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Introduction: Stress, anxiety, and depression are of high prevalence among all mental disorders. Their high prevalence among those students directly dealing with the public health, made us investigate the prevalence of these disorders and their related parameters such as the body mass index and body image perception among the medical students in Azad Medical University. Materials and methods: This is a cross-sectional – analytical study conducted on 148 medical students (51 male and 97 female; average age 19.51±1.03 years) of the Azad Medical Sciences University of Tehran in their 1st to 7th semesters in a period between December and June 2017.. The students were given a questionnaire encompassing demographic information, 21-folded depression, anxiety, and stress index (DASS-21), and the questionnaire of the body image perception; and the participants’ height and weight data were also collected. Results: 148 students filled in the questionnaires and 47%, 54% and 59.15% of them were diagnosed with depression, anxiety, and stress symptoms, respectively. These percentages were not significantly different between the basic medicals and physiopathology course groups. But the stress rate was lower among the physiopathology course group members (p=0.026). Furthermore, the BMI rate was normal in 61.5% of the students of the basic medicals courses and 71.9% of those of physiopathology courses. However, only 25.3% of the students perceived a correct image of their body, and this can provide grounds for the outbreak of depression among those students. Conclusion: The present study revealed that the prevalence of depression, stress, and anxiety are high among medical students, and this makes them perceive an incorrect image of their body which should be studied. Therefore, regarding the results of this study, it is imperative to conduct more research on this group, so that the outcomes would be communicated with the relevant authorities and educational academies, in line with reducing the pressure on the students.

Introduction: Parents’ challenges in care of the children with special health care needs expose them to a high risk of developing psychological symptoms, such as increased anxiety and worry. This study aimed to investigate the effect of integrated monotheistic therapy (MIT) on the mental health of mothers of children with special needs (physical illnesses) during the COVID-19 pandemic. Methods: From this statistical population, using convenience sampling method, 60 mothers with severely physically ill children were selected who after testing as a pre-test and 30 people were randomly divided into experimental and control groups. A mental health questionnaire was used. Then, the treatment sessions were held in 8 sessions of 120 minutes by observing the health protocol for the subjects in the experimental group. Then, the data were collected and analyzed by SPSS statistical software using analysis of covariance. Results: The results show that after controlling the pre-test scores, the effect of the group on the post-test scores of mental health was significant (p

COVID-19, as a global concern and pivotal issue in the healthcare system, could have various presentations, leading to difficulty in diagnosis and management. Neuroinvasive potency, as claimed by preliminary studies, is a considerable pathogenesis. Serious neurological disorders like multiple sclerosis (MS) were out of the blue to be the first demonstration of COVID-19. This report highlights the representation of MS in a young woman, which resulted in a COVID-19 diagnosis.

In this research, we develop a neuromorphic system to study neural signaling at the level of first order tactile afferents which are slowly adapting type I (SA1) and rapidly adapting type I (RA1) mechanoreceptors. Considering, the linearized Izhikevich model, two digital circuits are developed for both afferents and are executed on the field programmable gate array (FPGA). After implementation of the digital circuits, we investigate how much information is encoded by this hardware-based neuromorphic system. Indeed, the artificial spiking sequences are evoked by applying different force profiles to the sensor connected to the FPGA. Next, the obtained neural responses are classified based on the two fundamental neural coding for brain information processing: spike timing and rate coding. Considering temporal coding, k-nearest neighbors (kNN), support vector machine (SVM) and Decision Tree algorithms are used for forces recognition using acquired artificial spike patterns. The results of classification show that the digital RA1 is susceptible to signal variations, while the digital SA1, on the other hand, is sensitive to the ramp and hold inputs. Furthermore, these responses are better distinguishable to different stimuli when both artificial SA1 and RA1 afferents are regarded. These results, which are functionally compatible with biological observations, yield the promise for fabrication and development of new tactile sensing modules to be employed in bio-robotic and prosthetic applications.

The study of non-stationary effects of hydrological time series and land-use changes in urban areas is essential to predict future floods and their probable damage. In the current study, a novel method was proposed for analyzing their simultaneous impact. For this purpose, rainfall frequency and land-use changes analyses were conducted for two different long-term periods, and the results were compared. Then, hydrologic modeling of the catchment was carried out using the HEC-HMS model, and obtained hydrographs were fed to the HEC-RAS2D model for estimating flood inundation areas. Using the economic information of assets and their damage functions, flood damages related to these two periods were evaluated through the HEC-FIA model. The results indicated that in the low return periods (e.g., 2-year flood), the damage in the second period was increased with respect to the first one but increased for the return periods of 5 to 100 years. Furthermore, surface runoff showed a 4.65% increase due to land-use change and a 12% increase due to rainfall non-stationarity. Moreover, flood damage showed a 136% increase on average, and among the two studied factors, the non-stationarity of rainfalls is considerably more effective on flood intensification.

Theory of mind (ToM) is the ability to attribute subjective mental states to oneself and others and is significantly impaired in autism spectrum disorder (ASD). A frontal-posterior network of regions including the ventromedial prefrontal cortex (vmPFC) and temporoparietal junction (TPJ) is involved in ToM. Previous studies show an underactivation of these regions in ASD. Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation method for causally investigating brain-behavior relationships via induction of cortical excitability alterations. tDCS, mostly over the dorsolateral prefrontal cortex, has been increasingly applied for improving behavioral problems in ASD leaving other potentially interesting regions untouched. Here we investigated contribution of the vmPFC and right TPJ in ToM abilities of ASD children via tDCS in a pilot study. Sixteen children with ASD (mean age = 10.7 ± 1.9) underwent three tDCS sessions (1 mA, 20 min) in a randomized, sham-controlled design. Stimulation protocols included: (a) anodal vmPFC tDCS, (b) anodal r-TPJ tDCS, and (c) sham tDCS. ToM abilities were explored during tDCS using the theory of mind test (TOMT). Our results show that activation of the vmPFC with anodal tDCS significantly improved ToM in children with ASD compared with both, r-TPJ tDCS, and sham stimulation. Specifically, precursors of ToM (e.g., emotion recognition, perception, and imitation) and elementary ToM skills (e.g., first-order mental state reasoning) were significantly improved by anodal vmPFC tDCS. Based on these results, the vmPFC could be a potential target region for the reduction of ASD symptoms via noninvasive brain stimulation, which should be examined in larger detail in future studies. LAY SUMMARY: Theory of mind (ToM) is the ability to infer mental states of oneself and others, which is impaired in autism. Brain imaging studies have shown involvement of two brain regions in ToM (ventromedial prefrontal cortex, temporoparietal junction) which are underactivated in autism. We increased activation of these regions via noninvasive brain stimulation in this experiment to see how it would affect ToM abilities in autism. We found that increased activation of the ventromedial prefrontal cortex improved ToM abilities in children with autism.

The combination of non-invasive brain stimulation interventions with human brain mapping methods have supported research beyond correlational associations between brain activity and behavior. Functional MRI (fMRI) partnered with transcranial electrical stimulation (tES) methods, i.e., transcranial direct current (tDCS), transcranial alternating current (tACS), and transcranial random noise (tRNS) stimulation, explore the neuromodulatory effects of tES in the targeted brain regions and their interconnected networks and provide opportunities for individualized interventions. Advances in the field of tES-fMRI can be hampered by the methodological variability between studies that confounds comparability/replicability. In order to explore variability in the tES-fMRI methodological parameter space (MPS), we conducted a systematic review of 222 tES-fMRI experiments (181 tDCS, 39 tACS and 2 tRNS) published before February 1, 2019, and suggested a framework to systematically report main elements of MPS across studies. Publications dedicated to tRNS-fMRI were not considered in this systematic review. We have organized main findings in terms of fMRI modulation by tES. tES modulates activation and connectivity beyond the stimulated areas particularly with prefrontal stimulation. There were no two studies with the same MPS to replicate findings. We discuss how to harmonize the MPS to promote replication in future studies.

Transcranial direct current stimulation (tDCS) has been increasingly used in attention-deficit hyperactivity disorder (ADHD) with mixed results. Previous tDCS studies merely targeted the dorsolateral prefrontal cortex and right inferior frontal gyrus with partial or no improving effects on cognitive deficits respectively. Posterior parietal cortex is another region involved in attentional functioning of ADHD, however, its contribution to ADHD attention functions has not been explored in tDCS studies. Moreover, attention networks are not investigated in the previous tDCS studies in ADHD neither. Here, we explored the effects of anodal tDCS over the right posterior parietal cortex (r-PPC) on attentional functioning (i.e., attention networks, selective attention, shifting attention) and response inhibition in ADHD children. 19 children with ADHD were recruited and underwent anodal/sham r-PPC tDCS (1 mA, 20 min) during task performance in a randomized cross-over design. Our results show an improving effect of anodal r-PPC tDCS specifically on the orienting but alerting or executive networks, in line with findings of healthy populations. Furthermore, activation of the r-PPC had a deteriorating effect on the top-down attentional control required for selective attention measured by the Stroop test. Modeling of the current flow showed a stronger electrical field induced in the inferior PPC (BA 39,40) which mediates bottom-up attentional control. No significant effect on shifting attention and response inhibition was found. Our findings indicate a domain-specific involvement of the r-PPC in attention orienting network of ADHD children. Activation of the r-PPC improves bottom-up but hinders top-down attentional control suggesting a critical role of the r-PPC in ADHD bottom-up attentional control.

Since the introduction of noninvasive transcranial brain stimulation techniques decades ago, these tools have been applied in both basic and clinical research, with the aims to enhance knowledge about the pathophysiology as well as to develop new treatment options for neurological and psychiatric diseases. In this chapter, we describe the implementation of these techniques to monitor and modulate neurophysiological processes in the human brain, which enables a better understanding of disease-related pathophysiology, and the development of therapeutic interventions.

Many elderly New Zealanders live in houses larger than they need and some find it difficult to maintain them. Some struggle on while others prefer to move to retirement villages or similar options. This study takes a typical three bedroom house in New Zealand and shows it is possible to convert this into small units for the independent elderly to enable ageing in place. The aim was to see the resource use impacts of each housing option. Two schemes are shown, one with separate units and a shared entrance and one with separate en-suite bedsits with shared living spaces. The conversions meet the New Zealand Lifemark standards for such housing and provide the type of accommodation found in retirement village units. Seven scenarios were created for households in both the original and converted house. A life-cycle energy assessment of each scenario of the house before and after conversion, including its operating energy and the energy embodied in the building and furniture, appliances, and tools shows that occupancy and design are key factors in whether resources can be saved using this approach. Over 50 years the conversion with 4 occupants showed a 27% saving in life-cycle energy (per person). However, 2 people living in the original house used less energy per person over 50 years than either 2 or 3 people living in the same house converted into two separate units. This is because of the duplication of appliances and furniture and more use of hot water in the two bathrooms. The resources going into converting the house were minimal.

Network-level synchronization of theta oscillations in the cerebral cortex is linked to many vital cognitive functions across daily life, such as executive functions or regulation of arousal and consciousness. However, while neuroimaging has uncovered the ubiquitous functional relevance of theta rhythms in cognition, there remains a limited set of techniques for externally enhancing and stabilizing theta in the human brain non-invasively. Here, we developed and employed a new phase-synchronized low-intensity electric and magnetic stimulation technique to induce and stabilize narrowband 6-Hz theta oscillations in a group of healthy human adult participants, and then demonstrated how this technique also enhances cognitive processing by assaying working memory. Our findings demonstrate a technological advancement of brain stimulation methods, while also validating the causal link between theta activity and concurrent cognitive behavior, which may ultimately help to not only explain mechanisms, but offer perspectives for restoring deficient theta-band network activity observed in neuropsychiatric diseases.

Despite several pieces of evidence, which suggest that the human brain employs internal models for motor control and learning, the location of these models in the brain is not yet clear. In this study, we used transcranial direct current stimulation (tDCS) to manipulate right cerebellar function, while subjects adapt to a visuomotor task. We investigated the effect of this manipulation on the internal forward and inverse models by measuring two kinds of behavior: generalization of training in one direction to neighboring directions (as a proxy for inverse models) and localization of the hand position after movement without visual feedback (as a proxy for forward model). The experimental results showed no effect of cerebellar tDCS on generalization, but significant effect on localization. These observations support the idea that the cerebellum is a possible brain region for internal forward, but not inverse model formation. We also used a realistic human head model to calculate current density distribution in the brain. The result of this model confirmed the passage of current through the cerebellum. Moreover, to further explain some observed experimental results, we modeled the visuomotor adaptation process with the help of a biologically inspired method known as population coding. The effect of tDCS was also incorporated in the model. The results of this modeling study closely match our experimental data and provide further evidence in line with the idea that tDCS manipulates FM's function in the cerebellum.

Our ability to properly move and react in different situations is largely dependent on our perception of our limbs' position. At least three sources - vision, proprioception, and internal forward models (FMs) - seem to contribute to this perception. To the best of our knowledge, the effect of each source has not been studied individually. Specifically, role of FM has been ignored in some previous studies. We hypothesized that FM has a critical role in subjects' perception which needs to be considered in the relevant studies to obtain more reliable results. Therefore, we designed an experiment with the goal of investigating FM and proprioception role in subjects' perception of their hand's position. Three groups of subjects were recruited in the study. Based on the experiment design, it was supposed that subjects in different groups relied on proprioception, FM, and both of them for estimating their unseen hand's position. Comparing the results of three groups revealed significant difference between their estimation' errors. FM provided minimum estimation error, while proprioception had a bias error in the tested region. Integrating proprioception with FM decreased this error. Integration of two Gaussian functions, fitted to the error distribution of FM and proprioception groups, was simulated and created a mean error value almost similar to the experimental observation. These results suggest that FM role needs to be considered when studying the perceived position of the limbs. This can lead to gain better insights into the mechanisms underlying the perception of our limbs' position which might have potential clinical and rehabilitation applications, e.g., in the postural control of elderly which are at high risk of falls and injury because of deterioration of their perception with age.

Background For effects of transcranial direct current stimulation (tDCS), electrical field distribution and coverage of the target areas play a decisive role. Methods We explored the effect of different angle-orientations of tDCS electrodes applied over the upper limb motor cortex (M1) on motor cortex excitability in healthy volunteers. Sixteen individuals received 1 mA anodal or cathodal tDCS through 35 cm2 electrodes over M1 for 15 min. Transcranial magnetic stimulation was used to examine tDCS-generated cortical excitability effects. The M1 electrode-orientation was following the right-left longitudinal plane, or positioned with 45° deviation from the midsagittal plane. Coverage of underlying brain and electrical field orientation were also investigated. Results Cortical excitability modulation was observed only when the electrode was aligned with 45° angle, which covered a larger area of the motor cortex. Conclusion: an electrode angle-orientation of 45° induces superior neuroplastic effects of M1 due to a better alignment with the motor cortex.

During recent years, non-invasive brain stimulation, including transcranial electrical stimulation (tES) in general, and transcranial direct current stimulation (tDCS) in particular, have created new hopes for treatment of neurological and psychiatric diseases. Despite promising primary results in some brain disorders, a more widespread application of tES is hindered by the unsolved question of determining optimum stimulation protocols to receive meaningful therapeutic effects. tES has a large parameter space including various montages and stimulation parameters. Moreover, inter- and intra-individual differences in responding to stimulation protocols have to be taken into account. These factors contribute to the complexity of selecting potentially effective protocols for each disorder, different clusters of each disorder, and even each single patient. Expanding knowledge in different dimensions of basic and clinical neuroscience could help researchers and clinicians to select potentially effective protocols based on tES modulatory mechanisms for future clinical studies. In this article, we propose a heuristic spatiomechanistic framework which contains nine levels to address tES effects on brain functions. Three levels refer to the spatial resolution (local, small-scale networks and large-scale networks) and three levels of tES modulatory effects based on its mechanisms of action (neurochemical, neuroelectrical and oscillatory modulations). At the group level, this framework could be helpful to enable an informed and systematic exploration of various possible protocols for targeting a brain disorder or its neuroscience-based clusters. Considering recent advances in exploration of neurodiversity at the individual level with different brain mapping technologies, the proposed framework might also be used in combination with personal data to design individualized protocols for tES in the context of precision medicine in the future.

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.

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.

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.

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.

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).