phenomenon for remodeling of neural circuits. Both the addition and removal of synapses and axonal arbors are crucial of a process to the rewiring of the new circuit brought about by experience.
Plasticity of the Prefrontal Cortex
Structural plasticity observed in primary sensory maps is not exclusive to these regions. Structural and synaptic plasticity is also crucial for cortical regions outside of primary sensory areas, such as, the prefrontal cortex, which plays an important role in the integration of sensory information and attention, as well as memory and decision making (Miller & Cohen, 2001). For decades, reports have demonstrated that during adolescence the prefrontal cortex undergoes extensive pruning (Huttenlocher, 1979; Petanjek et al., 2011). Furthermore, a lack of pruning in adolescence is thought to underlie mental illness and schizophrenia (Sekar et al., 2016). Evidence suggests that long-range connections from the orbitofrontal cortex and basolateral amygdala that converge onto the apical dendrites of layer V excitatory neurons of the dorsomedial prefrontal cortex (dmPFC) stabilize instead of pruning during adolescence (Johnson, Loucks, et al., 2016). Synaptic remodeling is also key during learning in the dmPFC. Projections from the orbitofrontal cortex to the dmPFC underwent enhanced bouton turnover following a learning-related foraging task where the reward was tied to olfactory cues (Johnson, Peckler, Tai, & Wilbrecht, 2016). In the upcoming years, we are likely to observe the emergence of a body of literature on the integration of cortical networks, their role in the dynamic changes of the adult cortex, and how their morphological changes contribute to learning.
Molecular Pathways Governing Structural Plasticity
Hints to the molecular pathways involved in axonal pruning exist in the developing organism. During development, neural circuits are created in excess so the circuitry is allowed flexibility and room for error, and inappropriate connections can be removed by pruning. One way that developing circuits determine which circuits are kept or pruned is dependent on the availability of neurotrophins (for review, see Park & Poo, 2013). Neurotrophins, which are secreted by target neurons and tissue, signal neurite growth and cell survival (Chao, 2003; Koshimizu et al., 2009; Park & Poo, 2013; Reichardt, 2006). As axons compete for neurotrophins, some axons will be forced to prune (Singh et al., 2008; Vanderhaeghen & Cheng, 2010) due to the limited availability of neurotrophins. This phenomenon can readily be seen in in vitro experiments, where the removal of neurotrophins induces extensive axonal pruning (Cusack, Swahari, Hampton Henley, Michael Ramsey, & Deshmukh, 2013; Nikolaev, McLaughlin, O’Leary, & Tessier-Lavigne, 2009; Simon et al., 2012). In the developing organism the removal of neurotrophins initiates the interaction of death receptor 6 (DR6) with amyloid precursor protein (APP; Nikolaev et al., 2009; Olsen et al., 2014). This interaction initiates caspase activity by retrograde signaling to the cell body and activation of PUMA by Foxo3a/c-Jun, which in turn initiates an undetermined anterograde signal to induce caspase activity in the axon of interest (Simon et al., 2016), which leads to the destruction of the axon and its removal but does not result in cell death. In the adult, DR6 and APP are also necessary for axonal pruning following changes in experience (Figure 6; Marik et al., 2013, 2016). Whisker plucking, which induces axonal growth and pruning of excitatory horizontally projecting axons into the deprived cortex, does not elicit axonal pruning in animals lacking DR6 or APP. Caspase activity is also important in the removal of dendritic spines (Erturk, Wang, & Sheng, 2014). DR6 and APP’s role has not been determined for dendritic spine removal.
APP’s role at the synapse is important for understanding adult plasticity but also plays an important role in aging and neurodegeneration. The E1 portion of the APP mediates cell adhesion (Stahl et al., 2014), while the E2 portion is important in axonal pruning development (Olsen et al., 2014) and adult plasticity (Marik et al., 2016). APP has long been implemented in Alzheimer’s disease due to the ability to cleave APP with beta-secretase and gamma-secretase resulting in amyloid beta, a very sticky byproduct that can accumulate and form plaques within the brain (for review, see LaFerla, Green, & Oddo, 2007). The elucidation of APP as a key role in synapse formation (Stahl et al., 2014) and axonal pruning (Olsen et al., 2014) is important for understanding APP’s physiological role and how it may induce pathology. The non-pathological cleavage of APP by alpha-secretase lends to cleavage products of sAPPα and C83. While neither cleavage interferes with the E2 portion of the protein that is important for binding to DR6, it is not known whether the binding of APP and DR6 occurs as a receptor ligand paradigm or in a cis or trans conformation within the cell membrane. DR6 does not seem to contribute further to amyloid plaque formation in the PS2APP mouse line at 6–12 months (Kallop et al., 2014). A death receptor 6-amyloid precursor protein pathway regulates synapse density in the mature CNS but does not contribute to Alzheimer’s disease-related pathophysiology in murine models. However, it is not known whether DR6’s lack of pathological contribution is because the pathological APP mutation within the mouse line impacts APP’s binding with DR6 or not. Furthermore, the presence and loss of DR6 did not significantly add to plaque formation. While there was a trend of less plaque formation in the absence of DR6, it was not significant. It would be interesting to see whether DR6 plays a significant role at very early stages of plaque formation and when pathological synaptic loss begins. Once significant loss of synapse and a critical level of plaque formation accumulates, it is possible that DR6 might not add anything further.
Pathology of Cortical Plasticity in Aging and Disease
It is by now well established that synaptic turnover, with large-scale morphological changes, is associated with normal sensory experience, as well as functional changes following CNS and peripheral damage. But changes in the rate of pruning may not have an unalloyed benefit. Increased synaptic turnover has been observed in aging animals (Morrison & Baxter, 2012), especially in the dorsal lateral prefrontal cortex and the hippocampus (Dumitriu et al., 2010; Peters, Sethares, & Luebke, 2008). The cause of this is not clear. In females this might be due to the loss of estrogen (Dumitriu et al., 2010; Morrison and Baxter, 2012). Synaptic loss that accompanies aging has been associated with a decrease in speed of processing (Birren & Fisher, 1995), decrease in reaction time (Thompson & Botwinick, 1968), forgetfulness, and free recall memory loss (Crook & West, 1990; Gilbert & Levee, 1971).
Extensive evidence has been given demonstrating that the adult brain is not hard-wired throughout life. Furthermore, there is evidence that with aging, organisms’ neural circuits do not become inflexible but instead are more labile. Yet this increase in malleability of synapses does not improve cognition or memory. Rather the inverse is true, with aged mice performing poorly on the object recognition tasks (Burke, Wallace, Nematollahi, Uprety, & Barnes, 2010; Murai, Okuda, Tanaka, & Ohta, 2007). Specifically the aged mice perform at the same level with a novel object versus a familiar object, suggesting that they do not recognize the novel object as new and therefore do not familiarize themselves with it by investigating it for a longer period than objects that were presented on previous days. So while having a complex brain that is capable of rewiring is extremely important for higher cognition and memory, the determination of which synapses and circuits to prune and which ones to keep is of extreme importance for overall behavior and intellect. Excessive pruning is also observed in schizophrenia (Sekar et al., 2016).
Having the ability to create new synapses, connections, and to learn new tasks as we age is imperative to brain functioning in adulthood and older. One potential consequence of ongoing rewiring of cortical circuits during aging is the possibility of these molecular pathways going awry, thus resulting in excessive axonal pruning and neurodegenerative diseases. For example, one of the first steps of Alzheimer’s disease is axonal pruning (Bartus, Dean, Beer, & Lippa, 1982). This is later accompanied by excessive cell death; breakdown of the blood brain barrier, hyper-phosphorylation of tau, and the buildup of amyloid plaques. Amyloid plaques are composed of Aβwhich is the consequence of cleavage of APP by beta-secretase and gamma secretase (LaFerla et al., 2007). Aβ has been shown to wreak havoc in neurons by causing calcium dysfunction, synaptic dysfunction, and inhibiting mitochondria and proteasome functioning as well as oligomerizing to form amyloid plaques. Aβ plaques inhibit synaptic plasticity (Puzzo et al., 2017; Wei et al., 2010) and alter neuronal activity (Busche et al., 2012).
When axonal pruning mechanisms go awry, resulting in excessive axonal pruning, memories unravel. Recent research demonstrating the importance of APP in normal healthy axonal pruning suggests that learning and memory loss may be on the same continuum, leaving the possibility of these pathways going awry and tipping the balance toward excessive pruning.
Plasticity is a universal and enduring property of the cerebral cortex. It is involved in different forms of memory, including declarative or conscious memories and non-declarative or unconscious memories. There is increasing awareness of the underlying mechanisms at the level of different cortical connections and different cell types. The exuberance of experience dependent changes in cortical circuits poses several questions about how learned information is represented. How do different connections interact in order to encode novel experiences, how is previously acquired information retained in the face of encoding new information along with the attendant changes in cortical connections? Can training influence the course of plasticity and facilitate functional recovery following CNS lesions? What are the molecular pathways that control the alteration of cortical circuits, including those that delineate the critical period? How do any of these mechanisms—either at the circuit, synaptic, or molecular levels—account for brain disorders?
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