Opposing patterns of abnormal D1 and D2 receptor dependent cortico-striatal plasticity explain increased risk taking in patients with DYT1 dystonia

Patients with DYT1 dystonia caused by the mutated TOR1A gene exhibit risk neutral behaviour compared to controls who are risk averse in the same reinforcement learning task. It is unclear whether this behaviour can be linked to changes in cortico-striatal plasticity demonstrated in animal models which share the same TOR1A mutation. We hypothesised that we could reproduce the experimental risk taking behaviour using a model of the basal ganglia under conditions where cortico-striatal plasticity was abnormal. As dopamine exerts opposing effects on cortico-striatal plasticity via different receptors expressed on medium spiny neurons (MSN) of the direct (D1R dominant, dMSNs) and indirect (D2R dominant, iMSNs) pathways, we tested whether abnormalities in cortico-striatal plasticity in one or both of these pathways could explain the patient's behaviour. Our model could generate simulated behaviour indistinguishable from patients when cortico-striatal plasticity was abnormal in both dMSNs and iMSNs in opposite directions. The risk neutral behaviour of the patients was replicated when increased cortico-striatal long term potentiation in dMSN's was in combination with increased long term depression in iMSN's. This result is consistent with previous observations in rodent models of increased cortico-striatal plasticity at in dMSNs, but contrasts with the pattern reported in vitro of dopamine D2 receptor dependant increases in cortico-striatal LTP and loss of LTD at iMSNs. These results suggest that additional factors in patients who manifest motor symptoms may lead to divergent effects on D2 receptor dependant cortico-striatal plasticity that are not apparent in rodent models of this disease

Modeling trait anxiety:from computational processes to personality

Computational methods are increasingly being applied to the study of psychiatric disorders. Often, this involves fitting models to the behavior of individuals with subclinical character traits that are known vulnerability factors for the development of psychiatric conditions. Anxiety disorders can be examined with reference to the behavior of individuals high in “trait” anxiety, which is a known vulnerability factor for the development of anxiety and mood disorders. However, it is not clear how this self-report measure relates to neural and behavioral processes captured by computational models. This paper reviews emerging computational approaches to the study of trait anxiety, specifying how interacting processes susceptible to analysis using computational models could drive a tendency to experience frequent anxious states and promote vulnerability to the development of clinical disorders. Existing computational studies are described in the light of this perspective and appropriate targets for future studies are discussed

Cooperative Extension: A Century of Innovation

As Cooperative Extension celebrates its 100th anniversary in 2014, the Land-Grant System will be reflecting on the first century of accomplishments and preparing for a second century of education. This commentary is the first in a series of six throughout the year that will analyze the rich history of Cooperative Extension, examine its role in contemporary society, and help us collaboratively envision the future of this unique American educational endeavor

Compulsivity in opioid dependence

This study was part funded by an unrestricted educational grant provided by Schering-Plough and a grant by an Anonymous Trust. Study support was also provided by the Scottish Mental Health Research Network. AB has received educational grants from Schering Plough and he has received research project funding from Schering-Plough, Merck Serono, and Indivior.Objective: This study aimed to investigate the relationship between compulsivity versus impulsivity and structural MRI abnormalities in opioid dependence. Method: We recruited 146 participants: i) patients with a history of opioid dependence due to chronic heroin use (n=24), ii) heroin users stabilised on methadone maintenance treatment (n=48), iii) abstinent participants with ahistory of opioid dependence due to heroin use (n=24) and iv) healthy controls(n=50). Compulsivity was measured using Intra/Extra-Dimensional (IED) Task and impulsivity was measured using the Cambridge Gambling Task (CGT).Structural Magnetic Resonance Imaging (MRI) data were also obtained. Results: As hypothesised, compulsivity was negatively associated with impulsivity (p<0.02). Testing for the neural substrates of compulsivity versus impulsivity, we found a higher compulsivity/impulsivity ratio associated with significantly decreased white matter adjacent to the nucleus accumbens, bed nucleus of stria terminalis and rostral cingulate in the abstinent group,compared to the other opioid dependent groups. In addition, self-reported duration of opioid exposure correlated negatively with bilateral globus pallidus grey matter reductions. Conclusion: Our findings are consistent with Volkow & Koob’s addiction models and underline the important role of compulsivity versus impulsivity inopioid dependence. Our results have implications for the treatment of opioid dependence supporting the assertion of different behavioural and biological phenotypes in the opioid dependence and abstinence syndromes.PostprintPeer reviewe

Radiomics Machine Learning Analysis of Clear Cell Renal Cell Carcinoma for Tumour Grade Prediction based on Intra-tumoural Subregion Heterogeneity

Background: Renal cancers are among the top ten causes of cancer-specific mortality, of which the ccRCC subtype is responsible for most cases. The grading of ccRCC is important in determining tumour aggressiveness and clinical management.Objectives: The objectives of this research were to predict the WHO/ISUP grade of ccRCC pre-operatively and characterise the heterogeneity of tumour sub-regions using radiomics and ML models, including comparison with pre-operative biopsy-determined grading in a sub-group.Methods: Data were obtained from multiple institutions across two countries, including 391 patients with pathologically proven ccRCC. For analysis, the data were separated into four cohorts. Cohorts 1 and 2 included data from the respective institutions from the two countries, cohort 3 was the combined data from both cohort 1 and 2, and cohort 4 was a subset of cohort 1, for which both the biopsy and subsequent histology from resection (partial or total nephrectomy) were available. 3D image segmentation was carried out to derive a voxel of interest (VOI) mask. Radiomics features were then extracted from the contrast-enhanced images, and the data were normalised. The Pearson correlation coefficient and the XGBoost model were used to reduce the dimensionality of the features. Thereafter, 11 ML algorithms were implemented for the purpose of predicting the ccRCC grade and characterising the heterogeneity of sub-regions in the tumours.Results: For cohort 1, the 50% tumour core and 25% tumour periphery exhibited the best performance, with an average AUC of 77.9% and 78.6%, respectively. The 50% tumour core presented the highest performance in cohorts 2 and 3, with average AUC values of 87.6% and 76.9%, respectively. With the 25% periphery, cohort 4 showed AUC values of 95.0% and 80.0% for grade prediction when using internal and external validation, respectively, while biopsy histology had an AUC of 31.0% for the classification with the final grade of resection histology as a reference standard. The CatBoost classifier was the best for each of the four cohorts with an average AUC of 80.0%, 86.5%, 77.0% and 90.3% for cohorts 1, 2, 3 and 4 respectively.Conclusions: Radiomics signatures combined with ML have the potential to predict the WHO/ISUP grade of ccRCC with superior performance, when compared to pre-operative biopsy. Moreover, tumour sub-regions contain useful information that should be analysed independently when determining the tumour grade. Therefore, it is possible to distinguish the grade of ccRCC pre-operatively to improve patient care and management.<br/

Radiomics Machine Learning Analysis of Clear Cell Renal Cell Carcinoma for Tumour Grade Prediction based on Intra-tumoural Subregion Heterogeneity

Background: Renal cancers are among the top ten causes of cancer-specific mortality, of which the ccRCC subtype is responsible for most cases. The grading of ccRCC is important in determining tumour aggressiveness and clinical management.Objectives: The objectives of this research were to predict the WHO/ISUP grade of ccRCC pre-operatively and characterise the heterogeneity of tumour sub-regions using radiomics and ML models, including comparison with pre-operative biopsy-determined grading in a sub-group.Methods: Data were obtained from multiple institutions across two countries, including 391 patients with pathologically proven ccRCC. For analysis, the data were separated into four cohorts. Cohorts 1 and 2 included data from the respective institutions from the two countries, cohort 3 was the combined data from both cohort 1 and 2, and cohort 4 was a subset of cohort 1, for which both the biopsy and subsequent histology from resection (partial or total nephrectomy) were available. 3D image segmentation was carried out to derive a voxel of interest (VOI) mask. Radiomics features were then extracted from the contrast-enhanced images, and the data were normalised. The Pearson correlation coefficient and the XGBoost model were used to reduce the dimensionality of the features. Thereafter, 11 ML algorithms were implemented for the purpose of predicting the ccRCC grade and characterising the heterogeneity of sub-regions in the tumours.Results: For cohort 1, the 50% tumour core and 25% tumour periphery exhibited the best performance, with an average AUC of 77.9% and 78.6%, respectively. The 50% tumour core presented the highest performance in cohorts 2 and 3, with average AUC values of 87.6% and 76.9%, respectively. With the 25% periphery, cohort 4 showed AUC values of 95.0% and 80.0% for grade prediction when using internal and external validation, respectively, while biopsy histology had an AUC of 31.0% for the classification with the final grade of resection histology as a reference standard. The CatBoost classifier was the best for each of the four cohorts with an average AUC of 80.0%, 86.5%, 77.0% and 90.3% for cohorts 1, 2, 3 and 4 respectively.Conclusions: Radiomics signatures combined with ML have the potential to predict the WHO/ISUP grade of ccRCC with superior performance, when compared to pre-operative biopsy. Moreover, tumour sub-regions contain useful information that should be analysed independently when determining the tumour grade. Therefore, it is possible to distinguish the grade of ccRCC pre-operatively to improve patient care and management.<br/

Chronic heroin use disorder and the brain:current evidence and future implications

The incidence of chronic heroin use disorder, including overdose deaths, has reached epidemic proportions. Here we summarise and evaluate our knowledge of the relationship between chronic heroin use disorder and the brain through a narrative review. A broad range of areas was considered including causal mechanisms, cognitive and neurological consequences of chronic heroin use and novel neuroscience-based clinical interventions. Chronic heroin use is associated with limited or very limited evidence of impairments in memory, cognitive impulsivity, non-planning impulsivity, compulsivity and decision-making. Additionally, there is some evidence for certain neurological disorders being caused by chronic heroin use, including toxic leukoencephalopathy and neurodegeneration. However, there is insufficient evidence on whether these impairments and disorders recover after abstinence. Whilst there is a high prevalence of comorbid psychiatric disorders, there is no clear evidence that chronic heroin use per se causes depression, bipolar disorder, PTSD and/or psychosis. Despite the growing burden on society from heroin use, knowledge of the long-term effects of chronic heroin use disorder on the brain remains limited. Nevertheless, there is evidence for progress in neuroscience-based interventions being made in two areas: assessment (cognitive assessment and neuroimaging) and interventions (cognitive training/remediation and neuromodulation). Longitudinal studies are needed to unravel addiction and neurotoxic mechanisms and clarify the role of pre-existing psychiatric symptoms and cognitive impairments.PostprintPeer reviewe

Fronto-medial electrode placement for electroconvulsive treatment of depression

Electroconvulsive therapy (ECT) is the most effective treatment for severe treatment-resistant depression but concern about cognitive side-effects, particularly memory loss, limits its use. Recent observational studies on large groups of patients who have received ECT report that cognitive side-effects were associated with electric field (EF) induced increases in hippocampal volume, whereas therapeutic efficacy was associated with EF induced increases in sagittal brain structures. The aim in the present study was to determine whether a novel fronto-medial (FM) ECT electrode placement would minimize electric fields in bilateral hippocampi (HIP) whilst maximizing electric fields in dorsal sagittal cortical regions. An anatomically detailed computational head model was used with finite element analysis, to calculate ECT-induced electric fields in specific brain regions identified by translational neuroimaging studies of treatment-resistant depressive illness, for a range of electrode placements. As hypothesized, compared to traditional bitemporal (BT) electrode placement, a specific FM electrode placement reduced bilateral hippocampal electric fields two-to-three-fold, whilst the electric fields in the dorsal anterior cingulate (dAC) were increased by approximately the same amount. We highlight the clinical relevance of this specific FM electrode placement for ECT, which may significantly reduce cognitive and non-cognitive side-effects and suggest a clinical trial is indicated