Studies using transcranial magnetic stimulation/transcranial direct current stimulation (TMS/tDCS) and deep brain stimulation (DBS) have shown significant results in the treatment of addiction ranging from nicotine, cocaine, heroin to alcohol dependence. Specifically, research investigating the effects of neurofeedback on nicotine dependent patients showed that modulation of the anterior cingulate cortex can decrease smokers' craving for nicotine. In several studies decreased craving was found in alcohol dependent patients after TMS or tDCS stimulation of the anterior cingulate cortex or the dorsolateral prefrontal cortex. Changing the behavior of neural networks, either through the modulation of neural spiking or threshold of neural firing presents another dimension to rehabilitation through neural rewiring or ‘neural-smithing’.Neuromodulation through non-invasive brain stimulation techniques have been used beyond the treatment of addiction. The capability to modulate macro and micro brain networks through external stimulation have provided a long-term rehabilitation approach to solving neurological issues such as tinnitus, primary headaches, poststroke gait disorders, etc. The initial goal is to seek new advances in non-invasive brain stimulation techniques as a rehabilitation approach to solving neurological issues. The second goal is to understand how external neuromodulation effects brain networks by modifying cortical excitability, mimicking the long-term depression (LTD) of synaptic plasticity, and sliding of the modification threshold for increased excitation (or long-term potentiation, LTP) and decreased excitation (or LTD), as an example. Computational and mathematical models have been used to capture how neuromodulation effects the brain through the modeling of brain networks and hubs, neural networks mathematically represented as graphs, comprised of nodes (neuronal elements) and edges (their connections), and advanced signal processing techniques.Technological advances have focused on various computational and mathematical modeling approaches to external neuromodulation. External neuromodulation approaches include, but are not limited to, advanced signal processing techniques, i.e. Kalman filters, univariate linear and nonlinear measures (spectral power, wavelet energy and entropy, correlation dimensionality, conditional probability, Shannon entropy, wavelet synchrony, dynamic entrainment, and phase locking value). Approaches such as graph theory, brain networks and hubs, oscillator models, and spatio-temporal dynamics have also been implemented. Biologically-based neural networks have had a high degree of success in modeling neural behavior such as architectures involving feed-forward backpropagation, layer-recurrent feed-forward input time-delay backpropagation, Elman, and distributed time delay networks. The overall aim of this Special Issue is to disseminate and discuss recent advances in external neuromodulation methodologies, with a focus on the following subtopics:• Emerging methodologies in external neuromodulation—providing new tools that improve sensitivity and specificity performance.• Computational and Mathematical Modeling approaches to external neuromodulation.• IoT advances in external neuromodulation• Novel approaches to treatment and assessment.We will accept contributions in the form of original research articles, reviews, mini-reviews, systematic reviews, perspective, methods, Hypothesis and Theory, clinical trials, case report, opinion, brief research report, data reports, technology and code and study protocol articles.
Studies using transcranial magnetic stimulation/transcranial direct current stimulation (TMS/tDCS) and deep brain stimulation (DBS) have shown significant results in the treatment of addiction ranging from nicotine, cocaine, heroin to alcohol dependence. Specifically, research investigating the effects of neurofeedback on nicotine dependent patients showed that modulation of the anterior cingulate cortex can decrease smokers' craving for nicotine. In several studies decreased craving was found in alcohol dependent patients after TMS or tDCS stimulation of the anterior cingulate cortex or the dorsolateral prefrontal cortex. Changing the behavior of neural networks, either through the modulation of neural spiking or threshold of neural firing presents another dimension to rehabilitation through neural rewiring or ‘neural-smithing’.Neuromodulation through non-invasive brain stimulation techniques have been used beyond the treatment of addiction. The capability to modulate macro and micro brain networks through external stimulation have provided a long-term rehabilitation approach to solving neurological issues such as tinnitus, primary headaches, poststroke gait disorders, etc. The initial goal is to seek new advances in non-invasive brain stimulation techniques as a rehabilitation approach to solving neurological issues. The second goal is to understand how external neuromodulation effects brain networks by modifying cortical excitability, mimicking the long-term depression (LTD) of synaptic plasticity, and sliding of the modification threshold for increased excitation (or long-term potentiation, LTP) and decreased excitation (or LTD), as an example. Computational and mathematical models have been used to capture how neuromodulation effects the brain through the modeling of brain networks and hubs, neural networks mathematically represented as graphs, comprised of nodes (neuronal elements) and edges (their connections), and advanced signal processing techniques.Technological advances have focused on various computational and mathematical modeling approaches to external neuromodulation. External neuromodulation approaches include, but are not limited to, advanced signal processing techniques, i.e. Kalman filters, univariate linear and nonlinear measures (spectral power, wavelet energy and entropy, correlation dimensionality, conditional probability, Shannon entropy, wavelet synchrony, dynamic entrainment, and phase locking value). Approaches such as graph theory, brain networks and hubs, oscillator models, and spatio-temporal dynamics have also been implemented. Biologically-based neural networks have had a high degree of success in modeling neural behavior such as architectures involving feed-forward backpropagation, layer-recurrent feed-forward input time-delay backpropagation, Elman, and distributed time delay networks. The overall aim of this Special Issue is to disseminate and discuss recent advances in external neuromodulation methodologies, with a focus on the following subtopics:• Emerging methodologies in external neuromodulation—providing new tools that improve sensitivity and specificity performance.• Computational and Mathematical Modeling approaches to external neuromodulation.• IoT advances in external neuromodulation• Novel approaches to treatment and assessment.We will accept contributions in the form of original research articles, reviews, mini-reviews, systematic reviews, perspective, methods, Hypothesis and Theory, clinical trials, case report, opinion, brief research report, data reports, technology and code and study protocol articles.