Structural dynamics of dendritic spines in memory and cognition

Review Structural dynamics of dendritic spines in memory and cognition Haruo Kasai1, Masahiro Fukuda1, Satoshi Watanabe1, Akiko Hayashi-Takagi2 and Jun Noguchi1 1 Laboratory of Structural Physiology, Center for Disease Biology and Integrative Medicine, Faculty of Medicine, and Center for NanoBio Integration, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan 2 Department of Psychiatry and Behavioral Neurosciences, Johns Hopkins University School of Medicine, Baltimore MD 21287, USA Recent studies show that dendritic spines are dynamic structures. Their rapid creation, destruction and shapechanging are essential for short- and long-term plasticity at excitatory synapses on pyramidal neurons in the cerebral cortex. The onset of long-term potentiation, spine-volume growth and an increase in receptor trafficking are coincident, enabling a ‘functional readout’ of spine structure that links the age, size, strength and lifetime of a synapse. Spine dynamics are also implicated in long-term memory and cognition: intrinsic fluctuations in volume can explain synapse maintenance over long periods, and rapid, activity-triggered plasticity can relate directly to cognitive processes. Thus, spine dynamics are cellular phenomena with important implications for cognition and memory. Furthermore, impaired spine dynamics can cause psychiatric and neurodevelopmental disorders. Dendritic spines On pyramidal neurons in the cerebral cortex, excitatory synapses terminate at spines, which are short protrusions joined to the main dendrite by a thin neck. Discovered in the 19th century and intensely scrutinized in the 20th century, dendritic spines are found in higher animals [1,2] and some insects [3,4]. Spines exist only on certain types of neurons, including pyramidal neurons in the cortex, medium spiny neurons in the basal ganglia and Purkinje cells in the cerebellum. Spines are more abundant in higher brain regions and highly variable in shape. Moreover, dendritic spines are the most actin-rich structures in the brain [5,6], and their morphology and density are abnormal in several mental disorders [7]. The best-known example of input-specific, activity-dependent synaptic plasticity—Donald Hebb’s canonical basis for learning and memory [8] —is long-term potentiation (LTP) of spine synapses in the hippocampus [9]. The link between LTP and spine structure was suggested by the finding that the size of the postsynaptic density (PSD) is related to the size of the spine head [10] and the number of AMPA-type glutamate receptors within it [11–13]. These ultrastructural studies, however, could not determine the functional state of a spine. This structure–function relationship was first established in 2001 using two-photon uncaging of a caged–glutamate compound [14–20]. Later Corresponding author: Kasai, H. (hkasai@m.u-tokyo.ac.jp) reports showed that spine enlargements are associated with LTP in single identified spines (Figure 1a) [21], indicating that Hebb’s learning rule applies even at the level of a single synapse. Many studies have since confirmed that the induction of LTP or long-term depression (LTD), another form of activity-dependent plasticity, induces structural plasticity of spines in stimulated dendritic branches [22–30]. Given the apparent stability in vivo of dendritic and axonal arbors at low magnification [31–33], the properties that govern spine dynamics over the long-term could play a major role in reorganizing cortical circuitry throughout life [34,35]. In fact, spines are frequently generated and eliminated even in the adult neocortex, and these events have been suggested as substrates for stable memory formation [35–38]. Both formation and enlargement of spines are important during synaptic rearrangements in the visual cortex that follow sensory deprivation [39]. It is important to note that spine structural dynamics include broader phenomena than LTP/LTD. Namely, they include the generation and elimination of spines [31,32,40– 42], and long-term, activity-independent fluctuations (described in detail below) (Figure 1b) [42]. In addition, spines become larger in response to the force of actin polymerization [43], which occurs within seconds [21] of LTP induction [44]. Spines seem to display expansive force continuously to maintain their shape and function [43]. These findings suggest that spine synapses are not just electrochemical but also mechanical in nature. The purpose of this article is to present the new findings on spine dynamics that can be extrapolated to a broad spectrum of higher-order brain functions. We summarize the relationships between spine structural dynamics and functional plasticity, explain the long-term maintenance of spine structures, propose an explanation for the impairment of spine dynamics in mental disorders and introduce the possible relationships between rapid spine dynamics and cognitive processes. Activity-dependent structural plasticity of dendritic spines and receptor trafficking At the level of the dendritic spine, structural dynamics and receptor trafficking both contribute to functional plasticity. For example, spine enlargement occurs within a minute (Figure 1a) [21], a time course that matches the rapid 0166-2236/$ – see front matter ß 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.tins.2010.01.001 121 Review Trends in Neurosciences Vol.33 No.3 We speculate that receptor trafficking does not support the long-term maintenance of plasticity in the absence of structural changes (Figure 1c), based on the turnover rates for individual molecules: the lifetime of a spine is more than a thousand times greater than that of its molecular constituents. Even PSD-95, among the most stable molecules in the spine, redistributes within 3 h in both dissociated cultures and the intact brain in vivo [56,57]. Spine lifetimes, by contrast, can be more than a year in vivo [32,35]. The autophosphorylation of CaMKII, once thought to help maintain long-term changes in synaptic strength, has since been revealed to be rather transient [58]. Thus, maintenance of the molecular status of the spine can require some association with its structure. Given this evidence, it would be interesting to test whether the functional plasticity induced without structural changes to the spine [24,46,59,60] can persist as long as the functional plasticity that accompanies structural alterations. We next address how spine structure can be maintained over days (Figure 1b). Figure 1. Structural dynamics of dendritic spines. Rapid and slow changes in spine shape and size result in rapid and slow synaptic plasticities. (a) Time course of spine enlargement induced by the two-photon uncaging of a caged–glutamate compound. Spine enlargement is specific to the stimulated spine (red circle) and does not spread to the neighboring spine (white diamond). Enlargement occurs within a few seconds and lasts over 2 h. (b) Slow intrinsic fluctuations in spine sizes occur in the presence of inhibitors of NMDA receptors (NMDAR) over periods of days. The spines show relatively small changes in size (quantified in Figure 2). Spine generation (spines 4, 6, 7) and elimination (spines 8 and 10) still occur frequently. (c) Relationship between structural plasticity and receptor trafficking, the two mechanisms of synaptic plasticity. Structural changes induced by plasticity can be maintained for a long time, enabling the functional states of spines to also be maintained for a long time—if there are structure–function relationships. Such relationships can be caused by the allocation of AMPA receptors in proportion to the spine size, resulting in the ‘‘readout of structure.’’ Trafficking of receptors can facilitate and hasten the readout processes. Figures in (a) and (b) are reproduced with permission from the Nature publishing group [21] and the Society for Neuroscience [42], respectively. induction of LTP. Enlarged spines also explain the longterm maintenance of LTP, given that the number of functional AMPA receptors correlates with spine volume [14– 20] (Figure 1c). And spine enlargement [45,46], like the late phase of LTP and long-term memory itself [47], depends on protein synthesis. Moreover, spine structures are stable for days in cultured hippocampal slices (Figure 1b) [42,48] and for years in the cortex in vivo [31,32,34]. These data support the idea that structural plasticity is the central cellular mechanism that underlies memory formation. Receptor trafficking also affects functional plasticity by facilitating the readout of spine structure (Figure 1c). During LTP induction, AMPA receptors in cytosolic vesicles are inserted into the plasma membrane [49,50] and diffuse laterally to supply new receptors to the PSD [51,52]. These receptors are anchored to PSD proteins in an actin-dependent manner [53–55]. LTD protocols also affect the endocytosis of AMPA receptors [49]. Hence, numerous experiments support the link between LTP/LTD induction, receptor trafficking and the acquisition of long-term memory in behaving animals [49]. It is important to note, however, that the rapid exchange of AMPA receptors between synaptic and extrasynaptic regions [51] means that trafficking mechanisms by themselves cannot account for long-term changes in synaptic function. 122 Long-term intrinsic fluctuations and maintenance of spines Neuronal networks reflect the properties of spine populations, rather than those of a single spine, because the generation of action potentials in a neuron requires the activation of many synapses. We recently identified a key phenomenon that affects the long-term behaviors of spine populations. Through the observation and systematic quantification of spine dynamics over periods of days (Figure 1b) [42], it became apparent that spine volumes grew and shrank spontaneously. These volumetric changes occurred even when NMDA receptors and Na+ channels were completely blocked to prevent activity-dependent plasticity (Figure 2a). Such changes, termed ‘intrinsic fluctuations,’ encompass all phenomena (other than activity-dependent plasticity) that contribute to the structural dynamics of spines [42], and represent at least a part of fluctuations of spine volumes reported in vivo [38,61,62]. These fluctuations reflect an inevitable lack of structural stability in spines whose molecular components turn over with a time constant of less than 3 h [56,57]. Despite this constant change, the average daily volume change was close to zero for all spine sizes (Figure 2b). Thus, spine populations at various sizes are in equilibrium, and individual spines can act as analog memory elements despite the stochastic changes in spine volume (up to 20% per day in young hippocampus) (Figure 3c). At first glance, intrinsic fluctuations would seem incompatible with long-term memory storage. However, two additional findings reveal that this variability does not defeat the maintenance of spine function over time (Figure 1c). First, the volume of an average spine remains largely the same for a certain period of time [42], consistent with the persistence for weeks of LTP in vivo [42,63]. Second, spine lifetimes can be very long (Figure Ia in Box 1), in line with in vivo two-photon imaging [32,34] (for theory and examples, see Box 1). The high proportion of small spines in the volume distribution [64] (Figure 2d) can now be explained by a positive correlation between the size of the spine and the amplitude Review Trends in Neurosciences Vol.33 No.3 Figure 2. Long-term structural dynamics of dendritic spines. (a) Fluctuations in the head volume of an individual spine as determined by the fluorescence intensity of the spine head. There are two mechanisms controlling the long-term dynamics of spine volumes: activity-dependent plasticity (A, blue arrows) and intrinsic fluctuations (I, black arrows). Activity-dependent plasticity is dependent on the activation of NMDA receptors, whereas intrinsic fluctuations exist even in the presence of inhibitors of NMDA receptors and Na+ channels. Activity-dependent plasticity and intrinsic fluctuations represent learning mechanisms and maintenance processes, respectively. The Langevin equations used to model the plasticity are shown. We assume that every spine larger than 0.02 mm3 has a presynaptic partner, although the presynaptic terminal is drawn only for the small spine. (b) Mean values of fluctuations in spine head volume in the presence (black, I) and in the absence (blue, A) of NMDA receptor inhibitors. (c) Standard deviations of fluctuations in spine head volume in the presence (black, I) and in the absence (green, C) of NMDA receptor inhibitors. The activity-dependent fraction is displayed by blue (A). (d) Probability–density distributions predicted from the C and I data given in (b, c). The prediction is well fit by the actual data for spine head volumes in slice cultures incubated for 3 days in the absence (C) or presence (I) of NMDA receptor inhibitors [42]. of its intrinsic fluctuations (Figure 2c, I). Small spines accumulate because of smaller fluctuations, and larger spines become less common because of their greater fluctuation amplitudes (Figure 2c) [42] (for a theoretical basis, see Box 1). Similar spine–volume distributions have been found in various conditions in vivo and in vitro, supporting the interpretation that the mechanisms are the same. In contrast to the magnitude of intrinsic fluctuations, the frequency of activity-dependent structural changes is greater in smaller spines (Figure Id in Box 1; for quantification of LTP, see Box 1). All spines are considered to receive a synaptic contact [20,42], and small spines correspond approximately to the notion of ‘‘silent synapses’’ [65] that express NMDA but not AMPA receptors [14,16,20]. Small spines display greater increases in cytosolic Ca2+ induced by NMDA receptor stimulation [16,66] and are preferential sites for LTP induction [21,65]. The structural changes that accompany LTP include the enlargement of small spines and an increased number of medium-sized spines (Figure 2d, C). Such effects on the volume distribution, however, were relatively weak (Figure 2d, compare I and C) because intrinsic fluctuations occur constantly and appear to be the dominant mechanism in determining spine–volume distributions. Large spines form only gradually, because activity-dependent enlargement acts preferentially on small, rather than medium, spines (Figure 2a) [42]. This simple fact predicts that older spines will tend to be larger (Figure Ib in Box 1) [42] and have longer life expectancies (Figure Ia in Box 1). Thus, the history of a spine is reflected in its volume, unlike a one-bit memory element in a computer, which cannot indicate its own history. Indeed, this history effect explains a feature of memory first described by Ebbinghaus in 1885—that older memories are more persistent [67]. In his seminal work, Ebbinghaus estimated the decay of memories by memorizing a list of nonsense syllables. After a variable period, he memorized the list again and quan- tified memory as the decrease in memorization time. He found a rapid loss of memory in the first day, followed by a slower decline over the next 31 days. This non-exponential pattern of memory decay suggested that longer-lasting memories were more persistent. A graph of the time savings was fitted by a logarithmic curve (Figure Ic in Box 1, red dashed line) that was later called the ‘‘savings function’’ or ‘‘forgetting curve’’ [68]. Thus, the nature of memory depends on its history, which can be explained in turn by the history effect of the dendritic spine. If one posits that the creation or enlargement of a few small spines within Broca’s area represent memory traces for new syllables, then the idea of intrinsic fluctuations predicts a logarithmic decay in the mean volume of small spines (Figure Ic in Box 1, solid line) that is closely related to the savings function (red dashed line). At the same time, the model explains how a proportion of the memory encoded within small spines can be saved into larger spines as a result of intrinsic fluctuations (Figure 2a). Intrinsic fluctuations are random forces that alternately strengthen and destroy the smallest spines. But the stochastic elimination of existing spines (Figure 1b, spines 8 and 10), in turn, enables the spontaneous generation of new spines (Figure 1b, spines 4, 6, 7) by clearing space and recycling molecular resources. Indeed, the spontaneous creation of new spines continues into adulthood and is often detected [42] in the mature neocortex [35,37,61]. Although new spines start small (Figure 2a), they represent functional synapses that can readily enlarge in an activity-dependent manner (Figure 1a). The creation of new spines accelerates 20–60 min after neuronal activity [40,69], but the sprouting cannot be synapse-specific as there is no synapse before its generation, in principle. Thus, activity-dependent synapse formation seems to reflect considerable chance [48]. Perhaps the random nature of this make-and-test process enables animals to adapt to an unexpected environment. These new spines could be seeds of new memory [35,37,38]. 123 Review Trends in Neurosciences Vol.33 No.3 Box 1. Population dynamics of dendritic spines To understand the population behaviors of spines, we have introduced a mathematical model in which spine volumes change continuously in a semi-random manner. The properties of the changes or fluctuations depend on spine volume (Figure 2a). Similar approaches have been used in the study of many other biological problems, such as inter-spike intervals [116], population genetics and ecology [117]. The simplest mathematical model for random continuous fluctuations is Brownian motion W(t) [116,117]. In this framework, a continuous random variable or stochastic process V(t) that has an average change or drift of m(V) and a standard deviation of s(V) is described as dV ðtÞ dW ðtÞ ¼ sðV ðtÞÞ þ mðV ðtÞÞ; (1) dt dt where W(t) represents the standard Brownian motion with a variance of 1/day [116,118]. We have applied this Langevin equation to the volume fluctuations of spines V(t) using standard deviation and drift values obtained from experiments (Figure 2b,c). We found that the actual volume distributions of spines in control (C) treatments and in the absence of activity-dependent plasticity (I) are well predicted by the model (Figure 2d) [42]. This model also accounts for spine elimination by defining it as the shrinkage of spines past a certain threshold value (0.02 mm3) (Figure 2a). Thus, we can predict that larger spine will have a longer life (Figure Ia in Box 1). The life expectancy of a spine with a volume of 0.3 mm3 is 57 days in the absence of activity-dependent plasticity, assuming that the intrinsic fluctuations have a coefficient of variation (CV=s/V) of 20%/day [42]. If the CV is reduced by a factor of v, the life expectancy is prolonged by a factor of v2. If CV is 5%/day, we can predict that the lifetime of a spine with a volume of 0.3 mm2 is 2 years, consistent with in vivo measurements in mice [34]. If the CV is 1%/ day, the life expectancy is approximately 64 years [42]. Thus, if spine volumes fluctuate by only 1%/day, they could account for the lifelong persistence of some spines in a human being. This model also predicts that on average, older spines will have a larger mean volume (Figure Ib in Box 1) and a longer life expectancy. The average time course of spine volume for spines with the initial volume of 0.021 mm3 can be obtained using the same calculation (Figure Ic in Box 1), which can be fitted with the same saving function (the dashed red line in Figure Ic in Box 1) that represents the time course of forgetting. We can interpret our data on the activity-dependent plasticity of spines using two opposing processes, the enlargement (LTP) and shrinkage (LTD) of spines. If one assumes that the unitary amplitudes All the above studies are consistent with a model in which activity-dependent spine–volume changes regulate newmemory acquisition (by enlarging/stabilizing or eliminating the smallest spines) and existing-memory persistence (by changing volumes of spines) [70].Thus, activity-dependent plasticity selects memory content and modifies memory strength, supporting the random-generation-and-test model of new-memory acquisition. In contrast, intrinsic fluctuations in spine volume may change the strength of a memory but seldom affect its content. We will next discuss how spine dynamics can account for abnormal spine profiles in various neurological and psychiatric conditions. Abnormalities in spine dynamics and mental disorders: a working model Many clinical investigators have proposed that synapses are major sites of pathogenesis for mental disorders such as schizophrenia, autism and other conditions that show normal gross anatomy [7,71–73]. Here, we summarize the reports of dendritic spine abnormalities in these disorders and present a hypothesis for how these changes might yield such diverse symptoms. 124 for enlargement and shrinkage are qE and qS, respectively, and the rates of enlargement and shrinkage are lE and lS, respectively, the average change and standard deviation can be expressed as: m ¼ q E lE  q S lS ; s 2 ¼ qE2 lE þ qS2 lS : (2) Then, the rates of enlargement and shrinkage are expressed as: lE ¼ s 2 þ qS m ; qE ðq E þ q S Þ lE ¼ s2 þ qS m : q E ðqE þ q S Þ (3) If one applies this equation to the activity-dependent component of fluctuations (Figure 2b,c, A) by assuming qE=qS=0.02, then the frequencies of enlargement and shrinkage are predicted as shown in Figure Id in Box 1. Figure I. (a) Relationship between spine head volume and mean life expectancy of a spine according to model I. (b) Dependence on spine age of the mean head volume of a spine following model I. (c) Average time course of head volume changes for spines that obey model I and have initial head volumes of 0.021 mm3. The red dashed line represents the savings function, k/[(Log[at])c+k], where k=0.13, a=480 and c=1.2. Figures reproduced, with permission from the Society for Neuroscience, from Ref. [42]. (d) The predicted contributions of LTP and LTD to the activity-dependent fluctuations (A) obtained using Eq. (3) in Box 1. Dendritic spines in mental retardation In human patients [7,74,75] and most (but not all) animal models of mental retardations [76–79], dendritic spines tend to be abnormally small and immature. Many types of mental retardation have been traced to genetic defects in scaffolding and adhesion molecules thought to maintain synapses [80,81]. Presuming that defects in scaffolding and adhesion proteins would affect intrinsic fluctuations, these abnormally small spines might represent an inability to maintain spine structure. In effect, the lack of these molecules unleashes intrinsic fluctuations (Figure 3) to erode large spines and cause a proliferation of small spines (Figure 2d), thereby masking the effects of activity-dependent plasticity on volume distribution [42]. The inability to preserve large spines created by activity-dependent plasticity prevents the accumulation of proper knowledge in the developmental period, potentially causing a deficit in general intelligence. Dendritic spines are abnormally small in mutant mice that lack a copy of the shank-1 gene, which encodes a major scaffolding protein in the spine [79]. In these mice, interestingly, spatial reference learning was enhanced, Review Trends in Neurosciences Vol.33 No.3 Figure 3. Various forms of spine motility and mental disorders. There are three forms of spine motility: rapid, activity-dependent plasticity; long-term, activity-dependent plasticity; and long-term intrinsic fluctuations. Schizophrenia and mental retardation can arise from selective impairments in activity-dependent plasticity and intrinsic fluctuations, respectively. Intrinsic fluctuations or synaptic stability can also be impaired in autism. although the memory after 4 weeks was much worse than controls [79]. One interpretation of these results could be that the overproliferation of small spines augments newmemory formation, whereas the lack of large spines indicates poor retention of older memories [80]. The superior performance of the mutant on the spatial learning task might represent a murine equivalent of the exceptional abilities that sometimes manifest in autistic savants. Dendritic spines in schizophrenia Unlike autism or mental retardation, schizophrenia is rarely linked to abnormalities in spine size [82]. This observation suggests that synaptic maintenance, as influenced by intrinsic fluctuations, remains unimpaired among schizophrenics (Figure 3). Instead, several lines of evidence point to links between schizophrenia and abnormal activity-dependent plasticity (Figure 3) [71,83]. The strongest such evidence is referred to as the NMDA hypothesis [84,85]. It stems from the observation that phencyclidine, a NMDA receptor antagonist, produces diverse schizophrenic symptoms that are ameliorated by NMDA receptor agonists such as D-serine. The dopamine dysregulation model, which relates to the classical dopamine hypothesis of schizophrenia, suggests that the dopamine-fueled elevation of intracellular cAMP plays an important role in NMDA-dependent synaptic plasticity [86]. Furthermore, the expression products of many schizophrenia-susceptibility genes are localized near glutamatergic synapses in a statistically significant manner (and can exist in other regions as well) [83,87] (Figure 3). Reduced spine density in the prefrontal cortex of schizophrenics [71,86] indicates a loss of balance between synaptogenesis and elimination [42,88]; both processes are strongly regulated by NMDA receptor transmission [89]. Therefore, the reduced density of dendritic spines in schizophrenic patients must represent a reduced generation relative to elimination of synapses [42]. Adult-onset schizophrenia can also develop in response to an inevitable post-adolescent decline in activity-dependent plasticity [90]. At this age, prodromal individuals who exhibit some schizophrenia-associated problems [91] already demonstrate a reduced capacity for activity-dependent synaptic plasticity. This growing deficit is often obscured, however, by high levels of synaptic remodeling at this age. When the decrease in activity-dependent plasticity is uncovered in adulthood, it is often accom- panied by evidence of abnormal neural connectivity and the full onset of symptoms (Figure 3). Although positive symptoms such as delusions and hallucinations remain the major criteria for schizophrenia diagnosis, clinicians have given greater attention in recent decades to a range of cognitive deficits and negative symptoms [91]. Schizophrenics experience the loss of attention, working memory, episodic memory, verbal fluency, emotion and volition [91]—nearly every higher-order brain function is impaired except intelligence quotient [92], which can be spared because it is believed to reflect cognitive functions acquired during childhood and adolescence [93]. In the context of spine dynamics, two synergic mechanisms could explain the symptoms of schizophrenia. The first mechanism rests on the idea that normal spine dynamics regulate experience-dependent plasticity within neuronal networks [71,83]. By extension, abnormal spine dynamics can permit the chaotic reorganization of neuronal networks, culminating in schizophrenia. Accordingly, the disordered thoughts and infrequent brainwave synchrony has led to the characterization of schizophrenia as a disease of disconnected brain regions [94,95]. Particularly because spiny glutamatergic neurons and their projections are so prevalent in the affected brain regions, spine dysfunction could be the culprit in disrupting normal connectivity. But a second, complementary and more provocative possibility is that spine dynamics take part directly in cognition (Figure 4). Rapid structural dynamics of spines in cognition: a new hypothesis Within the brain, the coordinated firing of neurons in space and time underlies myriad functions, including the cognitive processes of perception, emotion and volition [8,96,97]. A major challenge in neuroscience is to delineate the physical conditions of the conscious brain [96,98], sometimes referred to as the neuronal correlates of consciousness [99]. Unfortunately, many studies of these correlates reduce every neuron to its action potential. This presents an incomplete picture of cognitive function, and indeed the brain is more than its ions. Recent experiments using optogenetic tools suggest that mechanisms other than spikes can participate in the creation of internal representations [100]. In this section, we attempt to identify connections between the 125 Review Figure 4. Hypothetical consequences of synchronous activities in a neuronal network. These figures illustrate the positive feedback mechanism in which recursive spike chains trigger the enlargement of specific spines, which in turn facilitate the spike chains. Each neuron displays characteristic neuronal motility in response to a spike chain, and an assembly of neurons exhibits synchronous and complex neural motility. (a) Selective enlargement of a specific set of spines in a neuron (yellow) by distinct spike chains 1 and 2. (b) Characteristic neuronal motilities induced by various spike chains. (c) Possible consequences of spine enlargement on synaptic functions. Spine enlargement enhances AMPA receptor function, applies positive pressure on a presynaptic terminal and can enhance neurotransmitter release. cognitive and synaptic neurosciences to suggest a new synaptic basis for cognitive function. The first connection is about attention. As a physical event, attention manifests as the synchronous firing of a neuronal population that responds preferentially to attended stimuli [97,101]. Synchronous firing can also provide a solution to the binding problem [102,103], in which the brain recognizes an object with diverse attributes as a unitary percept. Therefore, the question arises: How does the brain recognize synchronous firing among spatially scattered neurons? From synaptic physiology, we know that the synchronous firing of pre- and postsynaptic neurons induces rapid spine enlargement [45,104]. In this way, a single spine can encode information from both neurons, and the thousands of spines (Figure 4b) in a pyramidal neuron have the potential to detect synchronous spikes from thousands of other neurons scattered throughout the brain [45] (Figure 4a). The rapid structural dynamics could even reflect a specific spike chain (Figure 4a,b). Thus, a pyramidal neuron can detect and register the synchronous firing of neuronal assemblies. During cognitive processes, billions of spiny neurons show synchronous motilities in their spines. 126 Trends in Neurosciences Vol.33 No.3 The second connection concerns speed. The speed of thought is not instantaneous under any circumstances, but particularly not for cognitive processes. For example, visual perception takes at least 0.1 s of recursive network computation [96], and tactile perception and free will each require 0.5 s of electrical activity in the human cortex [105]. These delays are on the order of the delay in spine enlargement after repetitive stimulation (0.5–3 s). The slight lag is attributed to the Ca2+ triggering of actin polymerization; once begun, the extension of actin filaments occurs rapidly (0.03–1 mm/s) [43,106]. This rapid enlargement does not seem to rely on the same enzymatic reactions needed for long-term enlargement, which involve CaMKII, Kalirin7, Rac and PAK [43,58,107]. The third connection relates to the ordering of long-term changes. Just as cognition precedes the formation of explicit memory [108], rapid enlargement precedes long-term enlargement of spines (Figure 1a). Thus, if cognitive processes involve spine dynamics, they use the same substrates as memory, and effectively leave their traces for memory. The fourth connection explores unconsciousness. General anesthetics easily and completely dispel cognition. Spine enlargement can also be blocked by anesthesia, because rapid spine motility has never been described in an anesthetized animal under natural stimulation—not even when the neurons continued to fire action potentials [109,110]. In support of this consideration, volatile anesthetics are reported to interfere with actin organization in resting cells [111]. In addition, the cerebellum is not a direct source of consciousness [112], although the cerebellar Purkinje cells have prominent spines. This could be correlated with the fact that these cells lack NMDA receptors and thus would not show rapid activity-dependent enlargement. The fifth connection addresses similarities between the phenomena of cognition and structural dynamics. Cognitive processes are specific—meaning defined or individual or particular—but their ultimate origin and subsequent progression are stochastic [113]. Likewise, spine motilities—which represent connections between specific neurons—are very rich cellular behaviors caused by the complex interplay of chemical and physical forces from the cytoskeleton, plasma membrane and surrounding cells. They, too, are often stochastic. The sixth and last connection recognizes self-modification. Cognitive processes are self-modifying, giving rise to memory, emotion and executive functions [113]. Likewise, spine enlargement can dynamically alter the functions of neuronal networks. Although we know that enlargement is associated with increased functional expression of AMPA receptors [21], we suspect that there are other functional consequences of spine enlargement that await discovery. The massive actin polymerization generates an expansive force [43] that acts on surrounding tissues (Figure 4c), but to what effect? One possibility is that it exerts a mechanical force on neighboring spines to alter their functions as well [43]. Another possibility is that the mechanical force enhances presynaptic function. This hypothesis is based on evidence that physical forces or chemical stimuli applied to the plasma membrane promote Review exocytosis [114,115]. If such forms of regulation can indeed be exerted in presynaptic terminals, then spines can actively maintain and trigger the firing of a particular neuronal network or spike chain through an increase in the release probability of presynaptic terminals. This type of regulation must be highly complex and stochastic, as described above. Thus, spine–volume changes could alter synaptic connections in various ways and rapidly selfmodify neuronal–network functions. The rapid, responsive movement of synapses shares many features with cognition. Dendritic spines can take part directly in cognitive processes to make them more individual, active and stochastic—unlike a computer, in which memory elements obey simple and deterministic rules. Thus, cognitive processes can be easier to understand when we take account of the spine structural dynamics. Direct imaging of spine motilities in vivo has the ability to substantiate these possibilities. Concluding remarks We have described the close relationship between spine structure and function, and introduced the hypothesis that intrinsic fluctuations in spine volumes account for the longterm maintenance of spines. The biophysical properties of these fluctuations could mirror the psychological properties of complex behaviors such as forgetting. Intrinsic fluctuations also predict the spontaneous generation of abundant new spines, leading to the random-generationand-test model of new-memory acquisition. We propose that abnormalities in the two types of spine structural plasticity—intrinsic fluctuations and activitydependent plasticity—are involved in the pathogenesis of mental retardation and schizophrenia, respectively. We also introduce the idea that rapid spine dynamics underlie cognition. These are testable hypotheses that can be examined quantitatively in the future. Regardless of those future results, the ability to visualize and manipulate spine dynamics in vivo will be useful in our investigations of cognitive processes and the synaptic bases of psychiatric disorders. Such investigations could provide new avenues for studying brain functions and can lead to novel diagnostic and therapeutic approaches for psychiatric disorders. Acknowledgements We thank M. Fukuda, K. Kasai, A. Sawa and K. Toyama for helpful discussions. This work was supported by Grants-in-Aids from the Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan (H.K., J.N.). References 1 Ramon y Cajal, S. (1995) Histology of the Nervous System of Man and Vertebrate, Oxford University Press 2 Greenough, W.T. and Bailey, C.H. (1988) The anatomy of a memory: convergence of results across a diversity of tests. 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