Abstract
Since the brain was found to be somehow flexible, plastic, researchers worldwide have been trying to comprehend its fundamentals to better understand the brain itself, make predictions, disentangle the neurobiology of brain diseases, and finally propose up-to-date treatments. Neuroplasticity is simple as a concept, but extremely complex when it comes to its mechanisms. This review aims to bring to light an aspect about neuroplasticity that is often not given enough attention as it should, the fact that the brain’s ability to change would include its ability to disconnect synapses. So, neuronal shrinkage, decrease in spine density or dendritic complexity should be included within the concept of neuroplasticity as part of its mechanisms, not as an impairment of it. To that end, we extensively describe a variety of studies involving topics such as neurodevelopment, aging, stress, memory and homeostatic plasticity to highlight how the weakening and disconnection of synapses organically permeate the brain in so many ways as a good practice of its intrinsic physiology. Therefore, we propose to break down neuroplasticity into two sub-concepts, “upward neuroplasticity” for changes related to synaptic construction and “downward neuroplasticity” for changes related to synaptic deconstruction. With these sub-concepts, neuroplasticity could be better understood from a bigger landscape as a vector in which both directions could be taken for the brain to flexibly adapt to certain demands. Such a paradigm shift would allow a better understanding of the concept of neuroplasticity to avoid any data interpretation bias, once it makes clear that there is no morality with regard to the organic and physiological changes that involve dynamic biological systems as seen in the brain.
Similar content being viewed by others
Introduction
The etymology of neuroplasticity breaks it down into two basic morphemes: neuro- plastic; with the “plastic” originally meaning “suitable for molding” as it comes first from the Greek term “plastikós” and later from the Latin “plasticus”. As a concept, neuroplasticity means the nervous system’s ability to reorganize its structure and functioning in response to some stimuli, either intrinsic or extrinsic [1]. Although simple in definition, the historical ground behind this concept relies entirely on the shoulders of magnificent and pioneering scientists. From a philosophical perspective, plasticity roots, as a reference to the nervous system, are still a matter of debate and have been tracked back to Willian James (1890), Santiago Ramón y Cajal (1894) and Demor (1896) in the late 1800s or Lugaro (1906) and Minea (1909) in the early 1900s [2,3,4]. However, the practical relevance of the neuroplasticity only began to be unveiled in the mid-20th century by Paul Bach-y-Rita who built the concept of sensory substitution by proposing that other brain areas may assume functions previously mediated by a lost neural tissue [5]. Similarly, based on Wilder Penfield works that showed a motor and sensory cortical representation of the body [6], Michael Merzenich noted that the adult brain is actually a dynamic structure, with the cortical body representations constantly shifting its boundaries [7]. Intriguing, Merzenich’s findings were initially criticized by David Hubel and Torsten Wiesel, two eminent neuroscientists at Johns Hopkins who had found a critical period for visual cortex plasticity to allow visual processing input to be replaced by the non-deprived eye at the expense of the deprived one [8, 9].
All the field advancement has not come without the controversy of the unknown and, not surprisingly, for so long the adult brain was considered hard-wired, incapable of any accommodation. However, after some breakthroughs, neuroplasticity is now well recognized as a fundamental and lifelong brain property. Although partially compromised, compared to early neurodevelopment, adult brain remodeling is still salient and fairly required for learning/experience- and internal milieu-based behavioral adjustments [10].
Bearing in mind a broader perspective of a flexible brain, we will go through studies related to early neurodevelopment, aging, stress, memory, and homeostatic plasticity to propose that brain flexibility goes beyond the obvious to include the concepts of “upward” and “downward” to neuroplasticity. Such sub-concepts aim to clarify how neuroplasticity is not only about building, but also about dismantling.
Neuronal disconnection of a prolix brain as a natural consequence of the neurodevelopment
Humans are born with tens of billions of brain cells and a single neuron may contact as far as 15,000 other cells, moreover a 3 years old child’s brain has established nearly 1000 trillion synapses [11]. The immature nervous system has a fundamentally redundant neuro-circuitry, as evidenced by the cortical dendritic spine density exceeded in childhood by one and a half to threefold that of adult’s brain [12, 13]. Higher synaptic density of the cerebral cortex was also found in early postnatal development of monkeys [14, 15], rats [16] and kittens [17], compared to adult animals. Cajal in the late 19th had already noticed that the spine density of pyramidal neurons throughout early postnatal development is greater than in adulthood [18].
Overabundant synapses were proposed as an endeavor by the brain to generate a diversity of connections beyond genetic anchorage, as activity-induced stabilization selects from the overproduced synapses those to remain [19, 20]. Accordingly, multiple retinal inputs converge onto immature lateral geniculate nucleus in both mice and ferrets, most of which are eliminated to just a few be maintained into adulthood [21, 22]. It was then proposed that the fuzzy retinogeniculate connections from early development would support the polishing of geniculocortical connections in late development to sharpen thalamocortical topography and likely also fine tune the orientation of cortical receptive fields [22]. Neuromuscular junction (NMJ) has also been a highly successful model to study developmental synaptic pruning. Early in development, one muscle fiber stablishes weak connections with about ten overly intermingled motor nerve axons, most of which (except for one) are further eliminated through the next postnatal couple of weeks [23]. Interestingly, increasing divergence in synaptic strength was observed before one of the two axonal inputs was removed from the shared mice muscle fiber, as the survivor earned vigor by increasing its quantal content and the defeated one became gradually weaker up to full withdrawal within 1 to 2 days [24]. Indeed, axons broaden their territory in response to previously evacuated sites, since the “soon-to-be-eliminated axon” still takes over the NMJ after laser removal of the strongest input [25]. Similarly, such refinement is widely established throughout early neurodevelopment on several occasions as a matter of axonal disconnection, as it follows: thalamic connection with the layer IV cells of the visual cortex is disrupted [26]; preganglionic inputs are disconnected from submandibular ganglion cells [27]; and climbing fibers disassemble from cerebellar Purkinje cells [28, 29]. The hippocampus is also target of developmental remodeling as the projections of pyramidal cells from the hippocampus to the medial septum are transient in rats and abruptly withdraw after birth [30]. Furthermore, the mossy fibers (infrapyramidal bundle) carrying intra hippocampal axons from the dentate gyrus (DG) to CA3 dramatically retract between the third and fourth week after mice are born [31].
Interestingly, early neurodevelopmental refinement has also been noticed from non-mammalian species, such as in the auditory systems of chicks [32] and in the visual system of tadpoles [33]. Drosophila metamorphosis prompts also to the loss of the dendritic arbor and axonal branches after puparium formation [34, 35]. Which means that the brain’s basic genetic program responsible for the ability to trigger early development-based abundance of synapses is, at least partially, presumable as evolutionarily conserved in different classes of animals, including invertebrates such as insects. However, different cellular mechanism may still be involved in synaptic pruning under different circumstances [36]. Besides, according to most evidence, developmental synaptic pruning or input disconnection has been usually acknowledged to be complete around early phases, or even when young people reach puberty or late adolescence. Consistently, callosal axons in newborn monkeys outnumber those in adults, and about 70% of these axons disappear months after birth [37]. Still, while young mice had 73% of the spines in layer-5 pyramidal neurons (primary visual cortex) stable over one-month interval, with changes primarily related to spine removal, adult mice had 96% of the spines stable for longer than 13 months [38].
Therefore, throughout early development, nervous system maturation has the highly dynamic process of neuronal remodeling as its hallmark and synaptic pruning is ubiquitously part of this complex rearrangement [39]. So, an embryonic and transient template, generous in promiscuous synapses, gradually gives place to a keen adult pattern of activity-driven neuronal connectivity as the repertoire of possible circuit configurations is pruning-based refined [36]. More than a passive process, developmental pruning follows Hebb’s rule whereas asynchrony, but no synchrony, between action potential achieves competing terminals to trigger input concurrence and further synaptic elimination [40].
Altogether, activity-guided neuronal disconnection is part of a successful and universal developmental program, occurring naturally throughout the nervous system, as well as in different species, and timely-frame coordinated according to each network, in order to forge and fine-tune connections to then enable minute idiosyncratic adaptations that fit into individual experiences and distinct surrounding factors. Even at lower levels, be aware that the adult brain is still undergoing remodeling whereas spine lifespans vary widely. For instance, notwithstanding dendritic branches in the barrel cortex of adult mice are quite stable over weeks and close to 50% of dendritic spines may persist for at least a month, surprisingly the remaining ones are only present for a few days [41]. For the human prefrontal cortex (PFC), developmental remodeling of the brain based on the pruning of superfluous spines extends even into the third decade of life [42]. Outstanding, these latest findings from human brain enlarge neurodevelopment beyond adolescence, and such elongated phase of reorganization implies the prefrontal cortex longer vulnerable to the environment in its most labile arrangement, thus a sensitive substrate for late-onset neuropsychiatry disorders [42]. For a brief overview of the topic, see Fig. 1.
Although the unmatured brain overflows with synaptic connections, such a developmental program goes beyond any triviality. Excessive synapses are extremely important for the brain to be able to readily fine-tune idiosyncratic connections to the demands of a rather “naïve” brain. In this way, seemingly superfluous connections make the brain prone to adapt, evolve and be molded around the behavioral repertoires that best predict continued future success. Within a concept, the time frame of promiscuous synapses could be understood as a window of opportunity in which the set of actions involving synaptic disconnections would be the tools that really makes this a good and profitable occasion. So, since the very newborn brain beginning, connections are ready to be plastically refined under the guidance of neuronal activities that mirror the nearby niche of an ever-changing environment. Furthermore, from an evolutionary perspective, pruning-based neuronal refinement of a redundant brain is likely a highly conserved mechanism as it can be found from insects to humans.
Neuronal disconnection as a natural consequence of brain aging
Developmental synaptic pruning related to neuronal refinement shifts over a continuum of time to a non-pathological and cumulative process of synaptic deterioration that irreversibly affects all maturing organisms, causing aging to intrinsically make the individual unable to properly adapt to the environment. Although aging is a genuine event, it is a major risk factor for the emergence of neurodegenerative and psychiatric diseases [43, 44]. Nevertheless, the occurrence of age-related cognitive decline is highly variable and is generally within the range for which aging may not yet be considered part of any neuropathology [45]. For a brief overview of the topic, see Fig. 2.
After a period of developmental neural refinement based on synaptic pruning, all organisms undergo non-pathological, cumulative, and irreversible synaptic deterioration as they age. Morphological changes in aged brains may be subtle as they are region-specific and often restricted to neuronal types or dendritic branches, usually resulting in synaptic strength deficits and some sort of brain shrinkage. Although synaptic corrosion in the elderly is usually associated with some level of cognitive decline, healthy aging is expected to be unrelated to any pathological problem. However, even for healthy aging, a small cognitive decline associated with synaptic pruning can be a burden in practice as it makes the individual less able to readily adapt to the surrounding environment.
Aging-related microstructural synaptic disconnection
Back to 1955, Brody claimed that age-linked shrinkage of the human brain was due, in part, to a decay in the number of cortical neurons [46]. Subsequent studies corroborated it by showing a decline in cortical neuron density, as well as cell loss in cortical and subcortical areas of elderly humans and non-human primates [47,48,49]. However, with the advancement of stereological methods new studies have identified these earlier data as confounding and likely biased by shrinkage artefacts and the mistaken inclusion of diseased samples [50, 51]. Yet, presynaptic terminal count was negatively correlated with age in individuals older than 60 years, averaging thus a 20% decrease in density of presynaptic terminals within the frontal cortex [52]. A 10% reduction per decade was also found in the total length of myelinated fibers, the main components of the white matter, adding up to 45% when the comparison was made directly between individuals aged around 20 and 80 years [53]. Similar to humans, in addition to decreased dendritic complexity, age-related changes in the non-human primate neocortex include a significant reduction in the number and density of spines [54, 55]. Further, ultrastructural inspection of white matter fiber tracts in aged rhesus monkey’s brain suggests an overall defective orchestrated driving of neuronal signals with the breakdown of the myelin sheath structure [56,57,58]. Indeed, such aging disconnection has been supported by independent rodent studies as, rather than loss of neurons, numerous cortical and subcortical areas show subtle, neuronal type-specific morphological changes involving the number of synaptic contacts and/or dendritic branches [59,60,61,62,63,64,65]. Some changes though may depend on whether the aged animal behaves cognitively normal or not [66]. Finally, even though the effects of aging are mostly drawn from the vertebrate’s brain, typically mammals, notably aged invertebrates such as insects, including fruit flies, honeybees, crickets and cockroaches also manifest a range of disconnection-like synaptic alterations [51].
Aging-related changes in functional synaptic plasticity
While long-term potentiation (LTP) is a persistent increase in synaptic strength that favors signal transmission between neurons, its counterpart long-term depression (LTD) is a long-lasting reduction in neurotransmission efficacy [67]. Both are activity-dependent phenomena, which happen according to some pattern of electrical stimulation. In general, aging is associated with certain levels of hippocampal LTP impairment [68,69,70,71,72,73]. Interestingly, aging-related LTP deficit may depend on whether the animals show cognitive shortage [74]. As non-invasive brain stimulation methods have been increasingly useful in extending the science of LTP to humans as well [75], paired associative stimulation has shown a reduced LTP-like plasticity in the motor cortex of elderly humans [76, 77]. Alternatively to transcranial magnetic stimulation, photic tetanus induced a LTP-like visual plasticity that was not properly augmented in older humans [78]. On the other hand, older rats seem to be more sensitive to hippocampal LTD induction or LTP reversal [72, 79,80,81]. Strikingly, serial electron microscopy of the CA1 synapses highlighted the existence in aged mice of multi-innervated dendritic spines, which would otherwise rarely be seen [82]; while 3D reconstructions of somatic, dendritic and axonal mitochondria in DG and CA1 found niche-specific morphological alterations by comparing aged to young adult mice [83]. Both respective findings could somehow, at least partially, explain the previously described age-related changes in synaptic strength. For example, multi-innervated dendritic spines may be redundant and their assembly appears to compete for downstream signaling linked to LTP induction [82]. Besides, mitochondria play a pivotal role in Ca2+ buffering and therefore tune neuronal communication and plasticity, in addition to defining axonal and dendritic growth according to their location [83].
Disturbances of cell proliferation in the aged brain
Although the brain’s cell proliferation program is extensively abolished throughout neurodevelopment, still some neuronal niches retain their proliferative precursors and thus are continually capable of producing adult-born neurons. That is particularly true for the subventricular zone of the lateral ventricles (SVZ) and the subgranular zone of the hippocampal DG (SGZ). While SVZ neuroblasts advance along the rostral migratory stream to mainly reach the olfactory bulb and differentiate into interneurons, SGZ neuroblasts shortly migrate to adjacent granular cell layer to differentiate into excitatory neurons [84]. To make this topic brief and of easier reading, below we will focus on the SGZ as it is of greater interest to humans and better studied than the SVZ.
Subpopulations of neural stem cells (NSC) in the SGZ switch from a mitotically active to a quiescent state in aged mice [85], which likely accounts for the progressive age-related impairment of proliferation, and consequently also for the disruption of either the survival, migration or differentiation of DG newborn cells [86,87,88,89,90,91]. However, both the quiescent and amplifying NSCs were actually found to continuously decrease over aging, once after exiting the quiescent state the NSC sustains a journey of asymmetric divisions to establish neuronal progenies before definitively becoming a mature astrocyte [92]. Such an interesting model to simultaneously explain aging-related phenomena involving the emergence of new astrocytes, the wane of neurogenesis and the decay of hippocampal NSCs. A prominent and progressive age-related decline in DG neurogenesis has also been found from non-human primates [93,94,95]. A comparison among different rodent strains and three other non-human primate species suggests that the decline in NSC proliferation, to a lesser extent also the decline in neurogenesis, actually follows absolute rather than relative age [96]. Hence, a sharp decrease in postnatal neurogenesis appears in rodents at intermediate to older phases whereas in non-human primates this happens at much earlier stages. Since a groundbreaking study revealed that the human hippocampus keeps generating neurons throughout life [97], adult neurogenesis has become a hot topic in human research. Likewise non-human primates, studies have observed in human DG that the number of neuroblasts decreases drastically over the early postnatal years, but that those remaining into adulthood still moderately decay with aging [98,99,100]. Making of neurogenesis actually a burning topic, some studies have contradictorily claimed that if DG neurogenesis happens during adulthood, it is nevertheless extremely rare [101, 102]. Although negative results from immunohistological post-mortem samples were argued to not have been optimized for detecting adult DG neurogenesis [103], even small neurogenesis levels are hypothesized to provide a reservoir of cells that would operate like a bottleneck within the reverberated hippocampal circuitry [104] as immature attributes of adult-born DG neurons appear to be long-lasting in human [105] and nonhuman primates [106] while holding a critical period of improved synaptic plasticity [107,108,109].
Network-related changes in the human aged brain
By detecting and correlating activity oscillations in segregated areas, functional magnetic resonance imaging (fMRI) studies have consistently found older humans with lower connectivity between regions of the default mode network (DMN), which includes the lateral parietal cortex, precuneus, posterior cingulate (Cg) cortex, hippocampus and medial PFC - mPFC [110, 111]. The DMN maintains strong connectivity and functional organization during ongoing resting state, but decreases its functionality (intrinsic activity) under attention-demanding tasks [112]. DMN activity has been associated to several cognitive skills, in addition to assist emotional processing, recall of previous experiences and self-referential mental activities [112, 113], and reduced DMN functional connectivity in healthy aging predicts poorer performance on memory tasks and executive functions [114,115,116]. Resembling social networks, brain is a complex large-scale network, topologically proficient and anatomically wired to balance energetic costs, that covers subnetworks or systems/modules of highly intricate nodes - neurons or brain areas [117]. Regions of interest (ROI)- and graph theoretical-based fMRI connectome analyses broadly agree that elders at rest exhibit lower within- and higher between-modules functional connectivity than younger adults [118, 119], which suggests some level of shortage regarding the independence of brain systems. Accordingly, aging comprises less subnetworks segregation, and the lower the system specialization the worse the episodic memory scores [120]. A longitudinal study used a linear mixed modeling-based fMRI analysis to assess the complex brain networks of healthy older adults along 4 years and found that the gradual erosion of subnetworks segregation at rest correlated with aging-associated decline in cognitive performance [121]. Moreover, older adults also appear to have less modularity and less local efficiency compared to youngers [122, 123]. Modularity (number of functional modules) and local efficiency are fairly related to each other as denser local connections between the topologically closest neighbors of a node are prone to make the brain network more modular, which would favor local efficiency by enabling better adaptability of specialized information processing and its segregated transfer within reasonably autonomous dynamic brain subnetworks [117].
Age-related differences in the structural white matter connectome bring patterns of results similar to those of functional connectivity studies. For example, based on graphical theoretical indices derived from diffusion MRI tractography, older individuals were noted to have poorer anatomical organization related to global cortical connectivity (i.e. bigger network cost) and local efficiency [124]. Using a similar method, global/local network efficiency and strength of intra/inter-modular connections were observed to peak around the third decade of life to then progressively decline with aging, whereas hub (nodes with high degree of network connectivity) integration and long-range connections linearly diminished throughout aging [125]. Indeed, long-range functional connectivity density was proposed to be more susceptible to the intrinsic effects of healthy aging [126]. Tract-based spatial statistics analysis of diffusion tensor imaging data also disclosed a profile of larger extracellular volumes and lower membrane densities as a consequence of a widely disrupted white matter in a diverse set of brain structures in older compared with younger adults [127, 128]. Therefore, whether by structural or functional connectome, integrity of the brain’s network seems to undergo an overall and solid decline from midlife onwards.
Synaptic disruption as a consequence of stress
Unlike to intrinsic factors driving synaptic pruning throughout neurodevelopment and aging, the ongoing art of living also includes a myriad of extrinsic factors, such as life stressors, still capable of disconnecting the brain. Stress could be conceptualized as a perception of threat, real or imagined, actual or anticipated, emotional or physical, where an individual undergoes some level of emotional discomfort and physiological changes that might lead to maladaptive behavioral adjustments. Stress responses are rather complex and dynamic, comprising a symphony of molecular and neuronal rearrangements that are coordinated at multiple levels to ideally orchestrate an optimal response to threatening challenges [129]. Thus, the stress response has originally evolved to bring the organism back to body homeostasis, protecting individuals from acute stress and maintaining their fundamental sense of well-being [130]. Additionally, in its optimal construct, the stress response may even be crucial for an individual’s optimal adaptation to the environment by triggering better experience-based homeostatic power [130]. However, prolonged exposure to stress often unbalances the stress response system towards a maladaptive homeostasis, from which several neuropsychiatric illnesses could be precipitated, including depression and post-traumatic stress disorder – PTSD [131]. Below, some stress-related brain changes. For a brief overview of the topic, see Fig. 3.
As an extrinsic factor capable of altering the brain, the concept of stress is based on nothing more than our bodily ability to perceive threats, real or imagined, actual or anticipated, emotional or physical. Such a realization is then accompanied by a sense of emotional inconvenience and extensive physiological changes that should, in principle, help us orchestrate the best adaptive behavior for survival, but which may actually lead to maladaptive behavioral adjustments. Indeed, a myriad of conditions can be perceived as stressful and the extent of brain effects may vary depending on the interplay between individual resilience and how long that stress lasts. Such effects include microstructural and subsequent macrofunctional changes, both of which are usually, but not only, coupled with the triggering of synaptic disconnections. Interestingly, stress-induced brain morphofunctional changes are generally recoverable. The effects of stress can overlap with the effects of neurodevelopment and aging on the brain, as we are all susceptible to stressors throughout our lives.
Macrostructural changes in a stressed brain
Lifelong stress exposure may induce cumulative brain structural changes, including frontoparietal network disturbances and decreased PFC/insula gray/white matter [132,133,134]. Shrinkage of several cortical structures has also been reported in children who have suffered physical/sexual assault [135, 136]. Childhood stress also promotes lasting brain changes beyond youth as retrospective and prospective studies detail how early life adversities can cause the thinning of various cortical structures in adulthood [137,138,139,140,141]. Strikingly, personal experiences of abuse may head brain changes specifically to regions involved with that particular adverse sensory input. Accordingly, cortical structures relevant to self-awareness and self-assessment were thinner in the case of adult women emotionally abused in childhood, while early sexual abuse was mainly associated with thinning of the genital representation field lying over the primary somatosensory cortex [142]. In addition, adults exposed to parental verbal abuse during childhood showed loss of integrity in neuronal pathways involved with language development [143]. Also, the gray matter of the visual cortex is shrunken in young adults who have been sexually abused or witnessed domestic violence during childhood [144, 145]. The hippocampus of adult individuals was also found to be smaller the greater their lifetime exposure to stressful situations, including financial problems [146,147,148]. Additionally, the higher the level of perceived stress or the earlier the adverse experience, the lower the hippocampal volumes in adolescents [149, 150]. Also, a thorough analysis showed that adults who experienced childhood trauma had lower volumes of all hippocampal subfields [151, 152].
Therefore, a myriad of different stressors is capable of inducing neuronal disconnection, especially those carried early on life. Importantly, each type of stressor may induce specific synaptic disruption, but keep in mind that brain rearrangement goes further, as some neurocircuits are actually reinforced rather than weakened. It’s also worth noting that although we have focused primarily on data about cortical and subcortical areas, such as hippocampus, the stress-triggered changes are in fact pervasive throughout the brain [153].
Microstructural changes in hippocampal neurons triggered by adulthood stress
Strikingly, animal studies have found that volumetric brain distortions are positively coupled to neuronal remodeling, thus convincingly demonstrating that regional shifts in spine density and dendritic arborization likely underlie gray matter changes [154, 155]. So, in accordance to previous human data, hippocampus seems to be particularly damaged after stress exposure. For example, rats exposed daily to a restraint [156, 157] or immobilization [158] stress had a decrease in the total length and branch points of the CA3 hippocampal apical dendrites. The same changes were observed in tree shrews daily subordinated to a dominant male [159]. Hippocampus is a complex structure, with a reverberant circuitry where apical dendrites of CA3 pyramidal neurons accumulate spines or thorny excrescences along their proximal segments to synapse mossy fiber inputs from DG granule neurons [160]. Hence, those previous findings probably reflect ultrastructural changes in DG-CA3 synapses, once rats chronically restraint-stressed exhibited rearrangements in vesicle clusters and mitochondrial occupancy of mossy fiber terminals [161], along with a retraction in postsynaptic thorny excrescences and reduction of their endosome-like structures [162]. Accordingly, rats chronically restraint-stressed showed extensive loss of mossy fiber contacts on CA3 thorny excrescences [163]. In fact, a thorough reorganization has been observed across all hippocampal subfields in rats under a mild but chronic and unpredictable stress regime, changes which include atrophy, decreased spine density and dendritic length [164,165,166]. Longitudinal MRI of rats before and after a chronic restraint stress schedule corroborated a 3% reduction in hippocampal volume [167]. Even a short resident-intruder stress paradigm promoted a prolonged decrease in dendritic length and spine density of CA1 pyramidal neurons in socially defeated rats [168]. Hippocampal cell death has also been observed short-transiently after one-day stress or long-lasting after a chronic unpredictable stress protocol [89]. Another study found increased levels of apoptosis in DG and entorhinal cortex of subordinate tree shrews who underwent to resident-intruder model [169]. Importantly, although stress can affect hippocampal neuroplasticity similarly in its most dorsal and ventral part [170], some effects may appear stronger in the ventral hippocampus [171], what would be in line with its predominant functional role in modulating neuroendocrine and emotional/motivational responses to stress [172].
Hippocampal cell proliferation disturbances induced by adulthood stress
Although the functional role of SGZ adult-born neurons is still a matter of debate, several studies suggest that these neurons are important for neuronal pattern separation and contextual discrimination [173]. Focusing on hippocampus, hypothetically neurogenesis bias neuronal coding by avoiding proactive interferences and generalizations, making thus the brain capable of accurately updating spatiotemporal information (cognitive flexibility) to further build a proper stress response and adapt to the demands of an ever-changing environment [174]. So, it is not a surprise that stress disrupts DG neurogenesis. Accordingly, 6 weeks of rat restraint stress decreased the proliferation of DG precursor cells, attenuated the survival of adult-newborn neurons, while concurrently decreased the number of granule cells and consequently granule cell layer volume [175]. Furthermore, seven days after inescapable shocks, or 24 h after a 45-min restraint stress, rats exhibited a reduction in DG cell proliferation [176]. Indeed, escapable shocks immediately reduced DG cell proliferation while inescapable shocks induced a longer-lasting detrimental effect [177]. A 6 weeks protocol of chronic mild stress robustly attenuated DG neurogenesis by about 40% [165]. Interestingly, a day of stress affected DG cell proliferation for no longer than 24 h, however a 3-week regimen of chronic unpredictable stress induced a lasting impairment with only an incomplete recovery around the third week of no further stress [89]. Chronic mild stress also suppressed cell proliferation and reduced the total number of granule cells in the rat ventral granule cell layer [178]. Marmoset monkey’s brain is also vulnerable to stress, as a single exposure to a resident conspecific for 1 h reduced DG cell proliferation [179].
Impairment of hippocampal synaptic strength by stress
Since there are 5000 times more synapses than neurons in the brain [180], many studies report stress largely affecting functional synaptic plasticity as well. Accordingly, the induction of LTP in adult rats, specifically from stimulation of medial perforant inputs to DG or commissural pathways to CA3, was suppressed after a 21-day schedule of restraint stress [181]. A single exposure of adult rats to an elevated platform for 30 min was able to impair hippocampal CA1 LTP in vivo and favor the appearance of a reliable LTD, which under control conditions would not occur [182]. Interestingly, prejudice in CA1 LTP was more pronounced in mature animals exposed to uncontrollable Morris water maze stress (no platform), when compared to the controllable stress group whose platform was present [183]. An impairment of CA1 and DG LTP was also observed in ex vivo hippocampal slices of grown-up animals previously stressed in a 21-day variable stress paradigm [184]. Early-postnatal stress (fragmented maternal care) also had an impact over aging rats, as middle-aged, but not young adults, presented an impairment of ex vivo CA3 LTP and a decrease in the hippocampal complexity of CA1 dendritic tree [185]. It is important to pinpoint, though, that stress will not always induce a loss in LTP, actually it may be even enhanced, or unchanged, depending on many factors such as the feature of stress and stress response phase [186]. Besides, for a long time LTP and LTD were conceived as electrophysiological phenomena predominantly implicated in brain areas whose role was associated with memory processing, such as the hippocampus. However, now it is well known that LTP and LTD are indeed widespread, from spinal cord to neocortex, and that memory storage is much more complex than previously thought and requires a large and integrative brain network [180].
General effects of adulthood stress on cortical disconnection
The mPFC includes different subregions such as the Cg, prelimbic (PrL) and infralimbic (IL) areas, and the preclinical evidence gathered so far poses that all of these structures are largely affected by stress as well. For instance, in addition to shortening spine density, chronic daily restraint stress reduced the total length and branch numbers of the apical dendrites in Cg and PrL pyramidal neurons [187,188,189,190], an outcome that likely mirror the atrophy of terminals branches [191]. On closer examination, the same stress scheme reduced the volume, length and surface area of these apical dendritic spines, overall decreasing the density of the larger dendritic spines while increasing those of the thinner ones [192]. Seven days of daily brief restraint-stress was still capable of inducing a substantial shrinkage of the Cg apical dendrites [193]. Moreover, ten days of daily immobilization stress was sufficient to shoot down IL branch points and the overall length of apical dendrites from randomly selected or entorhinal cortex-projecting neurons [194]. Even a single exposure to the stressful forced swim [195] or elevated platform [196] arouse a retraction in apical dendrites of IL pyramidal neurons. Additionally, a chronic model of unpredictable stress led to a large volumetric shrinkage of the mPFC, as it includes all subregions, and additionally disrupted the PrL LTP acquired from high-frequency stimulation of the ventral hippocampus CA1 [197]. Excess of glucocorticoid is supposed to underlie the stress effects, at least partially, on synaptic disconnections [198]. Therefore, it is noteworthy that the brain changes triggered by corticosteroid treatment resemble those described previously for stressed animals [199].
Importance of downward plasticity for memory
Memory is an essential cognitive function that, due to complexity, is often described apart into at least three main stages: acquisition or encoding, consolidation and retrieval. During encoding, sensory information is received by the brain, neuronal excitability is altered and synaptic plasticity starts being established. Memory is then progressively consolidated through changes in synaptic connections as it is retained as a memory trace. Finally, under predictive cues memory retrieval emerges [200, 201].
Since we are all surrounded by an ever-changing environment and constantly subject to routine bodily sensory updating, after retrieving a memory multiple destinies are still possible, such as reconsolidation, extinction and forgetting [202]. By definition, reconsolidation opens a novel temporal window of lability that allows the original memory to be modified [203, 204]. On the other hand, the extinction process occurs when a new memory trace competes with the original one [205, 206]. Forgetting, in turn, is characterized as a physiological phenomenon in which unnecessary information decays over time [207].
Here, we will focus on studies showing that downward synaptic plasticity is required for adulthood memory formation as well as for post-retrieval processes to be established. Although counterintuitive, analogous to neurodevelopment, activity-dependent synaptic disconnection is also important during the animal’s adulthood so that its brain circuits, whose neuronal coding underlies memory storage, are refined [208]. Also, suppressing neuronal ensembles associated with the foundation of a previous memory may favor subsequent learning/memory by reducing any potential interference [209]. For a brief overview of the topic, see Fig. 4.
Downward structural changes such as spine removal and/or dendritic shrinkage underlie the functional weakening of synaptic strength that happens during LTD and in some specific phases of memory. Each memory phase has its particular cellular mechanism, however some of them are shared. The bottom figure abridges how AMPAR/GluA2 internalization triggered by NMDAR/GluN2B-mediated LTD is a fundamental shared cellular mechanism by which synaptic weakening happens in all these memory processes.
Impairment of synaptic strength for learning and memory
Classical studies correlate learning and memory with increased synaptic efficacy [210]. However, there is currently no doubt that decrement in synaptic efficacy may also support memory formation [211,212,213,214]. Accordingly, spatial learning triggered endogenous LTD [215, 216]. While LTD and LTP share some molecular mechanisms that are required for memory formation, e.g. activation of N-methyl-D-aspartate receptors (NMDARs), unlike LTP, LTD requires endocytosis of α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptors (AMPARs), which are generally found in postsynaptic density [211, 217, 218]. Hence, blocking the GluN2B subunit of NMDARs, or AMPAR endocytosis, disrupted both LTD- and hippocampus-dependent learning and memory [219,220,221,222]. Additionally, it was demonstrated that LTD mediated by GluA2 (AMPAR subunit) endocytosis in the apical inputs to CA1 neurons is crucial for the establishment of place fields during spatial learning [223]. At the circuitry level, it was shown that new experiences activate the locus coeruleus to release noradrenaline into the hippocampus, mediating thus spatial learning through LTD along Schaffer collaterals-CA1 synapses [224]. Therefore, it is clear that synaptic weakening and its underlying cellular mechanisms are essential for shaping hippocampus-dependent spatial memories.
Weakening of synaptic strength during memory extinction and forgetting
Extinction and forgetting may involve functional and structural reorganization of synapses that were potentiated throughout the original learning [207, 225]. First, be aware that the neurobiological mechanism underpinning the reduction in synaptic strength, in addition to LTD, might also imply depotentiation (induction of synaptic depression following LTP) and LTP decay [226]. Recent studies, in fact, have shown that the blockade of NMDARs in the hippocampus prevents forgetting [227] and also the LTP decay [228, 229], indicating that forgetting actually takes place as an active rather than a passive process. In addition, NMDAR-mediated calcium entry activates calcineurin [229] and synaptotagamin-3 [230], both of which ultimately lead to the removal of GluA2-AMPARs from hippocampal synapses and cause a reduction in synaptic strength [230, 231], then favoring forgetting. Likewise, blocking synaptic removal of GluA2-AMPARs prevented depotentiation [231]. Comparably to forgetting, GluA2-AMPAR endocytosis has also been implied on memory extinction [232]. Accordingly, extinction reduced the surface expression of AMPAR subunits to pre-conditioning levels whereas depotentiated the conditioning-induced synaptic potentiation from the internal capsule to the lateral amygdala in a NMDAR-dependent manner [233]. The neuronal response to fear conditioned tone was indeed shown to return to baseline levels after extinction, thus indicating that the synaptic connection from auditory sensory input to the lateral amygdala is somehow reset by extinction [234]. Extinction memory was also shown to suppress the reactivation of contextual fear engram cells while activating another distinct ensemble in the hippocampus [235]. At last, synaptic depression of basolateral amygdala (BLA) inputs to PrL and IL mPFC improved extinction memory as it led to a more efficient reduction in fear expression [236].
Although we have focused on describing how the decreased synaptic strength/activity of a previously fear consolidated memory might be a crucial mechanism for extinction and forgetting, it is worth mentioning that all memory phases are regulated by a complex and integrated brain circuit. Part of which might involve different input/output or modulatory pathways that would alternatively be enhanced [237,238,239].
Structural modifications underlying synaptic weakening and memory
Plenty of studies have associated shrinkage and elimination of dendritic spines with synaptic LTD [240,241,242]. In detail, an optically-induced LTD provoked a homogeneous lowering of hippocampal synaptic function, followed over days by synaptic refinement, as synapses most likely to release neurotransmitters regained their function over time whereas low-probability ones were removed [208]. On the other hand, repetitive induction of chemical LTD on consecutive days resulted in substantial retraction of mushroom spines, whose subtype is considered the most stable [243]. Additionally, mutant animals lacking the GluN2B subunit of NMDAR, in addition to deficits in learning and LTD expression, had an expressive reduction on dendritic spine density in CA1 neurons [219]. When it comes to memory-related dendritic shrinkage, structural alterations were supposed to engender the synaptic refinement vital for stablishing memory once fear conditioning induced a reduction in spine density specifically in hippocampal neurons that were active during learning, but not in those inactive [244]. Besides, fear conditioning was found to induce spine elimination also in other brain regions such as motor [245] and frontal association cortex [246]. The neurobiology of extinction memory has also been grounded on a complex structural remodeling that involves several brain areas. Although it is widely accepted that extinction has its foundations built on a new memory, following studies have implied that the original conditioned trace may still be affected by extinction as, in addition to the increase in number, all the structural spine changes of the anterior Cg, IL, BLA and auditory cortex were found reset back to preconditioning levels by extinction [247,248,249]. Overall, spine or dendritic removal/shrinkage underlies functional synaptic weakening during some particular phases of memory, such a scenario that gets more complex when entangled with upward neuroplasticity.
The downward flow of homeostatic plasticity
The intracellular measurement of excitatory and inhibitory synaptic input ratios establishes the key principle of excitatory and inhibitory (E/I) synaptic balance, a concept which helps to better understand how the neuronal firing rate is sustained within a physiological range and under a neurocircuitry perspective to maintain accurate and reliable any transmission of information. E/I ratios are kept stable over time despite fluctuating external interferences, and the mechanisms underlying its maintenance are diverse and intricate [250]. Tight coupling with a counterweight is thought to regulate how quickly and accurately neurons can respond to a stimulus, once it would function as gain and selectivity mechanisms so that it might favor output refinement while expanding the operational range necessary to drive neuronal activity [251].
Within the perspective of a single neuron the firing rate is homeostatically regulated by neuronal intrinsic excitability via voltage-dependent inward and outward currents [252], as such adjustment dictates how easily neurons will reach spike threshold [253]. Furthermore, postsynaptic activity dynamically adjusts the induction of plasticity through a sliding threshold between potentiation and depression so that it also happens to modulate subsequent plasticity - i.e., metaplasticity [254, 255]. When past activity is low, the synaptic threshold slides down and favors LTP induction, conversely higher overall activity slides the threshold up and favors LTD induction [256]. Additionally, it is assumed that homeostatic adjustments in synaptic strength are accompanied by changes in the accumulation of postsynaptic receptors such as NMDAR and AMPAR, characterizing synaptic scaling [257, 258]. All these changes can occur locally, at synaptic sites, or globally throughout the entire dendritic arborization. An important hallmark of synaptic scaling is that the number of synaptic receptors is modified following a multiplicative scaling factor so that preserves the relative differences between synaptic weights and properly conserves the information stored [259,260,261].
At a spine structural level, competitive interactions between spines are expected to maintain total excitatory inputs constant, within a dynamic range, as increased spine density was followed by decreased spine volume and individual synaptic response [262] and theta burst stimulation-induced LTP increased spine size while decreased overall spine density [263]. Indeed input-specific synaptic potentiation induced with high-frequency glutamatergic uncaging led to structural growth of the local dendritic spine of hippocampal CA1 pyramidal neurons, but shrank and weakened nearby unstimulated spines [264]. The same pattern of such heterosynaptic shrinkage of inactive and adjacent spines was witnessed in the principal neurons of the basolateral amygdala [265]. Accordingly, efficacy of spontaneous transmission in both cortical and hippocampal individual synapses was regulated by the extent of nearby synaptic co-activity [266]. Interestingly, activity-driven shrinkage of neighboring spines was limited to 10 µm radius inter-spine distance and the closest ones had the greatest shrinkage [267]. It is thought that heterosynaptic spine elimination could contribute to the compartmentalization of dendritic segments by clustering synaptic inputs, thus favoring the dendritic branch as a fundamental functional unit able of performing local computations and memory storage [268, 269].
From the standpoint of an entangled brain, this complex set of homeostatic regulatory mechanisms is important for modulating synaptic weights and neuronal activity in order to maintain neuronal network homeostasis at both spatial and temporal scales [259]. Although data on homeostatic plasticity are scarce when not focused to its own neurophysiological mechanisms, to make a better sense of this topic as an extension of our other topics we next describe some studies in which homeostatic plasticity has someway been involved with some aspect related to neurodevelopment, aging, stress and memory. For a brief overview of the topic, see Fig. 5.
Homeostatic plasticity is a global mechanism that comprises several forms of controlling brain activity within adequate physiological levels. Hyperactivity mobilizes downward homeostatic mechanisms that act by braking down and constraining neuronal activity over a physiological range. On another hand, a counteracting upward homeostatic force is recruited following hypoactivity. Regular cognitive process such as memory are tempered by homeostatic plasticity. The same regulation happens when individuals have to deal with stressful challenges. Since neurodevelopment, homeostatic mechanisms are observed and expressed in particular ways depending on the brain area, cell-type and age investigated. Also, homeostatic dysregulation may explain some of the aging-related phenomena such as hyperexcitability.
Neurodevelopment - homeostatic plasticity entailed to low brain activity
The extent which and location where homeostatic control takes place may be impacted by circuit maturation throughout neural development as in the visual cortex it is triggered by visual experience. For example, at postnatal day 16 to 21, layers 4 and 6 are under synaptic scaling regulation [270], while on later ages scaling switches to layers 2/3 [271, 272]. Disturbances of activity in the developing network may also be restored to normal levels with inhibitory and/or excitatory adjustments. Consistently, the density of inhibitory synapses decreased when a glutamate receptor antagonist was applied to the organotypic culture of the developing hippocampus [273]. In addition, homeostatic plasticity may involve specific cell-types as developing hippocampal granule cells exhibited reduced inhibitory input from parvalbumin-positive basket interneurons when excitatory drives were absent [274]. Pyramidal neurons in layer 4 of the visual cortex dynamically adjusted excitatory and inhibitory inputs, respectively, up and down to compensate for the reduction in sensory cues [275].
Homeostatic plasticity involved with high brain activity of adult animals
Memory
Based on the central idea that learning induces an increase in neuronal excitability a study observed that the compensatory reduction in hippocampal neuronal excitability, induced with the delivery of a high-frequency spike train specifically to fear engram cells, facilitated memory extinction while decreased the density of dendritic spines and increased the number of inhibitory synapses [276]. Contrary to the idea that neurons with higher excitability are more likely to be recruited for learning while learning itself would increase neuronal excitability [277,278,279], learning-induced hyperexcitability actually happened to rapidly trigger a counteracting force sufficient to decrease neuronal excitability [280]. However, in this case such homeostatic plasticity was suggested to be critical for consolidating memories without interference. Another study found that synaptic refinement induced by homeostatic downscaling sculpted an accurate associative taste memory after taste aversion conditioning initially induced a generalized response as synapses in the gustatory cortex were potentiated [281].
Stress
Homeostatic control might act as a protective physiological mechanism that supports appropriate coping strategy during stressful circumstances by maintaining the dynamics of behavioral and physiological adjustments within an optimal range through a precise E/I neuronal balance [282]. On the other hand, failure of homeostatic controls during stress might favor depression and anxiety-like behaviors [283]. Interesting, a study has demonstrated that resilient mice expressed an up-regulated hyperpolarization-activated current (Ih) in the dopaminergic neurons of the ventral tegmental area (VTA) after chronic stress. This Ih current was higher than the upregulation usually observed in depressed mice and it drove an excitatory force that was countered by an increase in K+ channel currents, a cell-specific compensatory mechanism that reduced VTA DA excitability to control levels [284].
Aging
Accumulating evidence demonstrates that neuronal hyperexcitability and hyperexcitable networks are hallmarks of normal [285, 286] and pathological aging [287]. Also, hyperexcitability in different brain regions as the hippocampus [285, 286, 288] and cortex [289] is associated with cognitive deficits [290, 291]. Many factors may account for hyperexcitability, including dyshomeostasis in Ca2+ [287, 291], alterations of GABAergic [292, 293] or glutamatergic [286] circuitry with effects on the E/I balance, as well as modifications on intrinsic excitability [288, 294]. Hypothetically, these age-associated changes could underlie failures on homeostatic control of excitability and influence neuronal firing rate [295]. Numerous proteins involved in AMPAR stabilization and AMPAR-mediated signaling cascades are down- or upregulate in the whole aging brain [296, 297], a potential mechanism for synaptic scaling and homeostatic control failures.
Concluding remarks
Since the brain was found to be somehow plastic, scientists urge for boosting this power up. Misleadingly, this often means increasing neuronal connections, once disconnections have been indiscriminately linked to all sorts of brain disorders. So, it is not uncommon to find “neuroplasticity impairment” in the scientific literature when it comes to data somehow related to synaptic disconnections - for an interesting review about molecular mechanisms of dendritic spine elimination, please see [242]. Although brain plasticity may have different primary minimalist meanings for neurodevelopment (redundancy and neuronal refinement), aging (synaptic deterioration and higher risk of neuronal disease), stress (adaptation or higher risk of psychiatric illness), and memory (learning and adaptability), it takes place on an interwoven continuum where neuroplasticity is indeed beyond the boundaries of any biological morality. Just as Bob Dylan criticizes how prior generation deals with social evolution and ends up saying “the times they are a-changin”, we propose a rationale for a paradigm shift that will hopefully shed some light on how synaptic disconnections fit within the concept of neuroplasticity.
Once aware of the broader picture regarding the role of brain flexibility, we should primarily speak of neuroplasticity as a balance between what we have named “upward” and “downward” neuroplasticity. Just common binary words to describe each path in which neuroplasticity could move along a direction vector. So, whatever route neuroplasticity takes, including whatsoever organically leads to synaptic disconnection or weakening, it does as a consequence of the brain’s ability to change itself. One could still say that once a synapse is gone, the brain would end up with lower levels of neuronal matter for the next move of neuroplasticity to take place. Indeed, neurodegenerative disorders make the brain less flexible as brain mass losses become massive over the course of the disease. So, for these cases it is unavoidable to think of an impairment of neuroplasticity. The same could be said about brain injury or stroke brain damage. However, under physiological conditions such as neurodevelopment, healthy aging, stress coping, memory and learning, synaptic turnover is part of a contingency plan to optimize the flow of neural information on demand. So, considering how complex the brain and its entangled neurocircuitry are, it is not worth compartmentalizing it to refine neuroplasticity concepts based on microniches as any change might be directly or indirectly counteracted at the cellular or neurocircuitry level by a completely opposite direction of neuroplasticity.
While we attempt to reduce the bias related to any moral judgment that could be even loosely associated with disconnection of synapses, we are aware that this approach is just the beginning as a paradigm shift has much more to deal with than handling sub-concepts. Indeed, any bias depends on the guided experience of a personal life, and therefore the concept of “downward” or “upward” neuroplasticity might still have some negative or positive connotation. We then want to make it as clear as possible that the idea of using such sub-concepts comes from the neutral view of direction vectors, an approach that should at least help to minimize any personal moral bias towards neuroplasticity.
As the island of knowledge grows larger, so does its horizon. The same happens when we include the “upward” and “downward” concepts to neuroplasticity. So now more than ever is a great opportunity to update the meaning of neuroplasticity, as our current knowledge that plasticity is an intrinsic and bidirectional property of neurons somewhat blurs the difference between “neuroplasticity” and “neurophysiology”. Although it sounds more of an epistemological issue, we propose that neuroplasticity is the ability of the brain to change itself in such a way that any gain or loss in brain function should be bigger or lower than the sum of its lost or gained parts. So, brain reorganization itself would not suffice to determine neuroplasticity without an amplified gain or loss of brain function, provided that the initial state of the brain is maintained and there is no change in the specific function of one region that is sufficient to reach and modify other areas of the brain. Such an updated concept would fit better with the new findings that place any single part of the brain directly or indirectly connected to any other single part of the brain, making even a small change likely to influence the entire brain in some way.
Therefore, upward and downward plasticity should at first be understood as complementary to each other, including in cases of psychiatric disorders. Both neuroplasticities co-exist, certainly interact with each other and thus one has nothing more especial than the other. Hence, it is important to give downward neuroplasticity as much attention as upward neuroplasticity given that brain flexibility would not be complete without one or the other. For good or bad, downward modifications are part of the neuroplasticity program, rather than being a deficiency of it. For a brief overview of the conclusion, please see Fig. 6.
The main goal of this review is to work on the idea of how neuroplasticity, in all its complexity, should be conceptually understood as a balance between what we have called here as “upward and downward neuroplasticity”. Developing brains, and partially also the adult ones, are flexible, moldable, and as it is, any capability for decreasing the density levels or structural complexity of spines and/or dendrites should be considered part of the neuroplasticity program, rather than being a deficiency of it. Thus, comparable to how puzzles fit together, upward and downward neuroplasticity work to complement each other so that the brain would eventually be able to reshape its connections by neuronal tuning to optimize network’s efficiency under certain demands. Within a broader landscape as seen with assembled puzzles, although neuroplasticity happens at first glance from the microscale changes of spines and dendrites according to a neuronal perspective, its consequences expand toward a macroscale outlook where individual orchestrated changes integrate into the account of different neuronal populations and neurocircuits. So, any cause or consequence neuroplastic change from an entangled brain, whether up or down, may be directly or indirectly connected to at least some other part of the brain that could still show a completely opposite direction of neuroplasticity. The orange, red and gray puzzles represent different populations of neurons that in the sum of the events globally present upward plasticity, while the green, yellow and purple puzzles represent different populations of neurons that in the sum of the events present global downward plasticity. Meanwhile, the blue and white puzzles represent neurons with a balanced number of events that represent both upward and downward neuroplasticity. Their connections should maintain the brain largescale of neuroplasticity at zero sum when considering downward and upward neuroplasticity within the topography of neuronal matter.
References
Mateos-Aparicio P, Rodríguez-Moreno A. The impact of studying brain plasticity. Front Cell Neurosci. 2019;13;66.
DeFelipe J. Brain plasticity and mental processes: Cajal again. Nat Rev Neurosci. 2006;7:811–7.
Berlucchi G, Buchtel HA. Neuronal plasticity: historical roots and evolution of meaning. Exp Brain Res. 2009;192:307–19.
Jones EG. Plasticity and neuroplasticity. J Hist Neurosci. 2004;13:293.
Bach-y-Rita P. Brain plasticity as a basis of sensory substitution. J Neuro Rehab. 1987;1:67–71.
Penfield W, Boldrey E. Somatic motor and sensory representation in the cerebral cortex of man as studied by electrical stimulation. Brain. 1937;60:389–443.
Buonomano DV, Merzenich MM. Cortical plasticity: from synapses to maps. Annu Rev Neurosci. 1998;21:149–86.
Hubel DH, Wiesel TN. Receptive fields of cells in striate cortex of very young, visually inexperienced kittens. J Neurophysiol. 1963;26:994–1002.
Hubel DH, Wiesel TN. Binocular interaction in striate cortex of kittens reared with artificial squint. J Neurophysiol. 1965;28:1041–59.
May A. Experience-dependent structural plasticity in the adult human brain. Trends Cogn Sci. 2011;15:475–82.
Brotherson S. Understanding brain development in young children. 2009. http://hdl.handle.net/10365/4954.
Huttenlocher PR. Synaptic density in human frontal cortex - developmental changes and effects of aging. Brain Res. 1979;163:195–205.
Huttenlocher PR, Dabholkar AS. Regional differences in synaptogenesis in human cerebral cortex. J Comp Neurol. 1997;387:167–78.
Bourgeois JP, Goldman-Rakic PS, Rakic P. Synaptogenesis in the prefrontal cortex of rhesus monkeys. Cereb Cortex. 1994;4:78–96.
Rakic P, Bourgeois JP, Eckenhoff MF, Zecevic N, Goldman-Rakic PS. Concurrent overproduction of synapses in diverse regions of the primate cerebral cortex. Science. 1986;232:232–5.
Aghajanian GK, Bloom FE. The formation of synaptic junctions in developing rat brain: a quantitative electron microscopic study. Brain Res. 1967;6:716–27.
Cragg BG. The development of synapses in the visual system of the cat. J Comp Neurol. 1975;160:147–66.
Ramon y Cajal S. Textura del sistema nervioso del hombre y de los vertebrados. Madrid: Moya; 1899, 1904.
Changeux JP, Danchin A, Changeux JP, Danchin A. Selective stabilisation of developing synapses as a mechanism for the specification of neuronal networks. Nature. 1976;264:705–12.
Hua JY, Smith SJ. Neural activity and the dynamics of central nervous system development. Nat Neurosci. 2004;7:327–32.
Chen C, Regehr WG. Developmental remodeling of the retinogeniculate synapse. Neuron. 2000;28:955–66.
Tavazoie SF, Reid RC. Diverse receptive fields in the lateral geniculate nucleus during thalamocortical development. Nat Neurosci. 2000;3:608–16.
Tapia JC, Wylie JD, Kasthuri N, Hayworth KJ, Schalek R, Berger DR, et al. Pervasive synaptic branch removal in the mammalian neuromuscular system at birth. Neuron. 2012;74:816–29.
Colman H, Nabekura J, Lichtman JW. Alterations in synaptic strength preceding axon withdrawal. Science. 1997;275:356–61.
Turney SG, Lichtman JW. Reversing the outcome of synapse elimination at developing neuromuscular junctions in vivo: evidence for synaptic competition and its mechanism. PLoS Biol. 2012;10;e1001352.
Hubel DH, Wiesel TN, LeVay S. Plasticity of ocular dominance columns in monkey striate cortex. Philos Trans R Soc Lond B Biol Sci. 1977;278:377–409.
Lichtman JW. The reorganization of synaptic connexions in the rat submandibular ganglion during post-natal development. J Physiol. 1977;273:155–77.
Crepel F, Mariani J, Delhaye‐Bouchaud N. Evidence for a multiple innervation of Purkinje cells by climbing fibers in the immature rat cerebellum. J Neurobiol. 1976;7:567–78.
Lohof AM, Delhaye-Bouchaud N, Mariani J. Synapse elimination in the central nervous system: functional significance and cellular mechanisms. Rev Neurosci. 1996;7:85–101.
Linke R, Pabst T, Frotscher M. Development of the hippocamposeptal projection in the rat. J Comp Neurol. 1995;351:602–16.
Bagri A, Cheng HJ, Yaron A, Pleasure SJ, Tessier-Lavigne M. Stereotyped pruning of long hippocampal axon branches triggered by retraction inducers of the semaphorin family. Cell. 2003;113:285–99.
Jackson H, Parks TN. Functional synapse elimination in the developing avian cochlear nucleus with simultaneous reduction in cochlear nerve axon branching. J Neurosci. 1982;2:1736–43.
Gaze RM, Keating MJ, Chung SH. The evolution of the retinotectal map during development in Xenopus. Proc R Soc London Ser B. Biol Sci. 1974;185:301–30.
Lee T, Lee A, Luo L. Development of the Drosophila mushroom bodies: sequential generation of three distinct types of neurons from a neuroblast. Development. 1999;126:4065–76.
Watts RJ, Hoopfer ED, Luo L. Axon pruning during Drosophila metamorphosis: evidence for local degeneration and requirement of the ubiquitin-proteasome system. Neuron. 2003;38:871–85.
Kantor DB, Kolodkin AL. Curbing the excesses of youth: molecular insights into axonal pruning. Neuron. 2003;38:849–52.
LaMantia AS, Rakic P. Axon overproduction and elimination in the corpus callosum of the developing rhesus monkey. J Neurosci. 1990;10:2156–75.
Grutzendler J, Kasthuri N, Gan WB. Long-term dendritic spine stability in the adult cortex. Nature. 2002;420:812–6.
Purves D, Lichtman JW. Elimination of synapses in the developing nervous system. Science. 1980;210:153–7.
Favero M, Cangiano A, Busetto G. Hebb-based rules of neural plasticity: are they ubiquitously important for the refinement of synaptic connections in development? Neuroscientist. 2014;20:8–14.
Trachtenberg JT, Chen BE, Knott GW, Feng G, Sanes JR, Welker E, et al. Long-term in vivo imaging of experience-dependent synaptic plasticity in adult cortex. Nature. 2002;420:788–94.
Petanjek Z, Judaš M, Šimić G, Rašin MR, Uylings HBM, Rakic P, et al. Extraordinary neoteny of synaptic spines in the human prefrontal cortex. Proc Natl Acad Sci USA. 2011;108:13281–6.
Cole JH, Marioni RE, Harris SE, Deary IJ. Brain age and other bodily “ages”: implications for neuropsychiatry. Mol Psychiatry. 2019;24:266–81.
Hou Y, Dan X, Babbar M, Wei Y, Hasselbalch SG, Croteau DL, et al. Ageing as a risk factor for neurodegenerative disease. Nat Rev Neurol. 2019;15:565–81.
Juan SMA, Adlard PA. Ageing and cognition. Subcell Biochem. 2019;91:107–22.
Brody H. Organization of the cerebral cortex. III. A study of aging in the human cerebral cortex. J Comp Neurol. 1955;102:511–56.
Ball MJ. Neuronal loss, neurofibrillary tangles and granulovacuolar degeneration in the hippocampus with ageing and dementia. A quantitative study. Acta Neuropathol. 1977;37:111–8.
Brizzee KR, Ordy JM, Bartus RT. Localization of cellular changes within multimodal sensory regions in aged monkey brain: possible implications for age-related cognitive loss. Neurobiol Aging. 1980;1:45–52.
Coleman PD, Flood DG. Neuron numbers and dendritic extent in normal aging and Alzheimer’s disease. Neurobiol Aging. 1987;8:521–45.
Burke SN, Barnes CA. Neural plasticity in the ageing brain. Nat Rev Neurosci. 2006;7:30–40.
Koch SC, Nelson A, Hartenstein V. Structural aspects of the aging invertebrate brain. Cell Tissue Res. 2021;383:931–47.
Masliah E, Mallory M, Hansen L, DeTeresa R, Terry RD. Quantitative synaptic alterations in the human neocortex during normal aging. Neurology. 1993;43:192–7.
Marner L, Nyengaard JR, Tang Y, Pakkenberg B. Marked loss of myelinated nerve fibers in the human brain with age. J Comp Neurol. 2003;462:144–52.
Page TL, Einstein M, Duan H, He Y, Flores T, Rolshud D, et al. Morphological alterations in neurons forming corticocortical projections in the neocortex of aged Patas monkeys. Neurosci Lett. 2002;317:37–41.
Duan H, Wearne SL, Rocher AB, Macedo A, Morrison JH, Hof PR. Age-related dendritic and spine changes in corticocortically projecting neurons in macaque monkeys. Cereb Cortex. 2003;13:950–61.
Peters A, Moss MB, Sethares C. Effects of aging on myelinated nerve fibers in monkey primary visual cortex. J Comp Neurol. 2000;419:364–76.
Peters A, Sethares C. Aging and the myelinated fibers in prefrontal cortex and corpus callosum of the monkey. J Comp Neurol. 2002;442:277–91.
Bowley MP, Cabral H, Rosene DL, Peters A. Age changes in myelinated nerve fibers of the cingulate bundle and corpus callosum in the rhesus monkey. J Comp Neurol. 2010;518:3046–64.
Grill JD, Riddle DR. Age-related and laminar-specific dendritic changes in the medial frontal cortex of the rat. Brain Res. 2002;937:8–21.
Eavri R, Shepherd J, Welsh CA, Flanders GH, Bear MF, Nedivi E. Interneuron simplification and loss of structural plasticity as markers of aging-related functional decline. J Neurosci. 2018;38:8421–32.
Calì C, Wawrzyniak M, Becker C, Maco B, Cantoni M, Jorstad A, et al. The effects of aging on neuropil structure in mouse somatosensory cortex-A 3D electron microscopy analysis of layer 1. PLoS One. 2018;13;e0198131.
Bloss EB, Puri R, Yuk F, Punsoni M, Hara Y, Janssen WG, et al. Morphological and molecular changes in aging rat prelimbic prefrontal cortical synapses. Neurobiol Aging. 2013;34:200–10.
Bloss EB, Janssen WG, Ohm DT, Yuk FJ, Wadsworth S, Saardi KM, et al. Evidence for reduced experience-dependent dendritic spine plasticity in the aging prefrontal cortex. J Neurosci. 2011;31:7831–9.
Mostany R, Anstey JE, Crump KL, Maco B, Knott G, Portera-Cailliau C. Altered synaptic dynamics during normal brain aging. J Neurosci. 2013;33:4094–104.
Von Bohlen Und Halbach O, Zacher C, Gass P, Unsicker K. Age-related alterations in hippocampal spines and deficiencies in spatial memory in mice. J Neurosci Res. 2006;83:525–31.
Buss EW, Corbett NJ, Roberts JG, Ybarra N, Musial TF, Simkin D, et al. Cognitive aging is associated with redistribution of synaptic weights in the hippocampus. Proc Natl Acad Sci USA. 2021;118:e1921481118.
Malenka RC, Bear MF. LTP and LTD: an embarrassment of riches. Neuron 2004;44:5–21. Volp
Barnes CA, McNaughton BL. Spatial memory and hippocampal synaptic plasticity in middle-aged and senescent rats. In: Stein D, editor. Psychobiology of aging: problems and perspectives. New York: Elsevier; 1980. p. 253–72.
Barnes CA, Rao G, Houston FP. LTP induction threshold change in old rats at the perforant path-granule cell synapse. Neurobiol Aging. 2000;21:613–20.
Bach ME, Barad M, Son H, Zhuo M, Lu YF, Shih R, et al. Age-related defects in spatial memory are correlated with defects in the late phase of hippocampal long-term potentiation in vitro and are attenuated by drugs that enhance the cAMP signaling pathway. Proc Natl Acad Sci USA. 1999;96:5280–5.
Dieguez D, Barea-Rodriguez EJ. Aging impairs the late phase of long-term potentiation at the medial perforant path-CA3 synapse in awake rats. Synapse. 2004;52:53–61.
Arias-Cavieres A, Adasme T, Sánchez G, Muñoz P, Hidalgo C. Aging impairs hippocampal- dependent recognition memory and LTP and prevents the associated RyR up-regulation. Front Aging Neurosci. 2017;9:111.
Pereda D, Al-Osta I, Okorocha AE, Easton A, Hartell NA. Changes in presynaptic calcium signalling accompany age-related deficits in hippocampal LTP and cognitive impairment. Aging Cell. 2019;18:e13008.
Tombaugh GC, Rowe WB, Chow AR, Michael TH, Rose GM. Theta-frequency synaptic potentiation in CA1 in vitro distinguishes cognitively impaired from unimpaired aged Fischer 344 rats. J Neurosci. 2002;22:9932–40.
Cooke SF, Bliss TVP. Plasticity in the human central nervous system. Brain. 2006;129:1659–73.
Fathi D, Ueki Y, Mima T, Koganemaru S, Nagamine T, Tawfik A, et al. Effects of aging on the human motor cortical plasticity studied by paired associative stimulation. Clin Neurophysiol. 2010;121:90–3.
Müller-Dahlhaus JFM, Orekhov Y, Liu Y, Ziemann U. Interindividual variability and age-dependency of motor cortical plasticity induced by paired associative stimulation. Exp Brain Res. 2008;187:467–75.
Spriggs MJ, Cadwallader CJ, Hamm JP, Tippett LJ, Kirk IJ. Age-related alterations in human neocortical plasticity. Brain Res Bull. 2017;130:53–9.
Norris CM, Korol DL, Foster TC. Increased susceptibility to induction of long-term depression and long-term potentiation reversal during aging. J Neurosci. 1996;16:5382–92.
Vouimba RM, Foy MR, Foy JG, Thompson RF. 17beta-estradiol suppresses expression of long-term depression in aged rats. Brain Res Bull. 2000;53:783–7.
Foster TC, Kumar A. Susceptibility to induction of long-term depression is associated with impaired memory in aged Fischer 344 rats. Neurobiol Learn Mem. 2007;87:522–35.
Aziz W, Kraev I, Mizuno K, Kirby A, Fang T, Rupawala H, et al. Multi-input synapses, but not LTP-strengthened synapses, correlate with hippocampal memory storage in aged mice. Curr Biol. 2019;29:3600–10.e4
Faitg J, Lacefield C, Davey T, White K, Laws R, Kosmidis S, et al. 3D neuronal mitochondrial morphology in axons, dendrites, and somata of the aging mouse hippocampus. Cell Rep. 2021;36:109509.
Bergmann O, Spalding KL, Frisén J. Adult neurogenesis in humans. Cold Spring Harb Perspect Biol. 2015;7:a018994.
Lugert S, Basak O, Knuckles P, Haussler U, Fabel K, Götz M, et al. Quiescent and active hippocampal neural stem cells with distinct morphologies respond selectively to physiological and pathological stimuli and aging. Cell Stem Cell. 2010;6:445–56.
Seki T, Arai Y. Age-related production of new granule cells in the adult dentate gyrus. Neuroreport. 1995;6:2479–82.
Kuhn HG, Dickinson-Anson H, Gage FH. Neurogenesis in the dentate gyrus of the adult rat: age-related decrease of neuronal progenitor proliferation. J Neurosci. 1996;16:2027–33.
Kempermann G, Kuhn HG, Gage FH. Experience-induced neurogenesis in the senescent dentate gyrus. J Neurosci. 1998;18:3206–12.
Heine VM, Maslam S, Joëls M, Lucassen PJ. Prominent decline of newborn cell proliferation, differentiation, and apoptosis in the aging dentate gyrus, in absence of an age-related hypothalamus-pituitary-adrenal axis activation. Neurobiol Aging. 2004;25:361–75.
Rao MS, Hattiangady B, Shetty AK. The window and mechanisms of major age-related decline in the production of new neurons within the dentate gyrus of the hippocampus. Aging Cell. 2006;5:545–58.
Ben Abdallah NMB, Slomianka L, Vyssotski AL, Lipp HP. Early age-related changes in adult hippocampal neurogenesis in C57 mice. Neurobiol Aging. 2010;31:151–61.
Encinas JM, Michurina TV, Peunova N, Park JH, Tordo J, Peterson DA, et al. Division-coupled astrocytic differentiation and age-related depletion of neural stem cells in the adult hippocampus. Cell Stem Cell. 2011;8:566–79.
Giachino C, Basak O, Lugert S, Knuckles P, Obernier K, Fiorelli R, et al. Molecular diversity subdivides the adult forebrain neural stem cell population. Stem Cells. 2014;32:70–84.
Gould E, Reeves AJ, Fallah M, Tanapat P, Gross CG, Fuchs E. Hippocampal neurogenesis in adult Old World primates. Proc Natl Acad Sci USA. 1999;96:5263–7.
Ngwenya LB, Heyworth NC, Shwe Y, Moore TL, Rosene DL. Age-related changes in dentate gyrus cell numbers, neurogenesis, and associations with cognitive impairments in the rhesus monkey. Front Syst Neurosci. 2015;9:102.
Amrein I, Isler K, Lipp HP. Comparing adult hippocampal neurogenesis in mammalian species and orders: influence of chronological age and life history stage. Eur J Neurosci. 2011;34:978–87.
Eriksson PS, Perfilieva E, Björk-Eriksson T, Alborn AM, Nordborg C, Peterson DA, et al. Neurogenesis in the adult human hippocampus. Nat Med. 1998;4:1313–7.
Knoth R, Singec I, Ditter M, Pantazis G, Capetian P, Meyer RP, et al. Murine features of neurogenesis in the human hippocampus across the lifespan from 0 to 100 years. PLoS One. 2010;5:e8809.
Spalding KL, Bergmann O, Alkass K, Bernard S, Salehpour M, Huttner HB, et al. Dynamics of hippocampal neurogenesis in adult humans. Cell. 2013;153:1219.
Mathews KJ, Allen KM, Boerrigter D, Ball H, Shannon Weickert C, Double KL. Evidence for reduced neurogenesis in the aging human hippocampus despite stable stem cell markers. Aging Cell. 2017;16:1195–9.
Franjic D, Skarica M, Ma S, Arellano JI, Tebbenkamp ATN, Choi J, et al. Transcriptomic taxonomy and neurogenic trajectories of adult human, macaque, and pig hippocampal and entorhinal cells. Neuron. 2022;110:452–69.e14.
Sorrells SF, Paredes MF, Cebrian-Silla A, Sandoval K, Qi D, Kelley KW, et al. Human hippocampal neurogenesis drops sharply in children to undetectable levels in adults. Nature. 2018;555:377–81.
Kempermann G, Gage FH, Aigner L, Song H, Curtis MA, Thuret S, et al. Human adult neurogenesis: evidence and remaining questions. Cell Stem Cell. 2018;23:25–30.
Leutgeb JK, Leutgeb S, Moser MB, Moser EI. Pattern separation in the dentate gyrus and CA3 of the hippocampus. Science. 2007;315:961–6.
Zhou Y, Su Y, Li S, Kennedy BC, Zhang DY, Bond AM, et al. Molecular landscapes of human hippocampal immature neurons across lifespan. Nature. 2022;607:527–33.
Kohler SJ, Williams NI, Stanton GB, Cameron JL, Greenough WT. Maturation time of new granule cells in the dentate gyrus of adult macaque monkeys exceeds six months. Proc Natl Acad Sci USA. 2011;108:10326–31.
Ge S, Yang CH, Hsu KS, Ming GL, Song H. A critical period for enhanced synaptic plasticity in newly generated neurons of the adult brain. Neuron. 2007;54:559–66.
Schmidt-Hieber C, Jones P, Bischofberger J. Enhanced synaptic plasticity in newly generated granule cells of the adult hippocampus. Nature. 2004;429:184–7.
Marín-Burgin A, Mongiat LA, Pardi MB, Schinder AF. Unique processing during a period of high excitation/inhibition balance in adult-born neurons. Science. 2012;335:1238–42.
Ferreira LK, Busatto GF. Resting-state functional connectivity in normal brain aging. Neurosci Biobehav Rev. 2013;37:384–400.
Dennis EL, Thompson PM. Functional brain connectivity using fMRI in aging and Alzheimer’s disease. Neuropsychol Rev. 2014;24:49–62.
Raichle ME. The brain’s default mode network. Annu Rev Neurosci. 2015;38:433–47.
Buckner RL, Andrews-Hanna JR, Schacter DL. The brain’s default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci. 2008;1124:1–38.
Andrews-Hanna JR, Snyder AZ, Vincent JL, Lustig C, Head D, Raichle MEE, et al. Disruption of large-scale brain systems in advanced aging. Neuron. 2007;56:924–35.
Damoiseaux JS, Beckmann CF, Arigita EJS, Barkhof F, Scheltens P, Stam CJ, et al. Reduced resting-state brain activity in the “default network” in normal aging. Cereb Cortex. 2008;18:1856–64.
Wang L, LaViolette P, O’Keefe K, Putcha D, Bakkour A, Van Dijk KRA, et al. Intrinsic connectivity between the hippocampus and posteromedial cortex predicts memory performance in cognitively intact older individuals. Neuroimage. 2010;51:910–7.
Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. 2009;10:186–98.
Damoiseaux JS. Effects of aging on functional and structural brain connectivity. Neuroimage. 2017;160:32–40.
Spreng RN, Stevens WD, Viviano JD, Schacter DL. Attenuated anticorrelation between the default and dorsal attention networks with aging: evidence from task and rest. Neurobiol Aging. 2016;45:149–60.
Chan MY, Park DC, Savalia NK, Petersen SE, Wig GS. Decreased segregation of brain systems across the healthy adult lifespan. Proc Natl Acad Sci USA. 2014;111:E4997–5006.
Ng KK, Lo JC, Lim JKW, Chee MWL, Zhou J. Reduced functional segregation between the default mode network and the executive control network in healthy older adults: a longitudinal study. Neuroimage. 2016;133:321–30.
Cao M, Wang JH, Dai ZJ, Cao XY, Jiang LL, Fan FM, et al. Topological organization of the human brain functional connectome across the lifespan. Dev Cogn Neurosci. 2014;7:76–93.
Geerligs L, Renken RJ, Saliasi E, Maurits NM, Lorist MM. A brain-wide study of age-related changes in functional connectivity. Cereb Cortex. 2015;25:1987–99.
Gong G, Rosa-Neto P, Carbonell F, Chen ZJ, He Y, Evans AC. Age- and gender-related differences in the cortical anatomical network. J Neurosci. 2009;29:15684–93.
Zhao T, Cao M, Niu H, Zuo XN, Evans A, He Y, et al. Age-related changes in the topological organization of the white matter structural connectome across the human lifespan. Hum Brain Mapp. 2015;36:3777–92.
Tomasi D, Volkow ND. Aging and functional brain networks. Mol Psychiatry. 2012;17:549–58.
Burzynska AZ, Preuschhof C, Bäckman L, Nyberg L, Li SC, Lindenberger U, et al. Age-related differences in white matter microstructure: region-specific patterns of diffusivity. Neuroimage. 2010;49:2104–12.
Damoiseaux JS, Smith SM, Witter MP, Sanz-Arigita EJ, Barkhof F, Scheltens P, et al. White matter tract integrity in aging and Alzheimer’s disease. Hum Brain Mapp. 2009;30:1051–9.
Joëls M, Baram TZ. The neuro-symphony of stress. Nat Rev Neurosci. 2009;10:459–66.
Chrousos GP. Stress and disorders of the stress system. Nat Rev Endocrinol. 2009;5:374–81.
American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th ed. Arlington, VA, USA: American Psychiatric Association; 2013.
Liston C, McEwen BS, Casey BJ. Psychosocial stress reversibly disrupts prefrontal processing and attentional control. Proc Natl Acad Sci USA. 2009;106:912–7.
Ansell EB, Rando K, Tuit K, Guarnaccia J, Sinha R. Cumulative adversity and smaller gray matter volume in medial prefrontal, anterior cingulate, and insula regions. Biol Psychiatry. 2012;72:57–64.
Moreno GL, Bruss J, Denburg NL. Increased perceived stress is related to decreased prefrontal cortex volumes among older adults. J Clin Exp Neuropsychol. 2017;39:313–25.
Hanson JL, Chung MK, Avants BB, Shirtcliff EA, Gee JC, Davidson RJ, et al. Early stress is associated with alterations in the orbitofrontal cortex: a tensor-based morphometry investigation of brain structure and behavioral risk. J Neurosci. 2010;30:7466–72.
Gold AL, Sheridan MA, Peverill M, Busso DS, Lambert HK, Alves S, et al. Childhood abuse and reduced cortical thickness in brain regions involved in emotional processing. J Child Psychol Psychiatry. 2016;57:1154–64.
Van Harmelen AL, Van Tol MJ, Van Der Wee NJA, Veltman DJ, Aleman A, Spinhoven P, et al. Reduced medial prefrontal cortex volume in adults reporting childhood emotional maltreatment. Biol Psychiatry. 2010;68:832–8.
Monninger M, Kraaijenvanger EJ, Pollok TM, Boecker-Schlier R, Jennen-Steinmetz C, Baumeister S, et al. The long-term impact of early life stress on orbitofrontal cortical thickness. Cereb Cortex. 2020;30:1307–17.
Holz NE, Boecker R, Hohm E, Zohsel K, Buchmann AF, Blomeyer D, et al. The long-term impact of early life poverty on orbitofrontal cortex volume in adulthood: results from a prospective study over 25 years. Neuropsychopharmacology. 2015;40:996–1004.
Underwood MD, Bakalian MJ, Escobar T, Kassir S, Mann JJ, Arango V. Early-life adversity, but not suicide, is associated with less prefrontal cortex gray matter in adulthood. Int J Neuropsychopharmacol. 2019;22:349–57.
Tomoda A, Suzuki H, Rabi K, Sheu YS, Polcari A, Teicher MH. Reduced prefrontal cortical gray matter volume in young adults exposed to harsh corporal punishment. Neuroimage. 2009;47:T66–71.
Heim CM, Mayberg HS, Mletzko T, Nemeroff CB, Pruessner JC. Decreased cortical representation of genital somatosensory field after childhood sexual abuse. Am J Psychiatry. 2013;170:616–23.
Choi J, Jeong B, Rohan ML, Polcari AM, Teicher MH. Preliminary evidence for white matter tract abnormalities in young adults exposed to parental verbal abuse. Biol Psychiatry. 2009;65:227–34.
Tomoda A, Navalta CP, Polcari A, Sadato N, Teicher MH. Childhood sexual abuse is associated with reduced gray matter volume in visual cortex of young women. Biol Psychiatry. 2009;66:642–8.
Tomoda A, Polcari A, Anderson CM, Teicher MH. Reduced visual cortex gray matter volume and thickness in young adults who witnessed domestic violence during childhood. PLoS One. 2012;7:e52528.
Papagni SA, Benetti S, Arulanantham S, McCrory E, McGuire P, Mechelli A. Effects of stressful life events on human brain structure: a longitudinal voxel-based morphometry study. Stress. 2011;14:227–32.
Szeszko PR, Betensky JD, Mentschel C, Gunduz-Bruce H, Lencz T, Ashtari M, et al. Increased stress and smaller anterior hippocampal volume. Neuroreport. 2006;17:1825–8.
Butterworth P, Cherbuin N, Sachdev P, Anstey KJ. The association between financial hardship and amygdala and hippocampal volumes: results from the PATH through life project. Soc Cogn Affect Neurosci. 2012;7:548–56.
Piccolo LR, Noble KG. Perceived stress is associated with smaller hippocampal volume in adolescence. Psychophysiology. 2018;55:e13025.
Humphreys KL, King LS, Sacchet MD, Camacho MC, Colich NL, Ordaz SJ, et al. Evidence for a sensitive period in the effects of early life stress on hippocampal volume. Dev Sci. 2019;22:e12775.
Teicher MH, Anderson CM, Polcari A. Childhood maltreatment is associated with reduced volume in the hippocampal subfields CA3, dentate gyrus, and subiculum. Proc Natl Acad Sci USA. 2012;109:E563-72.
Dahmen B, Puetz VB, Scharke W, Von Polier GG, Herpertz-Dahlmann B, Konrad K. Effects of Early-life adversity on hippocampal structures and associated HPA axis functions. Dev Neurosci. 2018;40:13–22.
Teicher MH, Samson JA. Annual research review: enduring neurobiological effects of childhood abuse and neglect. J Child Psychol Psychiatry. 2016;57:241–66.
Keifer OP, Hurt RC, Gutman DA, Keilholz SD, Gourley SL, Ressler KJ. Voxel-based morphometry predicts shifts in dendritic spine density and morphology with auditory fear conditioning. Nat Commun. 2015;6:7582.
Kassem MS, Lagopoulos J, Stait-Gardner T, Price WS, Chohan TW, Arnold JC, et al. Stress-induced grey matter loss determined by MRI is primarily due to loss of dendrites and their synapses. Mol Neurobiol. 2013;47:645–61.
Watanabe Y, Gould E, McEwen BS. Stress induces atrophy of apical dendrites of hippocampal CA3 pyramidal neurons. Brain Res. 1992;588:341–5.
Wood GE, Young LT, Reagan LP, Chen B, McEwen BS. Stress-induced structural remodeling in hippocampus: prevention by lithium treatment. Proc Natl Acad Sci USA. 2004;101:3973–8.
Vyas A, Mitra R, Shankaranarayana Rao BS, Chattarji S. Chronic stress induces contrasting patterns of dendritic remodeling in hippocampal and amygdaloid neurons. J Neurosci. 2002;22:6810–8.
Magariños AM, McEwen BS, Flügge G, Fuchs E. Chronic psychosocial stress causes apical dendritic atrophy of hippocampal CA3 pyramidal neurons in subordinate tree shrews. J Neurosci. 1996;16:3534–40.
Blackstad TW, Kjaerheim Å. Special axo-dendritic synapses in the hippocampal cortex: electron and light microscopic studies on the layer of mossy fibers. J Comp Neurol. 1961;117:133–59.
Magariños AM, García Verdugo JM, Mcewen BS. Chronic stress alters synaptic terminal structure in hippocampus. Proc Natl Acad Sci USA. 1997;94:14002–8.
Stewart MG, Davies HA, Sandi C, Kraev IV, Rogachevsky VV, Peddie CJ, et al. Stress suppresses and learning induces plasticity in CA3 of rat hippocampus: a three-dimensional ultrastructural study of thorny excrescences and their postsynaptic densities. Neuroscience. 2005;131:43–54.
Sandi C, Davies HA, Cordero MI, Rodriguez JJ, Popov VI, Stewart MG. Rapid reversal of stress induced loss of synapses in CA3 of rat hippocampus following water maze training. Eur J Neurosci. 2003;17:2447–56.
Qiao H, An SC, Xu C, Ma XM. Role of proBDNF and BDNF in dendritic spine plasticity and depressive-like behaviors induced by an animal model of depression. Brain Res. 2017;1663:29–37.
Bessa JM, Ferreira D, Melo I, Marques F, Cerqueira JJ, Palha JA, et al. The mood-improving actions of antidepressants do not depend on neurogenesis but are associated with neuronal remodeling. Mol Psychiatry. 2009;14:764–73.
Sousa N, Lukoyanov NV, Madeira MD, Almeida OFX, Paula-Barbosa MM. Reorganization of the morphology of hippocampal neurites and synapses after stress-induced damage correlates with behavioral improvement. Neuroscience. 2000;97:253–66.
Lee T, Jarome T, Li SJ, Kim JJ, Helmstetter FJ. Chronic stress selectively reduces hippocampal volume in rats: a longitudinal magnetic resonance imaging study. Neuroreport. 2009;20:1554–8.
Patel D, Anilkumar S, Chattarji S, Buwalda B. Repeated social stress leads to contrasting patterns of structural plasticity in the amygdala and hippocampus. Behav Brain Res. 2018;347:314–24.
Lucassen PJ, Vollmann-Honsdorf GK, Gleisberg M, Czéh B, De Kloet ER, Fuchs E. Chronic psychosocial stress differentially affects apoptosis in hippocampal subregions and cortex of the adult tree shrew. Eur J Neurosci. 2001;14:161–6.
Schoenfeld TJ, McCausland HC, Morris HD, Padmanaban V, Cameron HA. Stress and loss of adult neurogenesis differentially reduce hippocampal volume. Biol Psychiatry. 2017;82:914–23.
Christian KM, Miracle AD, Wellman CL, Nakazawa K. Chronic stress-induced hippocampal dendritic retraction requires CA3 NMDA receptors. Neuroscience. 2011;174:26–36.
Bannerman DM, Rawlins JNP, McHugh SB, Deacon RMJ, Yee BK, Bast T, et al. Regional dissociations within the hippocampus-memory and anxiety. Neurosci Biobehav Rev. 2004;28:273–83.
Hainmueller T, Bartos M. Dentate gyrus circuits for encoding, retrieval and discrimination of episodic memories. Nat Rev Neurosci. 2020;21:153–68.
Surget A, Belzung C. Adult hippocampal neurogenesis shapes adaptation and improves stress response: a mechanistic and integrative perspective. Mol Psychiatry. 2022;27:403–21.
Pham K, Nacher J, Hof PR, McEwen BS. Repeated restraint stress suppresses neurogenesis and induces biphasic PSA-NCAM expression in the adult rat dentate gyrus. Eur J Neurosci. 2003;17:879–86.
Vollmayr B, Simonis C, Weber S, Gass P, Henn F. Reduced cell proliferation in the dentate gyrus is not correlated with the development of learned helplessness. Biol Psychiatry. 2003;54:1035–40.
Malberg JE, Duman RS. Cell proliferation in adult hippocampus is decreased by inescapable stress: reversal by fluoxetine treatment. Neuropsychopharmacology. 2003;28:1562–71.
Jayatissa MN, Henningsen K, Nikolajsen G, West MJ, Wiborg O. A reduced number of hippocampal granule cells does not associate with an anhedonia-like phenotype in a rat chronic mild stress model of depression. Stress. 2010;13:95–105.
Gould E, Tanapat P, Mcewen BS, Flügge G, Fuchs E. Proliferation of granule cell precursors in the dentate gyrus of adult monkeys is diminished by stress. Proc Natl Acad Sci USA. 1998;95:3168–71.
Kim SJ, Linden DJ. Ubiquitous plasticity and memory storage. Neuron. 2007;56:582–92.
Pavlides C, Nivón LG, McEwen BS. Effects of chronic stress on hippocampal long-term potentiation. Hippocampus. 2002;12:245–57.
Xu L, Holscher C, Anwyl R, Rowan MJ. Glucocorticoid receptor and protein/RNA synthesis-dependent mechanisms underlie the control of synaptic plasticity by stress. Proc Natl Acad Sci USA. 1998;95:3204–8.
Kavushansky A, Vouimba RM, Cohen H, Richter-Levin G. Activity and plasticity in the CA1, the dentate gyrus, and the amygdala following controllable vs. uncontrollable water stress. Hippocampus. 2006;16:35–42.
Alfarez DN, Joëls M, Krugers HJ. Chronic unpredictable stress impairs long-term potentiation in rat hippocampal CA1 area and dentate gyrus in vitro. Eur J Neurosci. 2003;17:1928–34.
Brunson KL, Kramár E, Lin B, Chen Y, Colgin LL, Yanagihara TK, et al. Mechanisms of late-onset cognitive decline after early-life stress. J Neurosci. 2005;25:9328–38.
Joëls M, Krugers HJ. LTP after stress: up or down? Neural Plast. 2007;2007:93202.
Radley JJ, Rocher AB, Miller M, Janssen WGM, Liston C, Hof PR, et al. Repeated stress induces dendritic spine loss in the rat medial prefrontal cortex. Cereb Cortex. 2006;16:313–20.
Liston C, Miller MM, Goldwater DS, Radley JJ, Rocher AB, Hof PR, et al. Stress-induced alterations in prefrontal cortical dendritic morphology predict selective impairments in perceptual attentional set-shifting. J Neurosci. 2006;26:7870–4.
Radley JJ, Sisti HM, Hao J, Rocher AB, McCall T, Hof PR, et al. Chronic behavioral stress induces apical dendritic reorganization in pyramidal neurons of the medial prefrontal cortex. Neuroscience. 2004;125:1–6.
Hains AB, Vu MAT, Maciejewski PK, Van Dyck CH, Gottron M, Arnsten AFT. Inhibition of protein kinase C signaling protects prefrontal cortex dendritic spines and cognition from the effects of chronic stress. Proc Natl Acad Sci USA. 2009;106:17957–62.
Cook SC, Wellman CL. Chronic stress alters dendritic morphology in rat medial prefrontal cortex. J Neurobiol. 2004;60:236–48.
Radley JJ, Rocher AB, Rodriguez A, Ehlenberger DB, Dammann M, Mcewen BS, et al. Repeated stress alters dendritic spine morphology in the rat medial prefrontal cortex. J Comp Neurol. 2008;507:1141–50.
Brown SM, Henning S, Wellman CL. Mild, short-term stress alters dendritic morphology in rat medial prefrontal cortex. Cereb Cortex. 2005;15:1714–22.
Shansky RM, Hamo C, Hof PR, McEwen BS, Morrison JH. Stress-induced dendritic remodeling in the prefrontal cortex is circuit specific. Cereb Cortex. 2009;19:2479–84.
Izquierdo A, Wellman CL, Holmes A. Brief uncontrollable stress causes dendritic retraction in infralimbic cortex and resistance to fear extinction in mice. J Neurosci. 2006;26:5733–8.
Moench KM, Maroun M, Kavushansky A, Wellman C. Alterations in neuronal morphology in infralimbic cortex predict resistance to fear extinction following acute stress. Neurobiol Stress. 2015;3:23–33.
Cerqueira JJ, Mailliet F, Almeida OFX, Jay TM, Sousa N. The prefrontal cortex as a key target of the maladaptive response to stress. J Neurosci. 2007;27:2781–7.
Conrad CD. Chronic stress-induced hippocampal vulnerability: the glucocorticoid vulnerability hypothesis. Rev Neurosci. 2008;19:395–411.
Myers B, McKlveen JM, Herman JP. Glucocorticoid actions on synapses, circuits, and behavior: implications for the energetics of stress. Front Neuroendocrinol. 2014;35:180–96.
Abel T, Lattal KM. Molecular mechanisms of memory acquisition, consolidation and retrieval. Curr Opin Neurobiol. 2001;11:180–7.
Kandel ER, Dudai Y, Mayford MR. The molecular and systems biology of memory. Cell. 2014;157:163–86.
de Oliveira Alvares L, Do-Monte FH. Understanding the dynamic and destiny of memories. Neurosci Biobehav Rev. 2021;125:592–607.
Haubrich J, Nader K. Memory reconsolidation. Curr Top Behav Neurosci. 2018;37:151–76.
Nader K, Hardt O. A single standard for memory: the case for reconsolidation. Nat Rev Neurosci. 2009;10:224–34.
Quirk GJ, Mueller D. Neural mechanisms of extinction learning and retrieval. Neuropsychopharmacology. 2008;33:56–72.
Izquierdo I, Furini CRG, Myskiw JC. Fear memory. Physiol Rev. 2016;96:695–750.
Davis RL, Zhong Y. The biology of forgetting-a perspective. Neuron. 2017;95:490–503.
Wiegert JS, Oertner TG. Long-term depression triggers the selective elimination of weakly integrated synapses. Proc Natl Acad Sci USA. 2013;110:E4510–9.
Barron HC, Vogels TP, Behrens TE, Ramaswami M. Inhibitory engrams in perception and memory. Proc Natl Acad Sci USA. 2017;114:6666–74.
Kandel ER. The molecular biology of memory storage: a dialogue between genes and synapses. Science. 2001;294:1030–8.
Collingridge GL, Peineau S, Howland JG, Wang YT. Long-term depression in the CNS. Nat Rev Neurosci. 2010;11:459–73.
Dudek SM, Bear MF. Homosynaptic long-term depression in area CA1 of hippocampus and effects of N-methyl-D-aspartate receptor blockade. Proc Natl Acad Sci USA. 1992;89:4363–7.
Dunwiddie T, Lynch G. Long-term potentiation and depression of synaptic responses in the rat hippocampus: localization and frequency dependency. J Physiol. 1978;276:353–67.
Heynen AJ, Abraham WC, Bear MF. Bidirectional modification of CA1 synapses in the adult hippocampus in vivo. Nature. 1996;381:163–6.
Kemp A, Manahan-Vaughan D. Hippocampal long-term depression and long-term potentiation encode different aspects of novelty accquisition. Proc Natl Acad Sci USA. 2004;101:8192–7.
Goh JJ, Manahan-Vaughan D. Spatial object recognition enables endogenous LTD that curtails LTP in the mouse hippocampus. Cereb Cortex. 2013;23:1118–25.
Katche C, Cammarota M, Medina JH. Molecular signatures and mechanisms of long-lasting memory consolidation and storage. Neurobiol Learn Mem. 2013;106:40–7.
Beattie EC, Carroll RC, Yu X, Morishita W, Yasuda H, Von Zastrow M, et al. Regulation of AMPA receptor endocytosis by a signaling mechanism shared with LTD. Nat Neurosci. 2000;3:1291–300.
Brigman JL, Wright T, Talani G, Prasad-Mulcare S, Jinde S, Seabold GK, et al. Loss of GluN2B-containing NMDA receptors in CA1 hippocampus and cortex impairs long-term depression, reduces dendritic spine density, and disrupts learning. J Neurosci. 2010;30:4590–600.
Ge Y, Dong Z, Bagot RC, Howland JG, Phillips AG, Wong TP, et al. Hippocampal long-term depression is required for the consolidation of spatial memory. Proc Natl Acad Sci USA. 2010;107:16697–702.
Dong Z, Bai Y, Wu X, Li H, Gong B, Howland JG, et al. Hippocampal long-term depression mediates spatial reversal learning in the Morris water maze. Neuropharmacology. 2013;64:65–73.
Dong Z, Gong B, Li H, Bai Y, Wu X, Huang Y, et al. Mechanisms of hippocampal long-term depression are required for memory enhancement by novelty exploration. J Neurosci. 2012;32:11980–90.
Ashby DM, Floresco SB, Phillips AG, McGirr A, Seamans JK, Wang YT. LTD is involved in the formation and maintenance of rat hippocampal CA1 place-cell fields. Nat Commun. 2021;12:100.
Lemon N, Aydin-Abidin S, Funke K, Manahan-Vaughan D. Locus coeruleus activation facilitates memory encoding and induces hippocampal LTD that depends on b-adrenergic receptor activation. Cereb Cortex. 2009;19:2827–37.
Maren S. Seeking a spotless mind: extinction, deconsolidation, and erasure of fear memory. Neuron. 2011;70:830–45.
Hardt O, Nader K, Wang YT. GluA2-dependent AMPA receptor endocytosis and the decay of early and late long-term potentiation: possible mechanisms for forgetting of short- and long-term memories. Philos Trans R Soc Lond B Biol Sci. 2013;369:20130141.
Villarreal DM, Do V, Haddad E, Derrick BE. NMDA receptor antagonists sustain LTP and spatial memory: active processes mediate LTP decay. Nat Neurosci. 2002;5:48–52.
Xiao MY, Niu YP, Wigström H. Activity-dependent decay of early LTP revealed by dual EPSP recording in hippocampal slices from young rats. Eur J Neurosci. 1996;8:1916–23.
Sachser RM, Santana F, Crestani AP, Lunardi P, Pedraza LK, Quillfeldt JA, et al. Forgetting of long-term memory requires activation of NMDA receptors, L-type voltage-dependent Ca2+ channels, and calcineurin. Sci Rep. 2016;6:22771.
Awasthi A, Ramachandran B, Ahmed S, Benito E, Shinoda Y, Nitzan N, et al. Synaptotagmin-3 drives AMPA receptor endocytosis, depression of synapse strength, and forgetting. Science. 2019;363:eaav1483.
Migues PV, Liu L, Archbold GEB, Einarsson E, Wong J, Bonasia K, et al. Blocking synaptic removal of GluA2-containing AMPA receptors prevents the natural forgetting of long-term memories. J Neurosci. 2016;36:3481–94.
Dalton GL, Wang YT, Floresco SB, Phillips AG. Disruption of AMPA receptor endocytosis impairs the extinction, but not acquisition of learned fear. Neuropsychopharmacology. 2008;33:2416–26.
Kim J, Lee S, Park K, Hong I, Song B, Son G, et al. Amygdala depotentiation and fear extinction. Proc Natl Acad Sci USA. 2007;104:20955–60.
Quirk GJ, Repa JC, LeDoux JE. Fear conditioning enhances short-latency auditory responses of lateral amygdala neurons: parallel recordings in the freely behaving rat. Neuron. 1995;15:1029–39.
Lacagnina AF, Brockway ET, Crovetti CR, Shue F, McCarty MJ, Sattler KP, et al. Distinct hippocampal engrams control extinction and relapse of fear memory. Nat Neurosci. 2019;22:753–61.
Klavir O, Prigge M, Sarel A, Paz R, Yizhar O. Manipulating fear associations via optogenetic modulation of amygdala inputs to prefrontal cortex. Nat Neurosci. 2017;20:836–44.
Amano T, Unal CT, Paré D. Synaptic correlates of fear extinction in the amygdala. Nat Neurosci. 2010;13:489–94.
Saito Y, Matsumoto M, Otani S, Yanagawa Y, Hiraide S, Ishikawa S, et al. Phase-dependent synaptic changes in the hippocampal CA1 field underlying extinction processes in freely moving rats. Neurobiol Learn Mem. 2012;97:361–9.
Bukalo O, Nonaka M, Weinholtz CA, Mendez A, Taylor WW, Holmes A. Effects of optogenetic photoexcitation of infralimbic cortex inputs to the basolateral amygdala on conditioned fear and extinction. Behav Brain Res. 2021;396:112913.
Nägerl UV, Eberhorn N, Cambridge SB, Bonhoeffer T. Bidirectional activity-dependent morphological plasticity in hippocampal neurons. Neuron. 2004;44:759–67.
Okamoto KI, Nagai T, Miyawaki A, Hayashi Y. Rapid and persistent modulation of actin dynamics regulates postsynaptic reorganization underlying bidirectional plasticity. Nat Neurosci. 2004;7:1104–12.
Stein IS, Zito K. Dendritic spine elimination: molecular mechanisms and implications. Neuroscientist. 2019;25:27–47.
Hasegawa S, Sakuragi S, Tominaga-Yoshino K, Ogura A. Dendritic spine dynamics leading to spine elimination after repeated inductions of LTD. Sci Rep. 2015;5:7707.
Sanders J, Cowansage K, Baumgartel K, Mayford M. Elimination of dendritic spines with long-term memory is specific to active circuits. J Neurosci. 2012;32:12570–8.
Xu Z, Adler A, Li H, Pérez-Cuesta LM, Lai B, Li W, et al. Fear conditioning and extinction induce opposing changes in dendritic spine remodeling and somatic activity of layer 5 pyramidal neurons in the mouse motor cortex. Sci Rep. 2019;9:4619.
Lai CSW, Franke TF, Gan WB. Opposite effects of fear conditioning and extinction on dendritic spine remodelling. Nature. 2012;483:87–92.
Vetere G, Restivo L, Novembre G, Aceti M, Lumaca M, Ammassari-Teule M. Extinction partially reverts structural changes associated with remote fear memory. Learn Mem. 2011;18:554–7.
Heinrichs SC, Leite-Morris KA, Guy MD, Goldberg LR, Young AJ, Kaplan GB. Dendritic structural plasticity in the basolateral amygdala after fear conditioning and its extinction in mice. Behav Brain Res. 2013;248:80–4.
Lai CSW, Adler A, Gan WB. Fear extinction reverses dendritic spine formation induced by fear conditioning in the mouse auditory cortex. Proc Natl Acad Sci USA. 2018;115:9306–11.
Xue M, Atallah BV, Scanziani M. Equalizing excitation-inhibition ratios across visual cortical neurons. Nature. 2014;511:596–600.
Okun M, Lampl I. Instantaneous correlation of excitation and inhibition during ongoing and sensory-evoked activities. Nat Neurosci. 2008;11:535–7.
Turrigiano G. Too many cooks? Intrinsic and synaptic homeostatic mechanisms in cortical circuit refinement. Annu Rev Neurosci. 2011;34:89–103.
D’Angelo E. Homeostasis of intrinsic excitability: making the point. J Physiol. 2010;588:901–2.
Bienenstock EL, Cooper LN, Munro PW. Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex. J Neurosci. 1982;2:32–48.
Abraham WC. Metaplasticity: tuning synapses and networks for plasticity. Nat Rev Neurosci. 2008;9:387.
Lee HK, Kirkwood A. Mechanisms of homeostatic synaptic plasticity in vivo. Front Cell Neurosci. 2019;13:520.
Turrigiano GG, Nelson SB. Homeostatic plasticity in the developing nervous system. Nat Rev Neurosci. 2004;5:97–107.
Rich MM, Wenner P. Sensing and expressing homeostatic synaptic plasticity. Trends Neurosci. 2007;30:119–25. https://doi.org/10.1016/j.tins.2007.01.004.
Turrigiano G. Homeostatic synaptic plasticity: local and global mechanisms for stabilizing neuronal function. Cold Spring Harb Perspect Biol. 2012;4:a005736.
Abbott LF, Nelson SB. Synaptic plasticity: taming the beast. Nat Neurosci. 2000;3:1178–83.
Moulin TC, Rayêe D, Williams MJ, Schiöth HB. The synaptic scaling literature: a systematic review of methodologies and quality of reporting. Front Cell Neurosci. 2020;14:164.
Zito K, Knott G, Shepherd GM, Shenolikar S, Svoboda K. Induction of spine growth and synapse formation by regulation of the spine actin cytoskeleton. Neuron. 2004;44:321–34.
Bourne JN, Harris KM. Coordination of size and number of excitatory and inhibitory synapses results in a balanced structural plasticity along mature hippocampal CA1 dendrites during LTP. Hippocampus. 2011;21:354–73.
Oh WC, Parajuli LK, Zito K. Heterosynaptic structural plasticity on local dendritic segments of hippocampal CA1 neurons. Cell Rep. 2015;10:162–9.
Power JM, Sah P. Dendritic spine heterogeneity and calcium dynamics in basolateral amygdala principal neurons. J Neurophysiol. 2014;112:1616–27.
Winnubst J, Cheyne JE, Niculescu D, Lohmann C. Spontaneous activity drives local synaptic plasticity in vivo. Neuron 2015;87:399–410.
Bian WJ, Miao WY, He SJ, Qiu Z, Yu X. Coordinated spine pruning and maturation mediated by inter-spine competition for cadherin/catenin complexes. Cell. 2015;162:808–22.
Branco T, Häusser M. The single dendritic branch as a fundamental functional unit in the nervous system. Curr Opin Neurobiol. 2010;20:494–502.
Kastellakis G, Cai DJ, Mednick SC, Silva AJ, Poirazi P. Synaptic clustering within dendrites: an emerging theory of memory formation. Prog Neurobiol. 2015;126:19–35.
Desai NS, Cudmore RH, Nelson SB, Turrigiano GG. Critical periods for experience-dependent synaptic scaling in visual cortex. Nat Neurosci. 2002;5:783–9.
Goel A, Lee HK. Persistence of experience-induced homeostatic synaptic plasticity through adulthood in superficial layers of mouse visual cortex. J Neurosci. 2007;27:6692–700.
Petrus E, Anguh TT, Pho H, Lee A, Gammon N, Lee HK. Developmental switch in the polarity of experience-dependent synaptic changes in layer 6 of mouse visual cortex. J Neurophysiol. 2011;106:2499–505.
Marty S, Wehrlé R, Sotelo C. Neuronal activity and brain-derived neurotrophic factor regulate the density of inhibitory synapses in organotypic slice cultures of postnatal hippocampus. J Neurosci. 2000;20:8087–95.
Pieraut S, Gounko N, Sando R 3rd, Dang W, Rebboah E, Panda S, et al. Experience-dependent remodeling of basket cell networks in the dentate gyrus. Neuron. 2014;84:107–22.
Maffei A, Nelson SB, Turrigiano GG. Selective reconfiguration of layer 4 visual cortical circuitry by visual deprivation. Nat Neurosci. 2004;7:1353–9.
Mendez P, Stefanelli T, Flores CE, Muller D, Lüscher C. Homeostatic plasticity in the hippocampus facilitates memory extinction. Cell Rep. 2018;22:1451–61.
Han JH, Kushner SA, Yiu AP, Cole CJ, Matynia A, Brown RA, et al. Neuronal competition and selection during memory formation. Science. 2007;316:457–60.
Lau JMH, Rashid AJ, Jacob AD, Frankland PW, Schacter DL, Josselyn SA. The role of neuronal excitability, allocation to an engram and memory linking in the behavioral generation of a false memory in mice. Neurobiol Learn Mem. 2020;174:107284.
Yiu AP, Mercaldo V, Yan C, Richards B, Rashid AJ, Hsiang HL, et al. Neurons are recruited to a memory trace based on relative neuronal excitability immediately before training. Neuron. 2014;83:722–35.
Morgan PJ, Bourboulou R, Filippi C, Koenig-Gambini J, Epsztein J. Kv1.1 contributes to a rapid homeostatic plasticity of intrinsic excitability in CA1 pyramidal neurons in vivo. Elife. 2019;8:e49915.
Wu CH, Ramos R, Katz DB, Turrigiano GG. Homeostatic synaptic scaling establishes the specificity of an associative memory. Curr Biol. 2021;31:2274–85.e5.
Schutter DJ, Wischnewski M, Bekkering H. Stability through variability: homeostatic plasticity and psychological resilience. Behav Brain Sci. 2015;38:e118.
Kavalali ET, Monteggia LM. Targeting homeostatic synaptic plasticity for treatment of mood disorders. Neuron. 2020;106:715–26.
Friedman AK, Walsh JJ, Juarez B, Ku SM, Chaudhury D, Wang J, et al. Enhancing depression mechanisms in midbrain dopamine neurons achieves homeostatic resilience. Science. 2014;344:313–9.
El-Hayek YH, Wu C, Ye H, Wang J, Carlen PL, Zhang L. Hippocampal excitability is increased in aged mice. Exp Neurol. 2013;247:710–9.
Fischer C, Endle H, Schumann L, Wilken-Schmitz A, Kaiser J, Gerber S, et al. Prevention of age-associated neuronal hyperexcitability with improved learning and attention upon knockout or antagonism of LPAR2. Cell Mol Life Sci. 2021;78:1029–50.
Lerdkrai C, Asavapanumas N, Brawek B, Kovalchuk Y, Mojtahedi N, Olmedillas Del Moral M, et al. Intracellular Ca2+ stores control in vivo neuronal hyperactivity in a mouse model of Alzheimer’s disease. Proc Natl Acad Sci USA. 2018;115:E1279–E1288.
Oh MM, Simkin D, Disterhoft JF. Intrinsic hippocampal excitability changes of opposite signs and different origins in CA1 and CA3 pyramidal neurons underlie aging-related cognitive deficits. Front Syst Neurosci. 2016;10:52.
Ding Y, Chen T, Wang Q, Yuan Y, Hua T. Axon initial segment plasticity accompanies enhanced excitation of visual cortical neurons in aged rats. Neuroreport. 2018;29:1537–43.
Haberman RP, Koh MT, Gallagher M. Heightened cortical excitability in aged rodents with memory impairment. Neurobiol Aging. 2017;54:144–51.
Uryash A, Flores V, Adams JA, Allen PD, Lopez JR. Memory and learning deficits are associated with Ca2+ dyshomeostasis in normal aging. Front Aging Neurosci. 2020;12:224.
McQuail JA, Frazier CJ, Bizon JL. Molecular aspects of age-related cognitive decline: the role of GABA signaling. Trends Mol Med. 2015;21:450–60.
Stanley EM, Fadel JR, Mott DD. Interneuron loss reduces dendritic inhibition and GABA release in hippocampus of aged rats. Neurobiol Aging. 2012;33:431.e1-13.
Disterhoft JF, Oh MM. Learning, aging and intrinsic neuronal plasticity. Trends Neurosci. 2006;29:587–99.
Radulescu CI, Cerar V, Haslehurst P, Kopanitsa M, Barnes SJ. The aging mouse brain: cognition, connectivity and calcium. Cell Calcium. 2021;94:102358.
Chowdhury D, Hell JW. Homeostatic synaptic scaling: molecular regulators of synaptic AMPA-type glutamate receptors. F1000Res. 2018;7:234.
Gainey MA, Hurvitz-Wolff JR, Lambo ME, Turrigiano GG. Synaptic scaling requires the GluR2 subunit of the AMPA receptor. J Neurosci. 2009;29:6479–89.
Acknowledgements
The present research was supported by grants from FAPESP (2018/04250-5 and 2018/18014-1).
Author information
Authors and Affiliations
Contributions
Both CRAFD and APC equally conceptualized the article, wrote the initial draft, and contributed to the final version of the manuscript.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Diniz, C.R.A.F., Crestani, A.P. The times they are a-changin’: a proposal on how brain flexibility goes beyond the obvious to include the concepts of “upward” and “downward” to neuroplasticity. Mol Psychiatry 28, 977–992 (2023). https://doi.org/10.1038/s41380-022-01931-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41380-022-01931-x
This article is cited by
-
Lasting dynamic effects of the psychedelic 2,5-dimethoxy-4-iodoamphetamine ((±)-DOI) on cognitive flexibility
Molecular Psychiatry (2024)
