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

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

Increased neural response to social rejection in major depression

Background: Being a part of community is critical for survival and individuals with major depressive disorder (MDD) have a greater sensitivity to interpersonal stress that makes them vulnerable to future episodes. Social rejection is a critical risk factor for depression and it is said to increase interpersonal stress and thereby impairing social functioning. It is therefore critical to understand the neural correlates of social rejection in MDD. Methods: To this end, we scanned 15 medicated MDD and 17 healthy individuals during a modified cyberball passing game, where participants were exposed to increasing levels of social exclusion. Neural responses to increasing social exclusion were investigated and compared between groups. Results: We showed that compared to controls, MDD individuals exhibited greater amygdala, insula, and ventrolateral prefrontal cortex activation to increasing social exclusion and this correlated negatively with hedonic tone and self-esteem scores across all participants. Conclusions: These preliminary results support the hypothesis that depression is associated with hyperactive response to social rejection. These findings highlight the importance of studying social interactions in depression, as they often lead to social withdrawal and isolation

Automated brain tumour identification using magnetic resonance imaging:a systematic review and meta-analysis

BACKGROUND: Automated brain tumor identification facilitates diagnosis and treatment planning. We evaluate the performance of traditional machine learning (TML) and deep learning (DL) in brain tumor detection and segmentation, using MRI. METHODS: A systematic literature search from January 2000 to May 8, 2021 was conducted. Study quality was assessed using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Detection meta-analysis was performed using a unified hierarchical model. Segmentation studies were evaluated using a random effects model. Sensitivity analysis was performed for externally validated studies. RESULTS: Of 224 studies included in the systematic review, 46 segmentation and 38 detection studies were eligible for meta-analysis. In detection, DL achieved a lower false positive rate compared to TML; 0.018 (95% CI, 0.011 to 0.028) and 0.048 (0.032 to 0.072) (P < .001), respectively. In segmentation, DL had a higher dice similarity coefficient (DSC), particularly for tumor core (TC); 0.80 (0.77 to 0.83) and 0.63 (0.56 to 0.71) (P < .001), persisting on sensitivity analysis. Both manual and automated whole tumor (WT) segmentation had “good” (DSC ≥ 0.70) performance. Manual TC segmentation was superior to automated; 0.78 (0.69 to 0.86) and 0.64 (0.53 to 0.74) (P = .014), respectively. Only 30% of studies reported external validation. CONCLUSIONS: The comparable performance of automated to manual WT segmentation supports its integration into clinical practice. However, manual outperformance for sub-compartmental segmentation highlights the need for further development of automated methods in this area. Compared to TML, DL provided superior performance for detection and sub-compartmental segmentation. Improvements in the quality and design of studies, including external validation, are required for the interpretability and generalizability of automated models