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Drug craving is a dynamic neurocognitive emotional-motivational response to a wide range of cues, from internal to external environments and from drug-related to stressful or affective events. The subjective feeling of craving, as an appetitive or compulsive state, could be considered a part of this multidimensional process, with modules in different levels of consciousness and embodiment. The neural correspondence of this dynamic and complex phenomenon may be productively investigated in relation to regional, small-scale networks, large-scale networks, and brain states. Within cognitive neuroscience, this approach has provided a long list of neural and cognitive targets for craving modulations with different cognitive, electrical, or pharmacological interventions. There are new opportunities to integrate different approaches for carving management from environmental, behavioral, psychosocial, cognitive, and neural perspectives. By using cognitive neuroscience models that treat drug craving as a dynamic and multidimensional process, these approaches may yield more effective interventions for addiction medicine.

Addiction is a chronic relapsing brain disease with significant economical and medical burden on the societies but with limited effectiveness in the available treatment options. Better understanding of the chemical, neuronal, regional, and network alterations of the brain due to drug abuse can ultimately lead to tailoring individualized and more effective interventions. To this end, employing new assessment and intervention procedures seems crucial. Noninvasive brain stimulation (NIBS) techniques including transcranial electrical and magnetic stimulations (tES and TMS) have provided promising opportunities for the addiction medicine in two main domains: (1) providing new insights into neurochemical and neural circuit changes in the human brain cortex and (2) understanding the role of different brain regions by using NIBS and modulating cognitive functions, such as drug craving, risky decision making, inhibitory control and executive functions to obtain specific treatment outcomes. In spite of preliminary positive results, there are several open questions, which need to be addressed before routine clinical utilization of NIBS techniques in addiction to medicine, such as how to account for interindividual differences, define optimal cognitive and neural targets, optimize stimulation protocols, and integrate NIBS with other therapeutic methods. Therefore, in this chapter we revise the available literature on the use of NIBS (TMS and tES) in the diagnostic, prognostic, and therapeutic aspects of the addiction medicine.

Fuzzy controllers are being used in various control schemes. The aim of this study is to adjust the hemodialysis machine parameters by utilizing a fuzzy logic controller (FLC) so that patient's hemodynamic condition remains stable during hemodialysis treatment. For this purpose, a comprehensive mathematical model of the arterial pressure response during hemodialysis, including hemodynamic, osmotic, and regulatory phenomena has been used. The multi-input multi-output (MIMO) fuzzy logic controller receives three parameters from the model (heart rate, arterial blood pressure, and relative blood volume) as input. According to the changes in the controller input values and its rule base, the outputs change so that the patient's hemodynamic condition remains stable. The results of the simulations illustrate that applying the controller can improve the stability of a patient's hemodynamic condition during hemodialysis treatment and it also decreases the treatment time. Furthermore, by using fuzzy logic, there is no need to have prior knowledge about the system under control and the FLC is compatible with different patients.

Transcranial direct current stimulation (tDCS) has been proposed as a technique for brain activity modulation. In this technique, a weak current (usually 1–2 mA) is delivered to scalp through two sponge electrodes. There are two types of tDCS stimulation: cathodal and anodal, which inhibit and facilitate neuronal activity, respectively (Hansen, 2012). tDCS has been shown to be effective in Alzheimer's disease (AD). Several studies have revealed that tDCS application can improve memory performance in Alzheimer's patients (APs) (Ferrucci et al., 2008; Boggio et al., 2009, 2012). For example, results of a single session tDCS study (Ferrucci et al., 2008) revealed that anodal/cathodal tDCS significantly enhanced/worsened word recognition in AD patients. In another study, application of anodal stimulation over DLPFC of APs has led to recognition memory improvement in a visual memory task (Boggio et al., 2009). These effects seem to be persistent, as in a multi-session tDCS study (Boggio et al., 2012), improvement in patients' visual recognition lasted for 4 weeks. Current pathway through brain plays a key role in the observed effects. Currently, modeling studies provide the only way for determining the pattern of current flow during tDCS. In recent years, finite element modeling has been suggested as a reliable and helpful tool in clinical therapeutic applications (Bikson et al., 2012). A critical issue which is required to be considered in modeling studies is the inter-individual anatomical variations. A modeling study has shown the profound role of individual cortical morphology in determination of current flow distribution for healthy people (Datta et al., 2012). Also the impact of pathologic anatomy (skull defects and lesions) on modulation of current flow has been examined in some previous studies (Datta et al., 2010, 2011). Specifically, in AD loss of neuronal structures and synaptic damages result in cortex shrinkage and ventricular enlargement (Frisoni et al., 2010). This changes the volume of CSF- referred as “super highway” for current flow- and therefore can significantly alters current pathway in these patients' head compared to healthy subjects (Bikson et al., 2012). These studies suggest that it is not precise to determine the dosage of applied current only based on healthy human modeling or clinical trial outcomes. We hypothesize that change in cortical thickness due to brain atrophy has significant effects on current flow pattern. These anatomical alterations may shift the stimulated areas and peak current density location in head. They may even alter the expected results from tDCS application. We suggest that cortical thickness is required to be considered in modeling studies to obtain more precise pattern of current flow in head and the stimulated brain regions. Specifically, AD affects differently on each patient's brain structure. We suggest developing individualized models based on each patient's MRI data. These models can be used by clinicians to find the optimal electrode montage and current amplitude for each patient. Using Individual-based models for designing clinical protocols could provide us with better interpretation of the results.

In this study the effect of providing explicit information in a visuomotor adaptation task was investigated using two computational models. Both models were able to reproduce the observed behavior in previous studies. However, these models make different prediction about a localization task after adaptation. A relevant experiment may be designed and performed to test whether either of these predictions is valid or the models need to be improved.

We presented a hypothesis about the possible adaptation mechanisms employed in the brain based on error size. The proposed hypothesis can help to provide a better understanding of motor adaptation mechanism in brain. Further validation of the hypothesis requires more investigations and experiments. For example, adaptation in response to a gradual perturbation can be compared in deafferented subjects, cerebellar patients, and healthy individuals. This comparison may be performed regarding generalization patterns to untrained hand or to other contexts with the same hand, adaptation rate, wash-out rate, etc. It has been shown that deafferented individuals were able to adapt their reaches to altered visual feedback of the hand (Ingram et al., 2000; Bernier et al., 2006; Miall and Cole, 2007). Adaptation in these subjects may show different features compared to healthy ones.

Motor adaptation is tuning of motor commands to compensate the disturbances in the outside environment and/or in the sensory-motor system. In spite of various theoretical and empirical studies, mechanism by which the brain learns to adapt has not been clearly understood. Among different computational models, two lines of researches are of interest in this study: first, the models which assume two adaptive processes, i.e. fast and slow, for motor learning, and second, the computational frameworks which assume two types of internal models in the central nervous system (CNS), i.e., forward and inverse models. They explain motor learning by modifying these internal models. Here, we present a hypothesis for a possible relationship between these two viewpoints according to the computational and physiological findings. This hypothesis suggests a direct relationship between the forward (inverse) internal model and the fast (slow) adaptive process. That is, the forward (inverse) model and fast (slow) adaptive process can be two sides of the same coin. Further evaluation of this hypothesis is helpful to achieve a better understanding of motor adaptation mechanism in the brain and also it lends itself to be used in designing therapeutic programs for rehabilitation of certain movement disorders.

Gene regulatory network is a set of genes which interact with each other indirectly and thereby rates of gene expression to mRNA are controlled. DNA microarrays can measure the expression levels of thousands of genes. Because of noise in microarray data and probabilistic nature of Bayesian networks (BN), we used them to model causal relations between genes. One difficulty with this technique is that learning the BN structure is an NP-hard problem, as the number of possible structures is superexponential in the number of genes. So in this paper, genes are clustered based on gene ontology and then BN is applied to model relationships between co-clustered genes. On the other hand since time delay information exists in a real regulatory network and BN is a static network, we proposed a novel method that uses cross-correlation between co-clustered genes to incorporate time information in BN. This method is applied to reconstruct the regulatory network of 84 yeast genes from Saccharomyces cerevisiae cell cycle dataset. Comparing the simulations results with the KEGG pathway map show that using cross-correlation increases accuracy from 66% to 72%. Sensitivity of model is improved too, as the number of inferred links is increased from 70 to 101.

For understanding the function of an organism, it is necessary to know "which", "how fast", and "when" the genes are expressed. A gene regulatory network represents how and when the genes interact with each other. Using genetic network modeling, it is possible to explain the cell functions at molecular level. DNA microarrays can measure the expression levels of thousands of genes simultaneously. Most of methodologies have proposed so far for modeling gene networks from microarray data take into account only a small number of genes. In this paper, a two steps method is proposed that can model large-scale Gene Regulatory Networks using time series microarray data. Firstly, genes are clustered based on existing biological knowledge (Gene Ontology annotations) and then a higher-order Markov dynamic Bayesian network is applied in order to model causal relationships between genes in each cluster. Finally the learned subnetworks are integrated to make a global network. This method is applied to reconstruct the regulatory network of 75 yeast genes from cell cycle gene expression dataset collected by Spellman et al. (1998). Comparing the results with the KEGG pathway map, indicates that this approach is capable of finding 31% of true relationships between genes (69% if directionality and time delay is not considered).

For many years central court houses have been used in parts of Iran with different climates. Though initially there appear to be many similarities, some aspects of these houses vary from one climatic zone to another. Several studies have also suggested that users of these houses moved within them as they sought for better thermal situations. This article sets out how differing sizes, forms and dimensional ratios of central courts in two climate zones of Iran (Yazd, hot and dry, and Bushehr, hot and humid) can support this behaviour. In both places these central court forms can provide good situations for human comfort on various sides and levels of the court. Consequently, residents could move within the house with the seasons to get their desired level of thermal comfort but these traditional patterns of movement differ for each climate zone.

Non-invasive brain stimulation (NIBS) has been gaining increased popularity in human neuroscience research during the last years. Among the emerging NIBS tools is transcranial electrical stimulation (tES), whose main modalities are transcranial direct, and alternating current stimulation (tDCS, tACS). In tES, a small current (usually less than 3 mA) is delivered through the scalp. Depending on its shape, density, and duration, the applied current induces acute or long-lasting effects on excitability and activity of cerebral regions, and brain networks. tES is increasingly applied in different domains to (a) explore human brain physiology with regard to plasticity, and brain oscillations, (b) explore the impact of brain physiology on cognitive processes, and (c) treat clinical symptoms in neurological and psychiatric diseases. In this review, we give a broad overview of the main mechanisms and applications of these brain stimulation tools.