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Using behavioral, pharmacological, and molecular methods, lots of studies reveal that depression is closely related to the abnormal neural plasticity processes occurring in the prefrontal cortex and limbic system such as the hippocampus and amygdala. Meanwhile, functions of the brain-derived neurotrophic factor (BDNF) and the other neurotrophins in the pathogenesis of depression are well known. The maladaptive neuroplastic in depression may be related to alterations in the levels of neurotrophic factors, which play a central role in plasticity. Enhancement of neurotrophic factors signaling has great potential in therapy for depression. This review highlights the relevance of neurotrophic factors mediated neural plasticity and pathophysiology of depression. These studies reviewed here may suggest new possible targets for antidepressant drugs such as neurotrophins, their receptors, and relevant signaling pathways, and agents facilitating the activation of gene expression and increasing the transcription of neurotrophic factors in the brain.

We previously demonstrated that multipotent mesenchymal stromal cells (MSCs) that overexpress microRNA 133b (miR-133b) significantly improve functional recovery in rats subjected to middle cerebral artery occlusion (MCAO) compared with naive MSCs and that exosomes generated from naive MSCs mediate the therapeutic benefits of MSC therapy for stroke. Here we investigated whether exosomes isolated from miR-133b-overexpressing MSCs (Ex-miR-133b+) exert amplified therapeutic effects. Rats subjected to 2 h of MCAO were intra-arterially injected with Ex-miR-133b+, exosomes from MSCs infected by blank vector (Ex-Con), or phosphate-buffered saline (PBS) and were sacrificed 28 days after MCAO. Compared with the PBS treatment, both exosome treatment groups exhibited significant improvement of functional recovery. Ex-miR-133b+ treatment significantly increased functional improvement and neurite remodeling/brain plasticity in the ischemic boundary area compared with the Ex-Con treatment. Treatment with Ex-miR-133b+ also significantly increased brain exosome content compared with Ex-Con treatment. To elucidate mechanisms underlying the enhanced therapeutic effects of Ex-miR-133b+, astrocytes cultured under oxygen- and glucose-deprived (OGD) conditions were incubated with exosomes harvested from naive MSCs (Ex-Naive), miR-133b downregulated MSCs (Ex-miR-133b−), and Ex-miR-133b+. Compared with the Ex-Naive treatment, Ex-miR-133b+ significantly increased exosomes released by OGD astrocytes, whereas Ex-miR-133b− significantly decreased the release. Also, exosomes harvested from OGD astrocytes treated with Ex-miR-133b+ significantly increased neurite branching and elongation of cultured cortical embryonic rat neurons compared with the exosomes from OGD astrocytes subjected to Ex-Con. Our data suggest that exosomes harvested from miR-133b-overexpressing MSCs improve neural plasticity and functional recovery after stroke with a contribution from a stimulated secondary release of neurite-promoting exosomes from astrocytes.

Brain-computer interfaces (BCI) are used in stroke rehabilitation to translate brain signals into intended movements of the paralyzed limb. However, the efficacy and mechanisms of BCI-based therapies remain unclear. Here we show that BCI coupled to functional electrical stimulation (FES) elicits significant, clinically relevant, and lasting motor recovery in chronic stroke survivors more effectively than sham FES. Such recovery is associated to quantitative signatures of functional neuroplasticity. BCI patients exhibit a significant functional recovery after the intervention, which remains 6–12 months after the end of therapy. Electroencephalography analysis pinpoints significant differences in favor of the BCI group, mainly consisting in an increase in functional connectivity between motor areas in the affected hemisphere. This increase is significantly correlated with functional improvement. Results illustrate how a BCI–FES therapy can drive significant functional recovery and purposeful plasticity thanks to contingent activation of body natural efferent and afferent pathways. Brain-computer interface (BCI) can improve motor skills on stroke patients. This study shows that BCI-controlled neuromuscular electrical stimulation therapy can cause cortical reorganization due to activation of efferent and afferent pathways, and this effect can be long lasting in a brain region specific manner.

Speech recognition (SR) has been improved significantly by artificial neural networks (ANNs), but ANNs have the drawbacks of biologically implausibility and excessive power consumption because of the nonlocal transfer of real-valued errors and weights. While spiking neural networks (SNNs) have the potential to solve these drawbacks of ANNs due to their efficient spike communication and their natural way to utilize kinds of synaptic plasticity rules found in brain for weight modification. However, existing SNN models for SR either had bad performance, or were trained in biologically implausible ways. In this paper, we present a biologically inspired convolutional SNN model for SR. The network adopts the time-to-first-spike coding scheme for fast and efficient information processing. A biological learning rule, spike-timing-dependent plasticity (STDP), is used to adjust the synaptic weights of convolutional neurons to form receptive fields in an unsupervised way. In the convolutional structure, the strategy of local weight sharing is introduced and could lead to better feature extraction of speech signals than global weight sharing. We first evaluated the SNN model with a linear support vector machine (SVM) on the TIDIGITS dataset and it got the performance of 97.5%, comparable to the best results of ANNs. Deep analysis on network outputs showed that, not only are the output data more linearly separable, but they also have fewer dimensions and become sparse. To further confirm the validity of our model, we trained it on a more difficult recognition task based on the TIMIT dataset, and it got a high performance of 93.8%. Moreover, a linear spike-based classifier-tempotron-can also achieve high accuracies very close to that of SVM on both the two tasks. These demonstrate that an STDP-based convolutional SNN model equipped with local weight sharing and temporal coding is capable of solving the SR task accurately and efficiently.

One of the most fascinating developments in the field of neuroscience in the second half of the 20th century was the discovery of the endogenous capacity of the brain for reorganization during adult life. Morphological and functional mechanisms underlying brain plasticity have been extensively explored and characterized. However, our understanding of the functional significance of these plastic changes is still fragmentary. This book shows that brain plasticity plays an essential role in the regulation of hormonal levels. The second aim is to propose that hormones orchestrate the multiple endogenous plastic events of the brain for the generation of adequate physiological and behavioral responses in adaptation to and in prediction of changing life conditions. The book starts by introducing the conceptual backgrounds on the interactions of hormones and brain plasticity. It then devotes itself to the analysis of the role of brain plasticity in the regulation of the activity of endocrine glands. It examines different hormonal influences on brain plasticity. Then, it goes on to cover the interactions of hormones and brain plasticity along the life cycle under physiological and pathological conditions.

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.

In animal models of Alzheimer's disease (AD), mechanisms of cortical plasticity such as long-term potentiation (LTP) and long-term depression (LTD) are impaired. In AD patients, LTP-like cortical plasticity is abolished, whereas LTD seems to be preserved. Dopaminergic transmission has been hypothesized as a new player in ruling mechanisms of cortical plasticity in AD. We aimed at investigating whether administration of the dopamine agonist rotigotine (RTG) could modulate cortical plasticity in AD patients, as measured by theta burst stimulation (TBS) protocols of repetitive transcranial stimulation applied over the primary motor cortex. Thirty mild AD patients were tested in three different groups before and after 4 weeks of treatment with RTG, rivastigmine (RVT), or placebo (PLC). Each patient was evaluated for plasticity induction of LTP/LTD-like effects using respectively intermittent TBS (iTBS) or continuous TBS protocols. Short-latency afferent inhibition (SAI) protocol was performed to indirectly assess central cholinergic activity. A group of age-matched healthy controls was recruited for baseline comparisons. Results showed that at baseline, AD patients were characterized by impaired LTP-like cortical plasticity, as assessed by iTBS. These reduced levels of LTP-like cortical plasticity were increased and normalized after RTG administration. No effect was induced by RVT or PLC on LTP. LTD-like cortical plasticity was not modulated in any condition. Cholinergic activity was increased by both RTG and RVT. Our findings reveal that dopamine agonists may restore the altered mechanisms of LTP-like cortical plasticity in AD patients, thus providing novel implications for therapies based on dopaminergic stimulation.

Many aspects of the brain’s design can be understood as the result of evolutionary drive toward metabolic efficiency. In addition to the energetic costs of neural computation and transmission, experimental evidence indicates that synaptic plasticity is metabolically demanding as well. As synaptic plasticity is crucial for learning, we examine how these metabolic costs enter in learning. We find that when synaptic plasticity rules are naively implemented, training neural networks requires extremely large amounts of energy when storing many patterns. We propose that this is avoided by precisely balancing labile forms of synaptic plasticity with more stable forms. This algorithm, termed synaptic caching, boosts energy efficiency manifold and can be used with any plasticity rule, including back-propagation. Our results yield a novel interpretation of the multiple forms of neural synaptic plasticity observed experimentally, including synaptic tagging and capture phenomena. Furthermore, our results are relevant for energy efficient neuromorphic designs. The brain expends a lot of energy. While the organ accounts for only about 2% of a person’s bodyweight, it is responsible for about 20% of our energy use at rest. Neurons use some of this energy to communicate with each other and to process information, but much of the energy is likely used to support learning. A study in fruit flies showed that insects that learned to associate two stimuli and then had their food supply cut off, died 20% earlier than untrained flies. This is thought to be because learning used up the insects’ energy reserves. If learning a single association requires so much energy, how does the brain manage to store vast amounts of data? Li and van Rossum offer an explanation based on a computer model of neural networks. The advantage of using such a model is that it is possible to control and measure conditions more precisely than in the living brain. Analysing the model confirmed that learning many new associations requires large amounts of energy. This is particularly true if the memories must be stored with a high degree of accuracy, and if the neural network contains many stored memories already. The reason that learning consumes so much energy is that forming long-term memories requires neurons to produce new proteins. Using the computer model, Li and van Rossum show that neural networks can overcome this limitation by storing memories initially in a transient form that does not require protein synthesis. Doing so reduces energy requirements by as much as 10-fold. Studies in living brains have shown that transient memories of this type do in fact exist. The current results hence offer a hypothesis as to how the brain can learn in a more energy efficient way. Energy consumption is thought to have placed constraints on brain evolution. It is also often a bottleneck in computers. By revealing how the brain encodes memories energy efficiently, the current findings could thus also inspire new engineering solutions.

Key Points Twin and familial studies reveal that autism spectrum disorder (ASD) traits are highly heritable. The genetic landscape of ASD is made of common and rare variants and can be different from one individual to another. Most of the ASD-risk genes are involved in chromatin remodelling, regulation of protein synthesis and degradation, or synaptic plasticity. In cellular and animal models, mutations in the ASD-risk genes lead to a distortion of typical neuronal connectivity by decreasing or increasing synapse strength or number. Compensatory mechanisms, such as genetic buffering and synaptic homeostasis, could modulate the severity of these mutations. Recent years have seen considerable interest in the genetics of autism spectrum disorder (ASD). In this Review, Thomas Bourgeron examines the genetic architecture of this disorder and how ASD-linked mutations might affect synaptic plasticity, before exploring the synaptic homeostasis hypothesis of ASD. Genetics studies of autism spectrum disorder (ASD) have identified several risk genes that are key regulators of synaptic plasticity. Indeed, many of the risk genes that have been linked to these disorders encode synaptic scaffolding proteins, receptors, cell adhesion molecules or proteins that are involved in chromatin remodelling, transcription, protein synthesis or degradation, or actin cytoskeleton dynamics. Changes in any of these proteins can increase or decrease synaptic strength or number and, ultimately, neuronal connectivity in the brain. In addition, when deleterious mutations occur, inefficient genetic buffering and impaired synaptic homeostasis may increase an individual's risk for ASD.

Transcranial alternating current stimulation (tACS) represents a promising non-invasive treatment for an increasingly wide range of neurological and neuropsychiatric disorders. The ability to use periodically oscillating electric fields to non-invasively engage neural dynamics opens up the possibility of recruiting synaptic plasticity and to modulate brain function. However, despite consistent reports about tACS clinical effectiveness, strong state-dependence combined with the ubiquitous heterogeneity of cortical networks collectively results in high outcome variability. Introducing variations in intrinsic neuronal timescales, we explored how such heterogeneity influences stimulation-induced change in synaptic connectivity. We examined how spike timing dependent plasticity, at the level of cells, intra- and inter-laminar cortical networks, can be selectively and preferentially engaged by periodic stimulation. Using leaky integrate-and-fire neuron models, we analyzed cortical circuits comprised of multiple cell-types, alongside superficial multi-layered networks expressing distinct layer-specific timescales. Our results show that mismatch in neuronal timescales within and/or between cells—and the resulting variability in excitability, temporal integration properties and frequency tuning—enables selective and directional control on synaptic connectivity by tACS. Our work provides new vistas on how to recruit neural heterogeneity to guide brain plasticity using non-invasive stimulation paradigms.