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Abnormally strong neural synchronization may impair brain function, as observed in several brain disorders. We computationally study how neuronal dynamics, synaptic weights, and network structure co-emerge, in particular, during (de)synchronization processes and how they are affected by external perturbation. To investigate the impact of different types of plasticity mechanisms, we combine a network of excitatory integrate-and-fire neurons with different synaptic weight and/or structural plasticity mechanisms: (i) only spike-timing-dependent plasticity (STDP), (ii) only homeostatic structural plasticity (hSP), i.e., without weight-dependent pruning and without STDP, (iii) a combination of STDP and hSP, i.e., without weight-dependent pruning, and (iv) a combination of STDP and structural plasticity (SP) that includes hSP and weight-dependent pruning. To accommodate the diverse time scales of neuronal firing, STDP, and SP, we introduce a simple stochastic SP model, enabling detailed numerical analyses. With tools from network theory, we reveal that structural reorganization may remarkably enhance the network's level of synchrony. When weaker contacts are preferentially eliminated by weight-dependent pruning, synchrony is achieved with significantly sparser connections than in randomly structured networks in the STDP-only model. In particular, the strengthening of contacts from neurons with higher natural firing rates to those with lower rates and the weakening of contacts in the opposite direction, followed by selective removal of weak contacts, allows for strong synchrony with fewer connections. This activity-led network reorganization results in the emergence of degree-frequency, degree-degree correlations, and a mixture of degree assortativity. We compare the stimulation-induced desynchronization of synchronized states in the STDP-only model (i) with the desynchronization of models (iii) and (iv). The latter require stimuli of significantly higher intensity to achieve long-term desynchronization. These findings may inform future pre-clinical and clinical studies with invasive or non-invasive stimulus modalities aiming at inducing long-lasting relief of symptoms, e.g., in Parkinson's disease. Author summary: Synaptic weight and structural plasticity of neuronal networks determine their behavior, and abnormalities therein may underlie disordered states. Studying how different plasticity mechanisms govern network dynamics, particularly during (de)synchronization processes, holds clinical importance concerning, e.g., Parkinson's disease. The marked difference between the timescales at which neuronal spiking activity (milliseconds), synaptic weight modifications (minutes-hours), and structural changes (hours-days) occur in the brain makes plastic network models computationally expensive, which may limit the scope of studies. Here, we present a leaky integrate-and-fire (LIF) neuron network model with a standard spike-timing-dependent plasticity (STDP) rule for weight plasticity and a stochastic structural plasticity (SP) method. The model is computationally efficient, allowing for detailed numerical analyses of network dynamics and structure. Combining the model with tools from network science, we show that structural reorganization resulting from SP can optimize the network for synchronization, elevating the level of synchrony while concurrently reducing overall network connections. This leads to the emergence of structural correlations between the natural firing rates of neurons and the number of their pre- and post-synaptic partners. Additionally, we demonstrate that synchronized networks that evolved with SP can be more robust against desynchronization stimulation. [ABSTRACT FROM AUTHOR]

Background: Abnormal neuronal synchrony is associated with several neurological disorders, including Parkinson's disease (PD), essential tremor, dystonia, and epilepsy. Coordinated reset (CR) stimulation was developed computationally to counteract abnormal neuronal synchrony. During CR stimulation, phase-shifted stimuli are delivered to multiple stimulation sites. Computational studies in plastic neural networks reported that CR stimulation drove the networks into an attractor of a stable desynchronized state by downregulating synaptic connections, which led to long-lasting desynchronization effects that outlasted stimulation. Later, corresponding long-lasting desynchronization and therapeutic effects were found in animal models of PD and PD patients. To date, it is unclear how spatially dependent synaptic connections, as typically observed in the brain, shape CR-induced synaptic downregulation and long-lasting effects. Methods: We performed numerical simulations of networks of leaky integrate-and-fire neurons with spike-timing-dependent plasticity and spatially dependent synaptic connections to study and further improve acute and long-term responses to CR stimulation. Results: The characteristic length scale of synaptic connections relative to the distance between stimulation sites plays a key role in CR parameter adjustment. In networks with short synaptic length scales, a substantial synaptic downregulation can be achieved by selecting appropriate stimulus-related parameters, such as the stimulus amplitude and shape, regardless of the employed spatiotemporal pattern of stimulus deliveries. Complex stimulus shapes can induce local connectivity patterns in the vicinity of the stimulation sites. In contrast, in networks with longer synaptic length scales, the spatiotemporal sequence of stimulus deliveries is of major importance for synaptic downregulation. In particular, rapid shuffling of the stimulus sequence is advantageous for synaptic downregulation. Conclusion: Our results suggest that CR stimulation parameters can be adjusted to synaptic connectivity to further improve the long-lasting effects. Furthermore, shuffling of CR sequences is advantageous for long-lasting desynchronization effects. Our work provides important hypotheses on CR parameter selection for future preclinical and clinical studies. [ABSTRACT FROM AUTHOR]

This document is an editorial titled "Neuromodulation using spatiotemporally complex patterns" published in the journal Frontiers in Neuroinformatics. It discusses the use of different stimulation patterns in deep brain stimulation (DBS) for the treatment of Parkinson's disease (PD). The editorial highlights the potential benefits of stimulation with spatiotemporal patterns compared to conventional single-frequency stimulation. It also mentions other stimulation approaches, such as coordinated reset stimulation and adaptive DBS, and their potential clinical applications. The document emphasizes the importance of neuroplasticity as a target for the development of novel stimulation techniques. [Extracted from the article]