Search

Background: Cannabis is one of the most commonly used substances in the United States. Prior literature using task-based functional magnetic resonance imaging (fMRI) has identified that individuals with Cannabis use disorder (CUD) show impairments in emotion processing circuitry. However, whether the functional networks involving these regions are also altered in CUD remains poorly understood. Aims: Investigate changes in resting-state functional connectivity (rsFC) in regions related to emotional processing in CUD. Methods: Sixty-two participants completed resting-state fMRI, including 21 with CUD, 20 with histories of illicit substance use but no current CUD diagnosis, and 21 with no history of illicit substance use. Whole-brain seed-based connectivity analyses were performed and one-way analyses of covariance (ANCOVAs) were conducted to detect group differences in the bilateral amygdalae, hippocampi, and the anterior and posterior cingulate cortices. Results: The CUD group exhibited significant reductions in rsFC between the amygdala and the cuneus, paracentral lobule, and supplementary motor area, and between the cingulate cortices and the occipital and temporal lobes. There were no significant group differences in hippocampal functional connectivity. In addition, CUD symptom counts based on the Structured Clinical Interview for DSM-5 (SCID) and the Cannabis Use Disorders Identification Test (CUDIT) significantly correlated with multiple connectivity metrics. Conclusion: These data expand on emerging literature indicating that CUD is associated with dysfunction in the neural circuits underlying emotion processing. Dysfunction in emotion processing circuits may play a role in the behavioral impairments seen in emotion processing tasks in individuals with CUD, and the severity of CUD symptoms appears to be directly related to the degree of dysfunction in these circuits. [ABSTRACT FROM AUTHOR]

The prevalence of psychiatry disorders such as anxiety and depression has steadily increased in recent years in the United States. This increased risk for anxiety and depression is associated with excess weight gain, which is often due to over-consumption of western diets that are typically high in fat, as well as with binge eating disorders, which often overlap with overweight and obesity outcomes. This finding suggests that diet, particularly diets high in fat, may have important consequences on the neurocircuitry regulating emotional processing as well as metabolic functions. Depression and anxiety disorders are also often comorbid with alcohol and substance use disorders. It is well-characterized that many of the neurocircuits that become dysregulated by overconsumption of high fat foods are also involved in drug and alcohol use disorders, suggesting overlapping central dysfunction may be involved. Emerging preclinical data suggest that high fat diets may be an important contributor to increased susceptibility of binge drug and ethanol intake in animal models, suggesting diet could be an important aspect in the etiology of substance use disorders. Neuroinflammation in pivotal brain regions modulating metabolic function, food intake, and binge-like behaviors, such as the hypothalamus, mesolimbic dopamine circuits, and amygdala, may be a critical link between diet, ethanol, metabolic dysfunction, and neuropsychiatric conditions. This brief review will provide an overview of behavioral and physiological changes elicited by both diets high in fat and ethanol consumption, as well as some of their potential effects on neurocircuitry regulating emotional processing and metabolic function. [ABSTRACT FROM AUTHOR]

Rationale: The management of indeterminate pulmonary nodules (IPNs) remains challenging, resulting in invasive procedures and delays in diagnosis and treatment. Strategies to decrease the rate of unnecessary invasive procedures and optimize surveillance regimens are needed.Objectives: To develop and validate a deep learning method to improve the management of IPNs.Methods: A Lung Cancer Prediction Convolutional Neural Network model was trained using computed tomography images of IPNs from the National Lung Screening Trial, internally validated, and externally tested on cohorts from two academic institutions.Measurements and Main Results: The areas under the receiver operating characteristic curve in the external validation cohorts were 83.5% (95% confidence interval [CI], 75.4-90.7%) and 91.9% (95% CI, 88.7-94.7%), compared with 78.1% (95% CI, 68.7-86.4%) and 81.9 (95% CI, 76.1-87.1%), respectively, for a commonly used clinical risk model for incidental nodules. Using 5% and 65% malignancy thresholds defining low- and high-risk categories, the overall net reclassifications in the validation cohorts for cancers and benign nodules compared with the Mayo model were 0.34 (Vanderbilt) and 0.30 (Oxford) as a rule-in test, and 0.33 (Vanderbilt) and 0.58 (Oxford) as a rule-out test. Compared with traditional risk prediction models, the Lung Cancer Prediction Convolutional Neural Network was associated with improved accuracy in predicting the likelihood of disease at each threshold of management and in our external validation cohorts.Conclusions: This study demonstrates that this deep learning algorithm can correctly reclassify IPNs into low- or high-risk categories in more than a third of cancers and benign nodules when compared with conventional risk models, potentially reducing the number of unnecessary invasive procedures and delays in diagnosis. [ABSTRACT FROM AUTHOR]

Reward Deficiency Syndrome (RDS), particularly linked to addictive disorders, costs billions of dollars globally and has resulted in over one million deaths in the United States (US). Illicit substance use has been steadily rising and in 2021 approximately 21.9% (61.2 million) of individuals living in the US aged 12 or older had used illicit drugs in the past year. However, only 1.5% (4.1 million) of these individuals had received any substance use treatment. This increase in use and failure to adequately treat or provide treatment to these individuals resulted in 106,699 overdose deaths in 2021 and increased in 2022. This article presents an alternative non-pharmaceutical treatment approach tied to gene-guided therapy, the subject of many decades of research. The cornerstone of this paradigm shift is the brain reward circuitry, brain stem physiology, and neurotransmitter deficits due to the effects of genetic and epigenetic insults on the interrelated cascade of neurotransmission and the net release of dopamine at the Ventral Tegmental Area -Nucleus Accumbens (VTA-NAc) reward site. The Genetic Addiction Risk Severity (GARS) test and pro-dopamine regulator nutraceutical KB220 were combined to induce "dopamine homeostasis" across the brain reward circuitry. This article aims to encourage four future actionable items: 1) the neurophysiologically accurate designation of, for example, "Hyperdopameism /Hyperdopameism" to replace the blaming nomenclature like alcoholism; 2) encouraging continued research into the nature of dysfunctional brainstem neurotransmitters across the brain reward circuitry; 3) early identification of people at risk for all RDS behaviors as a brain check (cognitive testing); 4) induction of dopamine homeostasis using "precision behavioral management" along with the coupling of GARS and precision Kb220 variants; 5) utilization of promising potential treatments include neuromodulating modalities such as Transmagnetic stimulation (TMS) and Deep Brain Stimulation(DBS), which target different areas of the neural circuitry involved in addiction and even neuroimmune agents like N -acetyl-cysteine. • This article presents an alternative non-pharmaceutical treatment approach tied to gene-guided therapy. • Genetic and epigenetic insults affect brain reward circuitry resulting in a reduction of the release of dopamine at the NAc. • The neurophysiologically accurate designation of "Hypodopaminism" replaces blaming nomenclature for RDS behaviors. • RDS behaviors can be identified utilizing GARS testing, brain mapping, and neurocognitive and neuropsychological assessments. • PBM can be used in the treatment and prevention of substance use disorder, relapse, and the development of RDS behaviors. [ABSTRACT FROM AUTHOR]

Cannabis is the third most used psychoactive drug worldwide. Despite being legally scheduled as a drug with high harm potential and no therapeutic utility in countries like the USA, evidence shows otherwise and legislative changes and reinterpretations of existing ambiguous laws make this drug increasingly available by legal means. Nevertheless, this substance is able to generate clear addiction syndromes in some individuals who use it, which are accompanied by brain alterations resembling those caused by other addictive drugs. Moreover, there is no available pharmacological treatment for this disorder. This fact motivates a deep study and comprehension of the neural basis of addiction-relevant cannabinoid effects. Interestingly, the cerebellum, a hindbrain structure which involvement in functions not related to motor control and planning is being increasingly recognized in the last decades, seems to be involved in the effects of addictive drugs and addiction-related processes and also presents a high density of cannabinoid receptors. Preclinical research on the involvement of the cerebellum in cannabis' effects has focused in the drug's motor incoordinating actions, potentially underestimating its participation in addiction. Therefore, this review addresses the studies reporting cerebellar involvement in cannabis effects both in experimental animals and human subjects and the possible relevance of these changes for addiction. Additionally, future experimental approaches will be proposed and hopefully this work will stimulate research on the cerebellum in cannabis addiction and help recognizing it as an important part of the neural circuitry affected in cannabis-related disorders. [ABSTRACT FROM AUTHOR]

Objective, accurate, and fast assessment of civil infrastructure conditions is critical to timely assess safety risks. Current practices rely on visual observations and manual interpretation of reports and sketches prepared by inspectors in the field, which are labor intensive, subject to personal judgment and experience, and prone to error. Terrestrial laser scanners (TLS) are promising for automatically identifying structural condition indicators, as they are capable of providing coverage for large areas with accuracy at long ranges. Major challenges in using this technology are in storing significant amount of data and extracting appropriate features enabling condition assessment. This paper proposes a novel adaptive wavelet neural network (WNN)-based approach to compress data into a combination of low- and high-resolution surfaces, and automatically detect concrete cracks and other forms of damage. The adaptive WNN is designed to sequentially self-organize and self-adapt in order to construct an optimized representation. The architecture of the WNN is based on a single-layer neural network consisting of Mexican hat wavelet functions. The strategy is to first construct a low-resolution representation of the point cloud, then detect and localize anomalies, and finally construct a high-resolution representation around these anomalies to enhance their characterization. The approach was verified on four cracked concrete specimens. The experimental results show that the proposed approach was capable of fitting the point cloud, and of detecting and fitting the crack. The results demonstrated data compression of 99.4%, 72.2%, 92.4% and 78.9% for the four specimens when using low resolution fit for crack detection. For specimens 1, 2 and 3, 97.1%, 42.5% and 63.9% compression of data were obtained for crack localization, which is a significant improvement over previous TLS based crack detection and measurement approaches. Using the proposed method for crack detection would enable automatic and remote assessment of structural conditions. This would, in turn, result in reducing costs associated with infrastructure management, and improving the overall quality of our infrastructure by enhancing maintenance operations. [ABSTRACT FROM AUTHOR]

• DREADD stimulation of NAcc POMC projections increases reward learning. • Removal of POMC neuronal stimulation in NAcc increases responding across days. • NAcc POMC stimulation does not affect responding for palatable food during learning. Obesity is a crisis in the United States, producing many co-morbid diseases that can drastically decrease quality of life. While diet is a major focus for therapeutic intervention, the need to understand underlying appetitive neurocircuitry persists. Proopiomelanocortin (POMC) peptides are well-known for their anorexigenic activity, but also mediate reward and learning. The nucleus accumbens (NAcc) is best known for its role in reward-based learning, but the contribution of POMC projections to NAcc on feeding are controversial since the two major POMC-derived peptides (β-endorphin and α-MSH) have opposite effects on food intake. Our objective was to determine the effect of stimulating POMC projections in the NAcc on acquisition and maintenance of operant self-administration of a palatable food. Adult POMC Cre mice were microinjected into the NAcc with a Cre-dependent retrograde adeno-associated viral vector expressing Gq Designer Receptors Exclusively Activated by Designer Drugs (DREADDs). Mice were trained to self-administer palatable 20-mg pellets in daily operant sessions. Acquisition of self-administration (fixed ratio 30) and baseline self-administration were measured in daily sessions, with mice receiving injections of either JHU37152 (DREADD agonist) or saline (i.p.) 15 min prior to the sessions. POMC neuron stimulation (JHU injection) before training sessions produced a significant increase in rate of acquisition and accuracy compared to the saline treated group, with no significant effect on rewards earned. Removal of POMC neuron stimulation before sessions initially reduced consumption with a gradual increase in responding for reinforcer over 3 days of saline injections. Reinstatement of POMC neuron stimulation (JHU) before the session resulted in a significant decrease in responding and rewards earned. These results suggest a complex role of POMC peptides within the NAcc that increase reward learning for a novel palatable food while decreasing consumption of the reinforcer following experience with it. [ABSTRACT FROM AUTHOR]

The article provides information on aspects related to Early Childhood Education and mediation of cognitive and socio-emotional skills. Topics discussed include developmental approach for neural circuits, social exclusion of immigrant children, role of education in childhood, and fundamental human rights.

The work examines the potential causative link between the flow of protons, i. e. temperature of the particles that are coming from the Sun and forest fires in the USA. For determination of the degree of randomness for time series of input (temperature of protons) and output parameters (number of forest fires), the R/S analysis is conducted. The analysis of fractal dimension provides us an opportunity to compare self-similar processes in the influx of protons and the time series of forest fires flashes. Therefore we developed and conducted the sensitivity analysis of model based on hybrid neural networks ANFIS. As the calculations showed, only 16% of the real forest flashes cannot be predicted by the model. [ABSTRACT FROM AUTHOR]

Obesity in the US is treated as a medical condition, while its socioeconomic roots are all but ignored. Yet obesity was a univariate predictor of voting patterns in the 2016 presidential election at both state and county level. Health indicators, linked to socioeconomic variables, may be the new political force. [ABSTRACT FROM AUTHOR]

Vehicle collisions amount to a significant loss of life in America. Upward of 30,000 lives are lost each year in the United States alone. Hundreds of millions of dollars are spent each year on vehicle safety and roadway design in the United States, and many studies have been conducted on the causal factors for vehicle collisions. This study used artificial neural networks as a means to predict the occurrence of injury of a vehicle collision. The objective was to classify the levels of the 'injury occurrence' in vehicle collisions, given certain input variables, including demographic and environmental factors involved in crashes. Using Neural Ware's Predict software, a back-propagation neural network model was constructed, trained, tested, and validated using data from the 2006 and 2007 Florida Traffic Crash Database. The neural network's computational power was iteratively increased by adding hidden layers to boost its predicting power until the performance began to plateau. Classification rates as well as Kolmogorov-Smirnov (K-S) statistical analysis were used to validate the model. A sensitivity analysis was then applied to find the level of contribution that the input variables had on the injury occurrence. Accident severity, number of lanes, and road surface conditions were found to have the most positive impacts on the injury occurrence. The implications of the study are discussed at the end of the article. [ABSTRACT FROM AUTHOR]

The go-to cognitive enhancers of today are those that are widely available rather than optimal for the user, including drugs typically prescribed for treatment of ADHD (e.g., methylphenidate) and sleep disturbances such as narcolepsy (modafinil). While highly effective in their intended therapeutic role, performance gains in healthy populations are modest at best and profoundly inconsistent across subgroups and individuals. We propose a method for in silico screening of possible novel cognitive enhancers followed by high-throughput in vivo and in vitro validation. The proposed method uses gene expression data to evaluate the the collection of activated or suppressed signaling pathways in tissues or neurons of the cognitively enhanced brain. An algorithm maps expression data onto signaling pathways and quantifies their individual activation strength. The collective pathways and their activation form what we term the signaling pathway cloud, a biological fingerprint of cognitive enhancement (or any other condition of interest). Drugs can then be screened and ranked based on their ability to minimize, mimic, or exaggerate pathway activation or suppression within that cloud. Using this approach, one may predict the efficacy of many drugs that may enhance various aspects of cognition before costly preclinical studies and clinical trials are undertaken. [ABSTRACT FROM AUTHOR]

Abstract: Anxiety and addiction disorders are two of the most common mental disorders in the United States, and are typically chronic, disabling, and comorbid. Emerging evidence suggests the bed nucleus of the stria terminalis (BNST) mediates both anxiety and addiction through connections with other brain regions, including the amygdala and nucleus accumbens. Although BNST structural connections have been identified in rodents and a limited number of structural connections have been verified in non-human primates, BNST connections have yet to be described in humans. Neuroimaging is a powerful tool for identifying structural and functional circuits in vivo. In this study, we examined BNST structural and functional connectivity in a large sample of humans. The BNST showed structural and functional connections with multiple subcortical regions, including limbic, thalamic, and basal ganglia structures, confirming structural findings in rodents. We describe two novel connections in the human brain that have not been previously reported in rodents or non-human primates, including a structural connection with the temporal pole, and a functional connection with the paracingulate gyrus. The findings of this study provide a map of the BNST's structural and functional connectivity across the brain in healthy humans. In large part, the BNST neurocircuitry in humans is similar to the findings from rodents and non-human primates; however, several connections are unique to humans. Future explorations of BNST neurocircuitry in anxiety and addiction disorders have the potential to reveal novel mechanisms underlying these disabling psychiatric illnesses. [Copyright &y& Elsevier]

Abstract: Blast-related traumatic brain injury (TBI) has been one of the “signature injuries” of the wars in Iraq and Afghanistan. However, neuroimaging studies in concussive ‘mild’ blast-related TBI have been challenging due to the absence of abnormalities in computed tomography or conventional magnetic resonance imaging (MRI) and the heterogeneity of the blast-related injury mechanisms. The goal of this study was to address these challenges utilizing single-subject, module-based graph theoretic analysis of resting-state functional MRI (fMRI) data. We acquired 20min of resting-state fMRI in 63 U.S. military personnel clinically diagnosed with concussive blast-related TBI and 21 U.S. military controls who had blast exposures but no diagnosis of TBI. All subjects underwent an initial scan within 90days post-injury and 65 subjects underwent a follow-up scan 6 to 12months later. A second independent cohort of 40 U.S. military personnel with concussive blast-related TBI served as a validation dataset. The second independent cohort underwent an initial scan within 30days post-injury. 75% of the scans were of good quality, with exclusions primarily due to excessive subject motion. Network analysis of the subset of these subjects in the first cohort with good quality scans revealed spatially localized reductions in the participation coefficient, a measure of between-module connectivity, in the TBI patients relative to the controls at the time of the initial scan. These group differences were less prominent on the follow-up scans. The 15 brain areas with the most prominent reductions in the participation coefficient were next used as regions of interest (ROIs) for single-subject analyses. In the first TBI cohort, more subjects than would be expected by chance (27/47 versus 2/47 expected, p <0.0001) had 3 or more brain regions with abnormally low between-module connectivity relative to the controls on the initial scans. On the follow-up scans, more subjects than expected by chance (5/37, p =0.044) but fewer subjects than on the initial scans had 3 or more brain regions with abnormally low between-module connectivity. Analysis of the second TBI cohort validation dataset with no free parameters provided a partial replication; again more subjects than expected by chance (8/31, p =0.006) had 3 or more brain regions with abnormally low between-module connectivity on the initial scans, but the numbers were not significant (2/27, p =0.276) on the follow-up scans. A single-subject, multivariate analysis by probabilistic principal component analysis of the between-module connectivity in the 15 identified ROIs, showed that 31/47 subjects in the first TBI cohort were found to be abnormal relative to the controls on the initial scans. In the second TBI cohort, 9/31 patients were found to be abnormal in identical multivariate analysis with no free parameters. Again, there were not substantial differences on the follow-up scans. Taken together, these results indicate that single-subject, module-based graph theoretic analysis of resting-state fMRI provides potentially useful information for concussive blast-related TBI if high quality scans can be obtained. The underlying biological mechanisms and consequences of disrupted between-module connectivity are unknown, thus further studies are required. [Copyright &y& Elsevier]

The mainstay of treatment for opioid use disorder (OUD) is opioid agonist therapy (OAT), which modulates opioid receptors to reduce substance craving and use. OAT maintains dependence on opioids but helps reduce overdose and negative sequelae of substance abuse. Despite increasing availability of OAT, its effectiveness is limited by difficulty in initiating and maintaining patients on treatment. With the worsening opioid epidemic in the United States and rising overdose deaths, a more durable and effective treatment for OUD is necessary. This paper reviews novel treatments being investigated for OUD, including neuromodulatory interventions, psychedelic drugs, and other novel approaches. Neuromodulatory interventions can stimulate the addiction neural circuitry involving the dorsolateral prefrontal cortex and deeper mesolimbic structures to curb craving and reduce use, and multiple clinical trials for interventional treatment for OUD are currently conducted. Similarly, psychedelic agents are being investigated for efficacy in OUD specifically. There is a resurgence of interest in psychedelic agents' therapeutic potential, with evidence of improving mood symptoms and decreased substance use even after just one dose. Exact mechanism of their anti-addictive effect is not fully elucidated, but psychedelic agents do not maintain opioid dependence and some may even be helpful in abating symptoms of withdrawal. Other potential approaches for OUD include targeting different parts of the dopamine-dependent addiction pathway, identifying susceptible genes and modulating gene products, as well as utilizing vaccines as immunotherapy to blunt the addictive effects of substances. Much more clinical data are needed to support efficacy and safety of these therapies in OUD, but these proposed novel treatments look beyond the opioid receptor to offer hope for a more durably effective OUD treatment. • Opioid agonist therapy maintains opioid dependence and carries high risk of relapse. • Many proposed treatments for opioid use disorder target addiction neural circuitry. • Aim is to get lasting anti-addictive effect at fewer doses, not just abate dependence. [ABSTRACT FROM AUTHOR]

The article presents a research that investigates the correlation between the cognitive and emotional disturbances and neural circuitry of patients with post traumatic stress disorder (PTSD) in the U.S. Researchers found that both disturbances are associated with the neural circuitry of the emotion-cognition interaction of PTSD patients. Moreover, they said that their findings are vital in impairing and enhancing the implications of emotions.

Recent climatic patterns indicate that extreme weather events will increase in frequency and magnitude. Remote sensing offers unique advantages for large-scale monitoring. In this research, Landsat 5 remotely sensed imagery was used to assess flooding caused by Hurricane Katrina, one of the worst natural disasters in the US over the past decades. The objective of our work is to assess whether decisions associated with the classification process, such as location of reference data and algorithm choice, affected flooding results and subsequent analysis using census data. Maximum Likelihood (ML) and Back Propagation Neural Network (NN) were the tested algorithms, the former reflecting a simple and popular classifier, and the latter an advanced but complex method. Flooding estimations were almost identical within the reference sample area, 124.4 km2 for the ML classifier and 123.7 km2 for the NN classifier. However, large discrepancies were found outside the reference sample area with the ML predicting 462.5 km2 and the NN identifying 797.2 km2 as flooded, almost twice the amount. Further investigation took place to evaluate the influence of the classification method to a social study, namely the racial characteristics of flooded areas. Using Census 2000 data, our study area was segmented in census tracts. Results indicated a strong positive correlation between concentration of African Americans and proportional residential flooding. Pairwise T-Tests also verified that flooding among different African American concentrations was statistically different. There were no significant differences between the ML and NN methods in the results interpretation, which is mostly attributed to the significant geographic overlap between reference sample area and the examined census tracts. This study suggests that emergency responders should exercise significant caution in their decision making when using classification products from undersampled geographic areas. [ABSTRACT FROM AUTHOR]

As the racial composition of the population changes, intergroup interactions are increasingly common. To understand how we perceive and categorize race and the attitudes that flow from it, scientists have used brain imaging techniques to examine how social categories of race and ethnicity are processed, evaluated and incorporated in decision-making. We review these findings, focusing on black and white race categories. A network of interacting brain regions is important in the unintentional, implicit expression of racial attitudes and its control. On the basis of the overlap in the neural circuitry of race, emotion and decision-making, we speculate as to how this emerging research might inform how we recognize and respond to variations in race and its influence on unintended race-based attitudes and decisions. [ABSTRACT FROM AUTHOR]

Marijuana (MJ) acutely acts on cannabinoid receptors that are found in numerous brain regions, including those involved in reward processing and decision-making. However, it remains unclear how long-term, chronic MJ use alters reward-based decision-making. In the present study, using [15O]water PET imaging, we measured brain activity in chronic MJ users, who underwent monitored abstinence from MJ for approximately 24 h before imaging, and control participants, while they took part in the Iowa Gambling Task (IGT), a monetary decision making task that strongly relies on the ventromedial prefrontal cortex (vmPFC). During PET imaging, participants took part in the standard and a variant version of the IGT as well as a control task. Chronic MJ users performed equally well on the standard IGT, but significantly worse than controls on the variant IGT. Chronic MJ users and control subjects showed increased regional cerebral blood flow (rCBF) in the vmPFC on both versions of the IGT compared to the control task. In the two-group comparison, chronic MJ users showed significantly greater rCBF than controls in the vmPFC on the standard IGT and greater activity in the cerebellum on both versions of the IGT. Furthermore, duration of use, but not age of first use, was associated with greater activity in the vmPFC. Thus, chronic MJ users tend to strongly recruit neural circuitry involved in decision-making and reward processing (vmPFC), and probabilistic learning (cerebellum) when performing the IGT. [ABSTRACT FROM AUTHOR]

The growing worldwide obesity epidemic and obesity-related disorders present a huge unmet medical need for safe and effective anti-obesity medications. The discovery of leptin in 1994 was rapidly succeeded by a wave of related discoveries leading to the elaboration of a hypothalamic melanocortinergic neuronal circuit regulated by leptin and other central and peripheral signaling molecules to control energy homeostasis. The identification of specific neuronal subtypes along with their unique connections and expression products generated a rich target menu for anti-obesity drug discovery programs. Over the course of the last decade, several new chemical entities aimed at these targets have reached various stages or successfully completed the drug discovery/regulatory process only to be dropped or taken off the market. There are now in fact fewer options for anti-obesity drug therapies in late 2010 than were available in 2000. The challenge to discover safe and effective anti-obesity drugs is alive and well. [ABSTRACT FROM AUTHOR]

Psychiatric neurosurgery, specifically stereotactic ablation, has continued since the 1940s, mainly at a few centers in Europe and the US. Since the late 1990s, the resurgence of interest in this field has been remarkable; reports of both lesion procedures and the newer technique of deep brain stimulation (DBS) have increased rapidly. In early 2009, the US FDA granted limited humanitarian approval for DBS for otherwise intractable obsessive-compulsive disorder (OCD), the first such approval for a psychiatric illness. Several factors explain the emergence of DBS and continued small-scale use of refined lesion procedures. DBS and stereotactic ablation have been successful and widely used for movement disorders. There remains an unmet clinical need: current drug and behavioral treatments offer limited benefit to some seriously ill people. Understandings of the neurocircuitry underlying psychopathology and the response to treatment, while still works in progress, are much enhanced. Here, we review modern lesion procedures and DBS for OCD in the context of neurocircuitry. A key issue is that clinical benefit can be obtained after surgeries targeting different brain structures. This fits well with anatomical models, in which circuits connecting orbitofrontal cortex (OFC), medial prefrontal cortex (mPFC), basal ganglia, and thalamus are central to OCD pathophysiology and treatment response. As in movement disorders, dedicated interdisciplinary teams, here led by psychiatrists, are required to implement these procedures and maintain care for patients so treated. Available data, although limited, support the promise of stereotactic ablation or DBS in carefully selected patients. Benefit in such cases appears not to be confined to obsessions and compulsions, but includes changes in affective state. Caution is imperative, and key issues in long-term management of psychiatric neurosurgery patients deserve focused attention. DBS and contemporary ablation also present different patterns of potential benefits and burdens. Translational research to elucidate how targeting specific nodes in putative OCD circuitry might lead to therapeutic gains is accelerating in tandem with clinical use. [ABSTRACT FROM AUTHOR]

Although the preceding chapters discuss much of the new knowledge of neurocircuitry of neuropsychiatric diseases, and an invasive approach to treatment, this chapter describes and reviews the noninvasive methods of testing circuit-based theories and treating neuropsychiatric diseases that do not involve implanting electrodes into the brain or on its surface. These techniques are transcranial magnetic stimulation, vagus nerve stimulation, and transcranial direct current stimulation. Two of these approaches have FDA approval as therapies. [ABSTRACT FROM AUTHOR]

Various methods are used to estimate sediment transport through riparian buffers and grass filters, with the sediment delivery ratio having been the most widely applied. The U.S. Forest Service developed a sediment delivery ratio using the stiff diagram and a logistic curve to integrate some of the factors influencing sediment delivery heuristically. This study independently tested the Forest Service sediment delivery ratio contrasted with artificial neural networks to represent the multiple nonlinearities between important factors and sediment delivery. The Forest Service sediment delivery ratio was not adequate when compared to published sediment yields from 30 small experimental buffers from three countries, including four forested buffers. However, artificial neural networks gave estimates of the delivery ratio that were highly correlated to the observations. The 30 buffer observations produced such good estimates of the sediment delivery ratio with both seven and five buffer parameters that this study suggests that as few as 30 sediment yield observations can be the basis for applying neural networks to interpolate the complex, multiple nonlinearities of hydrology and sediment transport on riparian buffers. [ABSTRACT FROM AUTHOR]

The article presents an award given to Marcia K. Johnson for her contribution in science by the American Psychological Association. The award was given in recognition for her studies in the cognitive and neural processes that constitute the subjective experience in mental life. Johnson took up doctor's degree at University of California and became a professor of Psychology at Yale University. She began to focus on the mechanisms underlying the attribution of mental experiences to memory in order to confront some issues raised by constructive and reconstructive processes.

The dynamics of a pair of weakly interacting conductance-based neurons, firing at low frequency, ν, is investigated in the framework of the phase-reduction method. The stability of the antiphase and the in-phase locked state is studied. It is found that for a large class of conductance-based models, the antiphase state is stable (resp., unstable) for excitatory (resp., inhibitory) interactions if the synaptic time constant is above a critical value τ[sub s][sup c], which scales as | log ν | when ν goes to zero. [ABSTRACT FROM AUTHOR]

Presents an overview of the biological basis for emotional neuroscience in the United States. Role of the prefrontal cortex and amygdala in emotional systems; Forms of affective plasticity; Consideration of plasticity in the neural circuitry of emotions; Differences in amygdala activation; Relation between affective style and health.

Opioid abuse and related overdose deaths continue to rise in the United States, contributing to the national opioid crisis in the USA. The neural mechanisms underlying opioid abuse and addiction are still not fully understood. This review discusses recent progress in basic research dissecting receptor mechanisms and circuitries underlying opioid reward and addiction. We first review the canonical GABA-dopamine neuron hypothesis that was upheld for half a century, followed by major findings challenging this hypothesis. We then focus on recent progress in research evaluating the role of the mesolimbic and nigrostriatal dopamine circuitries in opioid reward and relapse. Based on recent findings that activation of dopamine neurons in the ventral tegmental area (VTA) and substantia nigra pars compacta (SNc) is equally rewarding and that GABA neurons in the rostromedial tegmental nucleus (RMTg) and the substantia nigra pars reticula (SNr) are rich in mu opioid receptors and directly synapse onto midbrain DA neurons, we proposed that the RTMg→VTA → ventrostriatal and SNr → SNc → dorsostriatal pathways may act as the two major neural substrates underlying opioid reward and abuse. Lastly, we discuss possible integrations of these two pathways during initial opioid use, development of opioid abuse and maintenance of compulsive opioid seeking. • Opioid reward is thought to be mediated by VTA GABAneuron inhibition and DA neuron disinhibition • This long-held canonical two-neuron hypothesis has been challenged by recent research • MORs are highly expressed in GABA neurons in the RMTg and SNr, but less in the VTA • Activation of dopamine neurons in both the VTA and SNc is equally rewarding • The RTMg → VTA → ventrostriatal and SNr → SNc → dorsostriatal pathways may underlie opioid reward [ABSTRACT FROM AUTHOR]

Introduction: Research Domain Criteria are coming to be required for applications for mental health research funding in the United States. Objectives: To translate contemporary neuroscience research into teaching medical residents how to prescribe psychiatric medications. Methods: We explore the neuroscience literature regarding neural circuitry and psychiatric symptoms and examine the neurotransmitters associated with those circuits. We associate psychiatric symptoms with the neural circuitry that produces those symptoms. We correlate medications with circuits which they might affect and symptoms they might ameliorate. Results: RDC is an alternative to DSM and ICD-10. Contemporary scientific diagnoses are not based on neuroscience. They are overlapping, contradictory, often vague, and hinder adequate research. Diagnoses are needed that are based on brain circuitry and function rather than "expert" opinion. The basis for RDC lies in psychiatric disorders being brain disorders with a primary focus on circuitry function. This contrasts with neurological disorders that have identifiable structural lesions. Symptoms are normally distributed and exist in everyone. RDC proposes to seek the distribution of traits and characteristics, defining abnormal as the extremes of these distributions rather than by defining mental disorders by signs and symptoms which give a diagnosis. We ask what are the brain system that primarily implements the traits, functions, and characteristics of interest. We explore what accounts for the development of dysregulation or dysfunction in these systems alongside normal-toabnormal dimensions? We describe resident reactions to this style of teaching and show greater comfort in prescribing medications. Conclusions: Translating Research Domain Criteria into psychiatric prescribing will move psychopharmacology into contemporary neuroscience. [ABSTRACT FROM AUTHOR]

Millions of people in the United States are affected by chronic pain, and the financial cost of pain treatment is weighing on the healthcare system. In some cases, current pharmacological treatments may do more harm than good, as with the United States opioid crisis. Direct electrical stimulation of the brain is one potential non-pharmacological treatment with a long history of investigation. Yet brain stimulation has been far less successful than peripheral or spinal cord stimulation, perhaps because of our limited understanding of the neural circuits involved in pain perception. In this paper, we review the history of using electrical stimulation of the brain to treat pain, as well as contemporary studies identifying the structures involved in pain networks, such as the thalamus, insula, and anterior cingulate. We propose that the thermal grill illusion, an experimental pain model, can facilitate further investigation of these structures. Pairing this model with intracranial recording will provide insight toward disentangling the neural correlates from the described anatomic areas. Finally, the possibility of altering pain perception with brain stimulation in these regions could be highly informative for the development of novel brain stimulation therapies for chronic pain. [ABSTRACT FROM AUTHOR]

The article presents an award given to Anthony D. Wagner by the American Psychological Association for his contributions in scientific early career in psychology. The award was given in recognition for his research on the neural basis of memory and executive control. He also used neuroimaging to show how prefrontal and medial temporal lobe systems are involved in the episodic memories. Wagner received a Bachelor of Arts in psychology from the University of California, Los Angeles and took up her graduate studies in Stanford University.

The article features a study that assesses the percentage of neural contrast spikes in the U.S. To determine neuronal spikes, researchers used inertial accelometers combined with gravity-measuring sensors. Results show that the first five percent of neural contrast spikes at about 94% of gravity vector, without prior knowledge of the environment.

The article presents a study that assesses the effect of ion channel grating randomness on spike firing time and mutual information in the U.S. Researchers used supra-threshold filtered Poisson process with an intensity of 10 [s-1] to 50 stochastic Hodgkin-Huxley (HH) neuron models. They found that mutual information was maximized and concluded that intrinsic supra-threshold stochastic resonance (SSR) played a vital role in excessive input signal processing of sensory nervous system.

The article presents a study that investigates the correlation between sensory input and neural responses in the U.S. To simplify its analysis, researchers used response conditioned stimulus ensemble (RCSE) and gaussian white noise (GWN) stimuli. Both schemes affect the quality of models and furnish insights on neural processing.

The article features a study that evaluates the significance of oscillatory interference in working memory of animal models in the U.S. To assess the behavior of entorhinal grid cells, researchers developed a technique that assumes oscillatory interference that encodes the order of items. They found that the activation pattern resulted to dendritic and somatic membrane potential oscillations (MPO) interference of various frequencies of parietal rank-order sensitive cells.

The article reports on the research that assesses polysynaptic excitatory connectivity of spiking cortical neurons in the U.S. Researchers used dynamic Bayesian networks to measure neuronal circuits and how these circuits perform brain functions in complex coordinated firing patterns. They found that several network dynamics, like polychrony and syn-fire chains, exhibit excitatory networks.

The article features a study that focuses on how information flows in neuronal networks in the U.S. To trace and to measure the activity of networks, researchers simulated the neocortical columns to measure the activity of networks and how it alter information flow in epileptic and schizophrenic animal models. They found that those networks with more internal connectivity have reduced information flow from inputs to output.

The article features the study conducted on the significance of Noradrenaline to the brain's interneuron activity in the U.S. It is a well-established neuromodulator in many parts of the brain and its impact on cells and synapses. Researchers used paired recordings from connected GABAergic interneurons in the hippocampus and they evaluated the novel type of noradrenegic modulation and they found that activation of ß-adrenergic receptors decreased gap junction-dependent electrical coupling between inhibitory interneurons. It revealed that the series of pharmacological interventions established was attributed to the activation of the cAMP/protein kinase A signaling cascade.

As many articles in this issue of IEEE Pulse demonstrate, interfacing directly with the brain presents several fundamental challenges. These challenges reside at multiple levels and span many disciplines, ranging from the need to understand brain states at the level of neural circuits to creating technological innovations to facilitate new therapeutic options. The goal of our multiuniversity research team, composed of researchers from Stanford University, Brown University, the University of California at San Francisco (UCSF), and the University College London (UCL), is to substantially elevate the fundamental understanding of brain information processing and its relationship with sensation, behavior, and injury. Our team was assembled to provide expertise ranging from neuroscience to neuroengineering and to neurological and psychiatric clinical guidance, all of which are critical to the overarching research goal. By employing a suite of innovative experimental, computational, and theoretical approaches, the Defense Advanced Research Projects Agency (DARPA) Reorganization and Plasticity to Accelerate Injury Recovery (REPAIR) team has set its sights on learning how the brain and its microcircuitry react (e.g., to sudden physiological changes) and what can be done to encourage recovery from such (reversible) injury. In this article, we summarize some of the team's technical goals, approaches, and early illustrative results. [ABSTRACT FROM PUBLISHER]

In this article, the author reflects on the aim of a representative of the IBM SyNAPSE group, an undertaking of the U.S. Defense Advanced Research Projects Agency, to construct an artificial cat-sized brain. He outlines the 2009 review by Lars Chittka and Jeremy Niven titled "Are Bigger Brains Better?," which suggests that bigger brains may possess more of the same of what is found in small brains. He stresses the correlation between greater behavioral complexity and greater brain-to-body ratio.

The article features a study that investigates the correlation between cortical responses and sensory stimuli and its importance in information theory approach in the U.S. To assess the application of adaptive algorithm in cortical neurons, researchers constructed cortical network model. They found that optimization of sensory stimuli can be used to define optimal sensory ensemble, while algorithms can be utilized to explore sensory information integration.

The article presents a study that investigates visual stimuli encoding of Hodgkin-Huxley neurons in the U.S. To assess neural circuitry, researchers applied neural decoding methods to recover complex stimuli in natural scenes. They found that majority of models encoded during early visual system have linear receptive field with non-linear spike generation mechanism.