Opposing Effects of Neuronal Activity on Structural Plasticity

Differences in the consolidation by spontaneous and evoked ripples in the presence of active dendrites

Ripples are a typical form of neural activity in hippocampal neural networks associated with the replay of episodic memories during sleep as well as sleep-related plasticity and memory consolidation. The emergence of ripples has been observed both dependent as well as independent of input from other brain areas and often coincides with dendritic spikes. Yet, it is unclear how input-evoked and spontaneous ripples as well as dendritic excitability affect plasticity and consolidation. Here, we use mathematical modeling to compare these cases. We find that consolidation as well as the emergence of spontaneous ripples depends on a reliable propagation of activity in feed-forward structures which constitute memory representations. This propagation is facilitated by excitable dendrites, which entail that a few strong synapses are sufficient to trigger neuronal firing. In this situation, stimulation-evoked ripples lead to the potentiation of weak synapses within the feed-forward structure and, thus, to a consolidation of a more general sequence memory. However, spontaneous ripples that occur without stimulation, only consolidate a sparse backbone of the existing strong feed-forward structure. Based on this, we test a recently hypothesized scenario in which the excitability of dendrites is transiently enhanced after learning, and show that such a transient increase can strengthen, restructure and consolidate even weak hippocampal memories, which would be forgotten otherwise. Hence, a transient increase in dendritic excitability would indeed provide a mechanism for stabilizing memories.

Long ascending propriospinal neurons are heterogenous and subject to spinal cord injury induced anatomic plasticity

Long ascending propriospinal neurons (LAPNs) are a subset of spinal interneurons that provide direct connectivity between distant spinal segments. Here, we focus specifically on an anatomically defined population of "inter-enlargement" LAPNs with cell bodies at L2/3 and terminals at C5/6. Previous studies showed that silencing LAPNs in awake and freely moving animals disrupted interlimb coordination of the hindlimbs, forelimbs, and heterolateral limb pairs. Surprisingly, despite a proportion of LAPNs being anatomically intact post- spinal cord injury (SCI), silencing them improved locomotor function but only influenced coordination of the hindlimb pair. Given the functional significance of LAPNs pre- and post-SCI, we characterized their anatomy and SCI-induced anatomical plasticity. This detailed anatomical characterization revealed three morphologically distinct subsets of LAPNs that differ in soma size, neurite complexity and/or neurite orientation. Following a mild thoracic contusive SCI there was a marked shift in neurite orientation in two of the LAPN subsets to a more dorsoventral orientation, and collateral densities decreased in the cervical enlargement but increased just caudal to the injury epicenter. These post-SCI anatomical changes potentially reflect maladaptive plasticity and an effort to establish new functional inputs from sensory afferents that sprout post-SCI to achieve circuitry homeostasis.

Harnessing the power of probiotics to enhance neuroplasticity for neurodevelopment and cognitive function in stunting: a comprehensive review

BACKGROUND

Stunting become a global concern because it's not only affecting physical stature, but also affecting on neurodevelopment and cognitive function. These impacts are resulting in long-term consequences especially for human resources, such as poor-quality labor, decreased productivity due to decreasing of health quality, including immunity and cognitive aspect.

DISCUSSION

This comprehensive review found that based on many studies, there is an altered gut microbiota, or dysbiosis, in stunted children, causing the impairment of brain development through Microbiota-Gut Brain Axis (MGB Axis) mechanism. The administration of probiotics has been known affect MGBA by improving the physical and chemical gut barrier integrity, producing antimicrobial substance to inhibit pathogen, and recovering the healthy gut microbiota. Probiotics, along with healthy gut microbiota, produce SCFAs which have various positive impact on CNS, such as increase neurogenesis, support the development and function of microglia, reduce inflammatory signaling, improve the Blood Brain Barrier's (BBB's) integrity, produce neurotropic factors (e.g. BDNF, GDNF), and promote the formation of new synapse. Probiotics also could induce the production of IGF-1 by intestinal epithelial cells, which functioned as growth factor of multiple body tissues and resulted in improvement of linear growth as well as brain development.

CONCLUSION

These properties of probiotics made it become the promising and feasible new treatment approach for stunting. But since most of the studies in this field are conducted in animal models, it is necessary to translate animal data into human models and do additional study to identify the numerous components in the MGB axis and the effect of probiotics on human.

Repetitive transcranial magnetic stimulation (rTMS) triggers dose-dependent homeostatic rewiring in recurrent neuronal networks

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive brain stimulation technique used to induce neuronal plasticity in healthy individuals and patients. Designing effective and reproducible rTMS protocols poses a major challenge in the field as the underlying biomechanisms of long-term effects remain elusive. Current clinical protocol designs are often based on studies reporting rTMS-induced long-term potentiation or depression of synaptic transmission. Herein, we employed computational modeling to explore the effects of rTMS on long-term structural plasticity and changes in network connectivity. We simulated a recurrent neuronal network with homeostatic structural plasticity among excitatory neurons, and demonstrated that this mechanism was sensitive to specific parameters of the stimulation protocol (i.e., frequency, intensity, and duration of stimulation). Particularly, the feedback-inhibition initiated by network stimulation influenced the net stimulation outcome and hindered the rTMS-induced structural reorganization, highlighting the role of inhibitory networks. These findings suggest a novel mechanism for the lasting effects of rTMS, i.e., rTMS-induced homeostatic structural plasticity, and highlight the importance of network inhibition in careful protocol design, standardization, and optimization of stimulation.

Learning potential and visuospatial memory could predict amyloid-beta positron emission tomography positivity in amnestic mild cognitive impairment

We investigate the role of neuropsychological tests, including the learning potential, in predicting amyloid-beta positron emission tomography (Aβ-PET) status in amnestic mild cognitive impairment (aMCI). This cross-sectional study included 64 patients with aMCI (31 Aβ-PET (-) and 33 (+)) who visited a memory impairment clinic at Pusan National University Hospital between 2014 and 2019. Patients underwent Aβ-PET scans using 18F-florbetaben and the Seoul Neuropsychological Screening Battery. Learning potential was determined based on the difference in scores between the first and third trials of the Seoul Verbal Learning test (SVLT). Binary logistic regression was used to demonstrate the association between Aβ-PET status and cognitive tests. Predictive ability of cognitive tests for Aβ deposition was investigated using receiver operating characteristic curves analysis. From logistic regression models, the SVLT learning potential and Rey-Osterrieth Complex Figure Test (RCFT) delayed recall were found to predict Aβ-PET positivity. The areas under the curve (AUC) of the SVLT learning potential and RCFT delayed recall were significantly different from 0.5. Our findings of an association between Aβ deposition status and learning potential and visuospatial memory suggest that these cognitive tests could be used to screen patients with aMCI for Aβ deposition status.

Synaptic weights that correlate with presynaptic selectivity increase decoding performance

The activity of neurons in the visual cortex is often characterized by tuning curves, which are thought to be shaped by Hebbian plasticity during development and sensory experience. This leads to the prediction that neural circuits should be organized such that neurons with similar functional preference are connected with stronger weights. In support of this idea, previous experimental and theoretical work have provided evidence for a model of the visual cortex characterized by such functional subnetworks. A recent experimental study, however, have found that the postsynaptic preferred stimulus was defined by the total number of spines activated by a given stimulus and independent of their individual strength. While this result might seem to contradict previous literature, there are many factors that define how a given synaptic input influences postsynaptic selectivity. Here, we designed a computational model in which postsynaptic functional preference is defined by the number of inputs activated by a given stimulus. Using a plasticity rule where synaptic weights tend to correlate with presynaptic selectivity, and is independent of functional-similarity between pre- and postsynaptic activity, we find that this model can be used to decode presented stimuli in a manner that is comparable to maximum likelihood inference.

Top-down and bottom-up stimulation techniques combined with action observation treatment in stroke rehabilitation: a perspective

Stroke is a central nervous system disease that causes structural lesions and functional impairments of the brain, resulting in varying types, and degrees of dysfunction. The bimodal balance-recovery model (interhemispheric competition model and vicariation model) has been proposed as the mechanism of functional recovery after a stroke. We analyzed how combinations of motor observation treatment approaches, transcranial electrical (TES) or magnetic (TMS) stimulation and peripheral electrical (PES) or magnetic (PMS) stimulation techniques can be taken as accessorial physical therapy methods on symptom reduction of stroke patients. We suggest that top-down and bottom-up stimulation techniques combined with action observation treatment synergistically might develop into valuable physical therapy strategies in neurorehabilitation after stroke. We explored how TES or TMS intervention over the contralesional hemisphere or the lesioned hemisphere combined with PES or PMS of the paretic limbs during motor observation followed by action execution have super-additive effects to potentiate the effect of conventional treatment in stroke patients. The proposed paradigm could be an innovative and adjunctive approach to potentiate the effect of conventional rehabilitation treatment, especially for those patients with severe motor deficits.

Growing dendrites enhance a neuron's computational power and memory capacity

Neocortical pyramidal neurons have many dendrites, and such dendrites are capable of, in isolation of one-another, generating a neuronal spike. It is also now understood that there is a large amount of dendritic growth during the first years of a humans life, arguably a period of prodigious learning. These observations inspire the construction of a local, stochastic algorithm based on an earlier stochastic, homeostatic, Hebbian developmental theory. Here we investigate the neurocomputational advantages and limits on this novel algorithm that combines dendritogenesis with supervised adaptive synaptogenesis. Neurons created with this algorithm have enhanced memory capacity, can avoid catastrophic interference (forgetting), and have the ability to unmix mixture distributions. In particular, individual dendrites develop within each class, in an unsupervised manner, to become feature-clusters that correspond to the mixing elements of class-conditional mixture distribution. Error-free classification is demonstrated with input perturbations up to 40%. Although discriminative problems are used to understand the capabilities of the stochastic algorithm and the neuronal connectivity it produces, the algorithm is in the generative class, it thus seems ideal for decisions that require generalization, i.e., extrapolation beyond previous learning.

Electrical stimulation methods and protocols for the treatment of traumatic brain injury: a critical review of preclinical research

BACKGROUND

Traumatic brain injury (TBI) is a leading cause of disabilities resulting from cognitive and neurological deficits, as well as psychological disorders. Only recently, preclinical research on electrical stimulation methods as a potential treatment of TBI sequelae has gained more traction. However, the underlying mechanisms of the anticipated improvements induced by these methods are still not fully understood. It remains unclear in which stage after TBI they are best applied to optimize the therapeutic outcome, preferably with persisting effects. Studies with animal models address these questions and investigate beneficial long- and short-term changes mediated by these novel modalities.

METHODS

In this review, we present the state-of-the-art in preclinical research on electrical stimulation methods used to treat TBI sequelae. We analyze publications on the most commonly used electrical stimulation methods, namely transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), deep brain stimulation (DBS) and vagus nerve stimulation (VNS), that aim to treat disabilities caused by TBI. We discuss applied stimulation parameters, such as the amplitude, frequency, and length of stimulation, as well as stimulation time frames, specifically the onset of stimulation, how often stimulation sessions were repeated and the total length of the treatment. These parameters are then analyzed in the context of injury severity, the disability under investigation and the stimulated location, and the resulting therapeutic effects are compared. We provide a comprehensive and critical review and discuss directions for future research. RESULTS AND CONCLUSION: We find that the parameters used in studies on each of these stimulation methods vary widely, making it difficult to draw direct comparisons between stimulation protocols and therapeutic outcome. Persisting beneficial effects and adverse consequences of electrical simulation are rarely investigated, leaving many questions about their suitability for clinical applications. Nevertheless, we conclude that the stimulation methods discussed here show promising results that could be further supported by additional research in this field.

Repetitive transcranial magnetic stimulation (rTMS) triggers dose-dependent homeostatic rewiring in recurrent neuronal networks

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive brain stimulation technique used to induce neuronal plasticity in healthy individuals and patients. Designing effective and reproducible rTMS protocols poses a major challenge in the field as the underlying biomechanisms remain elusive. Current clinical protocol designs are often based on studies reporting rTMS-induced long-term potentiation or depression of synaptic transmission. Herein, we employed computational modeling to explore the effects of rTMS on long-term structural plasticity and changes in network connectivity. We simulated a recurrent neuronal network with homeostatic structural plasticity between excitatory neurons, and demonstrated that this mechanism was sensitive to specific parameters of the stimulation protocol (i.e., frequency, intensity, and duration of stimulation). The feedback-inhibition initiated by network stimulation influenced the net stimulation outcome and hindered the rTMS-induced homeostatic structural plasticity, highlighting the role of inhibitory networks. These findings suggest a novel mechanism for the lasting effects of rTMS, i.e., rTMS-induced homeostatic structural plasticity, and highlight the importance of network inhibition in careful protocol design, standardization, and optimization of stimulation.

Synaptic Dysfunction and Plasticity in Amyotrophic Lateral Sclerosis

Recent evidence has supported the hypothesis that amyotrophic lateral sclerosis (ALS) is a multi-step disease, as the onset of symptoms occurs after sequential exposure to a defined number of risk factors. Despite the lack of precise identification of these disease determinants, it is known that genetic mutations may contribute to one or more of the steps leading to ALS onset, the remaining being linked to environmental factors and lifestyle. It also appears evident that compensatory plastic changes taking place at all levels of the nervous system during ALS etiopathogenesis may likely counteract the functional effects of neurodegeneration and affect the timing of disease onset and progression. Functional and structural events of synaptic plasticity probably represent the main mechanisms underlying this adaptive capability, causing a significant, although partial and transient, resiliency of the nervous system affected by a neurodegenerative disease. On the other hand, the failure of synaptic functions and plasticity may be part of the pathological process. The aim of this review was to summarize what it is known today about the controversial involvement of synapses in ALS etiopathogenesis, and an analysis of the literature, although not exhaustive, confirmed that synaptic dysfunction is an early pathogenetic process in ALS. Moreover, it appears that adequate modulation of structural and functional synaptic plasticity may likely support function sparing and delay disease progression.

Nine insights from internet engineering that help us understand brain network communication

Philosophers have long recognized the value of metaphor as a tool that opens new avenues of investigation. By seeing brains as having the goal of representation, the computer metaphor in its various guises has helped systems neuroscience approach a wide array of neuronal behaviors at small and large scales. Here I advocate a complementary metaphor, the internet. Adopting this metaphor shifts our focus from computing to communication, and from seeing neuronal signals as localized representational elements to seeing neuronal signals as traveling messages. In doing so, we can take advantage of a comparison with the internet's robust and efficient routing strategies to understand how the brain might meet the challenges of network communication. I lay out nine engineering strategies that help the internet solve routing challenges similar to those faced by brain networks. The internet metaphor helps us by reframing neuronal activity across the brain as, in part, a manifestation of routing, which may, in different parts of the system, resemble the internet more, less, or not at all. I describe suggestive evidence consistent with the brain's use of internet-like routing strategies and conclude that, even if empirical data do not directly implicate internet-like routing, the metaphor is valuable as a reference point for those investigating the difficult problem of network communication in the brain and in particular the problem of routing.

Biomarkers of Activity-Dependent Plasticity and Persistent Enhancement of Synaptic Transmission in Alzheimer Disease: A Review of the Current Status

Alzheimer disease (AD) is a chronic and heterogeneous neurodegenerative disorder characterized by complex pathological processes involving neuroinflammation, neurodegeneration, and synaptic dysfunction. Understanding the exact neurobiological mechanisms underlying AD pathology may help to provide a biomarker for early diagnosis or at least for assessment of vulnerability to dementia development. Neural plasticity is defined as a capability of the brain to respond to alterations including aging, injury, or learning, with a crucial role of synaptic elements. Long-term potentiation (LTP) and long-term depression (LTD) are important in regulating synaptic connections between neural cells in functional plasticity. Synaptic loss and impairment of the brain's plasticity in AD leads to cognitive impairment, and one of important roles of synaptic biomarkers is monitoring synaptic dysfunction, response to treatment, and predicting future development of AD. Synaptic biomarkers are undoubtedly very promising in developing novel approach to AD treatment and control, especially in the era of aging of societies, which is one of the most common risk factor of AD. Implementing a widespread measurement of synaptic biomarkers of AD will probably be crucial in early diagnosis of AD, early therapeutic intervention, monitoring progression of the disease, or response to treatment. One of the most important challenges is finding a biomarker whose blood concentration correlates with its level in the central nervous system (CNS). This review aims to present the current status of biomarkers of activity-dependent plasticity and persistent enhancement of synaptic transmission in Alzheimer disease.

Synaptic plasticity and mental health: methods, challenges and opportunities

Activity-dependent synaptic plasticity is a ubiquitous property of the nervous system that allows neurons to communicate and change their connections as a function of past experiences. Through reweighting of synaptic strengths, the nervous system can remodel itself, giving rise to durable memories that create the biological basis for mental function. In healthy individuals, synaptic plasticity undergoes characteristic developmental and aging trajectories. Dysfunctional plasticity, in turn, underlies a wide spectrum of neuropsychiatric disorders including depression, schizophrenia, addiction, and posttraumatic stress disorder. From a mechanistic standpoint, synaptic plasticity spans the gamut of spatial and temporal scales, from microseconds to the lifespan, from microns to the entire nervous system. With the numbers and strengths of synapses changing on such wide scales, there is an important need to develop measurement techniques with complimentary sensitivities and a growing number of approaches are now being harnessed for this purpose. Through hemodynamic measures, structural and tracer imaging, and noninvasive neuromodulation, it is possible to image structural and functional changes that underlie synaptic plasticity and associated behavioral learning. Here we review the mechanisms of neural plasticity and the historical and future trends in techniques that allow imaging of synaptic changes that accompany psychiatric disorders, highlighting emerging therapeutics and the challenges and opportunities accompanying this burgeoning area of study.

Dynamics of phase oscillator networks with synaptic weight and structural plasticity

We study the dynamics of Kuramoto oscillator networks with two distinct adaptation processes, one varying the coupling strengths and the other altering the network structure. Such systems model certain networks of oscillatory neurons where the neuronal dynamics, synaptic weights, and network structure interact with and shape each other. We model synaptic weight adaptation with spike-timing-dependent plasticity (STDP) that runs on a longer time scale than neuronal spiking. Structural changes that include addition and elimination of contacts occur at yet a longer time scale than the weight adaptations. First, we study the steady-state dynamics of Kuramoto networks that are bistable and can settle in synchronized or desynchronized states. To compare the impact of adding structural plasticity, we contrast the network with only STDP to one with a combination of STDP and structural plasticity. We show that the inclusion of structural plasticity optimizes the synchronized state of a network by allowing for synchronization with fewer links than a network with STDP alone. With non-identical units in the network, the addition of structural plasticity leads to the emergence of correlations between the oscillators' natural frequencies and node degrees. In the desynchronized regime, the structural plasticity decreases the number of contacts, leading to a sparse network. In this way, adding structural plasticity strengthens both synchronized and desynchronized states of a network. Second, we use desynchronizing coordinated reset stimulation and synchronizing periodic stimulation to induce desynchronized and synchronized states, respectively. Our findings indicate that a network with a combination of STDP and structural plasticity may require stronger and longer stimulation to switch between the states than a network with STDP only.

Translational neuronal ensembles: Neuronal microcircuits in psychology, physiology, pharmacology and pathology

Multi-recording techniques show evidence that neurons coordinate their firing forming ensembles and that brain networks are made by connections between ensembles. While “canonical” microcircuits are composed of interconnected principal neurons and interneurons, it is not clear how they participate in recorded neuronal ensembles: “groups of neurons that show spatiotemporal co-activation”. Understanding synapses and their plasticity has become complex, making hard to consider all details to fill the gap between cellular-synaptic and circuit levels. Therefore, two assumptions became necessary: First, whatever the nature of the synapses these may be simplified by “functional connections”. Second, whatever the mechanisms to achieve synaptic potentiation or depression, the resultant synaptic weights are relatively stable. Both assumptions have experimental basis cited in this review, and tools to analyze neuronal populations are being developed based on them. Microcircuitry processing followed with multi-recording techniques show temporal sequences of neuronal ensembles resembling computational routines. These sequences can be aligned with the steps of behavioral tasks and behavior can be modified upon their manipulation, supporting the hypothesis that they are memory traces. In vitro, recordings show that these temporal sequences can be contained in isolated tissue of histological scale. Sequences found in control conditions differ from those recorded in pathological tissue obtained from animal disease models and those recorded after the actions of clinically useful drugs to treat disease states, setting the basis for new bioassays to test drugs with potential clinical use. These findings make the neuronal ensembles theoretical framework a dynamic neuroscience paradigm.

Genetic control of neuronal activity enhances axonal growth only on permissive substrates

Background

Neural tissue has limited regenerative ability. To cope with that, in recent years a diverse set of novel tools has been used to tailor neurostimulation therapies and promote functional regeneration after axonal injuries.

Method

In this report, we explore cell-specific methods to modulate neuronal activity, including opto- and chemogenetics to assess the effect of specific neuronal stimulation in the promotion of axonal regeneration after injury.

Results

Opto- and chemogenetic stimulations of neuronal activity elicited increased in vitro neurite outgrowth in both sensory and cortical neurons, as well as in vivo regeneration in the sciatic nerve, but not after spinal cord injury. Mechanistically, inhibitory substrates such as chondroitin sulfate proteoglycans block the activity induced increase in axonal growth.

Conclusions

We found that genetic modulations of neuronal activity on both dorsal root ganglia and corticospinal motor neurons increase their axonal growth capacity but only on permissive environments.

Supplementary Information

The online version contains supplementary material available at 10.1186/s10020-022-00524-2.

Moment closure approximations of susceptible-infected-susceptible epidemics on adaptive networks

The influence of people's individual responses to the spread of contagious phenomena, like the COVID-19 pandemic, is still not well understood. We investigate the Markovian Generalized Adaptive Susceptible-Infected-Susceptible (G-ASIS) epidemic model. The G-ASIS model comprises many contagious phenomena on networks, ranging from epidemics and information diffusion to innovation spread and human brain interactions. The connections between nodes in the G-ASIS model change adaptively over time, because nodes make decisions to create or break links based on the health state of their neighbors. Our contribution is fourfold. First, we rigorously derive the first-order and second-order mean-field approximations from the continuous-time Markov chain. Second, we illustrate that the first-order mean-field approximation fails to approximate the epidemic threshold of the Markovian G-ASIS model accurately. Third, we show that the second-order mean-field approximation is a qualitative good approximation of the Markovian G-ASIS model. Finally, we discuss the Adaptive Information Diffusion (AID) model in detail, which is contained in the G-ASIS model. We show that, similar to most other instances of the G-ASIS model, the AID model possesses a unique steady state, but that in the AID model, the convergence time toward the steady state is very large. Our theoretical results are supported by numerical simulations.

Homeostatic control of synaptic rewiring in recurrent networks induces the formation of stable memory engrams

Brain networks store new memories using functional and structural synaptic plasticity. Memory formation is generally attributed to Hebbian plasticity, while homeostatic plasticity is thought to have an ancillary role in stabilizing network dynamics. Here we report that homeostatic plasticity alone can also lead to the formation of stable memories. We analyze this phenomenon using a new theory of network remodeling, combined with numerical simulations of recurrent spiking neural networks that exhibit structural plasticity based on firing rate homeostasis. These networks are able to store repeatedly presented patterns and recall them upon the presentation of incomplete cues. Storage is fast, governed by the homeostatic drift. In contrast, forgetting is slow, driven by a diffusion process. Joint stimulation of neurons induces the growth of associative connections between them, leading to the formation of memory engrams. These memories are stored in a distributed fashion throughout connectivity matrix, and individual synaptic connections have only a small influence. Although memory-specific connections are increased in number, the total number of inputs and outputs of neurons undergo only small changes during stimulation. We find that homeostatic structural plasticity induces a specific type of "silent memories", different from conventional attractor states.

Spike-Timing-Dependent Plasticity Mediated by Dopamine and its Role in Parkinson's Disease Pathophysiology

Parkinson's disease (PD) is a multi-systemic neurodegenerative brain disorder. Motor symptoms of PD are linked to the significant dopamine (DA) loss in substantia nigra pars compacta (SNc) followed by basal ganglia (BG) circuit dysfunction. Increasing experimental and computational evidence indicates that (synaptic) plasticity plays a key role in the emergence of PD-related pathological changes following DA loss. Spike-timing-dependent plasticity (STDP) mediated by DA provides a mechanistic model for synaptic plasticity to modify synaptic connections within the BG according to the neuronal activity. To shed light on how DA-mediated STDP can shape neuronal activity and synaptic connectivity in the PD condition, we reviewed experimental and computational findings addressing the modulatory effect of DA on STDP as well as other plasticity mechanisms and discussed their potential role in PD pathophysiology and related network dynamics and connectivity. In particular, reshaping of STDP profiles together with other plasticity-mediated processes following DA loss may abnormally modify synaptic connections in competing pathways of the BG. The cascade of plasticity-induced maladaptive or compensatory changes can impair the excitation-inhibition balance towards the BG output nuclei, leading to the emergence of pathological activity-connectivity patterns in PD. Pre-clinical, clinical as well as computational studies reviewed here provide an understanding of the impact of synaptic plasticity and other plasticity mechanisms on PD pathophysiology, especially PD-related network activity and connectivity, after DA loss. This review may provide further insights into the abnormal structure-function relationship within the BG contributing to the emergence of pathological states in PD. Specifically, this review is intended to provide detailed information for the development of computational network models for PD, serving as testbeds for the development and optimization of invasive and non-invasive brain stimulation techniques. Computationally derived hypotheses may accelerate the development of therapeutic stimulation techniques and potentially reduce the number of related animal experiments.

Time Course of Homeostatic Structural Plasticity in Response to Optogenetic Stimulation in Mouse Anterior Cingulate Cortex

Plasticity is the mechanistic basis of development, aging, learning, and memory, both in healthy and pathological brains. Structural plasticity is rarely accounted for in computational network models due to a lack of insight into the underlying neuronal mechanisms and processes. Little is known about how the rewiring of networks is dynamically regulated. To inform such models, we characterized the time course of neural activity, the expression of synaptic proteins, and neural morphology employing an in vivo optogenetic mouse model. We stimulated pyramidal neurons in the anterior cingulate cortex of mice and harvested their brains at 1.5 h, 24 h, and $48\,\mathrm{h}$ after stimulation. Stimulus-induced cortical hyperactivity persisted up to 1.5 h and decayed to baseline after $24\,\mathrm{h}$ indicated by c-Fos expression. The synaptic proteins VGLUT1 and PSD-95, in contrast, were upregulated at $24\,\mathrm{h}$ and downregulated at $48\,\mathrm{h}$, respectively. Spine density and spine head volume were also increased at $24\,\mathrm{h}$ and decreased at $48\,\mathrm{h}$. This specific sequence of events reflects a continuous joint evolution of activity and connectivity that is characteristic of the model of homeostatic structural plasticity. Our computer simulations thus corroborate the observed empirical evidence from our animal experiments.

Long-Term Desynchronization by Coordinated Reset Stimulation in a Neural Network Model With Synaptic and Structural Plasticity

Several brain disorders are characterized by abnormal neuronal synchronization. To specifically counteract abnormal neuronal synchrony and, hence, related symptoms, coordinated reset (CR) stimulation was computationally developed. In principle, successive epochs of synchronizing and desynchronizing stimulation may reversibly move neural networks with plastic synapses back and forth between stable regimes with synchronized and desynchronized firing. Computationally derived predictions have been verified in pre-clinical and clinical studies, paving the way for novel therapies. However, as yet, computational models were not able to reproduce the clinically observed increase of desynchronizing effects of regularly administered CR stimulation intermingled by long stimulation-free epochs. We show that this clinically important phenomenon can be computationally reproduced by taking into account structural plasticity (SP), a mechanism that deletes or generates synapses in order to homeostatically adapt the firing rates of neurons to a set point-like target firing rate in the course of days to months. If we assume that CR stimulation favorably reduces the target firing rate of SP, the desynchronizing effects of CR stimulation increase after long stimulation-free epochs, in accordance with clinically observed phenomena. Our study highlights the pivotal role of stimulation- and dosing-induced modulation of homeostatic set points in therapeutic processes.

Self-Organized Structuring of Recurrent Neuronal Networks for Reliable Information Transmission

Simple Summary

Information processing in the brain takes places at multiple stages, each of which is a local network of neurons. The long-range connections between these network stages are sparse and do not change over time. Thus, within each stage information arrives at a sparse subset of input neurons and must be routed to a sparse subset of output neurons. In this theoretical work, we investigate how networks achieve this routing in a self-organized manner without losing information. We show that biologically inspired self-organization entails that input information is distributed to all neurons in the network by strengthening many synapses in the local networks. Thus, after successful self-organization, input information can be read out and decoded from a small number of outputs. We also show that this way of self-organization can still be more energy efficient than creating more long-range in- and output connections.

Abstract

Our brains process information using a layered hierarchical network architecture, with abundant connections within each layer and sparse long-range connections between layers. As these long-range connections are mostly unchanged after development, each layer has to locally self-organize in response to new inputs to enable information routing between the sparse in- and output connections. Here we demonstrate that this can be achieved by a well-established model of cortical self-organization based on a well-orchestrated interplay between several plasticity processes. After this self-organization, stimuli conveyed by sparse inputs can be rapidly read out from a layer using only very few long-range connections. To achieve this information routing, the neurons that are stimulated form feed-forward projections into the unstimulated parts of the same layer and get more neurons to represent the stimulus. Hereby, the plasticity processes ensure that each neuron only receives projections from and responds to only one stimulus such that the network is partitioned into parts with different preferred stimuli. Along this line, we show that the relation between the network activity and connectivity self-organizes into a biologically plausible regime. Finally, we argue how the emerging connectivity may minimize the metabolic cost for maintaining a network structure that rapidly transmits stimulus information despite sparse input and output connectivity.

3D Synaptic Organization of the Rat CA1 and Alterations Induced by Cocaine Self-Administration

The hippocampus plays a key role in contextual conditioning and has been proposed as an important component of the cocaine addiction brain circuit. To gain knowledge about cocaine-induced alterations in this circuit, we used focused ion beam milling/scanning electron microscopy to reveal and quantify the three-dimensional synaptic organization of the neuropil of the stratum radiatum of the rat CA1, under normal circumstances and after cocaine-self administration (SA). Most synapses are asymmetric (excitatory), macular-shaped, and in contact with dendritic spine heads. After cocaine-SA, the size and the complexity of the shape of both asymmetric and symmetric (inhibitory) synapses increased but no changes were observed in the synaptic density. This work constitutes the first detailed report on the 3D synaptic organization in the stratum radiatum of the CA1 field of cocaine-SA rats. Our data contribute to the elucidation of the normal and altered synaptic organization of the hippocampus, which is crucial for better understanding the neurobiological mechanisms underlying cocaine addiction.

Three-dimensional analysis of synaptic organization in the hippocampal CA1 field in Alzheimer's disease

Alzheimer's disease is the most common form of dementia, characterized by a persistent and progressive impairment of cognitive functions. Alzheimer's disease is typically associated with extracellular deposits of amyloid-β peptide and accumulation of abnormally phosphorylated tau protein inside neurons (amyloid-β and neurofibrillary pathologies). It has been proposed that these pathologies cause neuronal degeneration and synaptic alterations, which are thought to constitute the major neurobiological basis of cognitive dysfunction in Alzheimer's disease. The hippocampal formation is especially vulnerable in the early stages of Alzheimer's disease. However, the vast majority of electron microscopy studies have been performed in animal models. In the present study, we performed an extensive 3D study of the neuropil to investigate the synaptic organization in the stratum pyramidale and radiatum in the CA1 field of Alzheimer's disease cases with different stages of the disease, using focused ion beam/scanning electron microscopy (FIB/SEM). In cases with early stages of Alzheimer's disease, the synapse morphology looks normal and we observed no significant differences between control and Alzheimer's disease cases regarding the synaptic density, the ratio of excitatory and inhibitory synapses, or the spatial distribution of synapses. However, differences in the distribution of postsynaptic targets and synaptic shapes were found. Furthermore, a lower proportion of larger excitatory synapses in both strata were found in Alzheimer's disease cases. Individuals in late stages of the disease suffered the most severe synaptic alterations, including a decrease in synaptic density and morphological alterations of the remaining synapses. Since Alzheimer's disease cases show cortical atrophy, our data indicate a reduction in the total number (but not the density) of synapses at early stages of the disease, with this reduction being much more accentuated in subjects with late stages of Alzheimer's disease. The observed synaptic alterations may represent a structural basis for the progressive learning and memory dysfunctions seen in Alzheimer's disease cases.

Self-organized criticality in neural networks from activity-based rewiring

Neural systems process information in a dynamical regime between silence and chaotic dynamics. This has lead to the criticality hypothesis, which suggests that neural systems reach such a state by self-organizing toward the critical point of a dynamical phase transition. Here, we study a minimal neural network model that exhibits self-organized criticality in the presence of stochastic noise using a rewiring rule which only utilizes local information. For network evolution, incoming links are added to a node or deleted, depending on the node's average activity. Based on this rewiring-rule only, the network evolves toward a critical state, showing typical power-law-distributed avalanche statistics. The observed exponents are in accord with criticality as predicted by dynamical scaling theory, as well as with the observed exponents of neural avalanches. The critical state of the model is reached autonomously without the need for parameter tuning, is independent of initial conditions, is robust under stochastic noise, and independent of details of the implementation as different variants of the model indicate. We argue that this supports the hypothesis that real neural systems may utilize such a mechanism to self-organize toward criticality, especially during early developmental stages.

A computational framework for optimal control of a self-adjustive neural system with activity-dependent and homeostatic plasticity

The control of the brain system has received increasing attention in the domain of brain science. Most brain control studies have been conducted to explore the brain network's graph-theoretic properties or to produce the desired state based on neural state dynamics, regarding the brain as a passively responding system. However, the self-adjusting nature of neural system after treatment has not been fully considered in the brain control. In the present study, we propose a computational framework for optimal control of the brain with a self-adjustment process in the effective connectivity after treatment. The neural system is modeled to adjust its outgoing effective connectivity as activity-dependent plasticity after treatment, followed by synaptic rescaling of incoming effective connectivity. To control this neural system to induce the desired function, the system's self-adjustment parameter is first estimated, based on which the treatment is optimized. Utilizing this framework, we conducted simulations of optimal control over a functional hippocampal circuitry, estimated using dynamic causal modeling of voltage-sensitive dye imaging from the wild type and mutant mice, responding to consecutive electrical stimuli. Simulation results for optimal control of the abnormal circuit toward a healthy circuit using a single node treatment, neural-type specific treatment as an analogy of medication, and combined treatments of medication and nodal treatment suggest the plausibility of the current framework in controlling the self-adjusting neural system within a restricted treatment setting. We believe the proposed computational framework of the self-adjustment system would help optimal control of the dynamic brain after treatment.

Functional strengthening through synaptic scaling upon connectivity disruption in neuronal cultures

An elusive phenomenon in network neuroscience is the extent of neuronal activity remodeling upon damage. Here, we investigate the action of gradual synaptic blockade on the effective connectivity in cortical networks in vitro. We use two neuronal cultures configurations-one formed by about 130 neuronal aggregates and another one formed by about 600 individual neurons-and monitor their spontaneous activity upon progressive weakening of excitatory connectivity. We report that the effective connectivity in all cultures exhibits a first phase of transient strengthening followed by a second phase of steady deterioration. We quantify these phases by measuring GEFF, the global efficiency in processing network information. We term hyperefficiency the sudden strengthening of GEFF upon network deterioration, which increases by 20-50% depending on culture type. Relying on numerical simulations we reveal the role of synaptic scaling, an activity-dependent mechanism for synaptic plasticity, in counteracting the perturbative action, neatly reproducing the observed hyperefficiency. Our results demonstrate the importance of synaptic scaling as resilience mechanism.

Statistical analysis of unidirectional and reciprocal chemical connections in the C. elegans connectome

We analyze unidirectional and reciprocally connected pairs of neurons in the chemical connectomes of the male and hermaphrodite Caenorhabditis elegans, using recently published data. Our analysis reveals that reciprocal connections provide communication between most neurons with chemical synapses, and comprise on average more synapses than both unidirectional connections and the entire connectome. We further reveal that the C. elegans connectome is wired so that afferent connections onto neurons with large numbers of presynaptic neighbors (in-degree) comprise an above-average number of synapses (synaptic multiplicity). Notably, the larger the in-degree of a neuron the larger the synaptic multiplicity of its afferent connections. Finally, we show that the male forms two times fewer reciprocal connections between sex-shared neurons than the hermaphrodite, but a large number of reciprocal connections with male-specific neurons. These observations provide evidence for Hebbian structural plasticity in the C. elegans.

The Interplay of Synaptic Plasticity and Scaling Enables Self-Organized Formation and Allocation of Multiple Memory Representations

It is commonly assumed that memories about experienced stimuli are represented by groups of highly interconnected neurons called cell assemblies. This requires allocating and storing information in the neural circuitry, which happens through synaptic weight adaptations at different types of synapses. In general, memory allocation is associated with synaptic changes at feed-forward synapses while memory storage is linked with adaptation of recurrent connections. It remains, however, largely unknown how memory allocation and storage can be achieved and the adaption of the different synapses involved be coordinated to allow for a faithful representation of multiple memories without disruptive interference between them. In this theoretical study, by using network simulations and phase space analyses, we show that the interplay between long-term synaptic plasticity and homeostatic synaptic scaling organizes simultaneously the adaptations of feed-forward and recurrent synapses such that a new stimulus forms a new memory and where different stimuli are assigned to distinct cell assemblies. The resulting dynamics can reproduce experimental in-vivo data, focusing on how diverse factors, such as neuronal excitability and network connectivity, influence memory formation. Thus, the here presented model suggests that a few fundamental synaptic mechanisms may suffice to implement memory allocation and storage in neural circuitry.

Order Through Disorder: The Characteristic Variability of Systems

Randomness characterizes many processes in nature, and therefore its importance cannot be overstated. In the present study, we investigate examples of randomness found in various fields, to underlie its fundamental processes. The fields we address include physics, chemistry, biology (biological systems from genes to whole organs), medicine, and environmental science. Through the chosen examples, we explore the seemingly paradoxical nature of life and demonstrate that randomness is preferred under specific conditions. Furthermore, under certain conditions, promoting or making use of variability-associated parameters may be necessary for improving the function of processes and systems.

Modeling the Shape of Synaptic Spines by Their Actin Dynamics

Dendritic spines are the morphological basis of excitatory synapses in the cortex and their size and shape correlates with functional synaptic properties. Recent experiments show that spines exhibit large shape fluctuations that are not related to activity-dependent plasticity but nonetheless might influence memory storage at their synapses. To investigate the determinants of such spontaneous fluctuations, we propose a mathematical model for the dynamics of the spine shape and analyze it in 2D-related to experimental microscopic imagery-and in 3D. We show that the spine shape is governed by a local imbalance between membrane tension and the expansive force from actin bundles that originates from discrete actin polymerization foci. Experiments have shown that only few such polymerization foci co-exist at any time in a spine, each having limited life time. The model shows that the momentarily existing set of such foci pushes the membrane along certain directions until foci are replaced and other directions may now be affected. We explore these relations in depth and use our model to predict shape and temporal characteristics of spines from the different biophysical parameters involved in actin polymerization. Approximating the model by a single recursive equation we finally demonstrate that the temporal evolution of the number of active foci is sufficient to predict the size of the model-spines. Thus, our model provides the first platform to study the relation between molecular and morphological properties of the spine with a high degree of biophysical detail.

Restructuring of contralateral gray matter volume associated with cognition in patients with unilateral temporal lobe glioma before and after surgery

Glioma can cause variable alterations to the structure and function of the brain. However, there is a paucity of studies on the gray matter (GM) volume alterations in the brain region opposite the temporal glioma before and after surgery. Therefore, the present study was initiated to investigate the alternation in contralateral homotopic GM volume in patients with unilateral temporal lobe glioma and further, assess the relationship between GM volume alternations with cognition. Eight left temporal lobe glioma patients (LTPs), nine right temporal lobe glioma patients (RTPs), and 28 demographically matched healthy controls (HCs) were included. Using voxel‐based morphometry method, alternations in the contralateral homotopic GM volume in patients with unilateral temporal lobe glioma was determined. Furthermore, the correlation analysis was performed to explore the relationship between cognitive function and altered GM volume. In the preoperative analysis, compared to HCs, LTPs exhibited increased GM volume in right inferior temporal gyrus and right temporal pole (superior temporal gyrus), and, RTPs presented increased GM volume in left inferior temporal gyrus. In the postoperative analysis, compared to HCs, RTPs presented increased GM volume in left middle temporal gyrus. Furthermore, the increased GM volume was significantly positively correlated with the memory test but negatively correlated with the visuospatial test. This study preliminarily confirmed that there were compensatory changes in the GM volume in the contralateral temporal lobe in unilateral temporal lobe glioma patients. Furthermore, alterations of GM volume may be a mechanism for cognitive function compensation.

Constructing multilayered neural networks with sparse, data-driven connectivity using biologically-inspired, complementary, homeostatic mechanisms

The immense complexity of the brain requires that it be built and controlled by intrinsic, self-regulating mechanisms. One such mechanism, the formation of new connections via synaptogenesis, plays a central role in neuronal connectivity and, ultimately, performance. Adaptive synaptogenesis networks combine synaptogenesis, associative synaptic modification, and synaptic shedding to construct sparse networks. Here, inspired by neuroscientific observations, novel aspects of brain development are incorporated into adaptive synaptogenesis. The extensions include: (i) multiple layers, (ii) neuron survival and death based on information transmission, and (iii) bigrade growth factor signaling to control the onset of synaptogenesis in succeeding layers and to control neuron survival and death in preceding layers. Also guiding this research is the assumption that brains must achieve a compromise between good performance and low energy expenditures. Simulations of the network model demonstrate the parametric and functional control of both performance and energy expenditures, where performance is measured in terms of information loss and classification errors, and energy expenditures are assumed to be a monotonically increasing function of the number of neurons. Major insights from this study include (a) the key role a neural layer between two other layers has in controlling synaptogenesis and neuron elimination, (b) the performance and energy-savings benefits of delaying the onset of synaptogenesis in a succeeding layer, and (c) how the elimination of neurons in a preceding layer provides energy savings, code compression, and can be accomplished without significantly degrading information transfer or classification performance.

The road ahead in clinical network neuroscience

Clinical network neuroscience, the study of brain network topology in neurological and psychiatric diseases, has become a mainstay field within clinical neuroscience. Being a multidisciplinary group of clinical network neuroscience experts based in The Netherlands, we often discuss the current state of the art and possible avenues for future investigations. These discussions revolve around questions like "How do dynamic processes alter the underlying structural network?" and "Can we use network neuroscience for disease classification?" This opinion paper is an incomplete overview of these discussions and expands on ten questions that may potentially advance the field. By no means intended as a review of the current state of the field, it is instead meant as a conversation starter and source of inspiration to others.

3D Electron Microscopy Study of Synaptic Organization of the Normal Human Transentorhinal Cortex and Its Possible Alterations in Alzheimer's Disease

The transentorhinal cortex (TEC) is an obliquely oriented cortex located in the medial temporal lobe and, together with the entorhinal cortex, is one of the first affected areas in Alzheimer’s disease (AD). One of the most widely accepted hypotheses is that synaptopathy (synaptic alterations and loss) represents the major structural correlate of the cognitive decline observed in AD. However, very few electron microscope (EM) studies are available; the most common method to estimate synaptic density indirectly is by counting, at the light microscopic level, immunoreactive puncta using synaptic markers. To investigate synaptic morphology and possible alterations related to AD, a detailed three-dimensional (3D) ultrastructural analysis using focused ion beam/scanning EM (FIB/SEM) was performed in the neuropil of Layer II of the TEC in human brain samples from non-demented subjects and AD patients. Evaluation of the proportion and shape of asymmetric synapses (AS) and symmetric synapses (SS) targeting spines or dendritic shafts was performed using 3D reconstructions of every synapse. The 3D analysis of 4722 synapses revealed that the preferable targets were spine heads for AS and dendritic shafts for SS, both in control and AD cases. However, in AD patients, we observed a reduction in the percentage of synapses targeting spine heads. Regarding the shape of synapses, in both control cases and AD samples, the vast majority of synapses had a macular shape, followed by perforated or horseshoe-shaped synapses, with fragmented synapses being the least frequent type. Moreover, comparisons showed an increased number of fragmented AS in AD patients.

Self-organized reactivation maintains and reinforces memories despite synaptic turnover

Long-term memories are believed to be stored in the synapses of cortical neuronal networks. However, recent experiments report continuous creation and removal of cortical synapses, which raises the question how memories can survive on such a variable substrate. Here, we study the formation and retention of associative memory in a computational model based on Hebbian cell assemblies in the presence of both synaptic and structural plasticity. During rest periods, such as may occur during sleep, the assemblies reactivate spontaneously, reinforcing memories against ongoing synapse removal and replacement. Brief daily reactivations during rest-periods suffice to not only maintain the assemblies, but even strengthen them, and improve pattern completion, consistent with offline memory gains observed experimentally. While the connectivity inside memory representations is strengthened during rest phases, connections in the rest of the network decay and vanish thus reconciling apparently conflicting hypotheses of the influence of sleep on cortical connectivity.

Exercise during abstinence normalizes ultrastructural synaptic plasticity associated with nicotine-seeking following extended access self-administration

Nicotine-craving progressively increases, or incubates, over abstinence following extended access self-administration. While not yet examined for nicotine, the incubation of cocaine-seeking is accompanied by changes in synaptic plasticity in the nucleus accumbens. Here, we determined whether such changes also accompany enhanced nicotine-seeking following extended access self-administration and abstinence, and whether exercise, a potential intervention for nicotine addiction, may exert its efficacy by normalizing these changes. Given that in humans, tobacco/nicotine use begins during adolescence, we used an adolescent-onset model. Nicotine-seeking was assessed in male rats following extended access nicotine or saline self-administration (23-hr/day, 10 days) and 10 days of abstinence, conditions known to induce the incubation of nicotine-seeking, using a within-session extinction/cue-induced reinstatement procedure. A subset of rats had 2-hr/day access to a running wheel during abstinence. Ultrastructural alterations of synapses in the nucleus accumbens core and shell were examined using electron microscopy. Nicotine-seeking was elevated following extended access self-administration and abstinence (in sedentary group), and levels of seeking were associated with an increase in the density of asymmetric (excitatory) and symmetric (inhibitory) synapses onto dendrites in the core, as well as longer asymmetric synapses onto spines, a marker of synaptic potentiation, in both the core and shell. Exercise normalized each of these changes; however, in the shell, exercise and nicotine similarly increased the synapse length. Together, these findings indicate an association between nicotine-seeking and synaptic plasticity in the nucleus accumbens, particularly the core, and indicate that the efficacy of exercise to reduce nicotine-seeking may be mediated by reversing these adaptations.

Ketamine and selective activation of parvalbumin interneurons inhibit stress-induced dendritic spine elimination

Stress is a major risk factor for the onset of many psychiatric diseases. In rodent models, chronic stress induces depression and impairs excitatory neurotransmission. However, little is known about the effect of stress on synaptic circuitry during the development of behavioral symptoms. Using two-photon transcranial imaging, we studied the effect of repeated restraint stress on dendritic spine plasticity in the frontal cortex in vivo. We found that restraint stress induced dendritic spine loss by decreasing the rate of spine formation and increasing the rate of spine elimination. The N-methyl-D-aspartate receptor antagonist ketamine inhibited stress-induced spine loss mainly by protecting mushroom spines from elimination. Ketamine also induced re-formation of spines in close proximity to previously stress-eliminated spines. Electrophysiological and in vivo imaging experiments showed that ketamine enhanced activity of parvalbumin (PV) interneurons under stress and counterbalanced the stress-induced net loss of PV axonal boutons. In addition, selective chemogenetic excitation of PV interneurons mimicked the protective effects of ketamine on dendritic spines against stress. Collectively, our data provide new insights on the effects of ketamine on synaptic circuitry under stress and a possible mechanism to counteract stress-induced synaptic impairments through PV interneuron activation.

Position- and Time-Dependent Arc Expression Links Neuronal Activity to Synaptic Plasticity During Epileptogenesis

In mesial temporal lobe epilepsy (mTLE) an initial precipitating injury can trigger aberrant wiring of neuronal circuits causing seizure activity. While circuit reorganization is known to be largely activity-dependent, the interactions between neuronal activity and synaptic plasticity during the development of mTLE remain poorly understood. Therefore, the present study aimed at delineating the spatiotemporal relationship between epileptic activity, activity-dependent gene expression and synaptic plasticity during kainic acid-induced epileptogenesis in mice. We show that during epileptogenesis the sclerotic hippocampus differed from non-sclerotic regions by displaying a consistently lower power of paroxysmal discharges. However, the power of these discharges steadily increased during epileptogenesis. This increase was paralleled by the upregulation of the activity-related cytoskeleton protein (Arc) gene expression in dentate granule cells (DGCs) of the sclerotic hippocampus. Importantly, we found that Arc mRNA-upregulating DGCs exhibited increased spine densities and spine sizes, but at the same time decreased AMPA-type glutamate receptor (AMPAR) densities. Finally, we show that in vivo optogenetic stimulation of DGC synapses evoked robust seizure activity in epileptic mice, but failed to induce dendritic translocation of Arc mRNA as under healthy conditions, supporting the theory of a breakdown of the dentate gate in mTLE. We conclude that during epileptogenesis epileptic activity emerges early and persists in the whole hippocampus, however, only the sclerotic part shows modulation of discharge amplitudes accompanied by plasticity of DGCs. In this context, we identified Arc as a putative mediator between seizure activity and synaptic plasticity.

Associative plasticity in supplementary motor area - motor cortex pathways in Tourette syndrome

The important role of the supplementary motor area (SMA) in the generation of tics and urges in Gilles de la Tourette syndrome (GTS) is underscored by an increased SMA-motor cortex (M1) connectivity. However, whether plasticity is also altered in SMA-M1 pathways is unclear. We explored whether SMA-M1 plasticity is altered in patients with Tourette syndrome. 15 patients with GTS (mean age of 33.4 years, SD = 9.9) and 19 age and sex matched healthy controls were investigated with a paired association stimulation (PAS) protocol using three transcranial magnetic stimulation (TMS) coils stimulating both M1 and the SMA. Standard clinical measures for GTS symptoms were collected. There was a significant PAS effect showing that MEP amplitudes measured in blocks during and after PAS were significantly higher compared to those in the first block. However, the degree of PAS was not differentially modulated between patients and controls as shown by a Bayesian data analysis. PAS effects in GTS correlated positively with the YGTSS motor tic severity. Plasticity previously reported to be altered in sensorimotor pathways in GTS is normal in SMA-M1 projections suggesting that the dysfunction of the SMA in GTS is not primarily related to altered plasticity in SMA-M1 connections.

Selective activation of parvalbumin interneurons prevents stress-induced synapse loss and perceptual defects

Stress, a prevalent experience in modern society, is a major risk factor for many psychiatric disorders. Although sensorimotor abnormalities are often present in these disorders, little is known about how stress affects the sensory cortex. Combining behavioral analyses with in vivo synaptic imaging, we show that stressful experiences lead to progressive, clustered loss of dendritic spines along the apical dendrites of layer (L) 5 pyramidal neurons (PNs) in the mouse barrel cortex, and such spine loss closely associates with deteriorated performance in a whisker-dependent texture discrimination task. Furthermore, the activity of parvalbumin-expressing inhibitory interneurons (PV+ INs) decreases in the stressed mouse due to reduced excitability of these neurons. Importantly, both behavioral defects and structural changes of L5 PNs are prevented by selective pharmacogenetic activation of PV+INs in the barrel cortex during stress. Finally, stressed mice raised under environmental enrichment (EE) maintain normal activation of PV+ INs, normal texture discrimination, and L5 PN spine dynamics similar to unstressed EE mice. Our findings suggest that the PV+ inhibitory circuit is crucial for normal synaptic dynamics in the mouse barrel cortex and sensory function. Pharmacological, pharmacogenetic and environmental approaches to prevent stress-induced maladaptive behaviors and synaptic malfunctions converge on the regulation of PV+ IN activity, pointing to a potential therapeutic target for stress-related disorders.

Accumulation of Dense Core Vesicles in Hippocampal Synapses Following Chronic Inactivity

The morphology and function of neuronal synapses are regulated by neural activity, as manifested in activity-dependent synapse maturation and various forms of synaptic plasticity. Here we employed cryo-electron tomography (cryo-ET) to visualize synaptic ultrastructure in cultured hippocampal neurons and investigated changes in subcellular features in response to chronic inactivity, a paradigm often used for the induction of homeostatic synaptic plasticity. We observed a more than 2-fold increase in the mean number of dense core vesicles (DCVs) in the presynaptic compartment of excitatory synapses and an almost 20-fold increase in the number of DCVs in the presynaptic compartment of inhibitory synapses after 2 days treatment with the voltage-gated sodium channel blocker tetrodotoxin (TTX). Short-term treatment with TTX and the N-methyl-D-aspartate receptor (NMDAR) antagonist amino-5-phosphonovaleric acid (AP5) caused a 3-fold increase in the number of DCVs within 100 nm of the active zone area in excitatory synapses but had no significant effects on the overall number of DCVs. In contrast, there were very few DCVs in the postsynaptic compartments of both synapse types under all conditions. These results are consistent with a role for presynaptic DCVs in activity-dependent synapse maturation. We speculate that these accumulated DCVs can be released upon reactivation and may contribute to homeostatic metaplasticity.

Associative properties of structural plasticity based on firing rate homeostasis in recurrent neuronal networks

Correlation-based Hebbian plasticity is thought to shape neuronal connectivity during development and learning, whereas homeostatic plasticity would stabilize network activity. Here we investigate another, new aspect of this dichotomy: Can Hebbian associative properties also emerge as a network effect from a plasticity rule based on homeostatic principles on the neuronal level? To address this question, we simulated a recurrent network of leaky integrate-and-fire neurons, in which excitatory connections are subject to a structural plasticity rule based on firing rate homeostasis. We show that a subgroup of neurons develop stronger within-group connectivity as a consequence of receiving stronger external stimulation. In an experimentally well-documented scenario we show that feature specific connectivity, similar to what has been observed in rodent visual cortex, can emerge from such a plasticity rule. The experience-dependent structural changes triggered by stimulation are long-lasting and decay only slowly when the neurons are exposed again to unspecific external inputs.

Study of the Size and Shape of Synapses in the Juvenile Rat Somatosensory Cortex with 3D Electron Microscopy

Changes in the size of the synaptic junction are thought to have significant functional consequences. We used focused ion beam milling and scanning electron microscopy (FIB/SEM) to obtain stacks of serial sections from the six layers of the rat somatosensory cortex. We have segmented in 3D a large number of synapses (n = 6891) to analyze the size and shape of excitatory (asymmetric) and inhibitory (symmetric) synapses, using dedicated software. This study provided three main findings. Firstly, the mean synaptic sizes were smaller for asymmetric than for symmetric synapses in all cortical layers. In all cases, synaptic junction sizes followed a log-normal distribution. Secondly, most cortical synapses had disc-shaped postsynaptic densities (PSDs; 93%). A few were perforated (4.5%), while a smaller proportion (2.5%) showed a tortuous horseshoe-shaped perimeter. Thirdly, the curvature was larger for symmetric than for asymmetric synapses in all layers. However, there was no correlation between synaptic area and curvature.

Branch-specific plasticity of a bifunctional dopamine circuit encodes protein hunger

Free-living animals must not only regulate the amount of food they consume but also choose which types of food to ingest. The shifting of food preference driven by nutrient-specific hunger can be essential for survival, yet little is known about the underlying mechanisms. We identified a dopamine circuit that encodes protein-specific hunger in Drosophila The activity of these neurons increased after substantial protein deprivation. Activation of this circuit simultaneously promoted protein intake and restricted sugar consumption, via signaling to distinct downstream neurons. Protein starvation triggered branch-specific plastic changes in these dopaminergic neurons, thus enabling sustained protein consumption. These studies reveal a crucial circuit mechanism by which animals adjust their dietary strategy to maintain protein homeostasis.

The malleable brain: plasticity of neural circuits and behavior - a review from students to students

One of the most intriguing features of the brain is its ability to be malleable, allowing it to adapt continually to changes in the environment. Specific neuronal activity patterns drive long-lasting increases or decreases in the strength of synaptic connections, referred to as long-term potentiation and long-term depression, respectively. Such phenomena have been described in a variety of model organisms, which are used to study molecular, structural, and functional aspects of synaptic plasticity. This review originated from the first International Society for Neurochemistry (ISN) and Journal of Neurochemistry (JNC) Flagship School held in Alpbach, Austria (Sep 2016), and will use its curriculum and discussions as a framework to review some of the current knowledge in the field of synaptic plasticity. First, we describe the role of plasticity during development and the persistent changes of neural circuitry occurring when sensory input is altered during critical developmental stages. We then outline the signaling cascades resulting in the synthesis of new plasticity-related proteins, which ultimately enable sustained changes in synaptic strength. Going beyond the traditional understanding of synaptic plasticity conceptualized by long-term potentiation and long-term depression, we discuss system-wide modifications and recently unveiled homeostatic mechanisms, such as synaptic scaling. Finally, we describe the neural circuits and synaptic plasticity mechanisms driving associative memory and motor learning. Evidence summarized in this review provides a current view of synaptic plasticity in its various forms, offers new insights into the underlying mechanisms and behavioral relevance, and provides directions for future research in the field of synaptic plasticity. Read the Editorial Highlight for this article on page 788. Cover Image for this issue: doi: 10.1111/jnc.13815.

Impaired Tuning of Neural Ensembles and the Pathophysiology of Schizophrenia: A Translational and Computational Neuroscience Perspective

The functional optimization of neural ensembles is central to human higher cognitive functions. When the functions through which neural activity is tuned fail to develop or break down, symptoms and cognitive impairments arise. This review considers ways in which disturbances in the balance of excitation and inhibition might develop and be expressed in cortical networks in association with schizophrenia. This presentation is framed within a developmental perspective that begins with disturbances in glutamate synaptic development in utero. It considers developmental correlates and consequences, including compensatory mechanisms that increase intrinsic excitability or reduce inhibitory tone. It also considers the possibility that these homeostatic increases in excitability have potential negative functional and structural consequences. These negative functional consequences of disinhibition may include reduced working memory-related cortical activity associated with the downslope of the "inverted-U" input-output curve, impaired spatial tuning of neural activity and impaired sparse coding of information, and deficits in the temporal tuning of neural activity and its implication for neural codes. The review concludes by considering the functional significance of noisy activity for neural network function. The presentation draws on computational neuroscience and pharmacologic and genetic studies in animals and humans, particularly those involving N-methyl-D-aspartate glutamate receptor antagonists, to illustrate principles of network regulation that give rise to features of neural dysfunction associated with schizophrenia. While this presentation focuses on schizophrenia, the general principles outlined in the review may have broad implications for considering disturbances in the regulation of neural ensembles in psychiatric disorders.